Materials Selection for Natural Fiber Composites [1st Edition] 9780081022771, 9780081009581

Table of contents :
Content:
Front Matter,Copyright,PrefaceEntitled to full text1 - Introduction, Pages 1-21
2 - Natural fiber composites, Pages 23-48
3 - Materials selection, Pages 49-71
4 - Material selection for composites, Pages 73-105
5 - Material selection of natural fiber composites, Pages 107-168
6 - Material selection of natural fiber composites using the analytical hierarchy process, Pages 169-234
7 - Material selection of natural fiber composites using other methods, Pages 235-272
Index, Pages 273-278

Citation preview

Materials Selection for Natural Fiber Composites

Related titles Handbook of natural fibre composites: Properties, processes, failure and applications (ISBN 978-0-85709-524-4) Multi Criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design (ISBN 978-0-08-099386-7) Thermoplastics and Thermoplastic Composites (ISBN 9781455778980)

Materials Selection for Natural Fiber Composites

Faris M. AL-Oqla Mohd S. Salit

An imprint of Elsevier

Woodhead Publishing is an imprint of Elsevier The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom Copyright © 2017 Elsevier Ltd. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-08-100958-1 (print) ISBN: 978-0-08-102277-1(Online) For information on all Woodhead publications visit our website at https://www.elsevier.com/books-and-journals

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Preface

Alhamdulillah, all praises to Almighty Allah who has enabled us to complete writing of the book titled Materials Selection for Natural Fiber Composites. The book is based on our long and solid experience with the topic. Books about natural fiber composites have, in fact, been published, but the emphasis has been mainly on the characterization and performance of such materials. It was noticed that there was the real need to write a book on material selection for natural fiber composites, as no book (either edited or authored) is found in the market dealing with this subject, let alone for natural fiber composites. Therefore, we strongly believe, the readers will greatly benefit from this book, as the majority of the materials contained therein are considered new to the research community. This book is the first systematic effort toward developing a better understanding of material selection for natural fiber composites. In this book, we’ve put a lot of emphasis on the use of material-selection tools, particularly the Analytical Hierarchy Process (AHP), a simple and yet very powerful tool in solving different issues in material selection for natural fiber composites, along with other established tools and techniques that can be implemented in natural fiber composite selection to expand the sustainable design possibilities, as well as to support the cleaner production needs for the future. The focus of this book is on natural fiber composites only—even though a book on the material selection of composite materials itself is still missing. This is because we have been dealing most, specifically, with natural fiber composites for more than15 years, and we felt, our contribution to this part of the topic was much greater and more information that was based on our own experiences in dealing with natural materials could be shared. Indeed, natural fiber composites, in the recent years, have become important materials used in different industries such as automotive, aerospace, defence, medical, furniture, and the building and construction industries, and the need for developing tools and techniques of material selection for such materials has become more apparent. In consequence, this book is filling a gap, documenting the latest research and developments, as well as providing a better understanding for a bio-based material selection system for various industrial applications with various conflicting criteria, as we have successfully introduced and established new methodologies for enhancing the selection of natural fiber composites and their constituents for sustainable industries, as well as having established new assessment methods for the material selection process in this field. Furthermore, this book is sufficiently important to support advancements in recent knowledge about natural fiber composite material selection, because without this book, researchers will have difficulty obtaining a step-by-step approach for this process. This does not mean the reader will be given a tool like software to help them with

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Preface

material selection, but this book can help and guide the reader to perform material selection for bio-based materials in uncertain environments, with simultaneous conflicting criteria and parameters, to achieve successful, low-cost, eco-friendly products, as well as satisfying both technical and customer satisfaction attributes in a fairly optimized manner. In dealing with natural fiber composites, we are more concerned with material selection criteria such as strength, stiffness, low cost, lightweight, availability, renewability, recyclability, biodegradability, and environmental friendliness. Most of these criteria are unique to natural fiber composites and therefore many industries are very serious about adopting these materials for their products. We are hopeful that this book will become a good guide for those who are dealing with material selection, and with design and product development from natural fiber composites. F. “Mohammed Khair” AL-Oqla Zarka, Jordan M.S. Salit Serdang, Malaysia

Introduction 1.1

1

Background

Materials and their characteristics are not only significant for determining the mechanical properties of a product, but also to explore products’ metaphysical features as they play a cornerstone role in customer satisfaction attributes (AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Sapuan, 2014c). Bearing in mind the tremendous need for sustainability as well as the awareness of the environmental impact, the proper synergy of the performance and recyclability of a particular product’s material has recently been given higher priority in engineering design (AL-Oqla & Omari, 2017). As it is of paramount importance for a particular design to attain success at low cost, proper material selection becomes critical in the engineering design field to make successful sustainable products. In fact, several issues, constrains, and limitations can disturb the implementation of a specific type of materials into a particular application (AL-Oqla & Sapuan, 2015a; Ashby, 2005; Dweiri & AL-Oqla, 2006). This makes compromising such constrains for the proper selection process of materials a complex matter where keen decisions must be made (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015b; Jahan, Ismail, Sapuan, & Mustapha, 2010; Rao & Patel, 2010). Due to the inherent cooperative interactions between the materials and their costs, obtainability, machinability, ease of forming, recyclability and performance, modern techniques like expert systems, optimizations, and informative decisions, are now employed to result in proper material selections (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015a; Dağdeviren, Yavuz, & Kılınc¸, 2009; Jahan, Ismail, Sapuan, et al., 2010). On the other hand, researchers in the modern industry, as well as engineers all over the world are continuously examining and exploring new material types and improved processes to enhance the development of products that achieve higher levels of customer satisfaction as well as modern aspects of functionality (Agoudjil, Benchabane, Boudenne, Ibos, & Fois, 2011; Ahuja, Mir, & Kumar, 2007; AL-Oqla & Sapuan, 2014a; AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015; Blume & Walther, 2013; Cosnier, 1999). This maintains industrial sectors’ competitive positions and enhances their profit margins. Many traditional materials that have been utilized in various engineering applications are now being replaced by new green materials to facilitate meeting the growing demand of environmental issues, weight reduction, as well as performance enhancement (AL-Oqla & Sapuan, 2014b; AL-Oqla & Sapuan, 2014c; Blume & Walther, 2013). In a scenario where the accessible set of materials is rapidly growing, multiple interactions among the various selection parameters occur, making the selection of the most appropriate material type for a particular application a difficult task (AL-Oqla & Omar, 2012, 2015; Dweiri & AL-Oqla, 2006; Jahan, Ismail, Sapuan, et al., 2010). In fact, it is estimated that more than 80,000 material types, including metallic alloys and nonmetallic engineering materials, are now

Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00001-3 Copyright © 2017 Elsevier Ltd. All rights reserved.

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Materials Selection for Natural Fiber Composites

available worldwide. Such materials include ceramics, glasses, plastics, composite materials, and semiconductors. For practical purposes, designers and engineers have to take into account several conflicting factors and materials-related features during the material selection process to achieve low-cost successful designs. These factors include physical properties (such as density, porosity, melting point, transparency, dimensional stability, and optical properties), mechanical properties (like Young’s Modulus, strength, fatigue, hardness, creep resistance, yield stress, ductility, and toughness), magnetic characteristics, electrical properties (permittivity, resistivity, dielectric strength), manufacturing abilities (machinability, castability, weldability, formability, heat treatability, etc.), material cost, durability, material impact on the environment, recyclability, performance characteristics, reliability, availability, fashion, market trends, cultural aspects, esthetics, thermal and radiation (conductivity, specific heat, reflectivity, transmissivity, diffusivity, emissivity), and surface (texture, corrosivity, wear resistance). Moreover, metaphysical properties, as well as the user-interaction aspects like perceptions, appearance and, emotions also must be considered during selecting materials in addition to the userinteraction aspects that affect the usability and personality of a product (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012; AL-Oqla & Sapuan, 2014b, 2014c; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015c; Ashby, 2005; Ayağ, 2014; Biron, 2013; Bledzki, Faruk, & Sperber, 2006; Dalalah, AL-Oqla, & Hayajneh, 2010; Jahan, Ismail, Sapuan, et al., 2010). Materials, on the other hand, are sometimes chosen either by trial and error or based upon what has been used before. Though this frequently works, systematic methods for materials selection are recently considered as a part of the engineering design process (AL-Oqla & Omar, 2012; Dalalah et al., 2010; Rao & Davim, 2008). Usually, several alternatives of materials are suitable for an application, however, the final selection is a compromise between their desired advantages and their disadvantages (AL-Oqla & Sapuan, 2015b; Karana, 2012). Therefore, the material selection process is an interdisciplinary effort and it often requires various fields of study, including industrial engineering, material science and engineering, mechanical engineering, as well as other expertise in the field of application.

1.2

Materials and design

Human beings deal with thousands of products in everyday use, from waking up in the morning through arriving at work, until returning home. Beds, mattresses, toothbrushes, cans, spoons, computers, sensors, tools, etc. are some of these products. Every single product is designed and manufactured in a specific way. Thus, it is worth trying to recognize the importance of materials, economic, processing, and quality decisions that are needed before a manufacturing process has started.

1.2.1

The design process

In the design process, efforts are made to translate the product ideas or market needs into detailed information by which products can be created. Typically, designers are responsible of choosing suitable materials for a product, as understanding the physical principles, the appropriate functioning and the manufacturing system would

Introduction

3

dramatically help in satisfactorily designing and producing the part (Biron, 2013). It is believed that questions should be asked by the designer to become familiar with the product for making design decisions. Such questions may include: l

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What does this product do? How does it do it? How much is this product worth? What does this product look like? How will the product be user-friendly? How can the product support its marketing?

Such questions, as well as others, are considered essential to allow for analysis of the product. After that, a step forward can be taken through other questions to help in design specifications. This may include a series of questions such as: l

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What are the (electrical, mechanical, ergonomic, appealing) requirements on every single part of the product? What is the function of every single component? How do these components work? What should every part be made of and why? Based on the number needed to be manufactured, which is the most appropriate manufacturing process or series of processes for each part? What are the feasible alternatives of materials and designs, and what are their pros/cons?

The answer of such questions would usually make a guide for the design process of the products. However, occasionally designers need to think of other appropriate specific questions for the products and components ( Jahan, Ismail, Mustapha, & Sapuan, 2010). Once the specification is realized, the next stage is to recognize how the materials are chosen or nominated. A good method for material selection is to plot two material properties on a chart, which is called a materials selection chart, so that designers can scan all possible materials from the reliable sources so no materials are overlooked. Once the materials are chosen, the next step is to start thinking of the processing routes and which manufacturing process is the most efficient for producing this product. In general, there are two main stages of selecting a suitable process: 1. Technical performance: the possibility of fabricating the product from the selected material. 2. Economics: the possibility of fabricating the product with a competitive cost.

Therefore, it is recommended to have a complete design to determine if the product should be either performance-driven or cost-driven. This also makes a large difference when one choose materials. For instance, a tennis racquet is a performance product because the cost is one of the last factors considered. On other hand, a drink bottle is cost-driven, as it will provide a sufficient performance that can do the task properly.

1.2.2

Materials performance

In engineering materials, there is always a discrepancy between theoretical properties and the tested ones (Almagableh, AL-Oqla, & Omari, 2017; Edwards, 2005; Sapuan, Haniffah, & AL-Oqla, 2016). In most cases, theoretical properties are more than those

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Materials Selection for Natural Fiber Composites

examined in the laboratory. In fact, there are many reasons behind this discrepancy, some include the fact that most of the materials are inhomogeneous, the internal cracks and defects, or the manufacturing imperfections, etc. This means that the practical life of the mechanical part will be less than the expected one. Component failure is the worst issue that designers struggle to avoid. The most vital factors that contribute to a component failure are: design mistakes, production defects, wrong materials selection, overloading the parts, and the insufficient maintenance. Therefore, engineered parts should be properly designed, and planes are to be set to prevent parts from any unexpected failure. To overcome this problem, many types of mechanical failure should be considered, starting from the design process and during the parts’ lifespan, such as: l

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Failure by fracture due to static overloads, either brittle or ductile failure. Buckling, in relatively long columns due to compressive overloading. Failure due to the impact loading or thermal shock. Failure by cyclic loading or fatigue fracture. Creep failure due to load, time, and temperature, even though the load is lower than yielding, and Failure due to extreme wear.

On the other hand, there are many causes for the mechanical failure that engineers should take into account during the design process which would dramatically affect the component’s life. These may include the following: l

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Manufacturing defects Improper or insufficient maintenance Unexpected vibrations Bad quality of material or wrong selection of materials Exceeding the service life Unexpected operating conditions Improper design or component integration Insufficient environmental protection Misuse or abuse Harsh environment such as chemical attack or corrosion Assembly faults Wrong heat treatments, and Inadequate quality assurance.

Consequently, selecting the correct materials, as well as implementing in a good design process would dramatically influence and enhance the service performance and engineering design because materials selection will affect the overall design configuration. The direct impact of the material properties on behavior in the final produced part and its indirect relation to the part geometry (Almagableh et al., 2017; Sapuan et al., 2016), as well as the manufacturing process are illustrated in Fig. 1.1. It clearly demonstrates that the component design occupied at the center of three cooperated or interconnected aspects, and the interaction among the product’s functional requirement, material characteristics, and the manufacturing process are also co-related, forming the cornerstone of the material selection process

Introduction

5

Functional requirement

Component design Manufacturing process

Material properties

Fig. 1.1 The Integration of factors that affect the design of a component.

(Barbero, 2010). That is, in the primary level of design, engineers should determine the mechanical, environmental, chemical, and other requirements. In advance levels, materials selection and design should satisfy all the requirements in order to build a successful, competitive, and reliable product.

1.3

Composite materials

The new technologies, as well as innovations of science, are primarily focusing on studying and reconsidering what has always been before our eyes, in simple structures of nature. Considering the advanced composite materials, nature paved the way thousands of years before our current advance composite materials and introduced very advanced and sophisticated examples of composite materials in a simple synergy everywhere, such as bones, wood, cellulose dispersed in lignin, etc. Currently, composites represent a huge progress in the science of materials, as they integrate the best attributes of several materials to empower them with outstanding desired characteristics. Thus, the study of composites is considered as a kind of philosophy of materials design toward enhancing the configuration of materials and their performance. It is thus that a science and a technology-oriented process, demanding a strict interaction between diverse subjects, such as the study of materials, design, structural analysis, and mechanics of materials. A composite material can be defined as the result of mixing two or more different materials to produce a new type of material with totally new properties. The new properties achieved are unique and superior in some aspects in comparison to the properties of the mixed constituents. Usually, one material of the composites is discontinuous, strong, and stiff, known as (strengthening), while the other(s) is/are weaker, less strong and continuous, known as matrix (Aridi, Sapuan, Zainudin, & AL-Oqla, 2016b). Some additional phases, known as interphase, could exist resulting from chemical reactions or other effects. In practice, three main factors can affect the composites’ properties: the properties of the constituents, the geometry/distribution of the phases, and the volume of the strengthening fraction with respect to the fibers

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Materials Selection for Natural Fiber Composites

(Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a). In fact, the distribution of the strengthening materials in the composites carries out the composites’ main features. As strengthening became less uniform, the composites became more heterogeneous. However, the geometry and orientation affect the anisotropy of the system (anisotropy: different properties in different directions). Composites, on the other hand, can generally be classified into three main categories: metal composites, ceramic composites, and polymeric ones (Ashby, 2005). However, there are two main families of composites according to the performance: low-to-medium performance composites, with usually short fibers or even particles as strengthening, which give limited levels of stiffness and local strength for the material. In such types of composites, the matrix is chiefly responsible of determining the mechanical characteristics of the composite. The other family, on the other hand, are the high-performance type of composites, regularly made out of continuous fibers that construct the frame of the composite and carry its stiffness and resistance in the direction of fibers (Fig. 1.2). The matrix phase here just protects and supports fibers, as well as transferring the strain from one fiber to another. The interphase, on the other hand, plays an important role in decreasing the failure mechanisms, enhancing the tensile strength, and controlling the stress-strain performance of the composite material (AL-Oqla, Alothman, Jawaid, Sapuan, & Es-Saheb, 2014; AL-Oqla & Omari, 2017). During the 1970s, composites applications became popular in many areas including aeronautics, automobiles, sports items, and biomedical applications. In the 1980s, a good development of properties occurred, where composites became stronger with high moduli. Nowadays, many researches focus on the development of modern composites to be able to work under a high temperature environment; this includes cement matrices mixed with mortar and resins. In fact, modern composites have recently wide applications where a specific oriented composite can be manufactured to satisfy particular application requirements. For instance, one of the most important modern materials is the nanoclay in natural fiber-polymer hybrid composites (Majeed et al., 2013; Najafi, Kord, Abdi, & Ranaee, 2012). This material type is very promising and it is a current subject for many research developments. Mixing the nanoclay with the natural fibers will improve the composite properties. For optimal properties of the

Fig. 1.2 Fiber-composite type of materials.

Introduction

7

hybrid composites, excellent dispersion and compatibility of the filler are required with the matrix. Hybrid natural fiber/nanoclay reinforced polymer composites can be obtained at a lower cost, while increasing their functionality and sustainability. Such composites have been widely used in many applications and the efforts towards green products are expected to lead to increasing the demands of such composites in the food industry, more specifically in packaging. An example of nanoclay is the polymer-silicate nanocomposites that could have three types of morphological structures (Alexandre & Dubois, 2000). A phase divided composite is gained if the matrix is incapable of intercalating between the silicate layers. These phases are shown in Fig. 1.3. Materials with improved mechanical properties are no doubt more desirable for various modern industries, including pharmaceutical and electronic packaging. However, regardless of their attractive characteristics, the lack of interfacial adhesion and compatibility between the fillers and the matrix limits their spread. Amendment of the resin in the hybrid composite can lead to designing and developing food packaging materials for a wide variety of applications, either stable or biodegradable. Another demand in such modern types of materials is to prevent the nanoparticles from the packaging materials to transfer to the packaged foods and to prevent their potential toxicological effects, if any. The applications of the modern advanced composites in general may include submerged pipes, boats, containers, space devices, sports equipment, civil industry application, automotive components, biomedical tools, high-tech applications, as well as many items that require high mechanical performance and dimensional stability, along with light weight (AL-Oqla & Omar, 2015; AL-Oqla & Sapuan, 2015a; Shah, 2013; Shen, Muduli, & Barve, 2015).

Cationic surfactant

Layered silicate

Polymer

Clay layer

Phase separated (microcomposite)

Intercalated (nanocomposite)

Exfoliated (nanocomposite)

Fig. 1.3 Three types of morphological structures of polymer-silicate nanoclay composites (AL-Oqla et al., 2017).

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Materials Selection for Natural Fiber Composites

1.4

Materials selection

Every distinguished material can be thought of as having a set of established characteristics or attributes. Designers usually seek a specific combination of these attributes or a property-profile for their products. Thus, the name of a particular material is the identifier for such a specific property-profile. The properties themselves are standard, such as the density, thermal and electrical conductivities, modulus, toughness, strength, and so on. Engineering materials can be classified into the six general families: metals, glasses, polymers, elastomers, ceramics, and hybrids (Chawla, 2012). This classification is based upon common features among each family. Every single family has the same properties, processing ways, and, often, general applications. l

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Metals have comparatively high moduli, in pure situations they are soft and easily deformed, they can be made strong with enough ductility by additives (alloying) and/or by heat treatment. Metal alloys can be formed by various manufacturing processes. Ceramics, on the other hand, also have high moduli, but they are brittle. Ceramics strengths in tension are much less than that of compression. Their strengths are usually about 15 times larger in compression. Besides, ceramics have no ductility. So, they are very sensitive to stress concentrations (holes, cracks, contact-stresses). Moreover, the strength of such brittle materials depends upon the volume of material under load and on loading time. So, ceramics are considered hard to design, unlike metals. However, ceramics have countless desired features like high stiffness, hardness, and resistance of abrasion and corrosion. Moreover, they can maintain their strength at high temperature levels. Glasses are amorphous materials, i.e., noncrystalline structure solids. The basic glass families are: (1) the soda-lime that has a content of about 75% (SiO2), (Na2O), (Na2CO3), (CaO), and other minor components. (2) Silicate glasses based on silica sand. They are usually brittle materials. Polymers have many advantages, such as being simply manufactured, low processing time, corrosion resistant, and a low friction coefficient. Polymers have low moduli, roughly 50 times less than metals have, but they can be strong—nearly as strong as metals. On the other hand, polymers have some disadvantages, like their weakness, low moduli, and they may creep at room temperature. Elastomers are amorphous polymers that having viscosity and elasticity as well. They are called viscoelastic materials and consist of long-chain polymers above their glass-transition temperature (Tg). They have weak intermolecular forces, and low elastic moduli. As their properties differ so much from those of other solids, special tests have evolved to properly characterize them. The common name for elastomers is rubber, as they are deformable and so can be used in seals, adhesives, tiers, and damping insulating parts. Hybrids are usually combinations of two materials or more from different material families. They combined to gather in some configuration and design in order to get the benefit from the advantages of each family, and avoid some disadvantages. Some examples of hybrids are: fiber and particle composites, sandwich structures, ropes, covers, foams, and definitely, the materials of nature (leaf, tree, bone, and skin). Most of these composites contain a polymer matrix and reinforcement. This reinforcement consists of fibers of glass or carbon. Hybrids have numerous advantages—they are light, stiff and strong, and can be tough. Because they contain polymers, hybrids usually cannot stand temperatures above 250°C.

Introduction

9

Due to the recent tremendous need and awareness regarding the environmental issues on one hand, and owing to the governmental emphasis upon the regulations with respect to sustainability concepts, the utilization of natural resources was encouraged. Accordingly, the natural fiber reinforced polymer composites (NFCs) became valuable alternative materials in the modern industries. In natural fiber composites, natural fibers (such as jute, sisal, hemp, oil palm, date palm, kenaf, rami, and flax) are used as fillers or reinforcements for polymer-based matrices. This would decrease the amount of waste disposal problems, and improve reduction in environmental pollution. Natural fiber composites are considered attractive from an environmental point of view and used as alternatives to the traditional glass/carbon composites. They can be implemented in different applications such as disposable accessories, building, packaging, furniture, insulation, and automotive industries (AL-Oqla, Sapuan, & Jawaid, 2016). Moreover, NFCs have numerous advantages over the traditional materials, including the low costs and densities, acceptable specific strengths and moduli, leading to low weight products. Furthermore, natural fiber composites are satisfactory from the environmental standpoint because they can contribute to producing recyclable and biodegradable goods after use (AL-Oqla, Sapuan, Ishak, & Nuraini, 2014). Comparable to the traditional synthetic fiber-composites, NFCs are considered much cheaper, and have good thermal and acoustic insulating features which would extend their industrial applications. In contrast, natural fibers have numerous advantages over the traditional glass fibers, such as: CO2 sequestration enhanced energy recovery, availability, reduced tool wear in machining, as well as reduced dermal and respiratory irritation (AL-Oqla et al., 2016; Faruk, Bledzki, Fink, & Sain, 2014; Kalia et al., 2011). However, natural fibers have some significant drawbacks like poor water resistance, low durability, and poor bonding with the matrix. These limitations of natural fiber composites may lead to undesirable properties and limit their industrial applications. On the other hand, materials have a serious role in engineering design and applications. They would dramatically affect the attributes of the designed products. Thus, materials selection is a process typically carried out by an engineer, designer, or materials scientist. Materials selection is a process aimed at selecting the most appropriate materials from a cluster of possible candidate materials by setting evaluation criteria after the proposed design has been finalized. For a very complex design, the process of selection becomes more difficult to perform where many functional and economic requirements should be explored before making the appropriate decision in choosing a particular material. If proper materials are successfully selected via systematic methodologies and techniques, producing successful sustainable products would then be achieved in a more efficient manner (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015d). It is believed that the proper compatibility between the material and its performance on one hand, and the synergy of the recyclability and the environment on the other, became critical in modern industries. In addition, developing new types of materials with desirable distinctive features would dramatically expand new design possibilities. Moreover, numerous criteria and limitations commonly affect the practice of a specific type of material in a precise application. Therefore, selecting a proper material

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Materials Selection for Natural Fiber Composites

for a specific application is a matter of multi-criteria decision-making problem, with a high uncertainty environment where proper decisions must be made based upon several conflicting evaluation standpoints (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015e; Al-Widyan & AL-Oqla, 2011, 2014; Saaty, 2013). In a scenario where the range of materials available to design engineers is wide and constantly increasing, a great challenge to designers appears. Thus, keen decisions in selecting the most suitable materials to achieve the desire functionality, as well as environmental issues and sustainability criteria, should be made make via huge efforts. This makes the material selection process a cornerstone in the engineering field. In fact, knowledge in various fields of engineering, as well as skills are required for proper materials selection, and such required knowledge may include an understanding of manufacturing processes, ergonomics, quality control, assembly processes, scheduling techniques, recycling schemes, maintenance, and safety as demonstrated in Fig. 1.4 (Pahl & Beitz, 2013). Therefore, various techniques and methods are usually utilized for selecting various materials. These include the conventional methods of materials selection, the famous Ashby chart, and various advanced materials selection tools and techniques. A flowchart of how to select materials is demonstrated in Fig. 1.5, where materials evaluation criteria and analysis, materials capabilities, data bases, experimental data, as well as modern computer-based selection techniques are usually utilized to perform the material selections. Such methods of selection, either using procedure-based tools or artificial intelligence tools, will be discussed in Chapter 3 of this book. Moreover, considering the selection of an appropriate material type from an environmental standpoint requires the study of the life cycle assessments (LCAs), particularly to compare between the natural and glass fiber composites. Such life cycle assessment is considered and identified as the key driver of using natural fibers and considering them as an attractive environmental performance alternative. LCA

Ergonomics

Quality control

Scheduling

Transport

Assembly

Production Knowledge required for materials selection

Recycling

Layout and form design

Maintenance

Safety

Costs

Fig. 1.4 Knowledge required for proper materials selection.

Operation

Introduction

11

Define the requirements of material

Processing performance analysis

Mechanical capability analysis

Economic properties analysis

Environmental properties analysis

List the candidate materials

Materials database

Experimental data initialization

Some computer-based selection methods like genetic algorithms (GA) and artificial neural networks (ANN) methods

GA

ANN

Coding and generate initial individual

Inputs

Crossover

ANN training

Mutation

Fitness evaluation N Y

Elimination of bad parents & children

Outputs

Elite individual Optimum solution

Fig. 1.5 A flow of material selection scheme. Adopted from Zhou, C.-C., Yin, G.-F., & Hu, X.-B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials & Design, 30, 1209–1215.

is a technique for evaluating the environmental features, along with the potential impacts associated with a product via collecting an inventory of related inputs and outputs of a product system and estimating the possible environmental impacts as a result of those inputs and outputs.

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Materials Selection for Natural Fiber Composites

Fiber crop cultivation

Monomer production

Natural fiber extraction & processing

GFRP component production

Compatibilizer production

Component use

Polymer production

Component end of life management -Land filling -Incineration -Composting

Fig. 1.6 Life cycle flowchart for comparing between natural and synthetic composites based components ( Joshi, Drzal, Mohanty, & Arora, 2004).

Moreover, the life cycle assessment method can read the consequences of both the inventory analysis and the impact assessments related to the objectives of the comparison study. The life cycle simple stages to compare between components prepared from natural fiber reinforced composite material and glass fiber reinforced polymer (GFRP) composites is displayed in Fig. 1.6. Generally, the natural fiber composites (NFCs) are likely to be environmentally superior to traditional glass fiber composites because of several reasons including: (1) Natural fibers production has lower environmental impacts comparable to that of glass fibers. (2) NFCs have higher fiber content of that of glass fiber composites for equivalent performance that enhance reducing more polluting base polymer content. (3) The light weight of NFCs has the capability to reduce emissions while using their products and can dramatically improve fuel efficiency, particularly in the automotive applications. (4) The natural fibers’ end of life incineration usually leads to recovered energy and carbon credits.

Furthermore, a study considered life cycle assessments of a side panel for Audi A3 car made from two dissimilar alternatives as (1) a design made from ABS co-polymer and (2) an alternative design made from hemp fiber (66 vol%)-epoxy resin composite has took into account both energy use and emissions up to the component manufacturing stage. Results of this study have proven that the natural fiber component uses 45% less energy, and results in lower air emissions, evidently proving that natural fibers have obvious superiority to the synthetic ones (Alves et al., 2010). However, as fertilizers are usually used in hemp cultivation, water emissions of nitrates, phosphates, and

Introduction

13

nitrogen oxide (NOx) emissions to air were higher than that of ABS. Moreover, most of the studies that considered the LCA for comparing natural and synthetic fibers have demonstrated that NFCs are environmentally superior to synthetic fiber composites on most performance metrics. On the other hand, regarding the matrix system, completely biodegradable materials are the most preferable materials to be used as matrices for the NFCs to fully satisfy regulations about using the eco-friendly and sustainable materials. However, the relative high cost of such types of materials is considered a disadvantage in comparison to commodity polymers such as Polypropylene that can be easily recycled with even lower costs, and exhibit a stable performance behavior during their expected lifetime and being capable of controlled degradation. The worldwide bio-based polymers production in various market segments in 2013 is illustrated in Fig. 1.7. The advantages, as well as the disadvantages of the natural fiber reinforced polymer composites, can be summarized in Table 1.1. Moreover, the appropriate selections and implementations of natural fibers in composites can be in advantage of the environmental performance, the economic growth as well as the industrial sustainability where available low-cost natural materials would be integrated with the industry to solve an environmental waste problem issue (AL-Oqla & Sapuan, 2014c). This would also result in achieving low-weight and cheap products with low energy consumption (Blume & Walther, 2013; Subramoniam, Huisingh, Chinnam, & Subramoniam, 2013). Besides, the availability of the natural fibers worldwide would also develop the productivity of the industry as no shortage of raw materials can occur (AL-Oqla & Sapuan, 2014c; AL-Oqla, Sapuan, Ishak, & Aziz, 2014). Furthermore, the desired degradability features of the natural Other 1%

Building and construction

Electrical and electronic

15%

Packaging—rigid (incl. food serviceware)

15% Packaging—flexible

4% 9%

Automotive and transports

16% 18% Textiles 18%

Functional 2% 3% Agriculture Consumer goods

Fig. 1.7 Worldwide bio-based polymers production in various market segments in 2013.

14

Materials Selection for Natural Fiber Composites

Table 1.1

Advantages and disadvantages of NFCs

Advantages l

Low densities.

l

High specific strength and stiffness. Durability can be improved considerably with treatment. Fibers are from renewable resources. Production requires little energy consumption. Production involves CO2 absorption, at the same time as returning oxygen to the environment. Low-cost fibers. Low hazard manufacturing processes. Low emission of toxic fumes at end of life. Less abrasive damage to processing equipment. More recyclability features.

l

l

l

l

l

l

l

l

l

Disadvantages l

Lower durability than synthetic-fiber composites.

l

High moisture absorption, which usually results in performance deteriorations. Lower strength, particularly the impact strength compared to synthetic-fiber composites. Greater variability of properties. Lower processing temperatures limiting the matrix options.

l

l

l

fibers would develop the environmental performance and the remanufacturing scheme by stimulating the end life products’ recyclability. Therefore, several applications are now utilizing the natural fiber composites for producing commercial products, like that of construction (like door/ceiling panels), food packaging, sporting goods, electrical/electronic components, furniture, and energy industry. Moreover, Greenline Jakob Winter Company in Germany has produced a laptop cover from organic bio plastics in addition to industrial cases and special brief-cases prepared from retainable natural fibers reinforced plastics (Saba, Jawaid, Sultan, & Alothman, 2017). Some of the natural fiber composites-based products are demonstrated in Fig. 1.8 for furniture, suitcases, and hemp eyewear, Fig. 1.9 for bathroom products and solid surface sinks, Fig. 1.10 for bio-based products in automotive and aerospace sectors like that of propeller system, electric charge station and interior car-door panels (Saba et al., 2017).

1.5

About this book

To enhance achieving more understanding about natural fiber composites for the sustainable modern societies, this book is the first systematic effort toward enhancing better understanding of materials selection for natural fiber composites. It primarily covers the use of various potential tools and techniques that can be implemented in natural fiber composite selection to expand the sustainable design possibilities, as well as supporting the cleaner production of them for the coming future. These techniques include the analytical hierarchy process, knowledge-based system; Java based materials selection system, artificial neural network, Pugh selection method, and digital

Introduction

15

Fig. 1.8 Some bio-based products (A) furniture, (B) suitcases, and hemp eyewear.

Fig. 1.9 Bio-based product like bathroom products and solid surface sink.

logic technique. However, the knowledge on related topics such as materials selection and design, natural fiber composites, and materials selection for composites will also be covered as a background for the main topic. On the other hand, current developments in the field of selecting the natural fiber composite material system, including

16

Materials Selection for Natural Fiber Composites

Fig. 1.10 Bio-based products in automotive and aerospace sectors including electric charge station, interior car-door panels and propeller systems.

the natural fiber composites and their constituents (fibers and polymers), is the main core of the book and it is discussed in detail from various technical, environmental, and economic points of view to enhance both environmental indices and the industrial sustainability theme. Moreover, recent developments in the topics of analytical hierarchy process in natural fiber composite materials selection, materials selection for natural fiber composites, and knowledge-based system for natural fiber composite selection are also discussed under uncertainty environment to enhance such bio-based materials selection process. This book, in addition, is filling a gap, documenting the latest research, as well as improving better understanding for such bio-based materials selection process for various industrial applications under wide conflicting criteria. On the other hand, the first author of this book, Dr. Faris M. AL-Oqla, had successfully introduced and established new methodologies for enhancing the selection of natural fiber composites and their constituents for sustainable industries, as well as establishing new assessment methods for material selection process in this field. Moreover, the co-author has worked with materials selection for composites and natural fiber composites for more than 15 years. He has published hundreds of publications in the topics of materials selection and natural fiber composites and several books on Materials selection and Design. As a consequence, the authors have the required knowledge for providing the information needed for promoting the natural fiber reinforced polymer composite selection systems, particularly when this information need has resulted from limited or unavailable sources in this topic in the form of

Introduction

17

books and monographs. The information in the forms of conferences and journal papers are also limited and scattered. Therefore, this book offers a systematic effort to fill the gap, as well as the real growing need to compile, discuss, compare, and present the information required for enhancing the natural fiber composite selection system in the form of a complete book to be beneficial for both academia and industrial sectors. Furthermore, this book is sufficiently important to support the advanced recent knowledge in natural fiber composite materials selection because without this book, researchers will be in a difficult situation to obtain a step-by-step approach in materials selection of natural fiber composites. It does not mean the readers will be given a tool like software to help them with materials selection, but this book will help and guide the readers to perform materials selection for bio-based materials under an uncertain environment, simultaneous conflicting criteria, and parameters to achieve successful low-cost, eco-friendly products, as well as satisfying both technical and customer satisfaction attributes to enhance future sustainable design possibilities in a fairly optimized manner. This book consists of seven chapters. The first chapter presents the background information about the book, such as the relationship between materials and design, the introduction to composite materials and natural fiber composites that will cover advantages and disadvantages of composites and applications of composite materials and a brief introduction of materials selection. Chapter 2 presents knowledge on natural fibers that are available as reinforcements for polymer composites. It also provides details on natural fiber composites such as advantages and drawbacks, matrix system, fiber ad matrix interfacial bonding, properties such as mechanical, thermal, environmental, chemical and physical properties of natural fiber composites and their applications. This chapter also discusses the challenging task in expanding the applications of natural fiber composites for several industrial applications. Chapter 3, on the other hand, focuses on various aspects and issues related to materials selection, including the role of materials selection in design and the need for materials selection. Discussion on tools and techniques in materials selection is also included in this chapter. Conventional methods of materials selection, the famous Ashby chart, and various advanced materials selection tools and techniques, either using procedure-based tools or artificial intelligence tools are also covered. In addition, Chapter 4 deals with materials selection for composites. This subject had been around for many decades, but there was no attempt in the past to compile them and to put them together in the form of books or parts of book. This chapter thus, provides knowledge and information on various issues in materials selection for composites, and selection of matrices and fibers. Research works on materials selection for composites and advanced techniques in composite materials selection are also treated. Chapter 5 considers the material selection of natural fiber composites. In this chapter, the introduction of materials selection for natural fiber composites is initially presented. The needs for materials selection for natural fiber composites, proper evaluations of the available natural fiber types and their capabilities for various industrial applications, as well as the appropriateness of a certain polymer type for particular fiber are then discussed. Moreover, the need for revealing new natural fiber types for natural fiber composites and its

18

Materials Selection for Natural Fiber Composites

relationship with environmental issues, pollutions and proper utilization of natural resources are also revealed. Issues and challenges in the traditional approaches in materials selection for natural fiber composites are presented too. In addition, a special topic on material selection of natural fiber composites using Analytical Hierarchy Process is presented in Chapter 6. It covers the appropriateness of the Analytical Hierarchy Process for natural fiber composite selection over other multi-criteria decision making methods. The added value of the simple and consistent pairwise comparisons for the selection of bio-based materials under uncertainty environment is presented as well. Several detailed case studies of evaluating and selecting natural fiber composite materials and their constituents utilizing the Analytical Hierarchy Process are also discussed to be a guideline for the reader in selecting, evaluating, and forming proper natural fiber composite for a particular application. The selection of the most appropriate fiber type, polymer matrix, and reinforcement conditions from various simultaneous technical, environmental, mechanical, chemical, as well as economic points of view for bio-based materials using Analytical Hierarchy Process are likely presented. Also the evaluation of various available natural fiber composites under conflicting criteria using Analytical Hierarchy Process is discussed to increase the reliability in the bio-based material selection process for sustainable design possibilities. The sensitivity analysis of the selection models using Analytical Hierarchy Process is also presented. This final chapter, on the other hand, elaborates on recent works on materials selection for natural fiber composites using various tools and techniques. These include Java-based materials selection, knowledge-based system, digital logic techniques, quality function deployment with environment, Pugh selection method, TOPSIS method, and artificial neural network approach.

References Agoudjil, B., Benchabane, A., Boudenne, A., Ibos, L., & Fois, M. (2011). Renewable materials to reduce building heat loss: Characterization of date palm wood. Energy and Buildings, 43, 491–497. Ahuja, T., Mir, I. A., & Kumar, D. (2007). Biomolecular immobilization on conducting polymers for biosensing applications. Biomaterials, 28, 791–805. Alexandre, M., & Dubois, P. (2000). Polymer-layered silicate nanocomposites: Preparation, properties and uses of a new class of materials. Materials Science & Engineering R: Reports, 28, 1–63. Almagableh, A., AL-Oqla, F. M., & Omari, M. A. (2017). Predicting the effect of nanostructural parameters on the elastic properties of carbon nanotube-polymeric based composites. International Journal of Performability Engineering, 13, 73. AL-Oqla, F. M., Almagableh, A., & Omari, M. A. (2017). Design and fabrication of green biocomposites. Green biocomposites. Cham, Switzerland: Springer. AL-Oqla, F. M., Alothman, O. Y., Jawaid, M., Sapuan, S. M., & Es-Saheb, M. (2014). Processing and properties of date palm fibers and its composites. Biomass and bioenergy. Cham, Switzerland: Springer. AL-Oqla, F. M., & Hayajneh, M. T. (2007). A design decision-making support model for selecting suitable product color to increase probability. In: Design challenge conference managing creativity, innovation, and entrepreneurship, Amman, Jordan.

Introduction

19

AL-Oqla, F. M., & Omar, A. A. (2012). A decision-making model for selecting the GSM mobile phone antenna in the design phase to increase over all performance. Progress in Electromagnetics Research C, 25, 249–269. AL-Oqla, F. M., & Omar, A. A. (2015). An expert-based model for selecting the most suitable substrate material type for antenna circuits. International Journal of Electronics, 102, 1044–1055. AL-Oqla, F. M., & Omari, M. A. (2017). Sustainable biocomposites: Challenges, potential and barriers for development. In M. Jawaid, S. M. Sapuan, & O. Y. Alothman (Eds.), Green biocomposites: Manufacturing and properties. Cham, Switzerland: Springer International Publishing (Verlag). AL-Oqla, F. M., & Sapuan, S. M. (2014a). Date palm fibers and natural composites. In: Postgraduate symposium on composites science and technology 2014 & 4th postgraduate seminar on natural fibre composites 2014, 28/01/2014, Putrajaya, Selangor, Malaysia. AL-Oqla, F. M., & Sapuan, S. M. (2014b). Enhancement selecting proper natural fiber composites for industrial applications. In: Postgraduate symposium on composites science and technology 2014 & 4th postgraduate seminar on natural fibre composites 2014, 28/01/ 2014, 2014, Putrajaya, Selangor, Malaysia. AL-Oqla, F. M., & Sapuan, S. M. (2014c). Natural fiber reinforced polymer composites in industrial applications: Feasibility of date palm fibers for sustainable automotive industry. Journal of Cleaner Production, 66, 347–354. AL-Oqla, F. M., & Sapuan, S. M. (2015a). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. Journal of the Minerals, Metals and Materials Society, 67(10), 2450–2463. AL-Oqla, F. M., & Sapuan, S. M. (2015b). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. JOM, 67, 2450–2463. AL-Oqla, F. M., Sapuan, S. M., Anwer, T., Jawaid, M., & Hoque, M. (2015). Natural fiber reinforced conductive polymer composites as functional materials: A review. Synthetic Metals, 206, 42–54. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Aziz, N. A. (2014a). Combined multi-criteria evaluation stage technique as an agro waste evaluation indicator for polymeric composites: Date palm fibers as a case study. BioResources, 9, 4608–4621. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2014b). A novel evaluation tool for enhancing the selection of natural fibers for polymeric composites based on fiber moisture content criterion. BioResources, 10, 299–312. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015a). Selecting natural fibers for industrial applications. In: Postgraduate symposium on biocomposite technology, March 3, 2015, Serdang, Malaysia. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015b). Decision making model for optimal reinforcement condition of natural fiber composites. Fibers and Polymers, 16, 153–163. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015c). Selecting natural fibers for bio-based materials with conflicting criteria. American Journal of Applied Sciences, 12, 64–71. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015d). A decision-making model for selecting the most appropriate natural fiber—Polypropylene-based composites for automotive applications. Journal of Composite Materials. http://dx.doi.org/ 10.1177/0021998315577233. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015e). Predicting the potential of agro waste fibers for sustainable automotive industry using a decision making model. Computers and Electronics in Agriculture, 113, 116–127.

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AL-Oqla, F. M., Sapuan, S. M., & Jawaid, M. (2016). Integrated mechanical-economic— Environmental quality of performance for natural fibers for polymeric-based composite materials. Journal of Natural Fibers, 13, 651–659. Alves, C., Ferra˜o, P., Silva, A., Reis, L., Freitas, M., Rodrigues, L., et al. (2010). Ecodesign of automotive components making use of natural jute fiber composites. Journal of Cleaner Production, 18, 313–327. Al-Widyan, M. I., & AL-Oqla, F. M. (2011). Utilization of supplementary energy sources for cooling in hot arid regions via decision-making model. International Journal of Engineering Research and Applications, 1, 1610–1622. Al-Widyan, M. I., & AL-Oqla, F. M. (2014). Selecting the most appropriate corrective actions for energy saving in existing buildings A/C in hot arid regions. Building Simulation, 7, 537–545. Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016a). Investigating morphological and performance deterioration of injection molded rice husk-polypropylene composites Due to various liquid uptakes. International Journal of Polymer Analysis and Characterization, 21(8), 675–685. Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016b). Mechanical and morphological properties of injection-molded rice husk polypropylene composites. International Journal of Polymer Analysis and Characterization, 21, 305–313. Ashby, M. F. (2005). Materials selection in mechanical design. Cambridge: ButterworthHeinemann. Ayağ, Z. (2014). An integrated approach to concept evaluation in a new product development. Journal of Intelligent Manufacturing, 27(5), 991–1005. Barbero, E. J. (2010). Introduction to composite materials design. Boca Raton, FL, USA: CRC press. Biron, M. (2013). Thermosets and composites: Material selection applications, manufacturing and cost analysis. Oxford, UK: Elsevier. Bledzki, A. K., Faruk, O., & Sperber, V. E. (2006). Cars from bio-fibres. Macromolecular Materials and Engineering, 291, 449–457. Blume, T., & Walther, M. (2013). The end-of-life vehicle ordinance in the German automotive industry—Corporate sense making illustrated. Journal of Cleaner Production, 56, 29–38. Chawla, K. K. (2012). Composite materials: Science and engineering. New York: Springer. Cosnier, S. (1999). Biomolecule immobilization on electrode surfaces by entrapment or attachment to electrochemically polymerized films. A review. Biosensors and Bioelectronics, 14, 443–456. Dağdeviren, M., Yavuz, S., & Kılınc¸, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36, 8143–8151. Dalalah, D., AL-Oqla, F., & Hayajneh, M. (2010). Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of cranes. Jordan Journal of Mechanical and Industrial Engineering, 4, 567–578. Dweiri, F., & AL-Oqla, F. M. (2006). Material selection using analytical hierarchy process. International Journal of Computer Applications in Technology, 26, 182–189. Edwards, K. (2005). Selecting materials for optimum use in engineering components. Materials & Design, 26, 469–473. Faruk, O., Bledzki, A. K., Fink, H. P., & Sain, M. (2014). Progress report on natural fiber reinforced composites. Macromolecular Materials and Engineering, 299, 9–26. Jahan, A., Ismail, M. Y., Mustapha, F., & Sapuan, S. M. (2010a). Material selection based on ordinal data. Materials & Design, 31, 3180–3187.

Introduction

21

Jahan, A., Ismail, M. Y., Sapuan, S. M., & Mustapha, F. (2010b). Material screening and choosing methods—A review. Materials & Design, 31, 696–705. Joshi, S. V., Drzal, L., Mohanty, A., & Arora, S. (2004). Are natural fiber composites environmentally superior to glass fiber reinforced composites? Composites Part A: Applied Science and Manufacturing, 35, 371–376. Kalia, S., Dufresne, A., Cherian, B. M., Kaith, B., Averous, L., Njuguna, J., et al. (2011). Cellulosebased bio-and nanocomposites: A review. Journal of Polymer Science, 2011, 1–35. Karana, E. (2012). Characterization of ‘natural’ and ‘high-quality’ materials to improve perception of bio-plastics. Journal of Cleaner Production, 37, 316–325. Majeed, K., Jawaid, M., Hassan, A., Abu Bakar, A., Abdul Khalil, H., & Salema, A. (2013). Potential materials for food packaging from nanoclay/natural fibres filled hybrid composites. Materials & Design, 46, 391–410. Najafi, A., Kord, B., Abdi, A., & Ranaee, S. (2012). The impact of the nature of nanoclay on physical and mechanical properties of polypropylene/reed flour nanocomposites. Journal of Thermoplastic Composite Materials, 25, 717–727. Pahl, G., & Beitz, W. (2013). Engineering design: A systematic approach. London, UK: Springer Science & Business Media. Rao, R., & Davim, J. (2008). A decision-making framework model for material selection using a combined multiple attribute decision-making method. International Journal of Advanced Manufacturing Technology, 35, 751–760. Rao, R., & Patel, B. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31, 4738–4747. Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61, 1101–1118. Saba, N., Jawaid, M., Sultan, M., & Alothman, O. Y. (2017). Green biocomposites for structural applications. Green biocomposites. Cham, Switzerland: Springer. Sapuan, S. M., Haniffah, W., & AL-Oqla, F. M. (2016). Effects of reinforcing elements on the performance of laser transmission welding process in polymer composites: A systematic review. International Journal of Performability Engineering, 12, 553. Shah, D. U. (2013). Developing plant fibre composites for structural applications by optimising composite parameters: A critical review. Journal of Materials Science, 48, 6083–6107. Shen, L., Muduli, K., & Barve, A. (2015). Developing a sustainable development framework in the context of mining industries: AHP approach. Resources Policy, 46, 15–26. Subramoniam, R., Huisingh, D., Chinnam, R. B., & Subramoniam, S. (2013). Remanufacturing decision-making framework (RDMF): Research validation using the analytical hierarchical process. Journal of Cleaner Production, 40, 212–220.

Further Reading Zhou, C. -C., Yin, G. -F., & HU, X. -B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials & Design, 30, 1209–1215.

Natural fiber composites 2.1

2

Natural fibers

Natural fibers are considered the main constituent type in the natural fiber reinforced polymer composites, as it is the independent constituent of the composite (AL-Oqla & Sapuan, 2015a, 2015b; AL-Oqla, Sapuan, & Jawaid, 2016). That is, the term “independent constituent” is proposed because the polymer matrix is typically nominated to be suitable for a particular natural fiber type, but not the opposite. This is because natural fibers have their own natural characteristics that cannot be dramatically changed or modified (as they are part of natural sources like plants) compared to that of polymers, which have characteristics that may be changed in the future, or it is possible to produce new polymers with more desirable capabilities, but the capabilities of natural fibers may not be significantly changed, even if some treatments are considered (AL-Oqla & Sapuan, 2014b, 2014c). A composite material is a system made of two or more materials in such a way to reach better desired properties and mechanical performance than those the individual constituents possess. Composites in general have two main types of materials; the strengthening one which is usually discontinuous, strong, and stiff (like fibers and particulates), and the other weaker type, less strength and continuous, which is known as a matrix. Some additional phases known as interphase could exist as a result of chemical reactions or other effects. The interfacial bonding between the strengthening materials and matrices has a vital role in governing the mechanical properties of produced composites. As stress is transferred inside the composite, good interfacial bonding between constituents is essential to attain the desired characteristics of the composite. In fact, three main factors affect the composites’ characteristics: The properties of the constituents, the geometry and distribution of the strengthening, as well as the volume of the strengthening fraction with respect to the matrix. The distribution of the strengthening usually carries out the composites features. As strengthening became less uniform, the composites became more heterogeneous, while the geometry and orientation affect the anisotropy of the produced composite, resulting in anisotropy composite, (different properties in different directions). After the Second World War, composites produced from reinforcing polymers with synthetic fibers were initiated. The mechanical properties of the final composites were dramatically enhanced compared to the properties of the involved polymers. Early in the 1960s, such polymeric-based composites were very expensive and were used in very limited applications. However, by the 1990s their costs were drastically decreased, and thus, were utilized in a wider range of applications (Agoudjil, Benchabane, Boudenne, Ibos, & Fois, 2011; AL-Oqla & Sapuan, 2015b; Shah, 2014). But this led to a huge amount of production, and substantially, in their wastes, caused a serious environmental issue (AL-Oqla & Sapuan, 2014c). Beside this, the increasing prices of petroleum and the depletion of their resources as well, developed Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00002-5 Copyright © 2017 Elsevier Ltd. All rights reserved.

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Materials Selection for Natural Fiber Composites

a new trend to utilize natural resources as well as the bio-based composites. These composites are based upon plants and agricultural stocks that are renewable (Agoudjil et al., 2011; AL-Oqla & Sapuan, 2014c; Mohanty, Misra, & Drzal, 2005). Without a doubt, it is not possible to completely replace all the petroleumbased products with bio-based ones. However, a combination of both can lead to useful products (Ahmed & Vijayarangan, 2008; Mohanty et al., 2005). The low-cost, eco-friendly, as well as low densities of the natural fiber composites on the other hand, have dramatically increased their importance for producing sustainable green products. Moreover, their less abrasiveness, renewability, and less energy consumptions during processing are extra added features demonstrating their increasing popularity. However, some challenges are facing the trend of utilizing the natural fibers in different applications in industry (Almagableh, AL-Oqla, & Omari, 2017; AL-Oqla & Omari, 2017; AL-Oqla, Almagableh, & Omari, 2017). One important challenge is their incompatibility with polymers, as the natural fibers are hydrophilic in nature, while most used polymers are hydrophobic. Many investigations and tests have been conducted to reduce this incompatibility by using coupling agents, and by means of physical and chemical treatments. The low permissible temperature is another challenge during forming the natural fiber composites to avoid thermal degradations of fibers, which also limits the polymer selection for such natural fibers. Natural fibers generally are those that already exist in nature and can be achieved via different mechanical and chemical processes. In fact, nature has offered humanity numerous types of natural fibers with various colors, sizes, and shapes. These fibers can be classified based on their origin into three main categories: l

l

l

Plants or vegetable fibers, based on natural cellulous/lignocelluloses fibers that consist of macroscopic particles (millimeters) and are obtained by specific technologies, like crushing the woody material which are picked from filaments, or long elements (meters) which are usually obtained from the strong and dense leaves of tropical plants in the wild. A third option to obtain plant fibers is usually performed via severe chemical treatments of the vegetable matrix from which they are to be extracted. Plant fibers can also be classified as bast fibers, leaf, fruit, and seed-hair fibers. The most well-known plant fibers are: oil palm, wood, rice straw, sisal, ramie, hemp, doum fruit, bagasse, pineapple leaf, cotton, flax, date palm, rice husk, wheat straw, curaua, coir, jowar, kenaf, bamboo, rapeseed waste, and jute (AL-Oqla & Omari, 2017; AL-Oqla, Alothman, Jawaid, Sapuan, & Es-Saheb, 2014; Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a; Jawaid & Abdul Khalil, 2011) Animal fibers, which are natural fibers consisting of certain proteins like silk, hair, wool, feathers and other uncommon sources. Mineral fibers (asbestos).

However, the term “natural fibers” in this book will be directed only to the first type of fibers, which is the lignocellulosic (plant) fibers. Natural fibers and their classifications are illustrated in Fig. 2.1. On the other hand, it is believed that implementing agricultural raw material sources into plastic industry would not only provide a renewable source of materials, but also contribute generating a nonfood source of economic development for more than a few countries; as for any long-term commercial development there must be a guaranteed of long-term resource supply.

Natural fiber composites

25

Fiber

Synthetic

Natural

Organic fiber Animal

Silk

Wool

Mineral Asbestos

Hair

Inorganic fiber

Aramid/ kevlar

Cellulose/lignocellulose

Glass

Polyethylene

Carbon

Aromatic polyester

Boron Silicacarbide

Bast

Leaf

Seed

Fruit

Wood Soft wood Hard wood

Stalk

Grass/reeds

Jute

Sisal

Kapok

Coil

Flax

Banana

Cotton

Oil palm

Hemp

Abaca

Loofah

Barley

Corn

Ramie

PALF

Milk weed

Maize

Sabai

Oat

Rape

Rye

Esparto

Kenaf

Henequen

Roselle

Agave

Mesta

Agave

Rice

Bamboo

Wheat

Bagasse

Cancry

Fig. 2.1 Natural fibers classifications (AL-Oqla, Alothman, et al., 2014).

Plants fibers consist of hollow cellulose fibrils held together by the lignin as a binder in the hemicellulose as a matrix, thus they are considered as composites. The cell wall of the plant fiber is inhomogeneous where each particular type of species of plant has its own unique fiber characteristics. Fibers have a complicated structure made up of many layers. The thin primary wall is surrounding a secondary wall throughout cell growth. The secondary wall, which consists of three different layers, the most important layer is the middle, since its thickness controls the ultimate mechanical behavior of the fiber. The middle layer is complicated and consists of helical cellular microfibrils of between 10 and 30 nm in length. In more detail, the microfibrillar angle, which is the angle between the axis of the fiber and the microfibrils, has some characteristic value that varies from one fiber to another. The quality of the fibers (smoothness and dexterity) is controlled by this microfibrillar angle. The amorphous matrix in the cell wall is very complex. It consists of hemicellulose, pectin, and lignin. The hemicellulose molecule is hydrogen bonded to cellulose and performs as cementing matrix between the cellulose microfibrils to build the main structural

26

Materials Selection for Natural Fiber Composites

Lumen Secondary wall S3 Hellically arranged crystalline cellulose microfibrils

Secondary wall S2

Amorphous region: mainly lignin and hemicellulose

Secondary wall S1

Primary wall

Disorderly arranged crystalline cellulose microfibrils

Fig. 2.2 A schematic structure of a natural fiber (AL-Oqla, Alothman, et al., 2014).

element of the fiber cell, which is called the cellulose-hemicellulose network. The main goal of hydrophobic lignin and pectin elements is to increase the stiffness of the cellulose/hemicellulose element. A schematic structure of a natural fiber is illustrated in Fig. 2.2.

2.1.1

Cellulose

Cellulose is the most available biopolymer on earth. Trees and grass are plants composed of this material. The most common and available type of cellulose is cotton. Cellulose is considered as a linear organic compound and has the formula (C6H10O5)n. Hundreds of D-glucose units are connected together by β (1–4) bonds (Fig. 2.3). They are capable of forming strong inter- and intra-molecular bonds and aggregated bundles

H

CH2OH

OH H

OH

H O

H

CH2OH

H H

OH

H

OH

H

O

H

H

CH2OH

n

Fig. 2.3 The chemical structure of cellulose.

O

O

O

O

CH2OH H

O H

OH

OH

Natural fiber composites

27

Cellulose fibrils in a plant cell wall

Cell wall

Fibril

Plant

OH

CH2OH OH

O

O OH

O

OH O OH

OH

CH2OH OH

OH O

OH O

O

O

O

O

CH2OH

CH2OH

β Glucose monomer

OH

OH

O

OH

CH2OH

O

O

OH

O

O

CH2OH

OH

CH2OH

CH2OH

OH

CH2OH

OH

OH

O

O

OH

OH O

OH

CH2OH

O

O OH

O

(microfibrils)

O OH

O

Cellulose molecules

OH O

OH

O

O

CH2OH

O

CH2OH

Fig. 2.4 Arrangement of fibrils, microfibrils, and a cellulous in a cell wall of a typical plant fiber.

of molecules due to the linearity behavior of cellulose molecules. In the literature, cellulose pack has been given many names: protofibrils, elementary fibrils, microfibrils, etc. Fig. 2.4 shows some arrangement of different structural aspects in regular plant fiber.

2.1.2

Hemicellulose

Hemicelluloses are heterogeneous bi-polymers (with a degree of polymerization from 200 to 300). Dry wood, for instance, has 20%–30% by weight of hemicelluloses. A wide range of hemicellulose biopolymers are made of monomers. The strength and hardness of the plant fibers are intrinsically linked with the percentage and extent of monomers in the polymers. Examples of monomers are mannose, arabinose, galactose, glucose, and xylose. Besides these monomers, acidic sugars like glucuronic and its acids also exist in hemicellulose polymers. Some of these components are removed during handling of the plant fibers for making plant-based composites.

2.1.3

Lignin

Lignin is a combination of heterogeneous tri-polymers. The weights of lignin in a plant fiber vary approximately from 2% up to 45% (AL-Oqla & Sapuan, 2014c; AL-Oqla, Sapuan, Ishak, & Nuraini, 2014). Lignin makes the plant fiber a compact

28

Materials Selection for Natural Fiber Composites

property. Lignin is polymers based on basically three monolinguals: p-coumaryl, sinapyl, and coniferyl alcohol. The main factors that influence reinforcing efficiency of fillers in a matrix are: fiber dispersion, orientation, length distribution, and matrix adhesion. Agro fibers are both hydrophilic and polar in nature. Polymers on the other hand, are hydrophobic and nonpolar. This conflict in nature for fibers and polymers usually causes a decrease in the true life of the composites. Therefore, the chemical compositions, as well as physical properties of a particular fiber type, have a major role in determining its mechanical performance and thus its appropriateness for the natural fiber reinforced polymer composites (AL-Oqla & Sapuan, 2014c; AL-Oqla, Sapuan, Ishak, & Nuraini, 2014, 2015d; AL-Oqla, Sapuan, & Jawaid, 2016; Sapuan, Haniffah, & AL-Oqla, 2016). The chemical compositions of some plant fibers are tabulated in Table 2.1 (Hakeem, Jawaid, & Rashid, 2014; Menon & Rao, 2012), whereas the physical (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; Jawaid & Abdul Khalil, 2011; Shah, 2013), and mechanical properties ( Jawaid & Abdul Khalil, 2011; Symington, Banks, West, & Pethrick, 2009) are mentioned in Tables 2.2 and 2.3, respectively.

2.2

Advantages and disadvantages of natural fiber composites

In fact, there are two main types of composites according to the performance standpoint: (1) the low-to-medium performance composites. This kind usually has short fibers or even particles as strengthening; they give a certain stiffness and local strength for the material, whereas the matrix is only responsible for determining the mechanical features of the material. (2) The high performance composites which are typically Table 2.1

The chemical composition of some natural fibers Chemical composition (% dry wt)

Fiber type

Cellulose

Hemicellulose

Lignin

Barley hull Barley straw Bamboo Banana waste Corn cob Cotton Cotton stalk Coffee pulp Eucalyptus Hardwood stems Rice straw Rice husk Wheat straw Wheat brain

34 36–43 49–50 13 32.3–45.6 85–95 31 33.7–36.9 45–51 40–55 29.2–34.7 29.7–35.6 35–39 10.5–14.8

36 24–33 18–20 15 39.8 5.1–15 11 44.2–47.5 11.1–18 24–40 23–25.9 11.9–29.3 22–30 35.5–39.2

19 6.3–9.8 23 14 6.7–13.9 0 30 15.6–19.1 29 18–25 17–19 15.4–20 13–16 8.3–12.5

Natural fiber composites

Table 2.1

29

Continued Chemical composition (% dry wt)

Fiber type

Cellulose

Hemicellulose

Lignin

Grasses Sugarcane bagasse Sugarcane tops Pine Poplar wood Olive tree biomass Jute fibers Switchgrass Winter rye Oilseed rape Softwood stem Oat straw Nut shells Sorghum straw

25–40 25–45 35 42–49 45–51 25.2 45–53 35–40 29–30 27.3 45–50 31–35 25–30 32–35

25–50 28–32 32 13–25 25–28 15.8 18–21 25–30 22–26 20.5 24–40 20–26 22–28 24–27

10.2–30 15–25 14 23–29 20–21 19.1 21–26 15–20 16.1 14.2 18–25 10.1–15 30–40 15–21

Table 2.2

Fiber type Oil palm Coconut coir Banana Pineapple leaves Jute Sisal Flax Cotton Ramie Kenaf (bast) Kenaf (core) Bagasse Bamboo Rice Corn Sunflower

The Physical properties of some natural fibers Fiber length (mm)

Fiber diameter (μm)

Thickness of single cell wall (μm)

Width of lumen (μm)

0.6–1.4 0.3–1.0

8.0–25.0 12.0–14.0

– 0.06–8.0

6.9–9.8 –

0.1–4.2 3.0–9.0

12.0–30.0 5.9–80.0

1.2–1.5 1.8–8.3

13.4–22.4 2.4–3

0.8–6.0 0.8–8.0 10.0–65.0 15.0–56.0 30.0–60.4 1.4–11.0

5.0–30.0 7.0–47.0 5.0–38.0 10.0–45.0 7.0–80.0 4.0–36.0

5.2–11.3 8.0–25.0 10.0–20.0 3.6–3.8 2.8–3 1.6–12.6

3.4–7.6 8.0–12.0 – 15.7–16.4 12.8–13.0 5.4–11.1

0.4–1.1

0.27–37.0

0.5–11.5

14.8–22.7

0.7–2.8 2.0–3.0 0.4–1.2 0.4–1.4 0.5–1.4

10.0–40.0 14.0–17.8 8.0–15.5 12.1–26.7 16.1–36.1

1.4–9.4 3.0–9.0 2.0–5.6 2.4–6.5 2.2–9.4

1.0–19.1 3.8–8.6 1.1–8.7 2.4–20.1 3.2–24.6

30

Materials Selection for Natural Fiber Composites

Table 2.3

The mechanical characteristics of some natural fibers

Types of Fiber

Density (g/cm3)

Young’s modulus (GPa)

Tensile strength (MPa)

Elongation at break (%)

Oil palm Ramie Banana Cotton Hemp Coir Sisal Kenaf Flax Jute Pineapple leaf Abaca Bamboo Date palm leaf Date Palm

0.7 1.5 1.3 1.5 1.5 1.2 1.3 1.1 1.5 1.5 1.4

3.2 44 33 12 70 44 38 53 58 60 4.4

248 500 355 400 550 500 600 930 1.339 860 126

2.5 2 5.3 3 1.6 2 2 1.6 3.2 2 2.2

1.5 0.9 0.9

6.2 35 11

764 503 309

2.6 1.4 2.7

1

2.7

377

13

made out of continuous fibers that construct the frame of the material and carry its stiffness and resistance in the direction of fiber. The matrix phase here protects and supports fibers, as well as transferring the strains from a fiber to another one (Aridi, Sapuan, Zainudin, & AL-Oqla, 2016b). The interphase between the fiber and polymer plays an important role in decreasing the failure mechanisms, the tensile strength, and controlling the strains/stresses performance of the material. The outstanding properties of fibers make them perfect for making particular types of composites. Some of these properties are: high failure stress, high modulus of elasticity in tension, light weight, and elastic behavior linearly up to failure. Therefore, such types of advanced composites (both synthetic and natural) are fiber role-oriented composites. The most common available fibers beside the natural (plant) ones are: glass, carbon, mineral fibers, and organic. They exist in many shapes: composites, continuous, or woven. Some details about each kind of fibers are shown below.

2.2.1

Glass fibers

Five types of glass fibers are listed according to their specifications and applications: 1. E-glass: used mainly for electrical applications. 2. S-glass: higher strength fibers. Tensile strength is about 33% more than strength of E-glass (see Fig. 2.5). 3. C-glass: generated from high alkaline glass, has super chemical resistance, but has low electrical properties.

Natural fiber composites

31

Fiber direction

Fig. 2.5 Uniaxial glass fibers. 4. D-glass: this type has great electrical properties. 5. L-glass: this type has lead, thus it is utilized as a good radiation protection. It is usually used as a pathway in the x-ray tracking of fibers.

Generally, all these glass types have high strength-to-weight ratios, although, these fibers are synthetic inorganic fibers with the highest densities comparable to other fiber types. Moreover, glass fibers keep about 50% of their mechanical properties under a temperature of 375°C, and around 25% under a higher temperature reaching to 538°C.

2.2.2

Carbon fibers

Despite all great properties for glass fibers, they are weak in tensile. That is why research on other kind of fibers began around three decades ago to make a step forward in this engineering science by using organic composites, carbon, and graphite (Fig. 2.6). Those kinds of fibers have higher mechanical properties because of their crystal structure. A graphite crystal structure shows overlapped layers of carbon atoms, those atoms bonded together by a strong bond called covalent bonding, whereas, the bond among different layers is weak, this is called Van-der-Waals. Depending on the manufacturing process, the crystal structure could be arranged in the preferred direction. Practically, it is very difficult to have perfect crystals. So, the effective properties will be less than expected. Currently, high-modulus fibers with low strength, and low-modulus with high strength fibers exist in the market.

2.2.3

Aramid fibers

Aramid fibers are strong synthetic fibers with high heat-resistant properties. They are used in military, aerospace, bicycle tires, and some other applications. In the 1970s, DuPont introduced Aramid as one of the most important fibers with high modulus.

32

Materials Selection for Natural Fiber Composites

Fig. 2.6 Uniaxial carbon fibers.

Firstly, the aramid fibers have been established to replace the wires made of steel in radial tires. The benefits were to reduce the weight, achieve higher resistance, and long durability (see Fig. 2.7). The production processes are similar to any other synthetic fibers: drawing, polymerization, extrusion, etc. The extrusion process of aramid fibers occurs at a temperature of about 200°C for molten polymers, while evaporation process is used to make the solvent. The extrusion can only happen via a solution, because the melting temperature of the fibers is higher than the decomposition temperature. At this phase, the product has only 15% of the strength and 2% of the stiffness of the final stage. The polymer has a structure

Fig. 2.7 Biaxial aramid fibers.

Natural fiber composites

33

produced of laminates with little orientation with respect to the axial of the fiber. Orientation and crystallization can be attained by stretching the fiber at 300–400°C.

2.2.4

Hybrid fabrics

Hybrid fabrics are used to achieve optimal ratio between the performances and the costs of the fabric. Various chemical compositions, different weights, and mechanical properties can be applied within the same fabrics. Therefore, a fabric can be designed in the required specification with low cost. For example, a mixture of carbon fibers and aramid in the weft and warp arrangement is possible, resulting in a composite with diverse elastic properties in the main directions of stress. Despite the above mentioned fibers, the natural fiber composites have major advantages over the synthetic based fibers. Beside the low cost and the light weight, biobased polymer composites (natural fiber reinforced composites) gained more attention due to their renewability and biodegradability. More advantages of natural fibers are listed below (Bledzki & Gassan, 1999): 1. They contribute to the consumption of CO2 gas. 2. The amount of the CO2 emission from burning fibers at the end of their lives is neutral. 3. The low abrasive nature of fibers makes their processing easier and more recyclable.

In addition to these advantages, the environmental concern, and the rate of consumption for petroleum are other factors pushing the whole world toward more use of the natural and sustainable resources and to go green. Joshi, Drzal, Mohanty, and Arora (2004) have investigated the life cycles for both the natural fibers and the glass fibers. From the environmental point of view, they concluded that the natural fibers are superior in most of the cases. This conclusion was justified by the following reasons: l

l

l

l

l

Processing the natural fibers will have less impact on the environment; For a given application, and to have same performance, a higher percentage of natural fibers will be required in comparison with the glass fibers. This will require reducing the resin percentage, and substantially, will lead to pollution reductions caused by polymers. Better efficiency is attained with the low density of the natural fibers. Thus, less pollution during operation in the automotive applications will be emitted. At the end of the natural fiber’s life, their burning produces energy and carbon. However, high emission of nitrate and phosphate may exist if the cultivation of the natural fibers used fertilizers. This can be very hazardous on the local water resources (Summerscales, Dissanayake, Virk, & Hall, 2010). The energy consumed in processing the natural fibers is much lower than that of the glass fibers. For example, the energy required to produce natural fibers from flax is about 17% of the energy required for the same quantity of glass fibers (Holbery & Houston, 2006). Moreover, Mohanty, Misra, and Drzal (2002a) have shown that the mechanical properties of the natural fiber composites are similar or even better than the glass fiber reinforced composites. Besides, natural fibers have other advantages comparable to the synthetic ones including the availability, low cost, good thermal and acoustical insulation characteristics, energy recovery, reduced tool wear in machining operations, degradability, and reduced dermal and respiratory irritation (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla,

34

Materials Selection for Natural Fiber Composites

Sapuan, & Jawaid, 2016; Kalia, James Njuguna, Alain Dufresne, & Cherian, 2011; Mir, Zitoune, Collombet, & Bezzazi, 2010; Sarikanat, 2010).

Therefore, implementing natural fibers into the plastic industry would dramatically enhance providing a renewable source of materials to guarantee long-term resources supply that evidently support the required industrial sustainability, as well as expand finding new green design possibilities at low costs to enhance the future cleaner production theme. However, replacing the traditional synthetic fibers by the natural ones is still a challenge. On the other hand, extensive research is still needed to be conducted to overcome the drawbacks before utilizing the natural fibers in polymer composites. One of these drawbacks is the high moisture absorption (AL-Oqla, Sapuan, Ishak, & Nuraini, 2014). This will lower the processing temperature and make the natural fibers nonuseful at elevated temperatures due to the degradation that alters the properties of the entire composite. To elaborate more, the high moisture absorption narrows the selection of applications where the composites can be used, and the low processing temperature on the other hand, narrows the choices of the matrix selection to those having low melting temperatures. One more drawback that limits the utilization of the natural fibers in polymers is their poor adhesion, or their incompatibility. Cellulose is hydrophilic in nature, while polymers are hydrophobic. This incompatibility can be overcome by means of three methods; using a coupling agent between the fibers and the polymers, enhancing the natural fiber properties prior to fabricating the composites, and carefully selecting the proper method for the composite production (AL-Oqla & Sapuan, 2014a; Mohanty, Misra, & Drzal, 2002b). Another drawback of the natural fibers is their irregularity in shape, i.e., the fibers do not have the same cross-sections along their lengths. This makes predicting the mechanical properties a difficult task (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015e; Summerscales et al., 2010). In comparison with conventional synthetic composites, natural fiber reinforced polymer composites (or natural composites) have much advantages and are better than those of traditional composites from various points of view as they have greater specific stiffness and specific strength, more resistance to corrosion, better recyclability, large fatigue strength, lower life-cycle costs, more impact absorption capacity, and have lower toxicity (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; Faruk, Bledzki, Fink, & Sain, 2012). Such advantages resulted, in fact, from the advantages of their constituents particularly the natural fibers. Moreover, both the characteristics and performance of products prepared from natural fiber composites are mainly influenced by the properties of their individual contents, compatibility, as well as the polymer/filler interfacial characteristics that enlarge the potentials of producing various exciting new materials with entirely new qualities (Alamri & Low, 2012). On the other hand, the growing usage of cellulosic-fiber composites instead of synthetic-fibers would provide numerous benefits to the infrastructure management, overall sustainability, and the cleaner production theme (AL-Oqla & Sapuan, 2015a; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015a, 2015b, 2015c; Alves et al., 2010). However, there is uncertainty in the performance of natural fibers associated with variability in

Natural fiber composites

35

natural fiber properties (AL-Oqla & Sapuan, 2015b; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla et al., 2015c). This requires better evaluations as well as keen selections of their most appropriate merits to enhance achieving more reliable and better design data (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla et al., 2015b, 2015c, 2015d).

2.3

Mechanical properties of natural fiber composites

The mechanical properties of the composite materials are the most important possessions to be considered even if the composites applications do not involve any carrying load, as they have to maintain their shapes when used. Thus, the mechanical properties of a composite are of paramount importance for its functional requirements. For short fiber composites (using short natural fibers), their mechanical properties are not easy to be predicted. This is due to the fact that some factors like the fibers orientations, dispersions, volume fraction, and their interface with the matrix cannot be accurately controlled (Ichhaporia, 2008). In fact, these factors cannot be accurately controlled due to the variation in the lengths of fibers and their distribution, as well as the unpredictable process variability. Thus, their mechanical properties can significantly be variable from piece to piece, bulk to bulk, and even from place to another on the same plant (Arnold, Hergenrother, & Mcgrath, 1992; De & White, 1996; Ichhaporia, 2008). Besides, factors like the volume fraction and the aspect ratio can significantly change the composite materials’ properties (Shalin, 1995). The stress transferred into the fibers will be insufficient if the aspect ratio is very small, leading to improper reinforcement for the composite. On the other side, if the aspect ratio is very high, fibers may become entangled, and this also leads to poor mechanical properties due to the improper distribution. At low density of fibers in the composite (low fiber loading), fibers will not be able to transfer the load to their neighbors, leading to a reduction in their strength properties. At high fiber loading, no sufficient matrix is penetrated between the fibers, leading to agglomeration, and thus, € blocking the load transfer ( Jacob, Thomas, & Varughese, 2004; Ozturk, 2010). Accordingly, the mechanical properties of natural fibers have large inconsistencies in the literature and this was dramatically reflected on their composites. Thus, large variations in the mechanical properties of natural fiber composites were reported (AL-Oqla et al., 2015c; AL-Oqla, Sapuan, Ishak, & Nuraini, 2016). It was reported that for the same polymer matrix, different fiber types, or a change in fiber loading for a particular fiber type, or even fiber treatment, can lead to dissimilar composite properties. Table 2.4 (AL-Oqla, Sapuan, Ishak, et al., 2016) validates this fact where various mechanical properties of different natural fibers with polypropylene (PP) polymer matrix are displayed. Therefore, in order to properly utilize NFCs for meeting future sustainable design requirements in one hand, and to maximize the desired composites’ characteristics on the other, proper evaluations and selection schemes for the available natural fiber types, as a major independent constituent of NFCs, have to be developed.

36

Materials Selection for Natural Fiber Composites

Table 2.4 Various mechanical properties of different natural fibers with polypropylene (PP) polymer matrix

Alternative Coir 15 wt %/PP Coir 20 wt %/PP Date palm 30 wt%/PP

Date palm 30 wt%/PP Flax 30 wt %/PP Flax 30 wt %/PP Flax 50 wt %/PP Flax 50 wt %/PP Jute 20 wt %/PP Jute 20 wt %/PP Jute 30 wt %/PP Kenaf 30 wt%/PP

Kenaf 40 wt%/PP Kenaf 40 wt%/PP Sisal 15 wt %/PP a

Treated composites.

Tensile strength (MPa)

Tensile modulus (MPa)

Flexural strength (MPa)

Flexural modulus (MPa)

Impact strength (J m21)

26.1a 28a 29a 26a 29.5a 24 18 21 17 29a 25a 28a 40.1 26 44.1a 43a 24

2210a 2400a

42.7a 26a

1830a 900a

22a 64a

3000a

33a

1150a

65a

700 1550 1610 650 800a 1350a 800a 5178 1700 5701.4a 2000a 2500

27.8 51

1572 2100

47

55a

2400a

53.5a

48

4500

67

68a

4600a

78a

42

700

45

2700a

83a

650a

65a

1680

45

1900

31

2490a 1880a 2250a

53.8a

2500a

44a

54a

2900a

51a

2600a 6000a 1774a

2700a 1678a

29a

5900 5000 1492 5500a 1648a

28

6000a 1778a

58a 58a 65a 36 a 60 19.67 24.01 71a 80a

2640a

48.8a

1430a

50a 53a 25.8 29.8a 28.7a 46a 23.18a 57a 50a 35 10.33 44a 19.84a 55a 28a

8000 1282

28a

26a

Natural fiber composites

2.4

37

Other properties of natural fiber composites

In fact, various factors can affect the properties of natural fiber composites according to the properties of their constituents (fibers and polymers). The biological, chemical, environmental, as well as economic assets of natural fibers can effectively enhance the final properties of the natural fiber composites and their implementation in various industrial applications. It is believed that utilizing natural fibers in natural fiber composites is not only an added value regarding the economic as well as environmental standpoints, but also from the sustainability point of view, as they are renewable raw materials for new green products (AL-Oqla & Sapuan, 2014b; AL-Oqla et al., 2015a). However, the characteristics of natural fibers themselves affect the composite performance. To make a high composites quality; clumping or accumulation of fibers must be avoided. Also, the polymer matrix should transfer the stress to fibers effectively. This can be increased by improving the adhesion between the fibers and matrix, and by increasing the fiber length when it is possible. Jute and coconut fibers are good examples of long filament fibers, this type results in better distribution of fibers through the polymer matrix. But long fibers usually lead to a rise in the degree of clumping and accumulation, which eventually decreases the efficiency of the final performance and properties of the natural fiber composites and their products. Chemical and physical treatment can also be used in order to have a good scattering and adhesion between the fiber and matrix. Furthermore, some special techniques and equipment are also used to blend agro fibers with plastics effectively to enhance the desired properties of the natural fiber composites. Besides, implementing the available natural fiber wastes in industry would fulfill the existence of low-cost materials that can dramatically expand the profit margins and support industrial sustainability. That is, the economic characteristics of natural fibers would also improve generating low-cost products while utilizing fewer resources, and enhance the recycling of the available agro wastes, resulting in less environmental pollution. Therefore, the integrated economic-environmental, as well as physical and biological properties of the natural fiber composites are influenced by those characteristics of their constituents, particularly the natural fiber ones. This can go hand in hand with the economy growth, as well as the environmental degradation indices, which satisfy the eco-efficiency strategy of the ecological sustainability. Furthermore, the degradability features of the natural fibers would intensely encourage the recyclability features of the natural fiber composites and thus the end life products made from such composites where the environmental performance can be improved. Consequently, various properties of natural fiber composites should be considered when evaluating their added values to the future industries which, by keen evaluation, can evidently contribute toward replacing the traditional composite materials with such natural cellulosic fiber composites.

2.5

Applications of natural fiber composites

Natural composites were used more than 100 years ago. Natural fiber composites were mainly utilized in building houses in various countries all over the world. They were used via mixing husks or sawdust with clay to give particulate composites and mixing

38

Materials Selection for Natural Fiber Composites

straws in clay to give a short fiber composite. Many other examples of reinforcements were used to improve the properties and therefore the performance of such composites. Natural composites have many applications now available in the market. They are used in outside deck floors, indoor furniture, landscaping timbers, park benches, fences, siding windows, and door frames. Natural fiber composites are more environmentally friendly than traditional ones and they need less maintenance than even the solid wood mixed with preservatives or rot-resistant solid wood in such composites, where a polymer is reinforced with some natural types of fillers. These polymericbased composites are used mainly in two ways. Firstly, to increase the performance over the unfilled polymers with lower costs, secondly, to increase the performance above the unfilled polymers by using reinforced fillers. Different combination of fillers, polymers, and additives will provide a hung variety of different performances in composites. Recently, researchers have not only focused on the cost of composites, but they are also paying more attention to the environmental issues of composites and trying to use more renewable materials to find a renewable nonfood source for raw materials, as tons of natural fibers are available cheaply in nature (most of them are burned) with wide range of sizes, colors, and shapes. On the other hand, plastics play an essential role for the most part in the markets; the applications of the polymers may include transportation, packaging, medical applications, construction, furniture, electronic components, etc. This huge variety of polymeric product application make the market dynamic and challenging in making the polymeric-based natural fiber composite products, offering service and financial issues. Some details about natural fiber reinforced polymeric composites’ applications are below:

2.5.1

Automotive and aerospace applications

As the automotive market and its supportive industries increase, huge amounts of materials are being consumed as final automotive-oriented products. However, at the end of the product life; products start another life cycle, either by recycling or disposal in various manners. Several national organizations, as well as governments pay more attention on the environmental effect of automobiles parts. As a consequence, various regulations have been established to save and protect the environment from these pollutions. The automotive industry is seriously considering these societal, governmental, and environmental needs and responsibilities. Thus, research and development of this field in European countries are enhanced by implementing natural fibers for polymericbased products (Kalia, Kaith, & Kaur, 2011) and are producing bio-based commercial vehicles, which are lighter and more economic as another new possibility for the consumer. These bio-based vehicles have many interior parts made from natural fibers (like hemp, sisal, jute, and coir) as reinforcements for biodegradable materials which have the ability to be composted or recycled when desired at the end of its life cycle. Moreover, it is believed that considering natural fibers as reinforcing agents in the automotive applications is efficient, as they are mainly utilized in components that

Natural fiber composites

39

do not have exposure to heavy loads, but have a good dimensional stability at low cost (Dittenber & Gangarao, 2011; Kalia, Kaith, et al., 2011). In the automotive sectors, the weight of the manufactured vehicle is a crucial factor as it dramatically influences the sustainability of the automotive sectors from the environmental standpoint. This is due to the fact that the weight directly affects the amount of fuel consumption, as well as the CO2 emission of the conventional and hybrid vehicles (Kastensson, 2014). It is estimated that a 10% mass reduction of vehicles would reduce their fuel consumption up to 4%–6% (Kastensson, 2014). The demanding regulations regarding fuel economy worldwide also dictate the weight reduction choice in the automotive industry. Consequently, utilizing any proper techniques or procedures that can produce new alternative materials capable of achieving lower weight vehicles with a satisfactory passengers’ feeling is encouraged toward improving the sustainability of the automotive sectors and their future green production. Therefore, one of the most feasible alternatives for the automotive industry to support its sustainability and attain better environmental performance as well is the employment of the natural fiber composites in their products to reduce weight (AL-Oqla & Sapuan, 2014c). That is, implementing the available natural fibers in the industry can increase the re-manufacturing process (Blume & Walther, 2012; Chiappetta Jabbour, Lopes De Sousa Jabbou, Govindan, Teixeira, & Ricardo De Souza Freitas, 2012), and satisfies the existence of cheap materials for the consumption flow in the production process to expand the profit margins. As a result, numerous automotive sectors, such as Mercedes-Benz, have already realized this and utilized the natural fiber composites in their products where noticeable weight reductions in door panels were achieved for Mercedes-Benz E-class when using the natural fiber composites (AL-Oqla & Sapuan, 2014c). Furthermore, Mercedes-Benz of Brazil allocated a budget of US$1.4 million in 1992 to explore the effect of using biomaterials in its products (Alves et al., 2010). Despite that, natural fiber reinforced polymeric-based composites have some cons like low thermal and dimension stability, degradation when exposed to sunlight for a long time, moisture absorption, and very large variations in distribution and orientation of reinforcement fibers. Some coupling agents with treatments of fibers can be used to reduce (but not eliminate) the ability of moisture absorption. For these reasons, natural fiber materials are used far away from contact with water and in indoor applications to minimize exposure to sunlight. Typically, plastics composites are used for interior automobile’s parts, for example, the dashboard. Fig. 2.8 shows the interior parts formed utilizing the natural fiber composites and the development of the dashboard for the BMW E-class. Natural fiber composites as a natural choice has many advantages: good mechanical properties, reducing weight, a decrease in fuel consumption, decent acoustic performance, lower costs, enhanced passenger safety, and improved biodegradability for interior parts. A good example of this are Mercedes E-class cars where the natural fiber composites offered a reduction of 20% in weight of the car and improved the mechanical properties which are a vital criterion for passenger protection in the event of accidents. Military and aerospace customers are considered to be challenging to plastic markets. The development of final a product has a long timeline and long life cycle as well. Suppliers of natural materials can build their planes for several years in advance

40

Materials Selection for Natural Fiber Composites

Rearview mirror

Sun visor Instrument panel

Mirror

Cigarette lighter

Windshield wiper controls

Vent Glove compartment

Turn signal lever

(A)

Steering column

Break pedal

Accelerator pedal

Heating controls

Radio controls

(B) Fig. 2.8 Schematic interior parts formed utilizing the natural fiber composites (A); and the development of dashboard for the BMW E-class (B).

based on final decisions on material for particular products in such fields. In this stage, competitors in natural composites are not yet able to prove their alternative materials for these applications.

2.5.2

Medical applications

Medical products are basically classified into two types, durable and disposable tools. However, medical disposable tools are row products with the concentration on cost. Reliability of products is a necessity. This integration of reliable, low-cost, disposable medical products is generating higher income for this field than for many other product markets. Various medical disposables like blood bags, IV bags, catheters, etc., are relatively high technology. Such disposable products are made of polymers and have the potential to be produced from natural fibers reinforced with polymer composites in the near future. The noticeable advantage of the conductive polymers, and their

Natural fiber composites

41

Solvent

Temperature pH

Conductivity

Synthesis

Doping

Method

“Small” dopant

“Large” dopant

Electric potential

Biodegradability Drug release

Biocompatibility Functionalization

Physical properties

Application

Colour Structure

Porosity

Mechanical properties

Fig. 2.9 The interconnecting possibilities of the conductive polymer composites to make factionalized products in various applications (AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015).

possibility to make conductive natural fiber reinforced polymer composites makes their implantation in various high tech applications, including the medical one, particularly valid. Such conductive natural composites allow their functions for various applications. The interconnection aspects of conductive composites are illustrated in Fig. 2.9 and the various aspects of the biomimetic conducting polymer-based composites are illustrated in Fig. 2.10.

2.5.3

Electrical and electronic market

This sector is one of the biggest markets for plastics products. Because the natural fiber reinforced polymer composites have great thermal and insulating properties, lightweight, and low cost, they are used as insulators, connectors, electric switches, receptacles for coffee makers, refrigerator interiors, refrigerators’ insulation and foams, etc. Moreover, the huge market of computers and their parts, like circuit boards and chips, get the advantages of outstanding properties of the high-performance composites that can withstand the stresses during assembly and strain during service life.

42

Materials Selection for Natural Fiber Composites Biological activity

Biodegradation

Enzymatically or hydrolytically cleavable

Mechanical properties

Topological properties

Fig. 2.10 Various aspects of the biomimetic conducting polymer-based composites (AL-Oqla, Sapuan, Anwer, et al., 2015).

2.5.4

Packaging market

The packaging market uses, on average, 30% of all plastics in use and is the largest single market for plastics. Nowadays, natural fiber polymeric composites have become the materials of special interest for the packaging area compared with traditional materials like paper, metals, and glass. This is because of the advantages of the composites, such as light weight, flexible, good protection against spoilage, easy to use, and lower shipping costs (light weight and small size). Moreover, the conductive features of the natural fiber-conductive polymer composites have recently been utilized in anti-bacterial packaging and other potential casing in high tech products. Many plastic resins are used for making natural fibers-polymeric based composites for packaging, particularly in the food packaging applications. These polymers include polypropylene, polystyrene, polyethylene, polyethylene terephthalate, and polyvinyl chloride. Plastic usage in packaging can be divided into the following types: l

l

l

l

Primary packaging for the end-user products like pouches, bags, bottles, or different containers. Secondary packaging for the primary products like, stretch films, bottle crates, and transit containers. Retail packaging, such as supermarkets bags, picketed bags, and shoppers. Consumer packaging, such as freezer bags and stick films.

2.5.5

Other applications

Natural fiber composites are currently used in various other applications including: l

l

Furniture and consumer goods: chairs, ironing boards, urns cases, helmets, and tables. Construction and infrastructure: roof panels, beams, and bridges.

Natural fiber composites l

43

Sports and leisure: tennis rackets, bicycle frames, and canoes, as well as other applications like wind energy, marine, bio-engineering and environmental uses.

Table 2.5 illustrates various types of applications for natural and synthetic/polymeric based composites.

Table 2.5

Applications of polymeric based composites

Application

Fiber type

Example

Application in car interior

Cellulose-polymer, kenaf, flax/sisal, wood fiber, jute, coconut

Application in construction, furniture, building, and others

Hemp, oil palm, stalk, wood, flax, cotton, rice husk, jute, coir, ramie, sisal bagasse

Marine and mechanical applications

Glass fiber reinforced plastic

Aerospace industry

Carbon fiber reinforced plastic, Basalt fiber reinforced polymer

Seats, floor trays, door panels, rear parcel shelf, engine shield, wheel shield, insulations, bumper, door covering (racks), spare tire-lining, upholstery, boot-liner, electronic device, side and back door panel, seat back, and hat rack. Window frame, panels, door shutters, decking, railing systems, and fencing, textile and yarn, goods, cordage, construction products, textiles, geotextiles, paper and packaging, bricks, furniture panels, electrical devices, manufacture bank notes, manufacture of pipes, and constructing drains and pipelines. Folded plates of various forms, walls and panels, doors, synclastic and anticlastic shells, skeletal structures, windows, ladders, staircases, chemical and water tanks, cooling towers, bridge decks, antenna dishes, etc. Engine cowlings, wing skin, front fuselage, access doors, under carriage doors, control surface fin and rudder, main torsion box, rotor blades, fuel tanks, fuselage structures and floor boards of helicopters, solar booms and solar horizontal and vertical tail, antenna dishes, stiffening spars, ribs, nose cones, fairings, cockpit and fuselage of Continued

44

Materials Selection for Natural Fiber Composites

Table 2.5

Continued

Application

Sports

2.6

Fiber type

Glass fiber reinforced plastic, Carbon fiber reinforced plastic

Example helicopters, wing root, motor casings, pressure bottles, propellant tanks, other pressurized systems, floor boards, interior decorative panels, partitions, cabin baggage racks and several similar applications. Ski poles, golf clubs, tennis and badminton rackets, fishing rods, hockey sticks, poles (pole vault), bicycle frames, etc.

Challenges in developing natural fiber composites for wide industrial applications

Considering the remarkable need and awareness of the environmental impact and the industrial sustainability, the synergy between the available natural resources and sustainability has been recently highly emphasized. It is believed that one of the most industrial and feasible solutions to maintain sustainability, and to achieve better environmental performance is the employment of the natural fiber reinforced polymer composites in new green products (Blume & Walther, 2013; Cheung, Ho, Lau, Cardona, & Hui, 2009; Hakeem et al., 2014). However, the variety in the available natural fibers dramatically affects their qualities, performance, and capabilities. This can alter the performance of the natural fiber composites. Replacing synthetic fiber composites with natural fiber composite materials is still challenging for the industry. This is due to the fact that the latter has some drawbacks that negatively affect this replacement process. The natural fiber composites’ drawbacks include their degradation, geometrical instabilities, mechanical loss, sensitivity to harsh environments, the high water absorption and the corresponding performance deteriorations that usually limit their industrial applications in wet environments. Moreover, the low permissible temperature is another drawback of the natural fiber composites that usually limits the polymer selection in such composites during the manufacturing processes to avoid thermal degradations of fibers. That is, natural fibers are complex mixtures of organic materials, and therefore improper thermal treatment can make several of the physical as well as chemical changes in the cellulosic fibers. Thus, thermal stability is a vital issue for the natural fibers, which is usually studied by thermo gravimetric analysis (TGA). In fact, the mechanical characteristics of plant fibers could be deteriorated due to their thermal degradation, particularly the toughness and bending strength. It was reported that thermal effects

Natural fiber composites

45

would dramatically alter the surface chemistry of the fibers and cause variations in the fiber/polymer bonding. Such variations or reduction in the adhesion forces and bonding between the fibers and polymers are responsible for the poor characteristics of natural fiber composites. Additionally, thermal degradation of fibers would result in producing volatiles at processing temperatures that make porous polymer products with poor mechanical properties. This facilitates and accelerates the biodegradability of such composites during the fabrication processes. In addition, the incompatibility between the hydrophilic natural cellulosic fibers and the hydrophobic polymers is a further drawback for the natural fiber composites. Moreover, several factors like the inherent variation in the geometry, shape, size, and capabilities of the plant fibers, as well as their low durability make controlling the process of natural fibers challenging for the modern industry (Mohanty et al., 2002a; Pickering, 2008). Therefore, the natural fiber composites reportedly occupied only about 1.9% of the 2.4 million tons of the total EU fiber reinforced plastic market in 2010 (Shah, 2013). This obviously reveals the need for better evaluating and selecting the natural fiber composites and their constituents in order to enhance achieving better performance for such eco-friendly materials to expand their implementations in various applications.

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AL-Oqla, F. M., & Sapuan, S. M. (2014b). Enhancement selecting proper natural fiber composites for industrial applications. In: Postgraduate symposium on composites science and technology 2014 & 4th postgraduate seminar on natural fibre composites, 28/01/2014, Putrajaya, Selangor, Malaysia. AL-Oqla, F. M., & Sapuan, S. M. (2014c). Natural fiber reinforced polymer composites in industrial applications: Feasibility of date palm fibers for sustainable automotive industry. Journal of Cleaner Production, 66, 347–354. AL-Oqla, F. M., & Sapuan, S. M. (2015a). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. Journal of the Minerals, Metals and Materials Society, 67(10), 2450–2463. AL-Oqla, F. M., & Sapuan, S. M. (2015b). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. JOM, 67, 2450–2463. AL-Oqla, F. M., Sapuan, S. M., Anwer, T., Jawaid, M., & Hoque, M. (2015). Natural fiber reinforced conductive polymer composites as functional materials: A review. Synthetic Metals, 206, 42–54. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Aziz, N. A. (2014a). Combined multi-criteria evaluation stage technique as an agro waste evaluation indicator for polymeric composites: Date palm fibers as a case study. BioResources, 9, 4608–4621. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2014b). A novel evaluation tool for enhancing the selection of natural fibers for polymeric composites based on fiber moisture content criterion. BioResources, 10, 299–312. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015a). Selecting natural fibers for industrial applications. In: Postgraduate symposium on biocomposite technology, March 3, Serdang, Malaysia. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015b). Decision making model for optimal reinforcement condition of natural fiber composites. Fibers and Polymers, 16, 153–163. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015c). Selecting natural fibers for bio-based materials with conflicting criteria. American Journal of Applied Sciences, 12, 64–71. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015d). A model for evaluating and determining the most appropriate polymer matrix type for natural fiber composites. International Journal of Polymer Analysis and Characterization, 20, 191–205. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015e). Predicting the potential of agro waste fibers for sustainable automotive industry using a decision making model. Computers and Electronics in Agriculture, 113, 116–127. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. (2016a). A decision-making model for selecting the most appropriate natural fiber—Polypropylene-based composites for automotive applications. Journal of Composite Materials, 50, 543–556. AL-Oqla, F. M., Sapuan, S. M., & Jawaid, M. (2016b). Integrated mechanical-economic— Environmental quality of performance for natural fibers for polymeric-based composite materials. Journal of Natural Fibers, 13, 651–659. Alves, C., Ferra˜o, P., Silva, A., Reis, L., Freitas, M., Rodrigues, L., et al. (2010). Ecodesign of automotive components making use of natural jute fiber composites. Journal of Cleaner Production, 18, 313–327. Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016a). Investigating morphological and performance deterioration of injection molded rice husk-polypropylene composites due to various liquid uptakes. International Journal of Polymer Analysis and Characterization, 21(8), 675–685.

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Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016b). Mechanical and morphological properties of injection-molded rice husk polypropylene composites. International Journal of Polymer Analysis and Characterization, 21, 305–313. Arnold, C. A., Hergenrother, P. M., & Mcgrath, J. E. (1992). An overview of organic polymeric matrix resins for composites. In T. L. Vigo & B. J. Kinzig (Eds.), Composite applications: The role of matrix, fiber, and interface. New York: VCH. Bledzki, A. K., & Gassan, J. (1999). Composites reinforced with cellulose based fibres. Progress in Polymer Science, 24, 221–274. Blume, T., & Walther, M. (2012). The end-of-life vehicle ordinance in the German automotive industry—Corporate sense making illustrated. Journal of Cleaner Production, 56, 29–38. Blume, T., & Walther, M. (2013). The end-of-life vehicle ordinance in the German automotive industry—Corporate sense making illustrated. Journal of Cleaner Production, 56, 29–38. Cheung, H. -Y., Ho, M. -P., Lau, K. -T., Cardona, F., & Hui, D. (2009). Natural fibre-reinforced composites for bioengineering and environmental engineering applications. Composites Part B Engineering, 40, 655–663. Chiappetta Jabbour, C. J., Lopes De Sousa Jabbou, A. B., Govindan, K., Teixeira, A. A., & Ricardo De Souza Freitas, W. (2012). Environmental management and operational performance in automotive companies in Brazil: The role of human resource management and lean manufacturing. Journal of Cleaner Production, 47, 129–140. De, S. K., & White, J. R. (1996). Short fibre-polymer composites. Boca Raton: CRC. Dittenber, D. B., & Gangarao, H. V. (2011). Critical review of recent publications on use of natural composites in infrastructure. Composites Part A Applied Science and Manufacturing, 43, 1419–1429. Faruk, O., Bledzki, A. K., Fink, H. -P., & Sain, M. (2012). Biocomposites reinforced with natural fibers: 2000–2010. Progress in Polymer Science, 37, 1552–1596. Hakeem, K. R., Jawaid, M., & Rashid, U. (2014). Biomass and bioenergy: Processing and properties. Cham: Springer International Publishing. Holbery, J., & Houston, D. (2006). Natural-fiber-reinforced polymer composites in automotive applications. JOM Journal of the Minerals Metals and Materials Society, 58, 80–86. Ichhaporia. P. K. (2008). Composites from natural fibers (Ph.D. thesis). Carolina State University. Jacob, M., Thomas, S., & Varughese, K. T. (2004). Mechanical properties of sisal/oil palm hybrid fiber reinforced natural rubber composites. Composites Science and Technology, 64, 955–965. Jawaid, M., & Abdul Khalil, H. (2011). Cellulosic/synthetic fibre reinforced polymer hybrid composites: A review. Carbohydrate Polymers, 86, 1–18. Joshi, S. V., Drzal, L., Mohanty, A., & Arora, S. (2004). Are natural fiber composites environmentally superior to glass fiber reinforced composites? Composites Part A Applied Science and Manufacturing, 35, 371–376. Kalia, S., James Njuguna, J., Alain Dufresne, A., & Cherian, B. M. (2011). Natural fibers, bioand nanocomposites. Journal of Polymer Science. http://dx.doi.org/10.1155/2011/735932. Article ID 735932. Kalia, S., Kaith, B., & Kaur, I. (2011). Cellulose fibers: Bio-and nano-polymer composites: Green chemistry and technology. Heidelberg: Springer. ˚ . (2014). Developing lightweight concepts in the automotive industry: Taking on Kastensson, A the environmental challenge with the Sa˚N€att project. Journal of Cleaner Production, 66, 337–346. Menon, V., & Rao, M. (2012). Trends in bioconversion of lignocellulose: Biofuels, platform chemicals & biorefinery concept. Progress in Energy and Combustion Science, 38, 522–550.

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Mir, A., Zitoune, R., Collombet, F., & Bezzazi, B. (2010). Study of mechanical and thermomechanical properties of jute/epoxy composite laminate. Journal of Reinforced Plastics and Composites, 29, 1669–1680. Mohanty, A., Misra, M., & Drzal, L. (2002a). Sustainable bio-composites from renewable resources: Opportunities and challenges in the green materials world. Journal of Polymers and the Environment, 10, 19–26. Mohanty, A. K., Misra, M., & Drzal, L. T. (2002b). Sustainable bio-composites from renewable resources: Opportunities and challenges in the green materials world. Journal of Polymers and the Environment, 10, 19–26. Mohanty, A. K., Misra, M., & Drzal, L. T. (2005). Natural fibers, biopolymers, and their biocomposites. Boca Raton: CRC Press. € Ozturk, S. (2010). Effect of fiber loading on the mechanical properties of kenaf and fiberfrax fiber-reinforced phenol-formaldehyde composites. Journal of Composite Materials, 44, 2265–2288. Pickering, K. (2008). Properties and performance of natural-fibre composites. Boca Raton: Elsevier. Sapuan, S. M., Haniffah, W., & AL-Oqla, F. M. (2016). Effects of reinforcing elements on the performance of laser transmission welding process in polymer composites: A systematic review. International Journal of Performability Engineering, 12, 553. Sarikanat, M. (2010). The influence of oligomeric siloxane concentration on the mechanical behaviors of alkalized jute/modified epoxy composites. Journal of Reinforced Plastics and Composites, 29, 807–817. Shah, D. U. (2013). Developing plant fibre composites for structural applications by optimising composite parameters: A critical review. Journal of Materials Science, 48, 6083–6107. Shah, D. U. (2014). Natural fibre composites: Comprehensive Ashby-type materials selection charts. Materials & Design, 62, 21–31. Shalin, R. E. (1995). Polymer matrix composites. London: Chapman & Hall. Summerscales, J., Dissanayake, N., Virk, A., & Hall, W. (2010). A review of bast fibres and their composites. Part 2—Composites. Composites Part A Applied Science and Manufacturing, 41, 1336–1344. Symington, M. C., Banks, W. M., West, D., & Pethrick, R. (2009). Tensile testing of cellulose based natural fibers for structural composite applications. Journal of Composite Materials, 43, 1083–1108.

Materials selection 3.1

3

Materials selection and design

Engineering materials are mainly divided into two categories: metallic and nonmetallic. These categories contain more than 120,000 useable materials including, but not limited to, glasses, metals, ceramics, plastics, composite materials, and semiconductors. From this huge number of materials, one needs some beneficial evaluative criteria to select the best materials for specific design and manufacturing processes to produce a successful, functional product with a high level of customer satisfaction attributes (ALOqla & Omari, 2017; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; Djassemi, 2012; Dweiri & AL-Oqla, 2006; Sapuan, Haniffah, & AL-Oqla, 2016). Designers and engineers have to consider numerous issues and materials-related criteria while assigning proper materials for their designs to attain low-cost successful products. Such issues include physical, mechanical, electrical, magnetic properties, cost, availability, manufacturing abilities, durability, environmental impact, recyclability, and others. In addition, both metaphysical properties and user-interaction aspects including appearance, perceptions, and emotions have also to be considered during the material selection process (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012; AL-Oqla & Sapuan, 2014a, 2014b; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015c). Thus, the process of material selection is an interdisciplinary work that typically involves various fields of study, such as material science, mechanical engineering, ergonomics, industrial engineering, as well as other expertise in the field of application. In fact, the design of an engineering component requires three interconnected tasks: specifying a shape, selecting a material, and choosing a proper manufacturing process. Attainment of an optimal realistic synergy between these tasks right the first time has various paybacks to any engineering-based business, including achieving a lower product cost, accelerating the time-to-market phase, reducing the number of in-service failures, and succeeding more business competition (Alexandre & Dubois, 2000; AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Omari, 2017; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015a; Alves et al., 2010). Due to the large number of available materials and various interactive selection parameters, the material selection process itself has multi-criteria decision-making problems and is still a big challenge for the industry (Abdollah, Shuhimi, Ismail, Amiruddin, & Umehara, 2015; Ali & Ba, 2013; AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Widyan & AL-Oqla, 2011, 2014). For instance, selection of the wrong materials means no longer competing in the market and then an engineering business finds itself out of date because other companies select the correct materials or more appropriate ones for a particular product regarding performance, reliability, cost, ecofriendly, and ease of manufacturing, etc. Therefore, selecting an adequate material is considered as a key driver for attaining the user satisfaction as well as market growth. Besides, it is believed that every engineering product has its own specific Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00003-7 Copyright © 2017 Elsevier Ltd. All rights reserved.

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Materials Selection for Natural Fiber Composites

characteristics and sequence of improvement events (AL-Oqla, Sapuan, Ishak, & Nuraini, 2014, 2016; AL-Oqla, Sapuan, & Jawaid, 2016; Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a, 2016b; Ashby, 2005). A well-organized business is capable of selecting the optimal combination of processes and materials, which is not an easy task and needs to be upgraded continuously. In order to develop a certain product, designers have to ask various questions, “What is to be developed? What does it do? And how does it do it? What are the requirements for the design? And which manufacturing processes are needed? What are the miscellaneous requirements if necessary?” Design in engineering is a systematic way of thinking and practical steps that are to be adopted in generating functional products and/or processes. The main fundamentals of design are the identification of the problem, functional desires, system definition, concept growth, materials and process selection, evaluation of the expected performance of the design, detailed design, creation of detailed drawing, and planning for the cost of materials and fabrication. Various aspects should be considered in designing a new product. The following aspects have usually to be realized in order to achieve a proper design (AL-Oqla, Sapuan, & Jawaid, 2016; Ashby & Johnson, 2013; Dweiri & AL-Oqla, 2006): The functionality or fitness for the purpose, the optimal materials, the ease of manufacturing, the quantity to be manufactured, the cost of the product, safety requirements, durability of the product, efficiency, running costs, the usage of the product, the finishing qualities, the ease of maintenance, and the environmental and social considerations. Useful product development guideline activities and their relation to material selections are illustrated in Fig. 3.1. In fact, there are several changes needed for modern societies so that dynamic and flexible scenarios can be achieved in the future. The developing situations of the modern world dramatically affect the boundary conditions of modern design. Among these situations are those related to selecting new materials and processes. The needs for finding new materials and processes for better designs are shown in Fig. 3.2.

3.2

The needs for materials selection

Materials selection plays a major role in the functionality, performance, customer satisfaction attributes, manufacturing processes, and in determining the physical and metaphysical properties of a new product (AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla et al., 2015c; Alves et al., 2010). During a selection process of the best material for a specific product, several requirements should be considered such as the design of the product, the manufacturing process to be used, health and safety issues, price, energy required for production, and disposing of it suitably at the end of its life (recycling process). Moreover, the environmental impact became a very important and challenging issue that faces the various current industries. The United States uses about 10%–15% of its consumed total energy in producing steel, plastics, aluminum, and paper industries. This kind of energy usually originates from fossil fuels. Therefore, this will contribute to

Materials selection

51

Market need: design requirements Material data needs

Design tools

Concept

Data for ALL materials, low precision and detail

Embodiment

Data for a SUBSET of materials, higher precision and detail

Detail

Data for ONE material, highest precision and detail

Function modelling Viabiliey studies Approximate analysis Geometric modelling Simulations methods Cost modelling Component modelling Finite-element modelling (FEM) DFM, DFA

Product specification Fig. 3.1 The relation between the design and material selection for particular product specifications (Ashby & Johnson, 2013).

carbon dioxide emissions causing environmental problems. Hence, engineers, designers and manufacturers should be aware in selecting materials and processes of their impact on environmental pollution. In addition to the growing global environmental awareness, both societal concerns and concepts of sustainability have integrated to trigger the exploration of new alternative products and processes wellmatched with the environment (eco-friendly alternatives).Therefore, recent attention to implementing recyclable and biodegradable materials in industries are given to decreasing the current production and accumulation of waste in order to result in less environmental pollution. For instance, in gear manufacturing, materials with high wear resistance and good shear strength should be used. Because the gears work under friction, then heat will be produced. In addition, light weight materials would help reduce the weight of the produced component and thus the overall machine. Such desired characteristics of the materials should be selected with minimum cost. A robust materials selection process would therefore dramatically help in such a complicated practice.

52

Materials Selection for Natural Fiber Composites

Market need

Market saturation and industrial design

Product safety and liability Service provision replacing product sales

Growing population and wealth

New or changed materials and processes

New science and technology

Market forces and competition

Concern for the individual

Miniaturization

Concern for the environment

Multi-functionality

Adapted products

Fig. 3.2 Need for selecting new materials for better product design (Ashby & Johnson, 2013).

Technically speaking, there is a growing need for better material selection processes as for good design and products, and engineers should be familiar with all material characteristics including: the mechanical properties (like hardness, tensile stress, toughness, etc.), physical properties (such as density, color, viscosity, melting temperature, etc.), electrical characteristics of resistivity and conductivity, and other properties like the availability, manufacturability, appearance, and cost, etc. Moreover, as a huge number of materials are available in the market, traditional methods and qualitative techniques for material selection became inappropriate and timeconsuming. Additionally, it is worth noting here that it is difficult to understand the precise relationship between material properties and design because the final product has properties different than those of the raw material because of processing and finishing applied to raw materials during all manufacturing steps. Generally, various factors can determine the needs of material selection. These factors may include the need to: l

l

Avoid only materials in the “comfort zone,” i.e., only materials that the designer is familiar with. Utilize new materials and processes to enhance innovation in design.

Materials selection l

l

l

l

l

l

53

Make materials information readily available to designers. Improve the performance of a specified product and eliminate the materials or service failures. Accommodate a change in component function. Reduce the costs of materials and their production, and to solve processing difficulties as well as taking advantage of new processing techniques to exploit a change in the availability of a material. Exploit the introduction of a new product, or regulate it to a decline in the market Accommodate a change in design, and to maintain customer satisfaction attributes and environmental issues.

In fact, approval processes should be considered before starting to use a new material in industry. After a new material has been identified for possible given applications, then the required specifications for the material are determined based on these applications. These specifications or properties include determining physical and mechanical properties like, density, cost, color, ductility, strength, heat and chemical resistance, etc. So any new material should pass all testes for a particular application before putting it in use regardless of their source or feedstock. If all tests results are positive, the next step is to create a prototype to test the performance. If the material passes the performance tests, then a third step of determining sourcing and purchasing process, i.e., cost effectiveness and assuring it exists in sustainable levels is usually required. The time to approve a new material is a serious matter. This time varies significantly for various materials and usually depends upon the products’ conditions. However, some steps may be expedited, but others could take more time than expected! For instance, in order to produce a composite, appropriate manufacturing process must be developed and the synergy between the composites constituents may have a major role in determining the mechanical properties and performance of the composites.

3.3

Tools and techniques of material selection

Exploring new materials and proper selection of the desired distinctive features and attributes of the available resources would normally enhance the innovation in design and expand new design possibilities (AL-Oqla & Omari, 2017; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; Ashby, 2005). However, some constrains and limitations would affect the usage of a particular type of material in a given application. Therefore, historically, specialists in the material selection process have been enthusiastic to practice their role according to the availability of types of materials. Materials are frequently selected either by trial and error or based on what has been used before. Although this frequently works, systematic approaches for material selection were recently considered as a part of the engineering design process (AL-Oqla & Omar, 2012; Dalalah, AL-Oqla, & Hayajneh, 2010; Rao & Davim, 2008). Several possible material alternatives are generally found to be suitable for a product in a given application, however, the final selection is a compromise between the alternatives’ desired advantages and their disadvantages (AL-Oqla & Sapuan, 2015b; Karana, 2012). Due to the inherent relationship between the materials

54

Materials Selection for Natural Fiber Composites

machinability, cost, availability, product design, recyclability, and the desired performance in the final product, new techniques such as informed decisions, combined evaluation schemes, optimizations, property indices, and expert systems are now utilized to come up with proper material selections. In fact, a range of quantitative selection approaches has been developed to explore the material selection process, thus a systematic evaluation can be made. For instance, some researchers have suggested various steps for material selection starting from defining the design, then analyzing the material properties, screening of candidate materials, then making an evaluation and decision for the optimal type of materials, and finally to verify that selections by tests (Chiner, 1988). Others had defined only three stages for the material selection process, namely, an initial screening stage, the development and comparison of the alternatives stage, and finally the stage of selecting the optimum material type (Farag, 2006). Moreover, other scientists have suggested basic materials selection activities to be formulating material evaluation criteria, making a set of candidate materials, comparing the candidate materials, and picking best one (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Oqla & Sapuan, 2015a; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015b, 2015e, 2015f; AL-Oqla, Sapuan, Ishak, et al., 2016; Dalalah et al., 2010; Dweiri & AL-Oqla, 2006; van Kesteren, Kandachar, & Stappers, 2006). In general, one of the most noticeable issues resulting from these findings is that regardless of the relation of design stages and the material selection, both screening and ranking are two vital steps in the material selection process.

3.3.1

Materials screening techniques

After the material alternatives have been proposed, the screening idea is to eliminate the obvious unsuitable materials and to focus attention on the remaining seemingly practical alternatives. Screening tools can dramatically narrow down the sets of alternatives to a manageable number for subsequent detailed evaluations. The common screening methods in material selection are illustrated in Fig. 3.3.

3.3.2

Materials comparing and choosing techniques

The selection of the most appropriate material type for a specific product is a difficult process. It deals with a great amount of information about the physical, mechanical, and other properties of materials. After narrowing down the possible material alternatives using one or a combination of the screening methods, another technique or approach is then required to compare and rank the available alternatives in order to choose and select a few optimal candidates from the available materials. Therefore, multi-criteria decision-making (MCDM) and optimization approaches are usually employed. MCDM techniques are dividing into two basic groups: the multiple attribute decision-making (MADM) and the multiple objective decision-making (MODM) approaches. There are several methods in such approaches. Methods can also be combined with each other to fine-tune achieving more practical solutions of the material selection issues in the engineering fields. Most of these comparing methods and screening tools are discussed in the following sections of this chapter.

Materials selection

55

Cost per unit property method

Chart method

Material screening methods

Questionnaire method Materials in products selection tools

Artificial intelligence methods

Computer-aided materials selection systems

Case-based reasoning

Knowledgebased systems

Neural networks

Fig. 3.3 Common screening methods in material selection ( Jahan, Ismail, Sapuan, et al., 2010).

3.4

Conventional materials selection techniques

In this section, most of the materials selection techniques that are conventionally used in both the screening stage and comparing and ranking ones are illustrated. However, both Ashby charts method and advanced materials selection techniques are discussed separately in subsequent topics in this chapter.

3.4.1

Cost per unit property method

Materials’ cost is a vital criterion that is usually considered in selecting a specific material for a given application. So, it is convenient to consider cost as a target at the beginning of the material selection process. In general, very expensive materials are excluded from this stage of the material selection process to achieve possible successful and functional products. At the end of material selection, a compromise between the materials’ cost and performance could exist. However, the most valuable evaluating factor for selecting a material is the cost per unit property that can improve the design performance. In the initial screening of materials for a given application, a particular property is usually set as the most important service requirement to achieve functionality, the cost per such unit property method is

56

Materials Selection for Natural Fiber Composites

strongly recommended. In this situation, we can estimate the cost that provides the most important needs for several kinds of materials. Usually, the cost per unit strength is one of the most valuable criteria for an appropriate mechanical performance of mechanical components and lower costs per unit strength material are desirable. This technique considers only one property as the most critical and discards the others, which limits its explanation.

3.4.2

Questionnaire method

Many researchers suggest the questionnaire method for material selections. The performance requirements are classified by Farag (2006) into two core classes: rigid (go-on-go) requirements and soft (relative) requirements. If the whole material type is being considered, then the rigid requirements should be encountered. These types of requirements are usually considered at the initial selection stages of materials in order to remove the inappropriate groups of alternatives. For example, if choosing materials for an electrical insulator, then metallic materials should be eliminated. The other requirements, namely the soft or the relative requirements, are negotiable and can be traded off. Edwards (2005) has introduced some important questions in order to increase the probability of attaining a better design solution. Such questions may include the following: l

l

l

l

l

l

l

Have all the distinct material properties (which are usually related to each other) been attained and understood? Have all the environmental issues been taken into account? Have all the economic constraints been considered? Will the design settings and requirements vary with time? Have the impacts of materials treatment, processing, and manufacturing conditions been taken into consideration? Have the consequences of the amounts as well as the rate of production of components been effectively taken into consideration? Has new raw material accessibility been considered?

In addition, van Kesteren et al. (2006) had suggested a question-oriented method for the user-interaction features of materials. This method consists of a list of questions for different stages in the user-product relations and a checklist of sensorial features. In this technique, both customers and the product designers usually predict, imagine, and discuss the interaction between the user and the new product in various stages starting from the first contact, try out, transporting, unpacking, usage, and rest stage. Moreover, the effect of customers, producers/sellers, users, and inventors on the selection of materials and procedures in manufacturing design are usually considered in this method. Stakeholders’ perceptions are also described and organized to be involved in the selection activities in such technique. In this material selection scheme, the influences that may affect the materials selection process are categorized as the following: strategic/commercial (customers), feasible/obtainable (manufacturers and sellers), perceptual/experiential (users), and circumstantial/personal (designers). One questionnaire form for designing a simple boat is illustrated in Fig. 3.4.

Materials selection

57

Occupation

Race

Age

Sex What is your favorite boat specifications

a)

Material type of boat you are interested in Fiberglass

b)

Wood

1

2

The less weight boat is better

3

The marginal safety is important in your boat

4

Low maintenance costs

5

Comfortable design

6

More capacity size, the larger boats needed

Excellent

Salary

Good

Average

Below RM1000 RM1000~3000 Above RM3000 Fair

Poor

Comment :

Fig. 3.4 A questionnaire form for a boat design project.

3.4.3

Materials in products selection methods

Materials in Products Selection (MiPS) methods are introduced by van Kesteren et al. (2006), and these methods are new for integrating the user-interaction parts into the selection process of materials. These methods help customers to specify the requirements clearly, which relates to the user-interaction parts and form agreement between them and the designer of the products in the primary material’s selection stage. In these MiPS, it would be better if the tools are not directly connected to the names of materials, the reason being, some materials may not be capable of fulfilling the main objectives of the project in terms of practical properties. Also, some new materials can be easily excluded when they are defined at the early stages of the project. MiPS methods mainly contain three tools as follows: picture tool (picture examples of products and their materials), sample tool (a real materials sample), and question tool (sensorial features of materials at different phases of the user-product interactions). To enhance the previous tools, it was suggested by the same authors that an additional improvement of emphasizing the converging step of the tools, as they stated that MiPS are only able to translate a small percentage of the user-interaction features into sensorial ones.

3.5

Ashby charts

Ashby (2005) has established a material selection system that focuses on data modeling aspects of the problem, where data is presented in charts. Materials and process selection charts that were founded by Ashby are tested and tried. Ashby’s material

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Materials Selection for Natural Fiber Composites

selection charts are valuable for preliminary screening of materials. Cambridge Engineering Selector (CES) is one of the powerful selection and analysis method software programs based upon Ashby’s materials selection procedure. In the mechanical design field, these charts play a simple and fast way of evaluating whether a material is appropriate for the existing case or not. Ashby chart method is easy to apply when the design of the component consists of a simple objective, such as minimizing the weight, and a single constraint like a specified stiffness, strength, or thermal conductance. The most thoughtful limitation of the chart method is that the chart limits the material selection decision to only solving two or three criteria. So, to solve this problem, multi-criteria decision making is established. The performance of a component depends on a combination of properties instead of a single property, such as the strength-weight ratio and stiffness-weight ratio to design a component with a light-weight characteristic. From this idea, one can build a plot of one property versus another, the drawing has field and subfield, the fields usually contain the material class, and the subfields have individual materials. For instance, the Young’s modulus, E, plotted against density, ρ, for various types of materials is shown in Fig. 3.5. This figure shows that the heavy envelopes enclose data for a given class of material. Moreover, it can illustrate preferable guidelines for minimizing mass with a particular strength for a given design.

Technical ceramics

Young’s modulus-Density

1000

Composites 100

SiC Si3N4 B4C

Al2O3

Steels Ni alloys Ti alloys

Al alloys GFRP

WC W alloys Cu alloys

Glass Mg alloys GFRP

Metals

Young’s modulus, E(GPa)

Wood

10 Longitudinal wave speed

1

Natural materials

Zinc alloys PEEK Concrete PET Epoxies PC

PS PP PE

104 m/s

Lead alloys

// grain Polyester

PMMA PA

PTFE ⊥ grain

10−1

Polymers and elastomers

Foams 10−2

EVA

103 m/s

Neoprene Flexible polymer foams

102 m/s

10−4 0.01

1/2

Guidelines for minimum mass design

Isoprene

10−3

E r

E r

Silicone elastomers Polyurethane

Cork

1/3

E r

Leather

Rigid polymer foams

Butyl rubber MFA. 04

0.1

1 Density, r (Mg/m3)

10

Fig. 3.5 A chart for Young’s modulus, versus density, for various types of materials (Ashby, 2005).

Materials selection

59

1000 Cu alloys

Thermal conductivity

Al alloys

SiC

AIN

Mg alloys B4C

Thermal conductivity (W/m K)

100

Carbon steels Al2O3 Si3N4

Lead alloys

10

Stainless steels Ti alloys Silicone elastomers

1

Epoxies Concrete

Glass

PA

PVC PMMA

Brick ABS

0.1

PP

Metals

Ceramics and glasses

Polymers and elastomers

Butyl rubber

0.01

Fig. 3.6 Thermal conductivity properties for families of solid utilizing a bar-chart (Ashby, 2005).

These charts compact huge information into accessible forms. They show correlations between material properties to help in solving real design issues. For example, utilizing a bar-chart like that in Fig. 3.6 to show the thermal conductivity properties for families of solids can enhance comparing such property for various materials in a simple manner to enable designers select the material type that fits according to this property. In this figure, each bar shows the range of conductivity offered by a material (only some of which are labeled) and each bar represents only one material. The length of each single bar demonstrates the range of conductivity exhibited by that material in its various forms. The materials are categorized by classes. Each class displays a characteristic range. That is; metals have high thermal conductivities. Ceramics on the other hand, have a wide range from low to high, while polymers have low conductivities. Much extra information is presented by another method of plotting materials properties, explained in the schematic of Fig. 3.7. Here, one property (the modulus of elasticity, E) is plotted versus another (the density, ρ) on logarithmic scales. The range of the axes is selected to take into account all materials, from the lightest, flimsiest foams, to the stiffest, heaviest metals. It is then realized that data for a given family of materials (like that of polymers) cluster together on the chart. That is, the sub-range related to one material family is, in all cases, much smaller than the full range of that property. Information for a given family can be surrounded in a property-envelope, as Fig. 3.7 shows. Inside it lie bubbles enclosing classes and sub-classes and this is the main idea of the Ashby charts by which Fig. 3.5 was established for various material types.

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Materials Selection for Natural Fiber Composites

1000 3 × 103 m/s

Modulus-density Ceramics

Metals

Young’s modulus, E (GPa)

100

103 m/s

Composites

10

3 × 102 m/s

Woods

1

102 m/s

Polymers

Foams 0.1

Longitudinal wave speed

Elastomers 0.01 0.1

1

10

100

Density (Mg/m3)

Fig. 3.7 The idea of a materials property chart: modulus-density chart (Ashby, 2005).

3.6

Advanced materials selection techniques

3.6.1

Artificial intelligence methods

Artificial intelligence (AI) is a study that lets computers implement intelligence for unstructured, dispersed knowledge processing in order to solve complex problems. As material selection process is one of these problems, it has an advantage in the vast amount of information that no engineer can match. In the past, an engineer could depend on journals, engineering handbooks, and his experience to choose a suitable material for an application, but these days, engineers are required to search for proper systematic techniques for managing and investigating engineering data on the everincreasing amount of materials. Some of the artificial intelligence methods for material selection include: computer-aided materials selection systems, knowledge-based systems, case-based reasoning, and neural networks.

3.6.1.1

Computer-aided materials selection systems

Because of the large amount of materials in the world, there is a serious need for information organization systems. Therefore, analytical methods exist for the computeraided engineering materials selection system. A computer-aided design system was proposed to suggest a candidate industrialization process and material grouping. Additionally, a cooperative computer program and synthetic smart methods to choose

Materials selection

61

a candidate material and early process integrations during the primary phases of the design were introduced for the material selection process ( Jahan, Ismail, Sapuan, & Mustapha, 2010). Furthermore, a program that supports decisions was established and called material selection program (MSP) to enhance and facilitate procedures in selecting materials ( Jahan, Ismail, Sapuan, et al., 2010). The program was based on a simple technique to support decisions considering the user stated restrictions to choose some potential materials from a database of information about numerous material features. Moreover, a microcomputer-based helps material selection for four frequently used material characteristics, namely: function, manufacturing, appearance, and cost. In addition, a computerized system for selecting materials that can take into account the environmental life-cycle effects of materials was established. Also, data systems and software were debated by Ashby (2005) for the main requirements of the materials required in engineering design in order to organize the materials’ information for ideal selection. Nevertheless, these methods are suitable tools for examining, but not for computations. While some material databases can help in the selection of materials systems, they are basically designed for data storage, and the process of selecting materials is not quit reliable.

3.6.1.2 Knowledge-based systems Knowledge-based systems (KBS) are systems dependent on the approaches and methods of artificial intellect, which is named an expert system. A knowledge-based system can be also known as a computer system designed to simulate human problem-solving through artificial brainpower and based on a database of information on a specific topic. Throughout the past decades, the input of engineering materials is classified into two groups: data and knowledge, the difference among the knowledge base and a database is blurred until now. The result of any measurement can be shown in numbers, while the “knowledge” is known as the connections among elements of data and most of the time, it is expressed in basic language. Rendering to these classes, there are two potential ways to preserve the material selection process, using material databases or knowledge-based systems. The best way of displaying the former is the computerized database, as it provides an easy process to the material’s information. The latter includes professional knowledge that can assist the user in an interactive way to solve variant problems and enquiries. Moreover, the knowledge-based system is active in a fully interactive mode and gives unbiased recommendations. It can search huge databases for best solutions (Farag, 2006). In fact, some efforts were made to establish knowledge-based systems to assist engineers to properly choose the suitable materials for various applications, such as building insulation materials, composite material, and materials for the pedal box system in the automotive sectors. Also, Sapuan (2001) offered a sample knowledge-based system for selection of polymeric-based composites. The rank of knowledge-based systems was clarified by Sapuan (2001) in the setting of concurrent engineering and was utilized to select material of polymeric-based composite. After that, the same author established a systematic application of knowledge-based systems to select ceramic matrix composite material for machine components. Furthermore, the procedure of

62

Materials Selection for Natural Fiber Composites

selecting appropriate manufacturing processes was described by Zha (2005). He also explained the materials in concurrent design referring to a fuzzy knowledge-based resolution support process. Getting the benefit of such fuzzy knowledge-based systems, a prototype web-based knowledge-intensive industrial consulting facility system with a client-knowledge server building was industrialized on order to help users/designers find better processes and materials.

3.6.1.3

Case-based reasoning

Amen and Vomacka (2001) have utilized systematic procedures for material selection based upon the solutions of similar previous problems. This procedure is called casebased reasoning (CBR). CBR is a useful technique for finding information as a technical solution through databases in real companies or industrial firms. Applying a database approach, massive amounts of information can be managed. Moreover, CBR systems can learn by receiving new knowledge, thus maintenance becomes easier.

3.6.1.4

Neural networks

Imagine that all information about material is stored in a professional network instead of handbooks: the selection of materials would then become more easy and interesting. Goel and Chen (1996) have presented some applications of skilled network in material selection via the following steps: l

l

l

Build a list of appropriate materials by neural network and expert system. Then arrange them according to the desired properties. Examine if the material is available in the storage or can be obtained in the desired time with knowledge about the costs using an expert system.

In fact, using neural network will provide a good tool in selecting a better alternative from the database. Neural network, however, may not be able to deliver only one best solution, it needs some modifications to be specified in material selection.

3.6.2

Goal programming

Goal programming (GP) is a type of modification of linear programming (LP). Although the main role of LP is to deal with only one single objective to be minimized or maximized, subjected to given constraints. GP, instead, is utilized as an effective method to handle a decision concerning multiple and conflicting goals. Temporarily, the objective function of a goal programming model may possibly consist in non-homogeneous units of measure. Goal programming is usually utilized for energetic material evaluation decisions.

3.6.3

Genetic algorithm and neural network

Neural network can provide proper input sets for the material selection process in achieving desired mechanical and physical properties. For instance, metal powder compaction and sintering are complex processes, as various mechanisms take place in determining the final properties of the material. Comparable to linear regression, neural

Materials selection

63

network was found reduce the time and cost in selecting the proper materials. Neural network and genetic algorithms were utilized for the selection of optimum composite material for various applications, as well as plastic selections. The relationship between both performance requirements and properties of plastics were selected utilizing fuzzy oriented models (AL-Oqla et al., 2015b). Moreover, optimal automotive body assemblies with various designs were achieved via multi-objective nonlinear mathematical programming models considering continuous, as well as discrete, variables. Such variables included the material types and the thicknesses of the panels ( Jahan, Ismail, Sapuan, et al., 2010). Such optimization problems, including material selections, were solved using multi-objective genetic algorithms. In fact, artificial neural networks as well as genetic algorithm approaches were utilized for optimizing multiple objective problems oriented to material selection for various applications.

3.6.4

Simple additive weighting method

A simple mathematics-based weighting of various properties technique was proposed by Farag (2006) to be utilized when several properties must be taken into account during selecting for proper materials for a particular application. In his method, relative importance of various criteria has to be calculated by the digital logic (DL) method. However, the amount of calculations rises quite rapidly as the number of alternatives increases, which requires more computational procedures. Generally, in the simple additive weight method, a weighted-property value is usually obtained by multiplying the numerical value of the property by a preset importance (weighting) factor. This method is frequently used in material selections to realize the overall material properties’ importance during the selection of the most appropriate type of material from a set of alternatives. Such a method can be integrated with other material selection schemes. But some comments were raised in this method, as it has a misleading approach and sometimes lacked the systematic sequence ( Jahan, Ismail, Mustapha, & Sapuan, 2010). Therefore, some new methods for calculating the relative importance of criteria (weights) in simple additive weighting method (SAW) were developed by modifying the DL way and integrating it with a nonlinear normalization. This modified DL approach in calculating the relative importance of criteria was found capable of providing more reasonable selections, but different than those obtained from the classical weighted method ( Jahan, Ismail, Mustapha, et al., 2010).

3.6.5

Optimization methods

Extensive optimization techniques have been suggested for enhancing the material selection, such as for mathematical programming, computer simulations, and genetic algorithms.

3.6.5.1 Mathematical programming Various mathematical programming and operations research principles were utilized to develop the process of material selection. For instance, a benefit-cost analysis scheme was utilized to select the optimum design-material-process combination.

64

Materials Selection for Natural Fiber Composites

An economic model was also offered for raw material selection, considering both initial cost and the costs that may occur due to inappropriateness of raw material quality. In addition, a methodology for structural synthesis for optimal material selection of truss structures was proposed as one of the mathematical programming schemes where assorted variables (including material aspects) were treated and expressed in terms of (0, 1) as design variables ( Jahan, Ismail, Mustapha, et al., 2010).

3.6.5.2

Computer simulation

Computer simulations as computer-aided design (CAD)/computer-aided manufacturing (CAM) programs have noticeably added value in developing better material selections for various applications. In fact, integrating the databases of material properties with design algorithms, as well as CAD/CAM programs, has various advantages in the material selection process including homogenization and sharing data more efficiently, reducing redundancy of effort, and reducing the cost of information storage and retrieval. Various software applications were utilized for achieving and assigning proper materials for a given design, like that of Moldflow software. Such software was utilized to evaluate various materials of different costs via simulating one material versus another, rather than doing trial and error in a given manufacturing process. In addition, the rapid progress in the finite element (FE) methods has permitted the implementation of computer simulation as a part of an integrated strategy for engineering design.

3.6.5.3

Genetic algorithm

As materials have various integrated performance related aspects, and because of the wide variety of material characteristics and attributes, genetic algorithms were utilized to optimize using appropriate material types, particularly the composites, as there are various combinations of reinforcement geometries and architectures in the engineering design of composites. Genetic algorithms are usually utilized for selecting optimal material constituents and microstructures based on the desired or optimized material properties.

3.6.6

Multiple attribute decision making methods (MADM)

Studying the performance of materials is usually accomplished by considering several evaluations, usually conflicting, and criteria rather than considering only one factor. The selection of an optimal material for an engineering design or manufacturing process among two or more alternative materials on the basis of two or more attributes is a multiple attribute decision-making (MADM) problem (AL-Oqla & Omar, 2012, 2015; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015d). These attributes can be expressed as numbers, like that of hardness and conductivity; others are Boolean, like the recyclability. Some can be stated only as a ranking (poor, good, excellent) such as resistance to corrosion, whereas some can be only studied by their images. Therefore, MADM approaches were developed to deal with such issues.

Materials selection

65

3.6.6.1 TOPSIS method An expert system called Technique of Ranking Preferences by Similarity to the Ideal Solution (TOPSIS) was founded to help in the process of material selection. This technique was frequently used in material selection and other applications. TOPSIS is usually integrated with other material selection techniques to improve reaching the desired type of materials. It was reported that TOPSIS was mixed with entropy as a function in computer-aided engineering (CAE) and that it is helpful in material selection process. Others also employed entropy and TOPSIS in material selection for gears and studied the effect of normalization norms on the ranking of the alternative materials. Moreover, TOPSIS as a multi attribute decision-making technique was utilized for determining the best reinforced natural fiber composite materials for automotive applications for the first time by AL-Oqla et al. (2015b). Rao and Davim (2008) alternatively offered a decision-making outline model for material selection based on a combined TOPSIS and analytical hierarchy process method.

3.6.6.2 Fuzzy multi-criteria decision-making methods For some material characteristics such as wear resistance, machinability, corrosion, and weldability, numerical values are rarely given, but materials are commonly ranked as very good, good, fair, poor, etc. Because wide materials’ characteristics stated in the engineering design handbooks are usually multi-dimensional and qualitative (steel attributes are non-deforming properties, wear resistance, safety in hardening, machinability, and toughness), The assessments of these characteristics are given by experts in linguistic terms as poor, fair, good, excellent, etc. In addition, the significance of the material properties is also different for diverse design requirements. Thus, the desired value and importance weight of a given material property is defined in a linguistic manner. As an example, it is “very important” that the wear resistance feature of the nominated material be “recommended” for it to work in a particular environment. Or, it is “important” that raw material cost must be “smaller or equal to a certain value.” That is, it is sometimes difficult to precisely quantify the rating of each alternative material. From this point of view, it seems that the final choice of material has a risk because of incomplete, approximate, and possibly incorrect information. For this reason, fuzzy logic seems to be useful to achieve unbiased wrong information. Fuzzy set theory was established with the idea that human thinking behavior is not based upon numbers, but linguistic terms, or labels of fuzzy sets. The implementation of fuzzy logic in the material and selection process is useful as decisions regarding these issues are made during the preliminary design stages with a high uncertainty environment. Therefore, it is recommended practicing fuzzy analysis in the earliest stages of design evaluation. Fuzzy set was proposed as a multiple criteria decisionmaking approach to select materials as the weights of various criteria to evaluate materials, as well as the material adequacy ratings of different alternatives and dissimilar evaluation criteria are given in linguistic terms. In addition, various fuzzy multicriteria decision-making methods for selecting the most appropriate materials were presented, some of which are the integrated fuzzy analytic hierarchy process

66

Materials Selection for Natural Fiber Composites

(AHP) method, which was frequently used in the decision-making, but this (FuzzyAHP) was proved as a falsified approach in more than a critical investigation and research (Zh€ u, 2013). Thus, the AHP method is preferable over the fuzzy-AHP, or any combination of fuzzy-multiple criteria decision-making tool mainly when the data is precisely known or when no subjectivity is involved in the problem (AL-Oqla et al., 2015b; Rao, 2013; Saaty & Shang, 2011). This is because converting the crisp data into a fuzzy layout would increase complexity, as well as the computational requirements, and rob the simple original data of their elegance, resulting in lower desirable outcomes (Rao, 2013). Furthermore, the AHP was capable of dealing with real life complexities under an uncertain environment more efficiently than fuzzy judgment (Saaty & Tran, 2007; Zh€ u, 2013). Thus, the fuzzy-AHP method has recently received criticism that its arithmetic operation violates the AHP reciprocal and continuity axioms, and the operational rule of consistency, which makes it questionable for decision-making problems (Zh€ u, 2013).

3.6.6.3

Individual methods in MADM

Rao (2006) presented a graph theory and matrix approach model for making materials selection. Any number of both quantitative and qualitative aspects of material evaluations can be considered in this technique. Therefore, it permits a more critical analysis than the DL method. However, the graph theory and matrix approach method do not have a condition for examining the consistency of the judgments made matrix. Also, this method is difficult to apply if the number of attributes is more than 20. Alternatively, the multi-criteria weighted average method was proposed utilizing the grey relational analysis to be used in the decision-making process, so as to rank the material options under various situations of uncertainties, nonlinear constraints, and conflicting objectives. Moreover, Rao (2008) has improved the compromise ranking method or VIKOR (VIsˇekriterijumsko KOmpromisno Rangiranje) method by utilizing the analytic hierarchy process to assign the weights of attributes and introducing a ranked value judgment on a fuzzy conversion scale for the qualitative values of attributes. It is worth noting here that the VIKOR method is usually helpful in the situation where the decision maker is not able to, or does not have knowledge of how to assign preference at the beginning of the decision-making process.

3.6.7

Multiple objective decision making methods (MODMM)

In material selection process, a compromise among the desired objectives should be performed to come up with the best solution. Materials selection and substitution decisions are unlike the exact sciences, where there is usually only one single correct solution to a problem. This is due to material selection requiring the consideration of conflicting advantages and limitations, necessitating compromises and trade-offs. Thus, different satisfactory solutions are possible. For instance, in designing the component, designers usually have a purpose; to make it as cheaply as possible, or as light, or perhaps as safe. This must be achieved subject to constraints: that the component

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should withstand the given loads without failure, that certain dimensions are fixed, and its cost is within certain limits.

3.6.7.1 Multi-attribute utility analysis (MAUA) Multi-attribute utility analysis (MAUA) models are mathematical tools utilized to evaluate and compare alternatives to support decision making about complicated alternatives. Such tools can help in assigning scores to alternatives in a decision situation where they can be identified and analyzed. A mapping of a multi-dimensional attribute domain into a single dimensioned preference is a utility function. The method of multi-attribute utility analysis uses questionnaires for measuring utility, however the interview manner is too lengthy and not feasible for most situations. Trade-off methods using utility functions usually lead to optimal solutions for two objectives, but for three, it is harder. Thus, some operations research tools are applied (like subjective probability assessment (SPA)) to measure the decision maker’s confidence during material selection in the levels of attributes of high degree of uncertainty. Then these probability distributions are used in conjunction with multi-attribute utility analysis to provide a consistent framework for making materials selection decisions.

3.6.7.2 Individual methods in MODM The mini-max dual method is usually utilized for solving the material selection structural optimization problems. Moreover, numerical optimizations for solving the multiobjective optimization problem can be applied for material selection process under multiple objective decision-making methods. According to advantages and disadvantages of the multiple objective decisions-making methods for material selections TOPSIS, ELECTRE, and AHP have been the most popular state-of-the-art methods in material choosing. However, all of Chart method, Computer-aided materials selection, and knowledge-based systems are the most prevalent approaches in material screening. Fuzzy methods, on the other hand, have commonly been used either individually or with other methods like genetic algorithm, neural networks, and MCDM methods. Generally, ELECTRE IV method was used for material selection to make lists of materials sorted from best to worst by considering various evaluation criteria. Various improvements of ELECTRE method were developed and used for materials selections as ELECTRE, ELECTRE I, ELECTRE III, and ELECTRE IV. The AHP is used in materials selection for many reasons: simple, easy to use, flexible, and has a measure to check the consistency in the decision-making process. Various researchers have used the AHP method for material selection in various applications (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Oqla & Omari, 2017; AL-Oqla & Sapuan, 2015a; AL-Oqla et al., 2015a, 2015b, 2015e, 2015f; AL-Widyan & AL-Oqla, 2011; AL-Widyan & AL-Oqla, 2014; Caputo, Pelagagge, & Salini, 2013; Dalalah et al., 2010; Dweiri & AL-Oqla, 2006; Lee, Kim, Kim, & Oh, 2012) and further discussions will be oriented to this method in the coming chapters of this book.

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In fact, TOPSIS, AHP, and ELECTRE methods have been the three most popular selection approaches since 2005. However, ELECTRE methods have a number of limitations, including the following: l

l

with increasing the number of alternatives, the amount of calculations increases quite rapidly. it can only determine the rank of each material, but not give numerical value for better understanding of differences between alternatives.

Conversely, AHP is a powerful and flexible decision-making technique capable of finding one set of priorities and making the best decision, including tangible and non-tangible aspects of a decision needed to be considered based upon the pair wise comparison manner. However, it can only compare a limited number of alternatives (usually not more than 15). TOPSIS in addition, is a good choice for material selection because of following reasons: l

l

l

l

it is useful for qualitative and quantitative data. it is relatively easy and fast, with a systematic process. the output can rank the candidate materials with a numerical value that offers a better understanding of differences and similarities among alternatives. TOPSIS is particularly useful when dealing with a large number of alternatives and evaluation criteria.

References Abdollah, M. F. B., Shuhimi, F. F., Ismail, N., Amiruddin, H., & Umehara, N. (2015). Selection and verification of kenaf fibres as an alternative friction material using weighted decision matrix method. Materials & Design, 67, 577–582. Alexandre, M., & Dubois, P. (2000). Polymer-layered silicate nanocomposites: Preparation, properties and uses of a new class of materials. Materials Science & Engineering R: Reports, 28, 1–63. Ali, A., & Ba, S. (2013). M.: Java based expert system for selection of natural fibre composite materials. Journal of Food, Agriculture and Environment, 11, 1871–1877. AL-Oqla, F. M., Almagableh, A., & Omari, M. A. (2017). Design and fabrication of green biocomposites. Green biocomposites. Cham, Switzerland: Springer. AL-Oqla, F. M., & Hayajneh, M. T. (2007). A design decision-making support model for selecting suitable product color to increase probability. In: Design challenge conference: Managing creativity, innovation, and entrepreneurship. Amman, Jordan. AL-Oqla, F. M., & Omar, A. A. (2012). A decision-making model for selecting the GSM mobile phone antenna in the design phase to increase over all performance. Progress in Electromagnetics Research C, 25, 249–269. AL-Oqla, F. M., & Omar, A. A. (2015). An expert-based model for selecting the most suitable substrate material type for antenna circuits. International Journal of Electronics, 102, 1044–1055. AL-Oqla, F. M., & Omari, M. A. (2017). Sustainable biocomposites: Challenges, potential and barriers for development. In M. Jawaid, S. M. Sapuan, & O. Y. Alothman (Eds.), Green

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biocomposites: Manufacturing and properties. Cham, Switzerland: Springer International Publishing (Verlag). AL-Oqla, F. M., & Sapuan, S. M. (2014a). Enhancement selecting proper natural fiber composites for industrial applications. In: Postgraduate symposium on composites science and technology 2014 & 4th postgraduate seminar on natural fibre composites, 28/01/2014, Putrajaya, Selangor, Malaysia. AL-Oqla, F. M., & Sapuan, S. M. (2014b). Natural fiber reinforced polymer composites in industrial applications: Feasibility of date palm fibers for sustainable automotive industry. Journal of Cleaner Production, 66, 347–354. AL-Oqla, F. M., & Sapuan, S. M. (2015a). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. Journal of the Minerals Metals and Materials Society, 67(10), 2450–2463. AL-Oqla, F. M., & Sapuan, S. M. (2015b). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. JOM, 67, 2450–2463. AL-Oqla, F. M., Sapuan, S. M., Anwer, T., Jawaid, M., & Hoque, M. (2015). Natural fiber reinforced conductive polymer composites as functional materials: A review. Synthetic Metals, 206, 42–54. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Aziz, N. A. (2014a). Combined multi-criteria evaluation stage technique as an agro waste evaluation indicator for polymeric composites: Date palm fibers as a case study. BioResources, 9, 4608–4621. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2014b). A novel evaluation tool for enhancing the selection of natural fibers for polymeric composites based on fiber moisture content criterion. BioResources, 10, 299–312. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015a). Selecting natural fibers for industrial applications. In: Postgraduate symposium on biocomposite technology, March 3, Serdang, Malaysia. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015b). Decision making model for optimal reinforcement condition of natural fiber composites. Fibers and Polymers, 16, 153–163. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2015c). Selecting natural fibers for bio-based materials with conflicting criteria. American Journal of Applied Sciences, 12, 64–71. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015d). A decision-making model for selecting the most appropriate natural fiber—Polypropylene-based composites for automotive applications. Journal of Composite Materials. http://dx.doi.org/10.1177/ 0021998315577233. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015e). A model for evaluating and determining the most appropriate polymer matrix type for natural fiber composites. International Journal of Polymer Analysis and Characterization, 20, 191–205. AL-Oqla, F. M., Sapuan, S. M., Ishak, M., & Nuraini, A. (2015f). Predicting the potential of agro waste fibers for sustainable automotive industry using a decision making model. Computers and Electronics in Agriculture, 113, 116–127. AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. (2016a). A decision-making model for selecting the most appropriate natural fiber—Polypropylene-based composites for automotive applications. Journal of Composite Materials, 50, 543–556. AL-Oqla, F. M., Sapuan, S. M., & Jawaid, M. (2016b). Integrated mechanical-economic— Environmental quality of performance for natural fibers for polymeric-based composite materials. Journal of Natural Fibers, 13, 651–659.

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Alves, C., Ferra˜o, P., Silva, A., Reis, L., Freitas, M., Rodrigues, L., et al. (2010). Ecodesign of automotive components making use of natural jute fiber composites. Journal of Cleaner Production, 18, 313–327. AL-Widyan, M. I., & AL-Oqla, F. M. (2011). Utilization of supplementary energy sources for cooling in hot arid regions via decision-making model. International Journal of Engineering Research and Applications, 1, 1610–1622. AL-Widyan, M. I., & AL-Oqla, F. M. (2014). Selecting the most appropriate corrective actions for energy saving in existing buildings A/C in hot arid regions. Building Simulation, 7, 537–545. Amen, R., & Vomacka, P. (2001). Case-based reasoning as a tool for materials selection. Materials & Design, 22, 353–358. Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016a). Investigating morphological and performance deterioration of injection molded rice husk-polypropylene composites due to various liquid uptakes. International Journal of Polymer Analysis and Characterization, 21(8), 675–685. Aridi, N., Sapuan, S. M., Zainudin, E., & AL-Oqla, F. M. (2016b). Mechanical and morphological properties of injection-molded rice husk polypropylene composites. International Journal of Polymer Analysis and Characterization, 21, 305–313. Ashby, M. F. (2005). Materials selection in mechanical design. Cambridge: ButterworthHeinemann. Ashby, M. F., & Johnson, K. (2013). Materials and design: The art and science of material selection in product design. Oxford, UK: Butterworth-Heinemann. Caputo, A. C., Pelagagge, P. M., & Salini, P. (2013). AHP-based methodology for selecting safety devices of industrial machinery. Safety Science, 53, 202–218. Chiner, M. (1988). Planning of expert systems for materials selection. Materials & Design, 9, 195–203. Dalalah, D., AL-Oqla, F., & Hayajneh, M. (2010). Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of cranes. Jordan Journal of Mechanical and Industrial Engineering, 4, 567–578. Djassemi, M. (2012). A computer-aided approach to material selection and environmental auditing. Journal of Manufacturing Technology Management, 23, 704–716. Dweiri, F., & AL-Oqla, F. M. (2006). Material selection using analytical hierarchy process. International Journal of Computer Applications in Technology, 26, 182–189. Edwards, K. (2005). Selecting materials for optimum use in engineering components. Materials & Design, 26, 469–473. Farag, M. M. (2006). Quantitative methods of materials selection. In M. Kutz (Ed.), Mechanical engineers handbook: Materials and mechanical design (3rd ed., pp. 466–488). Hoboken, NJ: Wiley. Goel, V., & Chen, J. (1996). Application of expert network for material selection in engineering design. Computers in Industry, 30, 87–101. Jahan, A., Ismail, M. Y., Mustapha, F., & Sapuan, S. M. (2010a). Material selection based on ordinal data. Materials & Design, 31, 3180–3187. Jahan, A., Ismail, M. Y., Sapuan, S. M., & Mustapha, F. (2010b). Material screening and choosing methods—A review. Materials & Design, 31, 696–705. Karana, E. (2012). Characterization of “natural” and “high-quality” materials to improve perception of bio-plastics. Journal of Cleaner Production, 37, 316–325. Lee, S., Kim, W., Kim, Y. M., & Oh, K. J. (2012). Using AHP to determine intangible priority factors for technology transfer adoption. Expert Systems with Applications, 39, 6388–6395.

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Rao, R. V. (2006). A material selection model using graph theory and matrix approach. Materials Science and Engineering A, 431, 248–255. Rao, R. V. (2008). A decision making methodology for material selection using an improved compromise ranking method. Materials & Design, 29, 1949–1954. Rao, R. V. (2013). Decision making in the manufacturing environment: Using graph theory and fuzzy multiple attribute decision making methods. London, UK: Springer. Rao, R., & Davim, J. (2008). A decision-making framework model for material selection using a combined multiple attribute decision-making method. International Journal of Advanced Manufacturing Technology, 35, 751–760. Saaty, T. L., & Shang, J. S. (2011). An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts. European Journal of Operational Research, 214, 703–715. Saaty, T. L., & Tran, L. T. (2007). On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process. Mathematical and Computer Modelling, 46, 962–975. Sapuan, S. M. (2001). A knowledge-based system for materials selection in mechanical engineering design. Materials & Design, 22, 687–695. Sapuan, S. M., Haniffah, W., & AL-Oqla, F. M. (2016). Effects of reinforcing elements on the performance of laser transmission welding process in polymer composites: A systematic review. International Journal of Performability Engineering, 12, 553. van Kesteren, I., Kandachar, P., & Stappers, P. (2006). Activities in selecting materials by product designers. In: Proceedings of the international conference on advanced design and manufacture, Harbin, China. Zha, X. F. (2005). A web-based advisory system for process and material selection in concurrent product design for a manufacturing environment. The International Journal of Advanced Manufacturing Technology, 25, 233–243. Zh€ u, K. (2013). Fuzzy analytic hierarchy process: Fallacy of the popular methods. European Journal of Operational Research, 236(1), 209–217.

Material selection for composites 4.1

4

Various issues in materials selection in composites

A composite material is a result of mixing two or more materials to produce a new material with new properties. The new properties must have better aspects in comparison to the properties of the mixed individuals (Hull & Clyne, 1996). Another definition that can be adopted for composite materials is composites are materials consisting of fibers or fillers and a matrix, and this definition comes from the function or objective of why these materials are created. That is, the load carriers or fibers will usually increase the stiffness of the polymer matrix or resin and thus the composites. To enhance the physical and the mechanical properties of this combination, some additives may become required. The composite materials can also be categorized based on the fibers’ size (short, or long), or the fiber’s type (synthetic, or natural). Composites, moreover, can be classified into three main categories: metal composites, ceramic composites, and polymeric composites (AL-Oqla & Omari, 2017). Nowadays, materials selection has become too complicated to be managed without help or utilizing any tool, and choosing the incorrect material type may incur some financial and other losses. Therefore, it is hard to see how design engineers can optimize their choice without some form of computerized selection tools (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012; Dweiri & AL-Oqla, 2006). The real issues of materials selection for composites are the presence of large permutations of: l

l

l

l

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Countless types of composite materials Numerous types of manufacturing processes for composites Large number of different fiber types So many types of matrices and So many arrangements of fibers

Consequently, the readers can obviously understand that selection of composite materials is not straightforward, unlike selection of monolithic materials. Composites propose great advantages compared to monolithic materials, for example, high stiffness, low density, long duration under fatigue, and high adaptability to diverse functions followed for the structure. Also, additional improvements can be reached for wear and tear, esthetics, sound isolation and thermal stability, behavior at several temperatures, resistance of corrosion, and thermal isolation and conductivity (AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015a). The important characteristics of the structural presentations of composites are the great specific resistance (resistance to density relation) and high exact stiffness (modulus elasticity (E) to density relation), and the anisotropic and heterogeneous kind of the material. Such characteristics make the composite system very Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00004-9 Copyright © 2017 Elsevier Ltd. All rights reserved.

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flexible, permitting an effective use of optimization of the material formation. Up to now, composites presented some limitations that traditional monolithic materials did not strongly submit. That is, composites proposed the benefit of high stiffness and great fiber resistance based on fiber dimensions. As the fibers became very strong, it became less ductile and required high amounts of energy to be produced. Moreover, the volume of fibers required to enhance the matrix strength usually allows the growth of widespread tensile mechanisms. In addition, fibers present comparatively great resistance to proper dispersion (AL-Oqla, Alothman, Jawaid, Sapuan, & Es-Saheb, 2014; AL-Oqla & Sapuan, 2015; AL-Oqla et al., 2015a). Furthermore, the tensile strength is basically reduced by the concentration of domestic stresses nearby fibers. Traditional materials on the other hand, are highly sensitive to the microstructure and to the local deficiencies impacting the hard and fragile material’s behavior (Aridi, Sapuan, Zainudin, & AL-Oqla, 2016b). By assuming that the material is almost uniform and regarding the macro mechanics scene, the anisotropy of the material can be exploited as a benefit. The material’s average behavior can be controlled and predicted by having knowledge of the properties of the material’s constituents. Nonetheless, the anisotropic analysis of composites is complicated and highly dependent on the procedure’s measurements, while the analysis of traditional materials is easier as a result of the material’s isotropy and regularity. In consequence, studying a composite structure needs the input of data linked to the average characteristics of the composite materials. The later can be predicted depending on the features and the arrangement of their ingredients. On the other hand, the experimental testing, analysis and independent characterization of a material require comprehensive software to identify the parameters of a great number of basic materials (Ahmed & Vijayarangan, 2008). For conventional materials, the mechanical characterization is easier than that of composites, because only two elastic constants and two values of resistance are required. Composites, moreover, have good properties compared with traditional materials, for instance, the specific density (reaches four times greater), and specific elasticity (reaches two times greater). This means that with equal stiffness, a structure made out of composite materials can weigh half the weight of one built with a traditional material (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015c; AL-Oqla et al., 2015a). The nature of the phases comprising the composite dramatically impacts the final properties conveyed to the material. Nevertheless, in order to obtain a composite with high mechanical strength, using “resistant” fibers is not enough: a good bonding between the matrix and the strengthening is of utmost importance (Sapuan, Haniffah, & Faris, 2016; Sapuan, Pua, El-Shekeil, & AL-Oqla, 2013). Bonding is usually enhanced by way of a third component applied in a very thin layer on the fiber surface in order to harmonize with the organic matrix (Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a; Sapuan, Haniffah, & Faris, 2016). Alongside the several advantages listed above, when designing a strengthening of a structural member, it is very useful to review all possible and feasible alternatives. The fact that a special type of intervention may be performed using fiber strengthened materials does not necessarily represent a condition sufficient by itself to make this the

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most suitable solution. The advantages and disadvantages of these applications should be reviewed one by one according to the specific conditions of the structure, according to other possible strengthening solutions available, and according to an accurate technical and economic evaluation. The mechanical properties of the composite materials are vital properties and thus have to be considered even if the composites are not designed or oriented for carrying load, as they have to maintain their shapes when utilized (Ichhaporia, 2008). The mechanical properties of the short fiber composites are not easy to be predicted as various issues are involved, such as the fibers orientations, their dispersions, their volume fraction, and their interface with the matrix (Ichhaporia, 2008). Such factors cannot be accurately controlled due to the variation in the length of fibers (particularly for the green fibers), their distribution as well as the unpredictable process variability. Thus, the mechanical properties can be significantly inconsistent from one composite to another (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015d; Arnold, Hergenrother, & Mcgrath, 1992; De & White, 1996; Ichhaporia, 2008). Moreover, factors like the volume fraction and the fiber’s aspect ratio can significantly change the composite materials’ properties (Shalin, 1995). The stress transferred into the fibers will be insufficient if the aspect ratio is very small, leading to improper reinforcement for the composite (AL-Oqla, Sapuan, Ishak, & Nuraini, 2016). On the other side, if the aspect ratio is very high, fibers may become entangled, and this also leads to poor mechanical properties due to the improper distribution. Moreover, at low fiber loading conditions, fibers will not be able to transfer load to their neighbors, leading to a reduction in composite strength properties. But at high density, no sufficient matrix is penetrated between the fibers, leading to agglomeration, and thus, blocking the load transfer € ( Jacob, Thomas, & Varughese, 2004; Ozturk, 2010). Therefore, various technical issues can affect the selection of the proper composites reinforcement conditions or processing of composites and the selection of the overall composites performance for various applications. In fact, processing of composites will be completely dependent on their design purposes; composites designed to withstand external loads (structural composites), as in some aerospace applications, will be different from those designed for esthetic purposes only (nonstructural composites). During the development stages, the material selection is crucial, as a wide range of material properties need to be considered (AL-Oqla & Sapuan, 2014c; AL-Oqla, Sapuan, & Jawaid, 2016). Using the material performance indices, Ashby (2005) was able to compare the relative performance of a wide range of materials for a specific element. The material performance indices are defined by the design criteria describing the objective and the constraint of the elements. In addition to the component function, the main objectives are, in general, minimizing the material weight, and reducing the production costs. The component function and constraint are normally the element stiffness and strength. For instance, it is believed that the most crucial material performance indices required to be maximized for a rod or a plate under pure axial tension are the specific tensile strength (tensile strength/material density) and the specific tensile stiffness

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(tensile stiffness/material density). However, for a beam or a plate under bending load, the element performance will be increased when the specific flexural stiffness is maximized, in addition to the specific flexural strength. Thus, for selecting the composites based on their performance indices, comparisons of combinations of materials properties are required.

4.2 4.2.1

Selection of matrices Selection of matrices for general composites

Matrices are made of different materials for composites such as, ceramic, metal, and plastic. Such matrices have many tasks and functions in the composite structure (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015e). They can: l

l

l

l

Provide fibers of the required stability. Incorporate the fibers to the required shape of the part. Isolate the fibers from the external surroundings and prevent them. Transfer the stress for the fibers.

They are different in their physical and mechanical properties, as well as costs. Table 4.1 compares the main types of matrices for composites. l

l

Ceramic matrices They are considered to be number one matrices regarding the mechanical and temperature properties. Currently, they are barely used for mainly three reasons: it is difficult to deal with them, difficult to distribute the fibers uniformly within the matrix, and there are also big challenges to eliminating the internal porosity. Aluminum oxides (Al2O3), and silicon oxide (SiO2) are two main ceramics matrices currently used. Metal matrices Metal matrices are considered as an intermediate type of matrices, between ceramics and plastics in their properties and costs. Their precise temperature limits depend on the metal Table 4.1 Main properties of various matrices of composites Matrix Type

Main properties

Polyester

Low temperature limit Medium/low mechanical properties Low cost Production of styrene Low temperature limit High cost High mechanical properties High temperature limit Medium cost Medium mechanical properties High temperature limit Low cost Medium mechanical properties

Epoxy

Metal matrix

Ceramic matrix

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l

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that composes the matrix, but it is more than the temperature with metal alone compared in equal conditions. The most useable metals are aluminum alloys, but when higher temperature operating conditions are needed, composites based on magnesium, nickel, titanium, and copper are usually selected. The main limitation of metal matrices is the possibility of chemical reactions between the matrices and the fibers; therefore, decreasing the part’s expected life. Plastic (polymers) matrices Two main families of plastic matrices with different performance and properties are utilized for composites; this scattering of properties is due to the spatial distribution of different kinds of modules and the degree of crystallinity. The first family is thermosetting. They are synthetic materials that can be strengthened by heating up, but if they are heated beyond a specific limit, they will be damaged irreversibly. Also, after polymerization, they cannot be remolded, reheated, or turned back into their initial state. Parts are intended to serve under high temperature environments; thermos matrix will be the best choice over the other family of plastics (thermoplastics). The most common thermosetting plastics (resins) are: polyester, phenolic, silicone, and epoxy. Matrices typically employed in the area of composites appear, before to application, in a more or less viscous state. At this phase, they have not undergone cross-linking yet and to trigger the process, specific agents should be added to the polymer, known as catalyzers in polyester matrices phase, and hardeners otherwise. The time of crosslinking can be controlled by adding some accelerators or inhibitors. The cross-linking time is highly influenced by temperature in opposite relationship (decreases as temperature increases). The type of matrix chosen hardly affects the mechanical properties of parts in the direction of fibers. The matrix is the part that is responsible of contacting with the surroundings, thus it should resist corrosion, heat, and abrasion. Some applications are containers of corrosive fluids, automotive components, food containers, etc. The second family is thermoplastic, which can be converted to liquid when heated up, but once cooled down, they retain their properties, and can be reshaped, remelted, and heat treated even after polymerization. Some applications of thermoplastics are impeded by low temperatures applications, used for complex part geometries in an easy and rapid way. These matrices consist of linear or branched thermoplastic polymers and can be melted and shaped by heating them up, and during the solidification no chemical change occurs. It can be manufactured by a forging process into any predetermined shape via different techniques, for instance, injection or extrusion processes. It is obtained by melting these polymers, and injecting them inside the mold and because it is in contact with the walls of the mold, it will solidify with time passing. This process can be repeated many times without making extensive changes in the performance of the resin, because they lose some hardness in the high-temperature stage, and so they always reacquire a solid state at a particular temperature. Thermoplastic resins are divided into: (1) crystalline type (has crystalline structure) which is opaque to light, and (2) amorphous, which has an amorphous structure and in general, transparent to light. The crystalline regions of such materials are categorized by melting temperatures. The amorphous regions are categorized by their glass transition temperature (the temperature to turn suddenly from a very stiff glassy state into a much softer, rubbery one). This transition occurs with the activation of some motions of the macromolecules that make the material. Below this temperature, the polymer chains have trouble moving and have very restrict positions. Using thermoplastic alone in structural applications will lack the stiffness necessary to withstand the load. Thus, fiber reinforcements are needed. Based on their chemical structures, thermoplastics can reach more than 50 different types. Some l

l

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Materials Selection for Natural Fiber Composites

Table 4.2

Characteristics of some plastic matrices Young’s modulus (N/ mm2)

Tensile strength (N/ mm2)

Resin

Type

Density (g/cm3)

Epoxy Phenol formaldehyde Polyester Acetal Nylon Polycarbonate Polyethylene

Thermosetting Thermosetting

1.1–1.4 1.2–1.4

2100–5500 2700–4100

40–85 35–60

Thermosetting Thermoplastic Thermoplastic Thermoplastic Thermoplastic

1.1–1.4 1.4 1.1 1.2 0.9–1.0

1300–4100 3500 1300–3500 2100–3500 700–1400

40–85 70 55–90 55–70 20–35

are: styrenics (e.g., polystyrene), acrylics (e.g., polymethylmetacrylate), polyolefins (e.g., polypropylene, polyethylene), fluoropolymers (e.g., polychlorotrifluoro-ethylene), vinyls (e.g., poly-vinylchloride), polyesters (e.g., polyethylene terephthalate), polymers containing sulfur (e.g., polysulfone), and many others. Table 4.2 contains the basic characteristics of some plastic matrices.

In general, the strength of the fiber for the polymeric-based fiber reinforced composites is generally larger than that of the matrix and the strain of the fiber is less than that of the resin, thus the composite characteristics are somehow in between these two constituents’ properties as seen in Fig. 4.1. Fig. 4.2 shows the strain to failure for various fibers (E-glass, S-glass, aramid, and high-strength grade carbon fibers) without being in a composite form. Here it can be illustrated that, for example, the S-glass fiber, with an elongation to break of about

Fig. 4.1 Schematic stress stain diagram of the polymeric based composites and their constituents. Tensile stress

Fibre

Composite Resin matrix

Strain

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79

S-glass 3000 Tensile stress (MPa)

HS carbon

Aramid

E-glass

2000

1000

Epoxy resin 1

2

3 4 Strain (%)

5

6

Fig. 4.2 Typical stresses and strain to break values for some synthetics fibers comparable to the Epoxy matrix.

5.6%, will require a resin with an elongation break of at least this value to achieve maximum tensile properties. Despite their higher cost in comparison to thermoset polymers, thermoplastics are justified for use due to their several advantages (Biron, 2007; Greco, Musardo, & Maffezzoli, 2007; Mohd Ishak, Leong, Steeg, & Karger-Kocsis, 2007) that may include the following: (1) They can be melted, reshaped, and reformed without losing their chemical and physical properties. However, after the first curing, thermosets cannot. (2) The processing time of the thermoplastics is much shorter than the time required for thermosets. (3) The shelf-life of thermoplastics is near infinite, while it is not more than six months for thermosets. (4) Thermoplastics have higher toughness and impact strength than thermosets. (5) Thermoplastics are recyclable with insignificant volatile organic compound released. On the other side, thermosets can only be dipped into the ground. (6) Thermoplastics require less energy during processing due to the reduction in the cycling time and temperature in comparison to thermosets.

However, thermoplastics have some disadvantages. One obvious disadvantage is the high viscosity compared to thermosets. The melting viscosity of thermoplastics is 500–1000 times more than the melting viscosity of thermosets. Thus, higher pressure is required during processing. Some problems can also occur due to the high viscosity, such as the formation of voids, and the disorder of fibers during consolidation. One more disadvantage of thermoplastics is the low temperature at which the creep takes place in comparison to thermosets; this is because no chemical cross-linking is formed at elevated temperatures (Biron, 2007). Despite these

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Table 4.3 Comparing thermoplastic to thermoset for general properties Property

Thermoset

Thermoplastic

Formulation Melt viscosity Fiber impregnation Prepeg stability Processing cycle Processing temperature Processing pressure Fabrication cost Mechanical properties 54 to 93°C, hot/wet Environmental durability Solvent resistance Damage tolerance Database

Complex Very low Easy Poor Long Low to moderate Low to moderate High Fair to good Good Excellent Poor to excellent Very large

Simple High Difficult Excellent Short to long High High Low Fair to good Unknown Poor to good Fair to good Small

disadvantages, the determination of using recyclable materials to replace the traditional materials is increasing. A general comparison between thermoplastics and thermoset is presented in Table 4.3.

4.2.2

Selection of matrices for the natural fiber reinforced polymeric based composites

Low-cost, eco-friendly, and the low density of the natural fiber composites increased their importance apparently. Moreover, their less abrasiveness, availability, renewability, and their less-energy consumptions during processing are extra added features demonstrating their increasing importance. However, some challenges are facing the trend of utilizing the natural fiber composite in different applications in industry (AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015b, 2015f). One important challenge is their incompatibility with polymers; the natural fibers are hydrophilic in nature, while most polymers used are hydrophobic (AL-Oqla, Sapuan, Ishak, & Nuraini, 2014). Thus, several investigations and tests have been conducted to reduce this incompatibility by using coupling agents, and by means of physical and chemical treatments. As the properties of the natural fiber composites strongly depend upon the matrix type, fiber type, and their interfacial bonding, studying the physiochemical, mechanical, and electrical behavior is a must for their performance optimization. The mechanical properties of the composite are heavily determined by the adhesion between the matrix and the reinforcing natural fibers. This is mainly because the stress transfer between the matrix and the reinforcement fibers determine the reinforcement efficiency and compatibility of the composite (Alawar, Hamed, & AL-Kaabi, 2009; AL-Oqla & Sapuan, 2014c; Arbelaiz et al., 2005).

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Factors that affect the polymer types for the natural fiber composites

Table 4.4

Level 1 Category Polymer properties for their selection

Level 2 Property/ characteristic Physical

Chemical Mechanical

Environmental

Other

Level 3 Criteria Specific heat, Thermal conductivity, Electrical conductivity, Coefficient of thermal expansion, Reflectivity, Opaque. Density, Flammability, Molecular weight (chain length), Thermal stability. Elastic modulus, Fracture toughness, Shear modulus, Yield strength, Poisson’s Ratio, Elongation to break, Hardness. Weather resistance, Thermal behavior (melting or degrading), Service temperature, Energy content. Thermoset or thermoplastics behavior, Toxicity, Price, Abrasion, Esthetic attributes (soft to hard, and warm to cool, muffled to ringing), Additive and Modifier properties.

Unfortunately, researchers usually select a polymer matrix type according to very limited criteria, like the availability, density, chemical resistance, and cost. Thus, an obvious lack of information regarding selecting polymers for the natural fiber composites considering wide various criteria exists. However, AL-Oqla and Sapuan (2014c) have recently established the major critical factors and criteria that affect the polymer selection for the natural fiber composites, where physical, mechanical, chemical, environmental, as well as thermal properties, in addition to the toxicity and cost of the polymer, should be taken into accounts. These factors are illustrated in Table 4.4. Therefore, proper decisions in selecting the proper polymer matrix type for a specific natural fiber can help avoid major drawbacks of the natural fibers, mainly the low permissible processing temperature (AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Sapuan, 2014b). These decisions can also help in achieving better interaction bonding between the fiber and the matrix and thus, meeting the required natural fiber composite characteristics and performance. This can be achieved via proper consideration of the various characteristics of the available polymers in order not only to facilitate the selection guidelines of the available alternatives polymers, but also to expand new sustainable eco-friendly design possibilities suitable for various industrial applications and the cleaner production themes (Almagableh, AL-Oqla, & Omari, 2017; AL-Oqla & Omari, 2017; AL-Oqla & Sapuan, 2014a; AL-Oqla et al., 2017).

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Materials Selection for Natural Fiber Composites

Starch

Polysaccharides

Protein

Biomass

Polylactic acid (PLA)

Polyhydroxylalkanoates (PHA)

Polyvinly alcohol (PVA)

Bio-synthesis from biomass

Microbial hydrolysis from biomass

Chemicalsynthesis from fossil product

Biopolymer

Fig. 4.3 A classification of major biodegradable polymers.

Biopolymers on the other hand, are very suitable for making a completely biodegradable or green composite. Biopolymers can be classified into four groups: biomass, bio-synthesized biomass, microbial hydrolyzed biomass and chemical-synthesized fossil product biopolymer. The application of biopolymers began in ancient times (Majeed et al., 2013). However, they have been replaced by fossil plastics due to their ease of availability, lower cost, and better properties. Starch is one of the most famous polysaccharide polymers and undergoes the highest amount of research. Polylactic acid (PLA), polyhydroxyalkanoates (PHA), and polyvinyl alcohol (PVA) are the main representatives for the categories of bio-synthesis from biomass, microbial hydrolysis from biomass, and chemical-synthesis from fossil product, respectively. A classification of main biodegradable polymers is shown in Fig. 4.3. Conventional polymers have had a significant position in biocomposite research in the last five years, and have been mainly used to compare the performance of biopolymer composites, showing that biodegradable polymers are suitable for application in advanced sectors.

4.3 4.3.1

Selection of fibers Selection of synthetic fibers

Generally, for the polymeric fiber reinforced composites, the improvements in most mechanical properties, including stiffness (flexural, shear, tensile, and compressive) and strength (compressive, tensile, flexural, shear, and impact), can be achieved by

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83

sf

Composite strength, sc

Matrix control failure

Fibre controlled failure

v f)

v f+ =sf

(1– s⬘ m

sc

sm sc = s

m (1–v ) f

s⬘m nf,min nf,crit

nf,max,prac nf,max,theo l Fibre volume fraction, nf

Fig. 4.4 An illustration for the variation of a unidirectional composite strength with fiber volume fraction.

increasing the fiber volume fraction of any fiber type. Although increasing the fiber content is attractive, there is a theoretical maximum fiber volume fraction (vf ) dependent on the fiber packing arrangement, which cannot be exceeded (Almagableh et al., 2017; Sapuan, Haniffah, & AL-Oqla, 2016). Fig. 4.4 illustrates the variation of the strength of a unidirectional composite made from a brittle-fiber and ductile-matrix with fiber volume fraction (Shah, 2013). It can be noticed that for low fiber fraction (fiber loading), the failure will occur depending upon the matrix characteristics only, and there must be a certain minimum fiber loading to make the failure controlled by the composite behavior and the strength of the matrix can be increased. This is accomplished by increasing the fiber fraction until a certain maximum value (the desired range of fiber fraction), after which the strength will be reduced due to the poor fiber distribution within the matrix and the poor interaction between the fiber and the matrix. The selection of synthetic fibers for the polymeric fiber reinforced composites is usually performed based upon the mechanical characteristics in addition to some physical properties of the fiber itself to enhance the composite performance, as the compatibility between the polymers and synthetic fibers, synthetic geometry, and length to diameter aspect ratio of synthetic fibers do not generally appear. Other considerations for selecting the synthetic fibers might also be considered, such as the ease of manufacturing, temperature resistance of the fibers, and its cost. However, much more consideration has to be realized when selecting the natural fibers for such composites.

84 l

Materials Selection for Natural Fiber Composites

Cost aspects of fibers Synthetic fibers are usually found in fabric forms to gain benefit of their mechanical properties to reinforce and strengthen the polymer matrix. Fabric cost usually depends upon various aspects like: The weight of fibers per square meter. The type of fibers. The distribution of the fibers through the fabric, for instance, multiaxial, monoaxial, or biaxial. Comparing the synthetic fibers regarding the cost criterion integrated with their mechanical performance can be summarized in the following points: - Glass fibers: The cheapest type, but they have relatively low mechanical properties. - Basalt fibers: A little higher cost, but with higher mechanical properties compared with glass. - Aramid fibers: This type is also intermediate between the glass and carbon fibers in both cost and properties. - Carbon fibers: Many levels of elastic moduli are available for carbon fibers. They have better elastic moduli, but more money is needed in a nonlinear manner for their various properties. For instance, doubled the modulus of elasticity, the cost will be tripled or may be quadrupled. l

l

l

However, fibers made from agro-waste (cellulosic) are the potential types for engineering sustainability from economic, as well as integrated desirable economic-mechanical-environmental, and other properties’ standpoint (AL-Oqla, Sapuan, & Jawaid, 2016; Sapuan et al., 2013). Natural fibers and properties will be discussed in the following section.

4.3.2

Selection of natural fibers

The advantages of natural fibers can be seen in most of the research journals that relate to them; many researchers have mentioned these advantages in their work (AL-Oqla, Alothman, et al., 2014; AL-Oqla et al., 2015a; Anuar & Zuraida, 2011; Aridi et al., 2016b; Elfehri Borchani, Carrot, & Jaziri, 2015). “Lignocellulosic fiber” is a scientific name that refers to natural fiber, because all plant fibers are constructed of just a few constituents (cellulose, hemicellulose, and lignin). Most of the plant fibers contain 50%–70% cellulose. Cellulose is the most abundant component in the world. Every biomass consists of a major amount of cellulose. Cellulose is the main source of the high performance of plant fibers; the Young’s modulus of crystalline cellulose is found to be higher than Kevlar and it is potentially stronger than steel (Lin & Dufresne, 2014). With respect to the environmental issue, biodegradable natural fiber can reduce the problem of solid waste yield and handling matter (AL-Oqla & Sapuan, 2015; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla et al., 2015b; Jawaid & Abdul Khalil, 2011). Other than the landfill issue, energy consumption is another aspect of environmental concern where a lower energy is required to produce the same amount of the natural

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85

fiber compared to the synthetic fiber. As a comparison for the total energy consumption between flax slivers, flax yarns, E-glass, and polypropylene fibers, their approximate magnitudes are 54–118, 81–146, 55, and 90 MJ/kg, respectively. Hence, minimal processing is becoming attractive with respect to the trend of minimizing the environmental impact of plant fibers reinforcements (Akil et al., 2011). Natural fiber has lower densities of 1.2–1.6 g/cm3 than 2.4 g/cm3 of glass fiber (Huda, Drzal, Mohanty, & Misra, 2006). A lower density of natural fiber has more volume or quantity of fibers for the same weight. This scenario means that natural fiber is fabricated with much lower energy, but with a much higher quantity of fibers.

4.3.2.1 Sources of natural fibers Natural fibers can be obtained from animal, mineral, and plant origins. However, this book will only consider the natural plant fibers. Plant fibers are primarily comprised of cellulose. Cotton, Coir, Jute, Hemp, Flax, and Sisal are the most common cellulose fibers sources that have already found applications in the industry. According to the literature, these fibers can be classified into the following categories: l

l

l

l

l

l

Seed fibers (like cotton): where fibers are collected from seeds or seed cases. Leaf fibers (like sisal, banana, date palm, and agave): where fibers are collected from leaves. Bast fibers (stem fibers or skin fibers), (like flax, jute, kenaf, hemp, and date palm): where fibers are collected from the skin or bast surrounding the stem of their plant. These fibers usually have higher tensile strength than other fibers. Fruit fiber (like coconut (coir), oil palm and kapok): where fibers are collected from the fruit of the plant. Stalk fibers (grass fibers), (like bagasse, rice, straw of wheat, barley, bamboo, and grass): where fibers are actually the stalks of the plant. Root fibers (like broom root): where fibers are the root of the plant.

4.3.2.2 Quality of natural fibers Due to their intrinsic composition, together with the conditions of possessing, natural fibers display wide variety in their physical, mechanical, and electrical properties. To ensure the quality of fibers to be used in industries, special fiber quality protocols usually assigned to make sure that both fiber non uniformity and dimensional variability between production batches does not dramatically affect the desired mechanical properties. The natural fiber composites are affected by problems in the properties of the natural fibers. Soil quality, seed densities, fertilization, field location, fiber location on the plant, climate, weather conditions, crop variety, and harvest timing are all possible factors that can affect the quality of the natural fibers (Alkaabneh, Barghash, & Mishael, 2013). Additionally, the differences in drying processes, extraction processing methods, the variation of the fibers’ cross-sectional area, as well as the damage incurred in handling and processing will also affect the quality of the natural fibers.

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4.3.3

Materials Selection for Natural Fiber Composites

Nanocrystalline cellulose

Nanocrystalline cellulose (NC) is the most popular reinforcement additive for many applications, as its dispersion state, weight ratio, and phase behavior are important factors affecting its performance. NC has needle-shaped cellulose fibers of less than 100 nm in size (Flauzino Neto, Silverio, Dantas, & Pasquini, 2013). NC is referenced by several terms in the literature: nanocrystalline cellulose (Cha, Wang, Cheng, He, & Jiang, 2014; Maddahy, Ramezani, & Kermanian, 2012; Zhang, Lu, Gao, Lv, & Yao, 2012), cellulose nanowhiskers, cellulose whiskers, crystalline cellulose (Rahman et al., 2014), cellulose crystals, and cellulose nanocrystals (Kumar, Negi, Choudhary, & Bhardwaj, 2014; Mtibe et al., 2015). Generally, there are two pathways to extract cellulose from lignocellulosic biomass. One is a chemical pulp treatment using soluble lignin and hemicellulose, which is followed by bleaching with oxidizing agents (Brinchi, Cotana, Fortunati, & Kenny, 2013). Alkaline treatment is the most used pathway (Mendes, Ferreira, Furtado, & de Sousa, 2015; Rathod, Haldar, & Basha, 2015). Another extraction treatment is the steam explosion process, and a better enzymatic hydrolysis results. Milled biomass is subjected to a high pressure for a short period of time. The fiber is then exposed to normal pressure after the steam opens. The fibers experience explosion and the breaking down of lignin and hemicellulose due to the sudden drop of pressure. Water soluble hemicellulose can be easily removed by water extraction, while other chemical treatments are needed to eliminate the lignin components. After pulp or steam explosion extraction, controlled sulfuric acid hydrolysis is used to isolate the NC. Acid is hydrolyzed in the amorphous region, while the crystalline regions are more resistant to acid. After this, repeated washing with water for dilution is needed to stop the hydrolysis reaction and remove the free acid molecules—a huge amount of water is needed to dilute the product. Mechanical dispersion, sonication, is used to disperse the agglomerated nanoparticles. Lastly, the drying of the products is performed to gain solid NC. Unfortunately, the long time taken in the production of NC limits its commercial availability. It has been accused of causing environmental issues due to the polluted washing water involved in the production. The remaining sulfate groups on the fiber’s surface may mean that hydrolysis continues, and an unexpected drop in properties may occur. These phenomena make sulfuric acid hydrolyzed NC unsafe for healthcare applications. Therefore, the microbial hydrolysis of NC is being proposed under a controlled anaerobic medium. Microcrystalline cellulose (MCC) sole carbon sources, from biomass, is placed in a salt medium with anaerobic gas (10% hydrogen, 10% CO2, and 80% nitrogen) and subjected to a shaking condition. The anaerobic microbial medium accelerates the enzyme to produce extra NC as storage for future usage. However, the long period of NC production was the shortage of microbial hydrolysis. The process of hydrolysis took a few days in complete darkness, to avoid photosynthesis. It was found that microbial hydrolyzed NC has a larger dimension due to the lower regions of amorphous needing to be cleaved

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Table 4.5 Source and form of cellulose reinforcement and production method Sources

Fiber form

Method

Reference

Kenaf fiber

CNF

Mechanical grinding

Canola straw Softwood

CNF CNF

Commercially bleached eucalyptus kraft pulp Cotton

CNF

Mechanical grinding High shear homogenization Mechanical actions with chemical or enzyme hydrolysis

Babaee, Jonoobi, Hamzeh, and Ashori (2015) Yousefi et al. (2013) Zhao et al. (2013)

NC

Microbial hydrolysis

Bamboo fiber

NC

Sugarcane bagasse Jute fiber Sugarcane bagasse MCC

NC NC NC NC

Microbial hydrolysis (Trichoderma reesei) Acid hydrolysis Acid hydrolysis Acid hydrolysis Acid hydrolysis

Bleached softwood pulp MCC

NC

Acid hydrolysis

NC

Acid hydrolysis

Corn husk MCC

NC NC

Acid hydrolysis Acid hydrolysis

Green seaweed, Ulva lactuca Grinded cellulose, KimWipes MCC

NC

Acid hydrolysis

NC

Acid hydrolysis

NC

Acid hydrolysis

Flax and hemp fiber Bleached aspen kraft pulp Cotton cellulose

NC NC

Microbial hydrolysis (Aspergillus oryzae) Acid hydrolysis

NC

Acid hydrolysis

Banana peel

CNF

Chemical and enzyme hydrolysis

Qing et al. (2013)

Nadanathangam and Satyamurthy (2011) Zhang et al. (2012) Kumar et al. (2014) Rahman et al. (2014) Zhu et al. (2011) Cordero, Amalvy, Fortunati, Kenny, and Chiacchiarelli (2015) Wang et al. (2015) Voronova, Surov, Guseinov, Barannikov, and Zakharov (2015) Brinchi et al. (2013) Atef, Rezaei, and Behrooz (2014) Mendes et al. (2015) Lalia, Guillen, Arafat, and Hashaikeh (2014) Voronova, Surov, and Zakharov (2013) Xu, Salmi, et al. (2013) Xu, Gao, et al. (2013) Pirani and Hashaikeh (2013) Tibolla, Pelissari, and Menegalli (2014) Continued

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Materials Selection for Natural Fiber Composites

Table 4.5

Continued

Sources

Fiber form

Cotton fiber

Banana pseudostem Bleached eucalyptus kraft pulp Bleached eucalyptus kraft pulp Pineapple leaf fiber

Method

Reference

NC

Microbial hydrolysis (Trichoderma reesei)

CNF

Chemical hydrolysis

CNF

Chemical hydrolysis

Satyamurthy, Jain, Balasubramanya, and Vigneshwaran (2011) Cordeiro, Mendonc¸a, Pothan, and Varma (2012) Tonoli et al. (2012)

CNF

High shear homogenization

CNF

Chemical hydrolysis

Syverud, ChingaCarrasco, Toledo, and Toledo (2011) Cherian et al. (2011)

(Peng, Dhar, Liu, & Tam, 2011). Trichoderma reesei is one of the most productive enzymes used in microbial hydrolysis. On the other hand, cellulose nanofiber (CNF) can be produced by mechanical grinding, high shear homogenization, a combination of mechanical action with chemical or enzyme hydrolysis. Enzymatic hydrolysis of CNF operates in a short time, to avoid further cellulose degradation (Zhu, Sabo, & Luo, 2011). Improved properties, like high surface area, strength, and stiffness, have been reported when using NC and CNF (Zhu et al., 2011). Various sources and forms of cellulose reinforcement and production methods are illustrated in Table 4.5.

4.3.4

Considering issues for selecting the natural fibers

Despite the above mentioned advantages of the natural fibers, replacing the glass fibers by the natural fibers is still a challenge. In fact, extensive research is desired to overcome the drawbacks before utilizing the natural fibers in polymeric composites. One of these drawbacks is the high moisture absorption of fibers. This will lower the processing temperature and make the natural fibers not useful at elevated temperatures due to the degradation that alters the properties of the entire composite. To elaborate, the high moisture absorption narrows the selection of applications where the composites can be used, and the low processing temperature narrows the choices of the matrix selection to those having low melting temperatures. One more drawback that limits the utilization of the natural fibers in polymers is their poor adhesion or their incompatibility. Cellulose is hydrophilic in nature, while polymers are hydrophobic. This incompatibility can be overcome by means of three methods; using a coupling agent between the fibers and the polymers, enhancing

Material selection for composites

Table 4.6

89

Factors affecting the selection of natural fibers

Level 1 Category Natural fibers properties for their selection

Level 2 Property/ characteristic Physical

Chemical and Biological

Mechanical

Technical

Environmental

Level 3 Criteria Texture, Density, Surface topology, Length/ Diameter Ratio, Form, and geometry (Fiber’s Diameter, Fiber’s Length, Microfibrillar Angle), Coefficient of thermal expansion, Sound absorption coefficient, Specific heat, Electrical conductivity, Thermal conductivity. Batch quality, Availability, Chemical composition (cellulose, lignin, etc.), Consistency of batch quality, planting limitations, Resource shortage, Odder emission, burning rate. Yield strength, Elastic modulus, Poisson’s Ratio, Elongation to break, Shear modulus, Specific modulus of elasticity, Specific yield strength, and Specific shear modulus. Processing energy consumption, Processing knowledge and time, Friendly processing, Processing time, Processing cost, Transferring cost, Raw fiber cost, Cost of energy input (fiber separation, fertilizers, machines, etc.) Government support, Biodegradability, Eco-friendly, Social positive view.

the natural fiber properties prior to fabricating the composites, and carefully selecting the proper method for the composite production (Mohanty, Misra, & Drzal, 2002). Another drawback of the natural fibers is their irregularity in shape; the fibers do not have the same cross-sections along their lengths. This makes predicting the mechanical properties a difficult task. Therefore, various proper evaluation criteria are required in the selection of natural fibers to make proper natural fiber composites where environmental, physical, biological, chemical, mechanical, and thermal properties, in addition to the availability, quality and the cost of the fiber, etc. have to be concerned. Most of these criteria are tabulated in Table 4.6 (AL-Oqla & Sapuan, 2014c).

4.4

Research work on materials selection for composites

Utilizing materials handbooks is a traditional way of performing materials selection, including composites when a limited number of candidate materials is available. The use of computerized materials selection systems has additional advantages for

90

Materials Selection for Natural Fiber Composites

encouraging design engineers to reflect and investigate their requirements for a material. It could be in the form of materials databases, Ashby charts, or any computerized materials selection tools. Databases like Cambridge Engineering Selector is a popular and a well-accepted computer-aided materials selection system. However, for the purpose of research and development, several methods have been established for selecting materials, particularly utilizing the artificial intelligent techniques like that of fuzzy logic, genetic algorithm, expert system, artificial neural network (ANN), and analytical hierarchy process (AHP). Various multi-criteria decision-making (MCDM) techniques have been used recently for materials selection as it is used in various engineering fields (AL-Oqla & Omar, 2015; AL-Widyan & AL-Oqla, 2011, 2014). For instance, AHP was used as a MCDM tool to select a composite material for automotive bumper beam that has to absorb the bulk energy and provides protection to the rest of vehicle (Hambali, Sapuan, Ismail, & Nukman, 2010). In this study, materials selection was achieved by means of Expert Choice software. Authors made an AHP frame work with a goal of materials selection of automotive bumper beam, whereas the main factors that affect the selection process were the energy absorption, weight, performance, cost, service conditions, environmental consideration, manufacturing process, and availability. The sub-criteria for the model included the impact toughness, flexural strength, raw material cost, flexural modulus, resistance to corrosion, low density, recyclability, water absorption, availability of raw materials shape, disposal, and availability of materials information. Their alternatives were represented by six different types of composite materials. In this practice, a pair-wise comparison flow was used to make judgements on all alternative materials with respect to each main criterion and sub-criterion. Moreover, VIKOR method was utilized to carry out composite material selection as VIKOR method can be utilized to rank and select the optimum composite material type. It facilitates ranking and selecting the alternatives with conflicting factors. The compromised ranking in VIKOR method is found by comparing the measure of closeness to an ideal alternative. Materials selection for engineering products made from composite materials were also performed utilizing an expert system approach with IF/THEN logic rules (Fairuz, Sapuan, Zainudin, & Jaafar, 2014). Various criteria and attributes such as density, tensile strength, tensile modulus, impact strength, flexural strength, flexural modulus, and water absorption of polymer based composites were reflected. The rule-based system in Logic Block designates the rules as units. However, for selecting materials, knowledge related to dependencies among facts are required. Such dependencies in the Logic Block system have a general form of rule: If\premise [Then\conclusion] means if the premises are true, then the conclusions should also be true. First rule: If premise A, Then conclusion A Second rule: If premise B, Then conclusion B Third rule: If premise C, Then conclusion C

Moreover, composite materials selection for automotive pedal box system was made by means of an expert system utilizing a commercial system shell called KEE. Over

Material selection for composites

91

thirty alternative composite materials were made available for the selection. Such alternatives were mainly taken from materials handbooks and materials databases like CAMPUS and FUNDUS. In addition, for the pedal box system, rule-based system implementing IF-THEN was also utilized, and only two main materials selection drivers; Young’s modulus and tensile strength were adopted for the selection process. The major pedal box system components included the mounting bracket, clutch, accelerator, and brake pedals. l

Selection for natural fiber composites Materials selection for natural fiber composites is a completely new field of research and it is very challenging and sophisticated because data and references related to these materials are very limited. Moreover, it is very difficult to separate the materials selection of natural fiber composites from the rest of materials. Natural fiber composite material selection is also a challenging task because of the anisotropic nature of the materials that requires personalized aspects regarding both products and materials. Additionally, because of the vast variation in fiber types, matrices, fiber dimension, fiber arrangement, and manufacturing methods, materials selection of natural fiber composites, as well as general composite materials, is a daunting task to perform. Thus, optimization methods, multiple criteria decisionmaking methods, multi-criteria materials selection system and life cycle assessment were developed for material selection of composites considering certain environmentally related criteria, such as greenhouse gas emissions, demand for fossil fuel, and Eco-Indicator 99 score. Moreover, natural fiber composites were also selected utilizing the ANN. Sapuan et al. (2011) also enhanced the selection of the natural fiber reinforced polymer composites materials using the AHP method to select the most suitable material for an automotive dashboard panel. Only density, Young’s modulus and tensile strength were the criteria for selecting the composites. They had 29 types of natural fiber composites as alternatives in the selection process. Then judgement was made using pair-wise comparison to calculate priority vectors. A consistency analysis was determined using a consistency ratio and the most suitable natural fiber polymer composite for the dashboard was selected. Mansor, Sapuan, Zainudin, Nuraini, and Hambali (2013) have made materials selection for automotive parking brake lever (Fig. 4.5) by means of analytical hierarchy process. The nominee materials for this product were natural fibers that are intended to be hybridized with glass fibers. The first part of the material selection process was the development of AHP hierarchical framework. The most appropriate natural fiber composite for the automotive parking brake lever was achieved by bearing in mind both main criteria and sub-criteria. Then pair-wise comparison between the candidate composites was made and sensitivity analysis was also performed to verify the results.

Moreover, a Java-based expert system was developed for the selection of natural fiber reinforced polymer composites (Ali, Sapuan, Zainudin, & Othman, 2013). The expert system was established using the software programming language NetBean JAVATM and the data were saved in relational database management system (RDBMS) software MySQL server database.

4.5

Advanced techniques in composite materials selection

Shah (2014) have utilized Ashby-type materials selection charts for the natural fiber composites and tried to establish a database for the properties of some natural fiber composites for structural applications. Such a database looked at helping to reveal

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Materials Selection for Natural Fiber Composites 5 1

6 4

3 2 Part No 1 2 3 4 5 6

Part Name Brake lever Bottom mounting Lock Rod Spring Release button

Fig. 4.5 CAD model of a center lever parking brake design in an assembly view, and exploded view

various issues on the tensile properties of bast fiber reinforced polymer composites, such as the effect of the matrix type (thermoplastic vs. thermoset), the reinforcement geometry and orientation (that of pellets, short-random nonwovens, and longaligned fibers for unidirectional and multi-axials), and the manufacturing technique (like that of hand lay-up, injection molding, vacuum infusion, compression molding, resin transfer molding and pre-pregging). Ashby also plotted the absolute tensile strength against the absolute tensile stiffness of natural fiber composites utilizing Ashby method as in Fig. 4.6 (Shah, 2013) and the specific tensile strength against the specific tensile stiffness of the natural fiber composites as in Fig. 4.7. Such plotting types are considered beneficial for several reasons as they can allow quick retrieval of the properties of certain type of material, permit quick comparison for the properties of various composites, facilitate the selection of the materials, as well as the manufacturing processes, and enable replacing a potential material instead of another. Such Ashby plots for the natural fiber composites can categorize them into four distinct sub-groups. Such groups can be ordered with increasing tensile properties as follow: (1) Injection molded (IM) natural fiber composites, whose mechanical properties are low comparable to the matrix material. (2) Natural fiber composites based on nonwoven reinforcements (like that of randomly oriented short fibers). (3) Natural fiber composites based on textile reinforcements (such as woven and stitched biaxials). (4) Unidirectional natural fiber composites.

It is also observed from such plots that both tensile strength and stiffness tend to increase linearly with each other. In addition, and observing the sub-groups difference

Material selection for composites

93

RTM, themoset, unidirectional

400

Tensile strength, s (MPa)

Compression moulded, thermoplastic, unidirectional

Prepreg, thermoset, unidirectional

als

300

e

idir

Un

Thermoplasmic resins

n ctio

Compression moulded, thermoset, unidirectional

Injection moulded, 3D-random Thermoset resins

200

Multiaxials Resins & IMs

100

Hand layup thermoset, unidirectional Compression moulded, thermoset, biaxial (stitched) Prepreg, thermoset, biaxial (woven) RTM, thermoset, biaxial (woven)

ns

ve wo n o

Vacuum infusion, thermoset, 2D-random Compression moulded, thermoset, 2D-random Compression moulded, thermoplastic, 2D-random

N

0

10

0

20 30 Tensile modulus, E (GPa)

40

Tensile strength/density, s /r (MPa/g cm–3)

Fig. 4.6 Ashby plot style for the tensile strength against the tensile stiffness of natural fiber composites (Shah, 2013)

RTM, themoset, unidirectional

Compression moulded, thermoplastic, unidirectional

300

Thermoplasmic resins

nals

ctio

dire Uni

Injection moulded, 3D-random

Compression moulded, thermoset, unidirectional

Thermoset resins

200

Prepreg, thermoset, unidirectional

Multiaxials Hand layup thermoset, unidirectional Compression moulded, thermoset, biaxial (stitched)

100

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s

en ov w n

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0 0

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10 20 Tensile modulus/density, E/r (GPa/g cm–3)

30

Fig. 4.7 Ashby plot style for the specific tensile strength against the specific tensile stiffness of the natural fiber composites (Shah, 2013).

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in properties, it can be detected that the thermoset-based natural fiber composites have better mechanical properties than thermoplastic-based ones. In addition to that, the manufacturing method can have a noticeable effect on natural fiber composites’ mechanical properties, particularly for those of the unidirectional natural fiber composites. Shah (2013) besides, revealed through comparisons that short-natural fiber reinforced composites (i.e., injection molded and nonwoven composites), have better tensile moduli (specific and absolute) and specific tensile strengths than the glass fiber reinforced polymer composites. Moreover, comparing long fiber reinforced composites (i.e., textile and unidirectional composites), has revealed that the natural fiber composites have better specific tensile moduli than the glass fiber reinforced polymer composites. But the specific tensile strength of the natural fiber composites is only up to half that of glass based polymeric composites. Despite this, Shah also mentioned the role of the cost of fibers in the selection process and suggested including it as one of the potential evaluation criteria in the selection process of the polymeric based composites. Comparison of the tensile properties (absolute and specific) of plant fiber reinforced plastics (PFRPs) and E-glass reinforced plastics (GFRPs) is shown in Fig. 4.8. As natural fiber composites can be fabricated in different processing methods and different fiber treatments, each method can produce different composites with various performances. Hence, it will be impractical to compare composites from different processing methods. However, basic ideas and approaches to qualify the green composites produced with different constituent elements are required. One of the most important selection ways is the ternary diagrams (ratio of the cost to the unit tensile strength) where the material with the lower ratio is the preferred. However, this selectivity has the drawback of considering only one property and ignoring all other ones. In order to overcome this drawback, the depth of the one-dimensional factors can be enriched by considering three bi-dimensional factors: cost per weight, specific strength, and specific stiffness. These factors are uncorrelated, and thus can be involved in the selection criteria. For instance, Fig. 4.9 shows some mechanical performance of several composites. For green composite selection, the E-glass as are not renewable, and thus they are excluded from the selection. However, the purpose of their presence in the figure was just to perform performance comparisons with the renewable ones. Two ternary diagrams are presented in Fig. 4.10 for the resin and Fig. 4.11 for the fibers to ease the comparison between different potential candidates for use in green composite materials. The ternary diagrams in Figs. 4.10 and 4.11 illustrate the best materials for various criteria weights as materials show up in different regions of the triangle’s area. By adopting this method, the decision makers can easily select the most appropriate candidate of materials referring to the percentage of importance provided along each of the three dimensional axes. The average specific strength values and the average specific stiffness values for fibers and resins were intentionally normalized. Same concept is applied on the cost factor but in inverse direction; which

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Unidirectional Thermosets Unidirectional Thermoplastics Multiaxial Thermosets Nonwoven Thermosets Nonwoven Thermoplastics Injection moulded Thermoplastics 0

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GFRPs

Fig. 4.8 Comparing the tensile properties (absolute and specific) of plant fiber reinforced plastics (PFRPs) and E-glass reinforced plastics (GFRPs) (Shah, 2014).

12 Av. Young modulus (GPa)

100 80 60 40 20

6 4 2

+j u PP te + PH flax BV + PH jut B+ e fl PP ax +j PL ute A PT +fla P+ x PL hem A+ p ra m PP PL ie A +g +j u la ss te +f ib er

ch ar

ar ch St

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0

10

St

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120

Fig. 4.9 Mechanical performance of several fiber composites.

indicates the less costly is the most preferred material. The materials that are not presented in the diagrams are not revealing the best combination for any property of the three considered factors in the ternary diagrams. The materials regions borders in the ternary diagrams were manually calculated. In a sequence of value tests, the areas that each material occupies inside the triangle were obtained. As an example

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20

% 80

%

10

%

% 90

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%

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%

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RAMIE

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%

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%

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KENAF 10%

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Fig. 4.11 Ternary diagram of renewable fibers for reinforcement.

90%

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Material selection for composites

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Fig. 4.12 Ternary diagram of green composites.

of using this method to make the selection decision (Fig. 4.11), consider the aim is 20%, 50%, and 30% for strength, stiffness, and cost, respectively. The best selection is found to be hemp. Reflecting the same concept on the green composite constituents will generate another diagram as presented in Fig. 4.12. Combining the three dominant resins (Fig. 4.10) with the fibers occupying similar areas, five different composites were considered for the comparison, these are: PLA-kenaf, PLA-flax, PLLA-hemp, PLLA-curaua, and PHB-ramie. The rule of mixture was adopted in calculating the mechanical performances for these composites by considering the values from Fig. 4.9. Their costs were calculated based on their constituents’ percentages (30% fibers and 70% resins). The results are not anticipated to be accurate in absolute, but can be considered as relatively accurate and sufficient in quick comparison between the different composites. On the contrary, green composites are rather challenging due to their decomposability. The biodegradability is one problem that has to be addressed, especially when the composites are intended for use in exteriors. Other aspects to be attentively considered are the reproducibility of the composites properties and their life cycles. Unfortunately, until this time, the cost of the bio-thermoplastics is the main limitation on their use spreading into various applications like that of the automotive industry. However, manufacturers are expected to come up with solutions to reduce their production costs with the increase of demands in the industry. A reversed trend can be developed with the rise environmental consciousness leading

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a premium price on environmental impact of current solutions. The focal point is whether it is possible to combine these materials to satisfy a level of performance yet at the lowest possible cost. Selecting materials that met some sustainability requirements without compromising structural functionality was introduced by Mayyas, Qattawi, Mayyas, and Omar (2013). Such a material selection model was developed based on the desirable selection criteria including economic, environmental, societal, and technical issues for automotive-bodies and was based on two ranking and evaluation methods. The first one was the preference selection index (PSI) and the second one was the principal component analysis (PCA). Both evaluation methods were utilized to select and rank various types of materials including steel, titanium alloy, aluminum alloys, magnesium alloys, and plastic reinforced composites for a vehicle panels with respect to their ability to meet sustainability requirements. This model of composite material selection was found to be reasonable, as there was no need to consider any relative importance between attributes and design goals, which can eliminate the bias associated with canonical materials selection methods. Algorithms for the PSI and the PCA are demonstrated in Figs. 4.13 and 4.14, respectively, and the scree plot of some candidate materials is demonstrated in Fig. 4.15 showing all principal components and their Eigenvalues according to the principal component analysis where a tiny change in the Eigenvalues appears after the fourth principal components.

Identifying the goal

Formulating decision matrix

Normalizing all decision matrix values

Compute preference variation value

Determine overall preference

Obtain preference selection index value

Rank of alternatives

Final decision and material selection

Fig. 4.13 Preference selection index (PSI) flowchart.

Material selection for composites

Collect data (attributes and candidate materials)

Calculate correlation or covariance matrix

Calculate Eigenvalues and Eigenvectors

Extract the first principle component that accounts for highest variance (highest Eigenvalue)

Check the constraints: b 211 + b 212 + ... + b 21p = 1

Extract the second principal component which accounts for the highest variance that was not accounted by first principal component (the second highest Eigenvalue)

Check the constraints: b 221 + b 222 + ... + b 22p = 1

Check the orthogonality between PC1 and PC2: b i1 b j1 + b i2 b j2 + ... + b ip b jp = 0

Repeat steps 4–8 for other pricipal components

Plots principal components vs. their Eigenvalues (scree plot)

Fig. 4.14 Algorithm for the principal component analysis (PCA).

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Scree plot 10

Eigenvalue

8 6 4 2 0 2

4

6

8 10 12 14 Component number

16

18

20

Fig. 4.15 Scree plot of candidate materials using the principal component analysis (Mayyas et al., 2013).

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Satyamurthy, P., Jain, P., Balasubramanya, R. H., & Vigneshwaran, N. (2011). Preparation and characterization of cellulose nanowhiskers from cotton fibres by controlled microbial hydrolysis. Carbohydrate Polymers, 83, 122–129. Shah, D. U. (2013). Developing plant fibre composites for structural applications by optimising composite parameters: A critical review. Journal of Materials Science, 48, 6083–6107. Shah, D. U. (2014). Natural fibre composites: Comprehensive Ashby-type materials selection charts. Materials & Design, 62, 21–31. Shalin, R. E. (1995). Polymer matrix composites. London: Chapman & Hall. Syverud, K., Chinga-Carrasco, G., Toledo, J., & Toledo, P. G. (2011). A comparative study of Eucalyptus and Pinus radiata pulp fibres as raw materials for production of cellulose nanofibrils. Carbohydrate Polymers, 84, 1033–1038. Tibolla, H., Pelissari, F. M., & Menegalli, F. C. (2014). Cellulose nanofibers produced from banana peel by chemical and enzymatic treatment. LWT - Food Science and Technology, 59, 1311–1318. Tonoli, G. H. D., Teixeira, E. M., Corr^ea, A. C., Marconcini, J. M., Caixeta, L. A., Pereira-daSilva, M. A., et al. (2012). Cellulose micro/nanofibres from Eucalyptus kraft pulp: Preparation and properties. Carbohydrate Polymers, 89, 80–88. Voronova, M. I., Surov, O. V., Guseinov, S. S., Barannikov, V. P., & Zakharov, A. G. (2015). Thermal stability of polyvinyl alcohol/nanocrystalline cellulose composites. Carbohydrate Polymers, 130, 440–447. Voronova, M. I., Surov, O. V., & Zakharov, A. G. (2013). Nanocrystalline cellulose with various contents of sulfate groups. Carbohydrate Polymers, 98, 465–469. Wang, C., Huang, H., Jia, M., Jin, S., Zhao, W., & Cha, R. (2015). Formulation and evaluation of nanocrystalline cellulose as a potential disintegrant. Carbohydrate Polymers, 130, 275–279. Xu, Q., Gao, Y., Qin, M., Wu, K., Fu, Y., & Zhao, J. (2013). Nanocrystalline cellulose from aspen kraft pulp and its application in deinked pulp. International Journal of Biological Macromolecules, 60, 241–247. Xu, Y., Salmi, J., Kloser, E., Perrin, F., Grosse, S., Denault, J., et al. (2013). Feasibility of nanocrystalline cellulose production by endoglucanase treatment of natural bast fibers. Industrial Crops and Products, 51, 381–384. Yousefi, H., Faezipour, M., Hedjazi, S., Mousavi, M. M., Azusa, Y., & Heidari, A. H. (2013). Comparative study of paper and nanopaper properties prepared from bacterial cellulose nanofibers and fibers/ground cellulose nanofibers of canola straw. Industrial Crops and Products, 43, 732–737. Zhang, Y., Lu, X. B., Gao, C., Lv, W. J., & Yao, J. M. (2012). Preparation and Characterization of Nano Crystalline Cellulose from Bamboo Fibers by Controlled Cellulase Hydrolysis. Journal of Fiber Bioengineering & Informatics, 5, 263–271. Zhao, J., Zhang, W., Zhang, X., Zhang, X., Lu, C., & Deng, Y. (2013). Extraction of cellulose nanofibrils from dry softwood pulp using high shear homogenization. Carbohydrate Polymers, 97, 695–702. Zhu, J. Y., Sabo, R., & Luo, X. (2011). Integrated production of nano-fibrillated cellulose and cellulosic biofuel (ethanol) by enzymatic fractionation of wood fibers. Green Chemistry, 13, 1339–1344.

Material selection of natural fiber composites 5.1

5

The need for materials selection of natural fiber composites

Final products made from natural fiber composites (NFCs) can be fabricated successfully to have the desired characteristics if physical and metaphysical properties of the products are improved through an effective materials selection process. Considering the tremendous need and concern of the environmental impact issues, as well as producing environmentally benign products and escalating petroleum prices, integrated with the industrial sustainability and clean production theme, the synergy between the available natural resources as well as wastes and the sustainable industry has been recently highly emphasized. This has led to the increasing use of NFCs in many different industries such as in automotive, building construction, and furniture industries. Moreover, attributes such as favorable cost, recycling, and abundance, has led the natural fibers to be appropriate candidates incorporated into polymer matrices to form composites in many engineering applications over its synthetic fiber counterpart. However, the variety in sources of natural fibers can dramatically affect their merits and capabilities regarding various standpoints that usually affect the final performance of their composites. The variation of natural fibers capabilities usually means a certain fiber type is preferable for a particular polymer matrix, as well as a specific application rather than others. But, there was an extreme lack in theoretical, as well as practical methodologies and tools, for evaluating the various ingredients of the NFCs. This is because of the wide conflicting criteria involved in selecting such constituents of the composites (natural fibers and polymers) that make it a very complicated issue. As aconsequence, an improper way of evaluating the NFCs and their constituents (particularly the natural fibers) with respect to the comprehensive desired criteria was indicated. This leads to disregarding some potential natural fibers in industry and keeps them as an environmental waste problem. Such improper evaluation of the natural fibers and their composites usually reduces the possibilities of taking full advantage of their desirable characteristics, as well as resulting in destroying the proper linkage between the sustainable design concepts and the industry. In addition, the improper selection of natural fibers can negatively affect the implementation of the NFCs in various industrial sectors. Therefore, developing and conducting proper evaluation and selection methodologies to enhance the selection of NFCs and their constituents are highly commended. In addition, it has been observed that only limited available natural fiber types are commercially utilized in industry while other plentiful types, like that of date palm Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00005-0 Copyright © 2017 Elsevier Ltd. All rights reserved.

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fiber (DPF), are not properly evaluated. This improper valorization of the available natural fibers is due to the facts that: l

l

Proper valuations of natural fibers have not been adequately discussed for industrial applications regarding a wide range of desired criteria. Selecting natural fibers for making NFCs is still subjected to the researchers’ estimations or limited evaluation standpoints.

Accordingly, a lack of information about selecting the most appropriate natural fiber type for a given application of NFCs was also indicated (AL-Oqla & Sapuan, 2014c). Furthermore, both physiochemical and mechanical behavioral knowledge of the natural fibers, as well as polymers, are required to optimize the performance of NFCs. This is because the final characteristics of the NFCs strongly depend on the type of matrix, fiber, and their interfacial bonding. That is, the compatibility, as well as the reinforcement efficiency between the composites constituents is essential for achieving the desired properties of the composites. However, a comprehensive review of this field reveals that there are: 1. No clear systematic and broad classifications of the aspects and criteria that have influence in the proper selection of the NFCs and their constituents were available before AL-Oqla and Sapuan (2014c). 2. Lack of information considering keen evaluations of the natural fibers’ capabilities regarding extensive, as well as combined beneficial criteria. 3. Shortage of precise decisions for selecting NFCs, as well as their constituents for industrial applications under uncertain environments. 4. Lack of information regarding predicting the behavior of various potential natural fiber types under wet conditions. 5. A shortage of evaluations of the available polymers with respect to a particular natural fiber and application bearing in mind wide simultaneous evaluation criteria. 6. Lack of information for properly determining and selecting the best reinforcement conditions of a given composite to maximize its overall performance with respect to a set of conflicting, but favorable, evaluation criteria simultaneously.

On the other hand, despite the accessibility of various computer-oriented database packages, as well as commercial material selection software types [like that of Cambridge engineering selector (CES)], expert systems, application programming interface (API) modules, commercial computer aided design (CAD) environment, and knowledge-based systems (KBS) that indicate different material possessions for the designers (Almagableh, AL-Oqla, & Omari, 2017; AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Omar, 2012; Biron, 2013), there are no distinguished evaluation methodologies for NFCs in such packages. They only have organization systems which usually mend and manipulate the data, in addition to graphical user interface to demonstrate the property data as material selection charts. For example, to select a certain material via CES software, a series of selection stages have to be performed. In each particular stage, either user-defined functions of material characteristics (such as the specific ultimate tensile strength) or a pair of material properties have to be stated as the axes for generating selection graphs, and thus, all materials in the database with related data entries are plotted on the graph. All materials that lie in the region that

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satisfies the selection criterion are then considered “passed” the selection stage. Subsequently, numerous stages have to be performed to quickly narrow the field of potential materials. But these results in such software types having no advantage over fast material screening tools. Nevertheless, as there are so many large conflicting criteria involved in the material selection, keen and appropriate evaluations of the NFCs and their constituents are of paramount importance to be established, developed, and performed to properly rank and identify the optimal one(s). But this has not occurred in the available commercial software types. Furthermore, material screening schemes, even if considering various evaluations through the “passed stages” scheme, would result in ignoring some potential material types that have better performance than a referenced one (AL-Oqla, Sapuan, & Jawaid, 2016b; Sapuan, Haniffah, & AL-Oqla, 2016; Sapuan, Pua, El-Shekeil, & AL-Oqla, 2013). Above that, the nonconcurrent combined material property evaluations would dramatically slow down the convergence in the material screening. Consequently, there are several practical limitations in the available material selection systems in evaluating and ranking the available material types with respect to various conflicting desirable features. As a result, new proper valorizing and evaluation methodologies are still required, predominantly for the NFCs to establish and enhance their selection process for various industrial applications. Consequently, there is a growing need for developing proper evaluation tools and models to accurately evaluate the capabilities of the available natural fiber types, as well as developing the selection process of the NFCs and their ingredients to expand their desirable features for further industrial applications and to increase sustainable design possibilities.

5.2

Traditional approaches

The availability, as well as variety in sources of natural fibers make differences and variations in their capabilities and affect their merits for various industrial applications. Moreover, the performance of products fabricated from NFCs are influenced by many internal and external factors, starting from the characteristics of their constituents as well as their compatibility, passing through integrated economicenvironmental standpoints. Despite that, the selection of the natural fibers and polymers for the NFCs are unfortunately still dependent upon traditional ways and limited individual evaluation criteria like that of cost, availability, and mechanical properties for the fibers, and the chemical resistance, mechanical properties, cost and ease of manufacturing for the polymers. Such traditional selection of the natural fiber constituents is due to improper understanding of the comprehensive criteria that affect the selection of the NFCs and their constituents. As a result, there is limited data to support the materials selection process for the NFCs, which is mainly due to a huge amount of possible NFCs that can be made with different properties. The main traditional approach in the field of NFCs is to develop some composites and then to experimentally investigate their mechanical, physical, and some other characteristics in order to determine their suitability for a particular industrial

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application. But a pre-systematics selection for the constituents (polymers and fibers) was hardly found before AL-Oqla and Sapuan (2014c, 2015b). Several researchers in the past had explored the performance of NFCs and found that such materials can be very competitive, and are suitable to be bonded with polymer matrices. They had investigated various techniques that can ensure the interfacial bonding between the fiber and the matrix, which can be enhanced by various means such as physical, mechanical, and chemical modifications. Several researchers attempted to make comparative studies on the functions and uses of various types of NFCs. However, the selection of the constituents was a traditionally oriented approach based only upon the researchers’ points of view without any scientific justifications other than some good mechanical properties and availability. For instance, the performance of jute fiber reinforced polymer composites were investigated by Alves et al. (2010), Mir, Zitoune, Collombet, and Bezzazi (2010)), and Sarikanat (2010). The considered performances included crystallinity, thermal stability, durability, fiber modification, and weathering resistance. The potential use of such materials in automotive components was also studied in search of green technology. In addition, Etaati, Pather, Fang, and Wang (2014)) investigated polypropylene (PP) polymers reinforced with hemp fibers for the fiber/matrix bond strength. In another development, hemp fiber reinforced polypropylene composites were studied in terms of mechanical properties and thermal stability of the composites. The work of Taniguchi (2001) was concerned with the development of several natural fibers [micro-fibrillated cellulose (MFC)] from seaweed, hemp, wood pulp, and cotton fibers fabricated using rotating twin disks with shear. Luz, Caldeira-Pires, and Ferrao (2010) on the other hand, investigated the use of bagasse fiber reinforced polypropylene composites in automotive components. In contrast, limited publications were devoted to the study of materials selection of NFCs for specific applications. Moreover, analytic hierarchy process (AHP) was used as a tool for materials selection process of NFCs for automotive components (dash board) (Sapuan et al., 2011). However, the materials selection drivers used in the study were only limited to density, Young’s modulus, and tensile strength. The candidate NFC materials were taken from the published literature. Only 29 candidate materials were selected and ranked for potential use in automotive dash board. Cheung, Ho, Lau, Cardona, and Hui (2009) on the other hand, reported their work on the development of natural (plant and animal based) fiber reinforced polymer composites for biomedical applications. They investigated the mechanical and thermal performance of such materials, but their main contribution was on using animal fibers as reinforcements. They proposed criteria affecting the materials selection process for NFCs in the biomedical field. Those criteria included bio-inertness, bio-functionality, and bio-activeness, just to name a few. As a result, there is limited data and information to support materials selection process for NFCs, which is mainly due to the huge amount of possible NFCs that can be made with different properties. However, there is a lack in practical methodologies and tools for evaluating the various ingredients, as well as the NFCs. Besides, there is a real need to perform pair-wise comparison studies of the NFCs based on different criteria influencing their proper selection process for a particular application. Doing so, the proper materials selection of NFCs will be made easier

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enabling design engineers and decision-making personnel to effectively select the most suitable materials that satisfy all criteria and constraints. To the best of the authors’ knowledge and after extensively investigating the published literature, it can be concluded that only limited work reporting the ranking of NFCs with respect to different materials selection criteria. The existing comparative studies on this topic were mainly done based on narrow criteria (focusing on mechanical properties and cost only), and they were not comprehensively performed. Such works were also lacking of systematic classification of the factors that influence the materials selection process for NFCs, as well as collective databases of materials selection criteria for NFCs that can be used as the major materials selection tools for design engineers. The performances of products fabricated from NFCs are influenced by many internal and external factors and criteria. AL-Oqla and Sapuan (2014c) have introduced a systematic classification of the factors affecting the materials selection process for NFCs, as well as collective database of materials selection criteria for NFCs to be used as the major materials selection tools for engineers. These criteria were initially categorized into different levels. Several levels were classified, such as levels for natural fibers, matrix system, NFCs, and the general and specific NFCs applications. Such levels are demonstrated in Fig. 5.1. In addition, the research involved in compiling, suggesting, and tabulating the criteria influencing the performance of NFCs and compartmentalizing them into different levels as shown. The categorization of NFCs criteria enables design engineers to utilize them as the main reference as well as the basis for comparing and selecting the most suitable NFCs for particular applications at given constraints. The research eventually proved that the date palm fiber (DPF)

Natural fiber properties

Specific composite performance

General composite performance

Polymer base properties Natural fiber composites selection

Composite characteristics

Fig. 5.1 Natural fiber composites selection aspects levels.

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was a fiber demonstrating great potential and very competitive for use as a material in automotive components. The research had also compared the performance of DPF with other natural fibers to reveal that the fiber was the real contender to be used in automobiles. Therefore, the work of AL-Oqla and Sapuan (2014c) was able for the first time in the field of NFC selection to propose and achieve three major objectives as: 1. To categorize factors and criteria affecting the materials selection process of NFCs for different applications, such as for automotive application. 2. To address the potential and competitive aspects of new fibers, not commonly used fibers, like that of DPFs as reinforcement in NFCs, compared to other natural fibers (like coir, hemp, and sisal) for automotive components in satisfying materials selection criteria previously categorized in their research. 3. To address the limited information in comparing NFCs against different criteria, and to emphasize the development of NFCs guidelines and databases to support NFCs material selection process. l

Criteria influencing the materials selection process of NFCs.

Unlike isotropic materials, components fabricated from composite materials are usually made based on the functional requirements. Composite materials are considered anisotropic materials (i.e., directional dependent properties) and the design of products from these materials are subjected to different factors and the performance of several elements, such as properties and arrangements of fibers, as well as matrices and the conditions of the curing process. Databases that contain various properties of different NFCs can greatly assist the materials selection process and can help design engineers to perform effective design tasks (AL-Oqla, Sapuan, Ishak, & Nuraini, 2014c; Majeed et al., 2013). But what is found in this field is that the properties of NFCs such as physical, mechanical, and chemical properties are determined without the need of specifying a particular application (AL-Oqla & Sapuan, 2014c; Alsaeed, Yousif, & Ku, 2012). Therefore, it is important to determine different properties of NFCs so that they can be considered as candidate materials for specific application by studying the properties of polymer matrices and natural fibers as well as their processing conditions. This was performed by AL-Oqla and Sapuan and proposed the following factors and materials selection criteria for NFCs: 1. The natural fiber properties level: It is where physical, chemical, mechanical, environmental, biological, and thermal properties, as well as availability, quality, and the cost of the fibers should be considered. 2. The polymer base (matrix) properties level: In this level, physical, chemical, mechanical, environmental, and thermal properties together with toxicity and cost of the polymer should be included. 3. The composite characteristics level: It is where characteristics are not necessarily exactly obeying any of these for fibers or matrices. Therefore, physical, mechanical, structural, thermal, environmental, technical requirements, along with cost and the occupational health and safety characteristics of the composites, should be considered in the materials selection process.

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4. The general composite performance level: It is a level where mechanical, specific strength, resistance to weather and environmental conditions, bio stability, life cycle, water absorption, bio-degradability, durability, and similar characteristics should be taken into account during the materials selection process. 5. The specific composite performance level: The specific requirements based on the desired function or application is considered. For the automotive industry: composites’ weight, thermal insulation properties, acoustic insulation properties, ease of maintenance, crash behavior, social impact, and occupational health and safety, should be investigated.

More suggested and collected criteria for NFCs are presented in Tables 5.1–5.4 for each level, which can be considered as the major database to be used by engineers for materials selection and design.

5.3

Proper assessment of natural fibers capabilities

In different industrial usages, materials that are lightweight, and have good insulation and vibrational damping behaviors can be good contenders. Lightweight materials in the automotive industry, for instance, are crucial in ensuring low fuel Table 5.1 Criteria affecting the selection of products made from natural fiber composite materials on fiber level Level 1 category

Level 2 property/characteristic

Natural fibers properties

Physical

Chemical and Biological

Mechanical

Technical

Environmental

Level 3 criteria Density, texture, form and geometry (fiber’s diameter, fiber’s length, microfibrillar angle, length/diameter ratio), surface topology, coefficient of thermal expansion, specific heat, thermal conductivity, electrical conductivity, sound absorption coefficient Batch quality, chemical composition (cellulose, lignin, etc.), availability, resource shortage, odder emission, consistency of batch quality, planting limitations, burning rate Yield strength, specific modulus of elasticity, elastic modulus, shear modulus, Poisson’s ratio, specific yield strength, elongation to break, specific shear modulus Processing energy consumption, processing knowledge and time, friendly processing, raw fiber cost, transferring cost, processing time, processing cost, cost of energy input (fiber separation, fertilizers, machines, etc.) Biodegradability, eco-friendly, government support, social positive view

Table 5.2

Criteria affecting the selection of NFCs on matrix level

Level 1 category

Level 2 property/characteristic

Polymer base properties

Physical

Chemical Mechanical

Environmental

Other

Level 3 criteria Specific heat, electrical conductivity, thermal conductivity, coefficient of thermal expansion, reflectivity, opaque Thermal stability, density, flammability, molecular weight (chain length) Shear modulus, fracture toughness, yield strength, elastic modulus, Poisson’s ratio, elongation to break, hardness Energy content, weather resistance, service temperature, thermal behavior (melting or degrading) Toxicity, additive and modifier properties, price, thermoset or thermos-plastics behavior, esthetic attributes (soft to hard, and warm to cool, muffled to ringing), abrasion

Table 5.3 Criteria affecting the selection of NFCs on composite characteristics level Level 1 category Composite characteristics

Level 2 property/ characteristic Physical

Chemical and Biological

Mechanical/ Structural

Technical

Level 3 criteria Surface topology, surface roughness, total density, texture, color and esthetic, coefficient of thermal expansion, electrical conductivity, opacity and translucency, specific heat, reflective index Biodegradability behavior, toxicity, recyclability, storage (on shelf storage), bio stability, life cycle time, weather resistance, sunlight and UV resistant, water absorption behavior, possibility of thermal recycling Poisson’s ratio, elastic modulus, impact strength, yield strength, compressive strength, shear modulus, fatigue strength, flexural modulus, elongation to break, fracture toughness, creep resistance, hardness, hardness Fabrication cost, sterilizeability, fabrication knowledge and time, reproducibility, product quality, thermal stability, packaging, process parameters (pressure, temperature, cure time, and surface finish requirements), secondary processability, life cycle cost, level of automation, possibility of producing homogenous/ nonhomogenous composites, labor protection and safety, cost of performance improvement

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Table 5.4 Criteria affecting the selection of NFCs on general and specific composite performance levels Level 1 category General composite performance

Level 2 property/characteristic

Level 3 criteria

Mechanical

Specific modulus of elasticity, specific strength per cost ratio, specific strength, other mechanical specific properties, shrinkage behavior, bio-degradation behavior, life cycle, burning behavior, insulation property, fiber orientation, dimensional stability, fiber volume content, damping behavior, adhesion force improvement (between fiber and matrix), contact squeaking, ease of handling, microorganism resistant, joining, ease of field construction, surface roughness quality, machinability Possibility to improve performance throughout properties modifications, durability, CO2 emissions, water absorption behavior, abrasion, tendency to burst, fogging, temperature effects, approval for use with foods Thermal insulation properties, ease of maintenance, total weight, acoustic insulation properties, occupational health and safety, crash behavior, social impact (acceptance and positive image), good resistance to micro cracking, low tearability, dirt resistance

Environmental

Specific composite performance

Specific requirements based on the desired function or application (here, automotive industry)

consumption and, in turn, saving the cost of operation. NFCs have been identified as good candidates for automotive components due to their design and manufacturing flexibility, can be conveniently integrated to other components made from different materials, in addition to their environmental friendliness (AL-Oqla & Sapuan, 2014a, 2014b; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015g; AL-Oqla, Sapuan, Ishak, & Nuraini, 2016a; Alves et al., 2010). The quality of natural fibers used in the automotive industry is monitored by means of fiber quality assurance protocols. That is, fiber quality assurance is important to ensure fiber consistency and to check

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that the variation in dimensions of different batches does not influence the mechanical performance of the fibers (AL-Oqla et al., 2016b; Karana, 2012). In fact, the performance of natural fibers directly related to the performance of NFCs. The quality of fibers is primarily determined by factors such as soil conditions, seed density, climate, and weather conditions, and the use of fertilizers (Dittenber & Gangarao, 2011; Kalia et al., 2011a). Further, the quality and properties of natural fibers are influenced by different factors, such as fiber extraction and drying methods, different fiber aspect ratios, fiber breakages and damages during extraction, and handling (AL-Oqla, Sapuan, Ishak, & Aziz, 2014b; Kalia et al., 2011a; Kalia, Kaith, & Kaur, 2011b). Natural fibers and their composites have been the materials of choice in automotive and industrial applications due to their specific properties, such as lightweight and environmental friendliness. For instance, in the Mercedes-Benz E-class model car, door panels were fabricated from NFCs. The door panels were made from flax/ sisal fiber reinforced epoxy hybrid composites. The use of NFCs in such door panels resulted in weight saving of 20% compared to metal counterparts and it significantly improved passengers’ protection during accidents (Kalia et al., 2011b). Moreover, Rieter Automotive was awarded JEC Composites Award 2005 for their product, an automotive under-floor module developed from natural fiber reinforced thermoplastic composites, with aerodynamic, thermal, and acoustic capabilities (Kalia et al., 2011b). Natural fibers on the other hand, like hemp, flax, and sisal, can potentially compete with the synthetic fibers, like the E-glass one if they are compared with respect to various properties such as cost, elasticity, tensile strength, availability, and elongation at failure. However, Glass fiber (E-type) is currently used as reference material, as this material is commonly used in various industries (Kalia et al., 2011b). Comparing the performance of new natural fibers with existing synthetic fibers is crucial for the use in different industries to demonstrate their potential and suitability. Effective comparisons can be achieved by taking into account a wide range of factors and selection criteria to demonstrate the capabilities of the available natural fiber types with respect to the synthetic ones, as well as comparing the natural fibers capabilities themselves.

5.3.1

Comparison of natural fibers for potential industrial use

The wide range of factors and criteria used for comparing different NFC products are summarized in Tables 5.1–5.4. In addition, these tables are useful for comparison and materials selection purposes of different constituents (matrices and fibers) that can be used to form NFCs for different applications. The properties of different natural fibers presented in Table 5.1 can be utilized to demonstrate the competitiveness and potential of the DPFs (as uncommonly used one) to be used in automotive components. This would enhance a systematic material selection process for the natural fibers in the automotive, as well as other industries. Not all criteria (selected only) are used here in the comparisons for two reasons, i.e., the available information is incomplete and insufficient regarding all the

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criteria tabulated in Table 5.1 on one hand, and showing the effectiveness with respect to all such criteria will take a long time on the other hand. This clearly indicates the need for comparing and assessing the capabilities of all available natural fibers regarding such comprehensive criteria and collecting them in a functional database to be properly utilized in enhancing the natural fiber selection for bio-based composites and applications. Moreover, innovation in design, as well as new design possibilities, would be achieved via exploring new materials and selecting their desired distinctive characteristics and attributes. This can be attained if proper assessments of the capabilities of the natural fibers (as main constituent of the NFCs) are performed regarding wide criteria. However, the selection process is sometimes sophisticated and needs several comparisons among various properties standpoints. In general, and in order to examine the possibility of utilizing a natural fiber to any specific industrial application, the capability and suitability of the new composite should be compared to existing material already being used. For such comparisons to be useful, they should be carried out to cover all important criteria and factors of interest. Natural fiber materials data used for comparing purposes were obtained, estimated, and calculated from the published literature. Data for DPFs were obtained from references (Abdal-Hay, Suardana, Jung, Choi, & Lim, 2012; Agoudjil, Benchabane, Boudenne, Ibos, & Fois, 2011; Ghosh, Nayak, Day, & Bhattacharyya, 2007; John & Anandjiwala, 2008; Kriker, Bali, Debicki, Bouziane, & Chabannet, 2008; Nasser & Al-Mefarrej, 2011) while for the remaining natural fibers were extracted from references (Agoudjil et al., 2011; Dittenber & Gangarao, 2012; Faruk, Bledzki, Fink, & Sain, 2012; Lewin, 2007; Majeed et al., 2013; Pilla, 2011). Table 5.5 depicts the data that compared the performance of different natural fibers, such as DPF, coir, hemp, and sisal against materials selection criteria.

Table 5.5 Data utilized for comparing the performance of different natural fibers Fiber type

Coir

Date palm

Hemp

Sisal

Density (g/cm3) Length (mm) Diameter (μm) Specific modulus (approx.) Annual world production (103 ton) Elongation to break (%) Cellulose (wt%) Lignin (wt%) Cost per weight (USD/Kg) Thermal conductivity (W/m K)

1.15–1.46 20–150 10–460 4 100

0.9–1.2 20–250 100–1000 7 4200

1.4–1.5 5–55 25–500 40 214

1.33–1.5 900 8–200 17 378

15–51.4 32–43.8 40–45 0.3 0.047

2–19 46 20 0.02 0.083

1–3.5 68–74.4 3.7–10 1.2 0.115

2–7 60–78 8–14 1.0 0.07

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Materials Selection for Natural Fiber Composites

Comparison criteria Density

Lower density is becoming of crucial importance in the automotive industry to minimize weight as much as possible and thus maximize efficiency. This makes density one of the main criteria that should be taken into account when comparing natural composite material to the synthetic counterpart. In addition, mechanical properties, such as the tensile strength and modus of elasticity are considered to be relative to the fibers density. Fig. 5.2 shows the results of comparison of density among DPFs with coir, hemp, and sisal. This comparison is based on the average values found in the literature. In this regard, DPF type shows the lowest density value and thus, less expected weight and that gives it an advantage over the other fiber materials in the automotive industry.

5.3.2.2

(Length/diameter) ratio

The length to diameter (L/D) aspect ratio is another very important property that must be taken into consideration when comparing various natural fibers systematically for the use in the automotive industry. This is mainly because the L/D aspect ratio determines how flexible a fiber could be for large-scale production, as it has been shown that discontinuous fibers (where length < 100
diameter) could be more suitable for mass production, as they are cheaper, isotropic in nature, and more flexible to fabricate complex parts (Kalia et al., 2011a). However, studies showed that fibers with higher aspect ratio (continuous long fibers) are generally stronger and stiffer than others with lower L/D (Asumani, Reid, & Paskaramoorthy, 2012; Etaati et al., 2014; Prajer & Ansell, 2014). Fig. 5.3 shows the comparison results of the aspect ratio of DPFs to other types. From such results, one can realize that a sisal fiber has the largest L/D whereas DPF lies in the middle. This result supports the findings of Fig. 5.2 in that

Density (g/cm3) Sisal

Hemp

Date palm

Coir 0

0.2

0.4

0.6

0.8

1

Fig. 5.2 Comparison between densities of various natural fibers.

1.2

1.4

1.6

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119

(L/D) ratio *103 Sisal

Hemp

Date palm

Coir 0

1

2

3

4

5

Fig. 5.3 Comparing various natural fibers with respect to their aspect ratio (L/D).

PDF can be a real competitor in the naturally isotropic complex parts in the automotive industry due to the possibility of fabricating either continuous or discontinuous fibers as the application requires.

5.3.2.3 Thermal conductivity Excellent heat and noise isolation properties are other important criteria in the automotive industry, especially in the interior design of the cabinet. This requires materials used in the cabinet to have low thermal conductivity. Results had shown that DPF has a thermal conductivity coefficient of 0.083 (W/m K) (Agoudjil et al., 2011; Pilla, 2011), which is lower than that of hemp (0.115 W/m K) and very near to that of sisal (0.07 W/m K). However, results show that coir has the lowest thermal conductivity coefficient with (0.047 W/m K). Fig. 5.4 summarizes these results. This demonstrates Thermal conductivity (W/m K)

Sisal

Hemp

Date palm

Coir 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Fig. 5.4 Date palm’s thermal conductivity with respect to other natural fiber types.

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that DPF can be a possible choice for the automotive applications when considering its appropriate thermal properties in addition to its lower density and moderate aspect ratio.

5.3.2.4

Cellulose and lignin content

Due to the difference in molecular composition and structure of natural fibers, they exhibit different mechanical and water absorption properties. The cell walls of these fibers are mainly composed of cellulose, hemicelluloses, and lignin in different percentages (Faruk et al., 2012). The amount of these polymers within a fiber can determine its mechanical and absorption properties. For instance, hemicelluloses and lignin work as reinforcing agents for the unidirectional cellulose microfibrils (AL-Oqla & Sapuan, 2014c). Natural fibers are organized as multiply construction with four layers of cellulose microfibrils with various angles to the fiber axis. The properties of the fiber, such as the elastic modulus of the fiber are strongly influenced by the spiral angle of those layers. In addition, lignin contents usually affect the structure properties and morphology characteristics of the fibers. A great advantage of the DPF over both hemp and sisal can be revealed through systematic keen investigation and comparison, which is that its cellulous content is greater than that of coir, but less than both hemp and sisal. This is illustrated in Fig. 5.5. This proves a reduction in the ability of water absorption (which is considered a disadvantage) comparing to other fiber types. This gives DPF types more desired mechanical properties over the coir. Moreover, unlike coir, the lignin content of DPF is less than a half of its cellulose content. Furthermore, the date palm’s cellulose content (similar to both hemp and sisal), is greater than that of its lignin content, which permits DPF to lay within these compared natural fibers to be noticeably competitive in the automotive applications.

Sisal

Hemp

Date palm

Lignin (wt%) Cellulose (wt%)

Coir

0

20

40

Fig. 5.5 Cellulose and lignin contents of different natural fibers.

60

80

Material selection of natural fiber composites

121

5.3.2.5 Availability of natural fibers One major factor that affects how much the industry may invest in utilizing natural fibers is its availability and the ease to get in hand. This is mainly due to the fact that this property will directly affect the continuity of the production and the price of both raw material and the final product. The annual production of raw natural fibers of plant origins varies from year to year and from type to type. Unfortunately, not enough research can be found in the literature covering the exact worldwide annual production of DPF, except AL-Oqla and Sapuan (2014c) and AL-Oqla, Alothman, Jawaid, Sapuan, and Es-Saheb (2014a). Examples of studies estimating the quantity of date palm trees can be found in Al-Khanbashi, Al -Kaabi, and Hammami (2005) and Jaradat and Zaid (2004). A study by Jaradat and Zaid (2004) stated that there are around 120 million date palm trees all over the world with more than two-thirds of them actually existing in the Arab countries. According to a study in (Al-Khanbashi et al., 2005), each palm tree can grow up to 100 years with an average 35 kg of palm residues per year. Calculations performed by AL-Oqla and Sapuan (2014c)) showed that the estimated annual palm tree raw fiber production is around 4200 tons. Another study in Nasser and Al-Mefarrej (2011)) stated that in Saudi Arabia alone, the seasonal trimming of date palm usually produces around 1 million metric tons of biomass wastes. These enormous quantities guarantee the availability and sustainability of the DPFs and make them very suitable for the automotive industry. Fig. 5.6 gives a direct comparison between the annual production of different natural fibers. From this figure one can realize that the annual estimated palm fiber production bests all other sources with more than 10 times of sisal fibers.

5.3.2.6 Raw fiber cost From the world production criterion, it can be seen that DPFs are the most available natural source of fibers. This makes these fibers the cheapest among the others, especially when compared to synthetic materials. This low cost can be considered as a key World production (103 ton) Sisal

Hemp

Date palm

Coir 0

1000

2000

3000

Fig. 5.6 The annual production of some natural fibers.

4000

5000

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Materials Selection for Natural Fiber Composites

Sisal

Hemp

Date palm

Coir 0

0.2

0.4

0.6 0.8 (USD/kg)

1

1.2

1.4

Fig. 5.7 Comparing the price of some natural fiber types used in the automotive industry with date palm fibers.

factor to direct the automotive industry toward using them wherever possible. It has been stated in (AL-Oqla & Sapuan, 2014c; AL-Oqla et al., 2014b, 2016b; Ghosh et al., 2007) that the excess amounts of date palm trees worldwide brings the prices down to no >0.02$/kg. Fig. 5.7 shows a direct comparison among the selected natural fiber types. This figure shows clearly that DPF is 15 times cheaper than coir, and 60 times less than hemp. Based on the low cost property, it is obvious that DPF is the most suitable choice for the automotive industry.

5.3.2.7

Elongation to break

Elongation to break is an important mechanical property that should be measured when considering the best natural fiber as a reinforcing option for the automotive industry. Fig. 5.8 demonstrates the elongation to break of different natural fiber types. It is seen from this figure that DPF has a moderate elongation break value that enables it to be acceptable for the automotive industry. Elongation to break of DPF is about three times less than that of coir, but twice greater than that of sisal.

5.3.2.8

Specific modulus of elasticity to cost ratio

Despite some poor mechanical properties of natural fibers, they do possess some other superior properties over conventional materials, like that of specific modulus of elasticity. To perform more meaningful assessments in this regard, one should consider the specific modulus of elasticity to cost ratio. This specific property becomes more significant if it is considered under cost-efficient bases. Having higher values of this ratio makes fibers more convenient to be used for industries such as automotive. Fig. 5.9 displays the comparison between the specific modulus of elasticity to cost ratio of different types of fibers being investigated together with the DPFs. In this figure, the specific modulus was calculated as the approximation of the average of extreme values of stiffness and of the density that was found in the literature. This

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123

Elongation to break (%) Sisal

Hemp

Date palm

Coir 0

5

10

15

20

25

30

35

Fig. 5.8 Comparing elongation to break percentage property of date palm fibers and other natural fiber types utilized in automotive sectors.

Specific modulus / Cost ratio Sisal

Hemp

Date palm

Coir 0

20

40

60

80

100

Fig. 5.9 Specific modulus of elasticity per cost ratio for the date palm fiber comparable to other commonly used types.

was taken in terms of (GPa/(g/cm3)) divided by the expected cost. It can be clearly demonstrated from Fig. 5.9 that DPF has dominant values over all other considered materials; the specific modulus of elasticity to cost ratio of DPF is twice that of hemp and three times that of sisal. Once again, this makes DPF a very imperative natural fiber type candidate to be used in the automotive industry.

5.3.2.9 Governmental support and social positive view Date palm trees do not need government license to be planted, unlike other crops like hemp. Quite the reverse, date palm has very high government support, social acceptance, and a positive view worldwide (Al-Shahib & Marshall, 2003;

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Materials Selection for Natural Fiber Composites

AL-Oqla & Sapuan, 2014c) and the fact that there are >120 million date palm trees in the world. More than two-thirds are in Arab countries. Moreover, such trees can grow and produce fruit for >100 years. About 2 million date palm trees are planted in Saudi Arabia, as they have government support. Such support comes from the fact that date palm produce the required raw materials for local industries, such as home accessories and furniture, and produce a very valuable food for humans. Date palms, in fact, bear a lot on the national economy in Saudi Arabia. The date fruit value is estimated to be $ 2.12 billion according to the base price of 2006 (AL-Oqla & Sapuan, 2014c). What is more, date palm trees are frequently used for garden decoration in Saudi Arabia. Such facts therefore, would assure the availability of DPFs at low prices and support its competitiveness for several industrial sectors, including automotive. Consequently, the proper assessment of the available natural fibers and conducting systematic evaluations for their capabilities regarding wide evaluation criteria would enhance the proper selection of the natural fibers and their composites to contribute in developing more green products to support the industrial sustainability and cleaner production theme. Moreover, systematic assessment of the natural fibers would also ereveal new potential fiber types to support and replace the commonly used fibers for various industries, which would improve finding new waste management practices.

5.4

Evaluating polymer matrices for natural fibers

Many matrix materials obtained from renewable resources have the potential to be good candidates for green applications regardless of being biodegradable or nonbiodegradable. The developing issue is the recyclability level and/or the decomposition at disposal. This criterion is one of the most desirable from an environmental point of view in selecting and assessing the available types of polymers. For the assumption of an existing 100% bio-based composite, even if the composite could not be recycled directly, there are many ways to dispose through incineration. No toxic gases will be emitted in incineration, and no gas at all in decomposition. On the other side, thermosets are not easy to be recycled, and thermoplastics have many processing limitations due to the high viscosity at melting. The bio-based thermosets using the plants oil as resins (novel thermosets) are difficult to recycle and reuse. However, in most cases, they can later be decomposed. Besides, some of the plant oil resins, such as soybean oil can be produced in a way to be biodegradable. Thermoset polymers produced from vegetable oils are obtained by cationic polymerization with some other monomers, such as cyclopentadiene, divinyl benzene, and styrene. But others, like epoxidized oils are converted directly, either in the presence of anhydrides as curing agent, or thermally latent catalysts in order to initiate the polymerization. Some of these polymers are biodegradable in soil. The additives themselves are synthetic and nonrenewable. Thus, they are not contributing to manufacturing totally green composites. Therefore, it would be more favorable to look for bio-thermoplastics that do not require the polymerization process, and that have the potential of combining both benefits of prospect disposal and

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125

recyclability. Moreover, to achieve optimal performance of the bio composites, the physiochemical, as well as mechanical behavior knowledge, is required because the properties of the NFCs strongly depend on the matrix type, fiber type, as well as their interfacial bonding. Therefore, the bond as well as compatibility between the matrix and the fibers is essential in defining the mechanical properties of the composite. This is due to the reinforcement efficiency and is determined by the stress transfer between matrix and the reinforcement fibers (Alawar, Hamed, & Al-Kaabi, 2009; Arbelaiz et al., 2005; AL-Oqla & Sapuan, 2014c). Therefore, to satisfactorily avoid major drawbacks of the bio-fibers, like the low permissible processing temperature, as well attaining better interfacial bonding between the fiber and the matrix, proper considerations in assessing and selecting the polymer matrix type for a particular natural fiber must be implemented to achieve the desired NFCs characteristics and performance. In order to expedite establishing selection guidelines of the available polymers, as well as to expand new sustainable eco-friendly design possibilities in a particular industry like that of the automotive application, desired evaluation criteria are utilized to assess and evaluate polymers using wide criteria. In a way to evaluate various polymer types regarding their mechanical performance, including the elastic modulus, fracture toughness, impact strength, yield strength, and elongation to break, which were selected from Table 5.2, experts have aggregated a priority for several polymers to be used with DPF for automotive application as illustrated in Fig. 5.10. The epoxy type was considered as the best choice followed by polypropylene regarding the general mechanical properties (AL-Oqla, Sapuan, Ishak, & Nuraini, 2015f). Similar comparisons for the polymers regarding the physical properties, including density, coefficient of thermal expansion, thermal conductivity, glass transition temperature, and acoustic insulation properties are demonstrated Priority/Mechanical properties Low density polyethylene (LDPE)

High density polyethylene (HDPE)

Epoxy

Polyester

Polypropylene 0

0.05

0.1

0.15

0.2

0.25

0.3

Fig. 5.10 Importance of various polymer alternatives regarding their mechanical properties.

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Materials Selection for Natural Fiber Composites

Priority / Physical properties

Low density polyethylene (LDPE)

High density polyethylene (HDPE)

Epoxy

Polyester

Polypropylene 0

0.05

0.1

0.15

0.2

0.25

Fig. 5.11 Importance of various polymer alternatives regarding their physical properties.

in Fig. 5.11. Polypropylene is the best of the polymers regarding the physical properties, followed by the low density polyethylene. This indicates that selecting the most appropriate polymer type for a particular fiber is subjected to conflicting criteria in an uncertain environment and usually leads to conflicting decisions throughout the traditional way of assessments. The illustration of such a polymer selection under an uncertain environment is illustrated in Fig. 5.12 (AL-Oqla & Sapuan, 2015a). Thus, more advanced and systematic ways of selection are still needed to achieve the most accurate and desired polymer type regarding wide evaluation criteria. Moreover, the importance of the various characteristics of polymer matrices usually differs for different natural fibers based on commonly and uncommonly used fiber types, like that of flax (a commonly used fiber type in automotive applications) and DPF (an uncommonly used one). For instance, the importance of the matrix properties for the date palm and flax fibers, as an experts’ feedback form various experts worldwide in the field of NFCs, is illustrated in Fig. 5.13. Such evaluation criteria included physical, mechanical, technical, chemical, environmental, and other characteristics of the polymers derived from those mentioned in Table 5.2. It can be noticed that the mechanical properties criterion is the most important evaluation for the matrix to achieve the required strength for a given application, both in commonly and uncommonly used fiber, whereas the technical and chemical criterion are the least critical in determining the appropriateness for the automotive applications. However, small variations in the importance are also noticed between the two types of fibers (AL-Oqla & Sapuan, 2015a).

Polymer 1

Polymer 2

.........

Polymer (N)

Uncertainty

Evaluation criteria

Physical properties of the polymer

Mechanical properties of the polymer matrix

Environmental and other properties of the polymer matrix

Chemical/technical properties of the polymer matrix

BEST POLYMER Fig. 5.12 Evaluating various polymers under uncertainty.

0.5 0.45 0.4

Importance

0.35 DPF

0.3

Flax

0.25 0.2 0.15 0.1 0.05 0

Physical properties

Mechanical properties

Chemical/Technical Environmental and properties other properties

Fig. 5.13 The importance of the evaluation criteria for both date palm and flax fibers.

128

5.5

Materials Selection for Natural Fiber Composites

Predicting the potential of new fiber types for industrial applications

Proper waste management has increasingly become crucial for both the environment and the industrial sustainability. Great amounts of agro waste are collected every year without being properly utilized. Some of this waste is burned, causing great environmental pollution. Moreover, partially utilizing the agro waste and natural fibers ignoring the rest will encourage the misuse of the natural resources available and would result in altering the ecological balance due to unplanned consumption (Louwagie, Northey, Finn, & Purvis, 2012; Zhang & Matsuto, 2011). Hence, more efforts are still needed in valorizing the waste and the available natural resources in more proper ways to contribute in solving such problems, as well as establishing more suitable and practical waste management practices, in addition to develop new potential applications (Zhang & Matsuto, 2011). In fact, selecting the suitable type of materials for a specific application can greatly enhance the customer satisfaction attributes and create successful sustainable practices. Considering new materials such as the bio-based composites for specific industrial applications is quite challenging and is affected by many limitations and constraints (Dweiri & AL-Oqla, 2006). Thus, proper and efficient material evaluations, as well as sufficient decision-making tools are required for the different applications in industry (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Oqla & Omari, 2017; AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015d; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015b; Al-Widyan & AL-Oqla, 2011, 2014; Dalalah, AL-Oqla, & Hayajneh, 2010; Hula, Jalali, Hamza, Skerlos, & Saitou, 2003; Rao, 2008). Unfortunately, up to the moment, this has not been properly conducted (Tahir, Ahmed, Saifulazry, & Ahmed, 2011; Zini & Scandola, 2011). The natural fibers capabilities are often evaluated using some single evaluation criteria (SEC) like Young’s modulus, tensile strength, elongation at fracture, specific modulus of elasticity, specific strength, etc. Only a small amount of work focused on evaluating the natural fibers using multiple criteria (Ghosh & Das, 2013; Majumdar, 2010; Majumdar, Sarkar, & Majumdar, 2004; Monteiro et al., 2011); a research work accomplished by AL-Oqla and Sapuan (2014c) introduced one combined criteria for evaluation (the ratio of the specific modulus with respect to the cost).

5.5.1

Combined multicriteria evaluation stage technique

An approach for better natural fibers evaluation by simultaneous integration of combined multi-criteria was demonstrated by AL-Oqla et al. (2014b)). In their approach (known as combined multicriteria evaluation stage technique (CMCEST)), the criteria influencing the selection of the natural fibers of the agro waste were combined and categorized as SEC, combined-double-evaluation-criteria (CDEC), combined-tripleevaluation-criteria (CTEC), etc. For the first stage (i.e., SEC), the evaluations were proposed based on single mechanical, physical, and economic criteria. For the second stage (i.e., CDEC), combined mechanical-physical criteria were utilized. For the third stage, combined mechanical-physical-economic criteria were considered (i.e., CTEC)

Material selection of natural fiber composites

129

in order to achieve more informed and consistent selection decisions. The effectiveness of this approach for the proper selection of the agro-waste fibers as reinforcement in the polymer composites is demonstrated by simultaneous pairwise comparisons between six different types of natural fibers at each selection stage. Each comparison for each single stage is interpreted separately. The flow chart and the stages of the CMCEST approach are demonstrated in Fig. 5.14. The performance and the

Combined Multi-Criteria Evaluation Scheme for Agro Waste Fibers for Natural Fiber Polymeric- Based Composites

Single-Evaluation-Criterion (SEC)

Stage 1 Physical Criterion

Economic Criterion

Mechanical Criterion

Information are enough and satisfactory for taking a decision?

Y

N

Combined- Double-Evaluation-Criterion (CDEC)

Stage 2 Combined Physical–Mechanical Criteria

Y

Information are enough and satisfactory for taking a decision? N

Combined-Triple-Evaluation-Criterion (CTEC)

Stage 3 Combined Physical-Mechanical-Economic Criteria

Y

Information are enough and satisfactory for taking a decision? N Y

More Combined Stages

Select the proper fiber type

Fig. 5.14 Stages of the CMCEST approach (AL-Oqla et al., 2014b).

Other Stages

130

Materials Selection for Natural Fiber Composites

capabilities of the NFCs strongly depend on the mechanical, physical, chemical, and economic characteristics, as well as the fiber-matrix interactions. Thus, for optimal benefits, pre-stage investigations are mandatory for the different properties and features in any industrial application. Accordingly, six different types of natural fibers were evaluated in the CMCEST approach, namely, date palm, coir, jute, oil palm, hemp, and kenaf to demonstrate their potential capabilities in a systematic new evaluation manner of the natural fibers. It is worth noting here that all the considered natural fibers are commonly used in various applications, particularly the automotive industry, except that of DPF. However, CMCEST approach can reveal the potential of the date palm for such an application systematically for the first time where mechanical, physical, and economic characteristics were considered simultaneously as an integrated evaluation scheme for the natural fiber capabilities. The fibers properties for such assessments in the CMCEST approach are obtained from the literature as listed in Table 5.6 (AL-Oqla et al., 2014b; Dittenber & Gangarao, 2011; Pilla, 2011), and the average of their values were considered for the comparison purposes. Before using these values in CDEC and CTEC, further calculations are performed; the fibers specific properties (relative to the average density values) are calculated and listed in Table 5.7. These properties were further measured relative to the cost ratio as a new scale and tabulated as provided in Table 5.8.

5.5.1.1 l

Single evaluation criterion comparison in the CMCEST approach

A single physical evaluation criterion comparison in the CMCEST approach

Table 5.6

Raw data used for evaluations (AL-Oqla et al., 2014b)

Fiber type

Density (g/cm3)

Coir

Kenaf

1.15–1.46 (1.31) 0.9–1.2 (1.05) 1.3–1.49 (1.4) 1.4–1.5 (1.45) 1.4

Oil palm

0.7–1.55 (1.13)

Date palm Jute Hemp

Tensile strength (MPa)

Tensile modulus (GPa)

Elongation to break (%)

Cost per weight (USD/kg)

95–230 (162.5) 97–275 (186.0) 320–800 (560.0) 270–900 (585.0) 223–930 (576.5) 80–248 (164.0)

2.8–6 (4.4)

15–51.4 (33.2) 2.0–19 (10.5) 1–1.8 (1.4)

0.3

1–3.5 (2.25)

1.3

1.5–2.7 (2.1)

0.5

17–25 (21.0)

0.3

Note: Average values are in parentheses.

2.5–12 (7.25) 8–78 (43.0) 23.5–90 (56.75) 14.5–53 (33.75) 0.5–3.2 (1.85)

0.02 0.3

Material selection of natural fiber composites

131

Table 5.7 Calculated specific properties and the cost ratios (AL-Oqla et al., 2014b) Fiber type Coir Date palm Jute Hemp Kenaf Oil palm

Specific tensile strength (MPa)/ (g/cm3)

Specific tensile modulus (GPa)/ (g/cm3)

Specific elongation (%)/ (g/cm3)

Cost ratioa

124.05 177.14

3.36 6.90

25.34 10.00

0.231 0.015

400.00 403.45 411.79 145.13

30.71 39.14 24.11 1.64

1.00 1.55 1.50 18.58

0.231 1.00 0.385 0.231

Example: Coir fiber has an average density of 1.31 and an average tensile modulus of 4.4, so the specific tensile modulus is 4.4/1.31 ¼ 3.36. a Cost ratios were found relative to the highest cost per weight (1.3); for example coir has a cost ratio of 0.3/1.3 ¼ 0.231.

Table 5.8

Calculated specific properties/cost ratios (AL-Oqla et al.,

2014b) Fiber type Coir Date palm Jute Hemp Kenaf Oil palm

Specific tensile strength (MPa)/ (g/cm3)/cost ratio

Specific tensile modulus (GPa)/ (g/cm3)/cost ratio

Specific elongation (%)/(g/cm3)/cost ratio

537.01 11,809.33

14.55 460.00

109.70 666.67

1731.60 403.45 1069.58 628.27

132.94 39.14 62.62 7.10

4.33 1.55 3.90 80.43

Example: The specific tensile strength of coir from Table 5.7 is 124.05 and its cost ratio is 0.231, so the specific tensile strength to cost ratio is 537.01.

A comparison between the various considered natural fibers was carried out regarding their densities as a SEC as demonstrated in Fig. 5.15. One can obviously notice that DPFs have the smallest value of density regarding other considered fibers. This is considered as an advantage in making light-weight products oriented to the automotive industry if DPFs are involved. Furthermore, such a comparison illustrates the closeness in density values of jute, kenaf, and hemp, and thus they have close priorities concerning this SEC. While such assessments can provide a primary significance to DPFs, better

132

Materials Selection for Natural Fiber Composites

Density (g/cm3) Oil palm Kenaf Hemp Jute Date palm Coir 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Fig. 5.15 A single-physical-evaluation-criterion comparison of natural fibers in CMCEST approach.

evaluation and information with respect to other single-evaluation criteria may be helpful for getting a more understandable idea about the assessed fiber types. l

A single economic evaluation criterion comparison in the CMCEST approach

As another SEC, the natural fibers were compared regarding the cost ratio (Fig. 5.16). It is clear that the DPFs are the cheapest among other considered types. Thus, date palms are very competitive economically. Furthermore, a remarkable variation between hemp, jute, and kenaf are obvious regarding this SEC. However, this criterion

1 0.9 0.8

Cost ratio

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Coir

Date palm

Jute

Hemp

Kenaf

Oil palm

Fig. 5.16 A single-economic-evaluation-criterion comparison of natural fibers in CMCEST approach.

Material selection of natural fiber composites

133

1100

Tensile strength (MPa)

1000 900 800 700 600 500 400 300 200 100 0 Coir

Date palm

Jute Hemp Fiber type

Kenaf

Oil palm

Fig. 5.17 A single-mechanical-evaluation-criterion comparison of natural fibers in CMCEST approach as tensile strengths (mean  standard deviation).

does not lead to a final decision of the best fiber type, and more series of SEC evaluations are required or instead, a more effective newly combined evaluation criteria are needed as a CDEC stage. l

A single mechanical evaluation criterion comparison in the CMCEST approach

The comparison between the different types of fibers was carried out regarding the mechanical properties as shown in Fig. 5.17. The variation of the mechanical properties among the different types is obvious and can be classified into two categories: hemp, jute, and kenaf as the first category with an average tensile strength exceeds 500 MPa, and coir, date palm, and oil palm as a second category with an average tensile strength below 200 MPa. Regarding this SEC, hemp is the most preferred, whereas fibers of the second category as the date palm are not. Considering the results from the previous two SEC, the results of this criterion contradicts those results leading to complicating the selections of the most appropriate among the different fiber types. Consequently, considering more SECs may lead to more complications, and thus, a new stage of CMCEST will be necessary.

5.5.1.2 Combined double-evaluation criterion (CDEC) in the CMCEST approach l

Combined mechanical-physical evaluation criterion

Although some SEC can provide basic information helpful for the selection purposes, combined evaluation criteria can provide more indications for the most appropriate among the many types of the natural fibers. To proceed to the second stage (i.e., CDEC), the same mechanical property (tensile strength) that was used for the SEC

134

Materials Selection for Natural Fiber Composites

Coir 450.00

Specific tensile strength

400.00 350.00

(MPa)/(g/cm3)

300.00 Oil palm

250.00

Date palm

200.00 150.00 100.00 50.00 0.00

Kenaf

Jute

Hemp

Fig. 5.18 Combined mechanical-physical evaluation criterion in the CMCEST approach.

is adopted here. Consequently, the specific strength values (tensile strength/density ratio) of the considered natural fibers are compared as shown in Fig. 5.18. Based on this combination between the physical property (density) and the mechanical property (tensile strength), kenaf seems slightly preferred over jute or hemp, and date palm is more preferred comparable to oil palm or coir. Due to this assessment, the priorities of hemp, jute, and kenaf are slightly changed from that obtained from the mechanical property when it was considered as a SEC. However, the order of the date palm, oil palm, and coir is not altered from the results of this criterion. This contradiction encourages using higher combined evaluation stages (i.e., CTEC).

5.5.1.3 l

Combined triple-evaluation criterion in the CMCEST approach

Combined mechanical-physical-economic evaluation criterion

The CTEC stage here combines mechanical, physical, and economic criteria simultaneously. The integration of these criteria would lead to more realistic selections of the optimal fibers types. This means that the simultaneous evaluation of the agro-waste fibers using the mechanical, physical, and economic characteristics can change the order of the most appropriate type of the natural fibers for a specific application.

Material selection of natural fiber composites

135

The economic characteristics are integrated with the mechanical and the physical properties to produce low-cost and more sustainable production. A comparison between the natural fibers using the combined mechanical, physical, and economic evaluation criterion is shown in Fig. 5.19. The ratio of the specific tensile strength to cost ratio was calculated for each fibers type. It is obvious that the value of this ratio for DPF type is about five times the value for jute. Thus, it can be concluded that among the six types of natural fibers considered, the date palms are the most appropriate. In a similar manner, a CTEC can be conducted on the natural fibers regarding the combined elongation to break, as shown in Fig. 5.20, and the combined tensile modulus properties as illustrated in Fig. 5.21. Both evaluation criteria comply with the concluded results of the previous criterion (specific tensile strength to cost ratio) regarding the superiority of the date palms over the other types of fibers. They also demonstrate the date palms capability in enhancing the industrial sustainability for any specific application (AL-Oqla et al., 2014b). Due to the obvious results, there is no need for more complex combined stages, as the CTEC was satisfactory. In other words, further stages will not greatly alter the priority of the DPFs relative to the other types due to the large gap with the next competitive fibers type. It is noteworthy here to mention that the CMCEST is extendable to further stages in case other evaluation

Coir 12,000.00

Specific tensile strength

10,000.00 8000.00 Oil palm

6000.00

(MPa)/(g/cm3) / Cost ratio Date palm

4000.00 2000.00 0.00

Kenaf

Jute

Hemp

Fig. 5.19 A combined mechanical-physical-economic evaluation criterion (tensile strength to cost ratio) in CMCEST approach.

136

Materials Selection for Natural Fiber Composites

Oil palm

Coir 500 450 400 350 300 250 200 150 100 50

Specific tensile modulus (GPa)/(g/cm3) / Cost ratio Date palm

0

Kenaf

Jute

Hemp

Fig. 5.20 A combined mechanical-physical-economic evaluation criterion (tensile modulus to cost ratio) in CMCEST approach.

aspects, such as the biological and the chemical characteristics, are requested. In conclusion, all the previously mentioned combined criteria yield that the date palm reinforcement composites can lead to a new level of composites in terms of the mechanical properties and the cost effectiveness. They can also reduce the environmental waste and enhance the environmental performance, as well as revealing new uncommonly used fibers to balance the consumption of the available natural resources and wastes. Therefore, AL-Oqla et al. (2014b)) were successfully able to introduce the CMCEST approach as a simple, novel, efficient, and functional systematic indicator to enhance the assessment and proper selection of the available natural fibers capabilities for polymeric composites. Inconsequence, conducting proper comparative studies regarding the fibers capabilities would not only enhance finding and revealing new potential natural fibers with optimum desired features like that of DPFs, but also improves the overall desired characteristics of the NFC materials, as well as establishing a comprehensive database for the fibers capabilities and merits for more proper evaluations and selections, which would dramatically expand their implementation in numerous green products to support positive environmental indices, as well as the industrial sustainability.

Material selection of natural fiber composites

137

Coir 700.00 600.00 500.00 Oil palm

400.00

Specific elongation (%)/(g/cm3) / Cost ratio

Date palm

300.00 200.00 100.00 0.00

Kenaf

Jute

Hemp

Fig. 5.21 A combined mechanical-physical-economic evaluation criterion (elongation to cost ratio) in CMCEST approach.

5.5.2

Moisture content criterion approach

The complete NFCs performance and capabilities intrinsically depend upon the physical characteristics, as well as the chemical compositions of the constituents ( Jawaid & Abdul Khalil, 2011; Sliwa, El Bounia, Charrier, Marin, & Malet, 2012). During their lifetime use, NFCs are exposed to different variable conditions including the hygroscopic conditions (Wang, Sain, & Cooper, 2006). The natural fiber hydrophilic behavior causes high moisture absorptions in wet and humid conditions. This would directly influence the mechanical performance of the fibers. The hygroscopic characteristics are very critical in the agro-waste natural fibers for their role in reducing the matrix-filler bonding, and thus, decreasing their mechanical performance (Azwa, Yousif, Manalo, & Karunasena, 2013; Sapuan et al., 2013; Wang et al., 2006). Hence, the moisture absorption is one of the most undesired characteristics in the natural fibers. It causes many catastrophic events such as reducing the mechanical properties, swelling, cracking, accelerating the fibers degradation, and inviting decay fungi (Azwa et al., 2013; Sapuan et al., 2013). Both moisture content and hemicellulose are considered the main factors affecting all of the moisture absorption amount in the natural fibers, their flammability, and their thermal and biological degradation (Azwa et al., 2013). Although much research has been conducted in the literature on

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investigating the effect of the moisture absorption on natural fibers mechanical performance, the results obtained were different and inconsistent (C
elino, Fr
eour, Jacquemin, & Casari, 2014). It was reported that the moisture absorption amount may worsen or enhance the performance of the composites depending on the types of both the matrix and the fibers (AL-Oqla et al., 2014c; Azwa et al., 2013). It was also reported that the moisture absorption may enhance a specific mechanical property for some types of fibers, whereas reduce it in others (Azwa et al., 2013; Placet, Cisse, & Boubakar, 2012; Symington, Banks, West, & Pethrick, 2009). However, the exact behavior of the fibers regarding the effect of moisture is not yet well known. Due to this, predicting the fibers relative performance for wet conditions is a very complex process. Moreover, the inconsistency and the contradictions among the results reported in the literature make the selection of a proper type of natural fibers a quite sophisticated process. It is believed that both hemicellulose and the moisture content of the natural fiber are the main factors responsible for the amount of its moisture absorption, thermal and biological degradation, and flammability (Azwa et al., 2013). Therefore, the chemical composition of the natural fibers is of paramount importance for most of the industrial applications, as it is important for the fiber capabilities from different standpoints like chemical, mechanical, degradable, and biological characteristics. Investigating the chemical composition of natural fibers and studying that for commonly used fibers, as well as those uncommonly used (like that of date palm) with respect to all of cellulose, hemicellulose and lignin contents express the influence of cellulose content on the mechanical characteristics of such fibers (AL-Oqla Sapuan, Ishak, & Nuraini, 2015c). Such comparisons of the chemical compositions of coir, date palm, hemp, jute, sisal and flax are illustrated in Fig. 5.22. Fibers with higher cellulose contents 80.0 71.2

69.0

70.0

67.0

65.3

60.0 50.0

46.0 42.5

40.0

37.9

30.0 20.0 20.0

19.6

18.7

18.0

10.0

17.0 12.4

11.0 12.1 6.9

10.0

2.3 0.0 Coir

Date palm Cellulose (wt%)

Hemp Lignin (wt%)

Sisal

Flax Hemicelluse (wt%)

Fig. 5.22 Chemical composition comparison for some natural fibers.

Jute

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139

Specific modulus (approx.) Jute Flax Sisal Hemp Date palm Coir 0.0

10.0

20.0

30.0

40.0

50.0

Fig. 5.23 Comparison of specific moduli for natural fibers (AL-Oqla et al., 2015c).

usually have better mechanical properties. This means that flax, sisal, hemp, and jute have higher specific moduli and specific strengths values than coir and date palm as demonstrated in Figs. 5.23 and 5.24, respectively, as a result of their higher cellulose contents (AL-Oqla et al., 2015c). Besides, cellulose content usually has a negative effect on natural fibers, as it can affect other beneficial features such as elongation to break property. That is why coir and date palm have much better elongation to break properties compared to jute, flax, sisal, and hemp (Fig. 5.25), i.e., due to the lower cellulose content of coir and date palm. This makes one realize that the chemical composition of the natural fiber has a major role in their capabilities and can dramatically affect and determine their expected performance, but at the same time makes the selection of the best natural fiber type for bio-composites more complicated with the conflicting criteria problem.

Spec. strength Jute Flax Sisal Hemp Date palm Coir 0.00

200.00

400.00

600.00

800.00

1000.00

Fig. 5.24 Comparison of specific strengths for natural fibers (AL-Oqla et al., 2015c).

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Materials Selection for Natural Fiber Composites

Jute Flax Sisal Hemp Date palm Coir 0.000

5.000

10.000 15.000 20.000 25.000 Elongation to break (%)

30.000

35.000

Fig. 5.25 Elongation to break of some natural fibers.

Lack of information in the literature regarding the proper evaluation and selection of the NFCs is obvious, particularly, evaluating the natural fibers for polymeric- based composites have not been sufficiently investigated (AL-Oqla et al., 2014b; AL-Oqla & Sapuan, 2014c). Therefore, more investigations are warranted. Moreover, inconsistencies and high levels of standard deviations of the results through the effects of water absorption on the fibers mechanical performance were observed in the literature (C
elino et al., 2014). This is mainly due to the variation in the qualities of fibers that may be different even for the same type, as well as due to the cumulative errors during the measurements of the fibers properties (Symington et al., 2009). Besides, there were no systematic or efficient evaluation methods to predict the fibers relative performance under wet conditions before the work presented by AL-Oqla et al. (2014c). They have established a novel and pioneer evaluation tool capable of predicting the relative performance for the natural fibers under the effect of moisture absorption. This established tool is expected to help designers properly and efficiently evaluate and select the most suitable types of fibers by enhancing the current selection processes. In other words, this tool is developed for evaluating the natural fibers based on the moisture content criterion (MCC) as a new and novel evaluation scheme.

5.5.2.1

MCC methodology

To better enhance the natural fibers selections, the relative performance for different types of fibers are illustrated based on the MCC. To perform the MCC approach, first, the performance value regarding a certain beneficial property of each fiber considered in the approach has to be normalized relative to the rest of the fibers properties values. Second, the fibers properties have to be calculated relative to their moisture contents (MCC values). Based on these relative performance values, new normalized values are also obtained. These two relative performances (before and after MCC) are then obtained relative to the best beneficial value. From this, the variations in performance

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141

between the normalized values (negative or positive) are obtained. These variations work as an indicator for the fibers capabilities with respect to a certain characteristic. Fibers of high positive increments in their performance values after using the MCC are preferred for further ecofriendly sustainable applications, whereas fibers of high negative decrements (reduction in performance) are not recommended. To elaborate more, fibers of negative decrements in the normalized performance values indicate negative influences (i.e., performance deteriorations) due to water absorption. Such deterioration in performance is explained due to the inherent moisture content in fibers that encourages swelling, aging, fungi decay, matrix incompatibility, and general mechanical instabilities. On the other side, fibers of highest positive deviations in their normalized performances are the best regarding the considered property, and the best choice is the type of fibers with the highest number of positive increment performances in different characteristics with the MCC being applied. For seeking clarity, various pairwise comparisons between the considered types of fibers have to be conducted with and without the MCC. Each comparison for each considered characteristic has to be performed and interpreted in a separate figure. The methodology described is illustrated in the flow chart provided in Fig. 5.26. Evaluating the natural fibers with respect to the MCC in the design stage is very essential in predicting the relative deterioration in performance due to the water absorption phenomenon. Thus, this method is able to qualitatively predict how the moisture affects the corresponding mechanical properties of natural fibers. More importantly, the MCC approach itself is very interesting and should attract much attention in the fields of both biology and material science. Overall, this study is interesting and provides a useful way of selecting natural fibers for polymer composites under wet conditions. As the moisture content varies in natural fibers based on their chemical compositions, the MCC method can predict the performance improvement or deterioration of natural fibers, including the uncommonly used ones, like that of DPFs. Fibers from date palms are considered competitive in comparison to other types of fibers with respect to their moisture contents as the weight variation is within 5%–12.5% from dry to wet conditions. Table 5.9 lists different properties of some common types of fibers used in industrial applications (coir, flax, hemp, jute, sisal, in addition to date palm) (AL-Oqla & Sapuan, 2014c; AL-Oqla et al., 2014b; Dittenber & Gangarao, 2011; Pilla, 2011). The comparisons between different types of fibers are conducted utilizing the MCC method to demonstrate its effectiveness as a tool for better evaluating and selecting the natural fibers, and to show the date palm competitiveness for sustainable designs. As the natural fibers of the same type can vary widely in their properties due to the variations in their qualities, the average of the reported values of natural fibers found in the literature were considered for the comparison (AL-Oqla et al., 2014b; AL-Oqla & Sapuan, 2014c; Rowell, Sanadi, Caulfield, & Jacobson, 1997). Fig. 5.27 shows the comparison regarding the fibers moisture contents. The variations between the different types of fibers are very obvious, where coir is the only fiber type having less values of moisture content than date palm. This gives the DPF superiority over other types like jute, flax, hemp, and sisal in resisting fungi, swilling, and reducing moisture absorption naturally. It is found that the moisture content of the date palm is only

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Start better evaluation of natural fibers

Step 1: Select fibers to be evaluated Step 2: Select a property to be evaluated Step 3: Find the average value of each fiber regarding that property Step 4: Find the normalized values found in step 3 with respect to the best beneficial one Step 5: Find the fibers average moisture content values Step 6: Calculate the ratios of the values found in step 3 with respect to the values found in step 5 Step 7: Find the normalized values of that found in step 6 with respect to the best one

Step 8: Find the differences between each value found in step 7 and their corresponding values in step 4

Step 9: The best choice of fibers regarding the considered property is the one with the highest positive value (for beneficial properties) or highest negative (for non-beneficial) in step 8 Step 10: Repeat steps 2-9 to find the best fiber type which is the best regarding the most desirable properties simultaneously

Fig. 5.26 The flow chart of MCC method. Table 5.9 Properties of natural fibers for conducting the MCC method Fiber type

Coir

Date palm

Flax

Hemp

Jute

Sisal

Density (g/cm3) Tensile strength (Mpa) Tensile modulus (GPa) Elongation to break (%) Moisture content (wt%)

1.15–1.46 95–230

0.9–1.2 97–275

1.4–1.5 343–2000

1.4–1.5 270–900

1.3–1.49 320–800

1.33–1.5 363–700

2.8–6

2.5–12

27.6–103

23.5–90

8–78

9–38

15–51.4

2–19

1.2–3.3

1–3.5

1–1.8

2–7

8

5–12.1

8–12

6.2–12

12.5–13.7

10–22

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143

16.00

Mmoisture content (%)

14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Coir

Date palm

Hemp

Sisal

Flax

Jute

Fig. 5.27 Average values of different natural fibers moisture contents.

two-thirds of jute, and one-half of sisal. Thus, it can be predicted that the date palm can behave more efficiently under the effect of moisture absorption than these other types of fibers. Although comparing the different mechanical properties, such as the tensile strength, can give useful information regarding the natural fibers capabilities, combined evaluations can provide more realistic ones. However, before proceeding in creating conclusions for the selection process, it is required to know how fibers will behave under wet conditions where water absorption is likely to occur to increase the reliability of the green products and to predict the fibers future performance deterioration and/or improvement. In other words, we have to be curious whether the relative performance of the fibers will be altered due to the effect of the water absorption after their utilization in products. Although massive and long-time efforts will be required for this, the developed MCC tool can give powerful contributions regarding this issue in a simple way, according to the procedure illustrated in Fig. 5.26. In order to predict the relative performance regarding a beneficial property (for instance the elongation-to-break), the elongation-to-break values are normalized according to the best value (33.2), which is for coir fibers, as displayed in Table 5.10. Fig. 5.28 demonstrates the average values of the fibers elongation to break properties. As another step, the elongation-to-break values are calculated relative to the moisture contents in fibers (EB/MC) and then normalized according to the best obtained value, which is (4.15) for coir. Then, the difference in the previous normalized values are calculated (NOR EB/MC)-(NOR EB) as tabulated (Table 5.10). These differences are illustrated in Fig. 5.29. They can predict the performances of fibers and whether deteriorations will occur or not. The studied case showed that deterioration in performance will occur in all types of fibers regarding the elongation-to-break

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Detailed calculations of the MCC evaluation tool in predicting the relative performance of natural fibers regarding the elongation to break property

Table 5.10

Moisture content (MC) (%) Elongation to break (EB) (%) Normalized EB (NOR EB) E B/MC Normalized EB/ MC (NOR EB/ MC) NOR(EB/MC)NOR(EB)

Coir

Date palm

Hemp

Sisal

Flax

Jute

8.000

8.550

9.100

16.000

10.000

13.100

33.200

10.500

2.250

4.500

2.250

1.400

1.000

0.316

0.068

0.136

0.068

0.042

4.150 1.000

1.228 0.296

0.247 0.060

0.281 0.068

0.225 0.054

0.107 0.026

0.000

0.020

0.008

0.068

0.014

0.016

35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 Coir

Date palm

Hemp

Sisal

Flax

Jute

Fig. 5.28 Demonstrate the average values of the fibers elongation to break properties.

relative to the coir performance, however, with different values. This yields that the best fiber is coir regarding the elongation-to-break performance, followed by hemp, as its performance will be the least affected under the effect of moisture absorption due to its moisture content. In contrast, sisal is considered the worst type of fiber regarding the elongation-to-break performance as large deterioration in its performance will occur due to water absorption. In fact, this result was not concluded before applying the MCC (Fig. 5.28).

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145

0.030 Predicted relative performance of elongation to break

0.020 0.010 0.000 –0.010

0.000 Coir

Date palm

Hemp –0.008

Sisal

–0.014

–0.020 –0.030

Flax

–0.020

Jute –0.016

–0.040 –0.050 –0.060 –0.070

–0.068

–0.080

Fig. 5.29 Elongation to break relative performance reduction predicted using MCC compared to coir fiber (AL-Oqla et al., 2014c).

From MCC, it is obvious that the performance capabilities of the different types of fibers can be changed. A good example of this is the sisal, as it has better performance regarding the elongation-to-break than jute, hemp, and flax. However, after applying the MCC, sisal seems inappropriate as its performance is highly negative, and high deterioration in its performance is predicted due to its moisture content. Further comparative studies can be conducted on natural fibers in a similar manner using the MCC evaluation tool. Predicting the fibers relative performance regarding the tensile strength under the effect of moisture absorption is conducted by following the same procedure as for the previously explained elongation-to-break property and is demonstrated in Fig. 5.30. Results revealed that sisal will exhibit reductions in the tensile performance of >15%. Also, jute is predicted to have >10% reduction in its tensile strength performance due to the water absorption phenomenon based on its MCC (AL-Oqla et al., 2014c). In contrast, hemp, coir, and date palm are predicted to have improvements in their performances under the water absorption effect where the maximum improvement is predicted for hemp (5% increment). The predicted results of the MCC regarding the improvements of the tensile performances is considered an added value to the previously achieved results on improving the NFCs under the effect of water absorption (Abral et al., 2014; Alamri & Low, 2012; Azwa et al., 2013; Dhakal, Zhang, & Richardson, 2007; Kuciel, Jakubowska, & Kuz´niar, 2014; Sapuan et al., 2013). In a similar manner, predicting the relative performances of fibers regarding the tensile modulus under the effect of moisture absorption, it is also performed and revealed that coir, hemp, and date palm will have tensile moduli improvements under the effect of water absorption with a maximum of 8.6% for hemp. Jute and sisal will have deteriorations in their performances as shown in Fig. 5.31 with 15.6% and 13.5%, respectively.

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Materials Selection for Natural Fiber Composites

Predicted relative performance of tensile strength

0.100 0.049 0.050

0.035

0.027 0.000

0.000 Coir

Date palm

Hemp

Sisal

Flax

Jute

–0.050

–0.100 –0.113 –0.150 –0.170

–0.200

Fig. 5.30 Predicted relative tensile strength performance using MCC method relative to flax fiber (AL-Oqla et al., 2014c).

Predicted relative performance of tensile modulus

0.100

0.086

0.050 0.017

0.019 0.000

0.000 Coir

Date palm

Hemp

Sisal

Flax

Jute

–0.050

–0.100

–0.150 –0.135

–0.156

–0.200

Fig. 5.31 The relative performance of tensile moduli predicted with MCC method (AL-Oqla et al., 2014c).

5.5.2.2

Results agreements with MCC method

Although the MCC tool is developed to perform comparative evaluations for the different types of natural fibers and for predicting the performance deteriorations and/or improvements in the fibers performances and capabilities due to the water absorption effect and thus enhance selecting the natural fibers for future green applications, the verifications will require huge amounts of time and effort. However, the MCC

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147

predicted results are in good agreement with the various results found in the literature. For instance, Symington et al. (2009) have conducted their studies on many cellulose based fibers under the effect of water absorption, regarding the tensile properties (the tensile strength, Young’s modulus, and elongation-to-break). These properties were tested in both dry and wet conditions, including soaked samples. In order to avoid the variability of fibers properties, 25 tests were conducted for each type of fibers for each case. The results of the soaked samples were considered as strong evidence for the validity of the MCC tool. Their results regarding the elongation-to-break property for coir, flax, jute, and sisal in both dry and wet (soaked) conditions are listed in Table 5.11. From the table it can be seen that a reduction in the elongation-to-break performance had occurred in all of jute, flax, and sisal compared to coir. This completely matches the predictions of the MCC tool. Coir fibers are having the best performance and this again complies with the results of the MCC. From Table 5.11, it can also be deduced that the maximum reduction in performance under the effect

Elongation to break of different natural fibers under the effect of water absorption (Symington et al., 2009)

Table 5.11

Elongation to break (%) Fiber

Dry

Water soaked

Performance

Percentage

Note

Sisal

2.9

2

0.9

31.0%

Jute

1.9

1.4

0.5

26.3%

Flax

2.5

1.9

0.6

24.0%

Coir

19

22.5

3.5

+18.4%

Reduction occurred as expected Reduction occurred as expected Reduction occurred as expected Improvement occurred as it is the best in elongation to break

The worst reduction achieved in sisal with 31% then in jute with 26.3%, and finally in flax with 24%, exactly as predicted from the MCC method, where it was predicted that sisal will be the worst fiber type then jute and flax

148

Materials Selection for Natural Fiber Composites

of water absorption was in sisal (31%) as the worst type of fibers, followed by jute and flax. The same results were predicted by the MCC tool (Fig. 5.29). These agreements clearly uphold the MCC as a novel and effective evaluation tool for the relative performances of the natural fibers under the effect of water absorption. Moreover, the accuracy in measuring the tensile strength due to the moisture absorption effect is reported in the same work (Symington et al., 2009), however, with less accuracy than the elongation-to-break and tensile moduli properties. This is because the very wide variations in the fibers property (tensile strength). This can add an advantage for the MCC tool as it is capable of predicting the relative performances of the natural fibers with more reliable results and less cumulative errors. That is, MCC is concerned in evaluating the relative performances of the natural fibers with less lab work, and as a consequence, with less measurement errors. Other strong agreements validating the MCC were found from investigating the tensile moduli performances of the natural fibers under the effect of moisture absorption. It was reported that the reduction in the tensile modulus property of sisal was from 17.8 GPa for dry to 17 GPa after wet conditions (4.5% decrement). Another reduction occurred in jute fibers (from 28 GPa at dry to 23.6 GPa at wet conditions) of about 15.7%. In contrast, coir fibers exhibit an improvement in the tensile modulus performance of about 40% (3.2 GPa at dry to 4.5 GPa at wet conditions). These results also conform to the results predicted by the MCC tool. More accurately, MCC was even able to predict the order of the relative performances as it predicted that jute will have worse reduction than that of sisal (Fig. 5.31). Further agreement were also found from other works even at the composite level. For example, a comparative study by Costa and D’almeida (1999) between sisal and jute composites regarding their mechanical properties due to the water absorption effect was conducted. After exposing the polyester-based composites to wet conditions for a long time (7500 h of soaking in distilled water), their ultimate tensile strengths were measured. For jute-polyester composites, it was reduced to 30.4 MPa from 43 MPa at dry conditions (29.3% reduction), whereas for sisal-polyester composites, the ultimate tensile strength was reduced to 18.8 MPa from 34.6 MPa at dry conditions (45.6% reduction). This means that the relative reduction in the tensile performance of sisal composites to jute composites was about 1.55 (45.6%/29.3%). This was exactly predicted by the MCC as depicted in Fig. 5.30, where a ratio of 1.5 was revealed (0.170/0.113). Moreover, the same authors had performed another comparative study between sisal-epoxy and jute-epoxy composites. A relative reduction in performance of jute composite to sisal composite was measured to be about 1.18 times for jute, while MCC predicted this relative value to be about 1.15 times for sisal. Overall, these preceded studies prove the validity of the MCC evaluation tool. However, the variations in the experimentally measured values (standard deviations) explain the slight difference for the values predicted from the MCC evaluation tool. At last, the MCC is considered as an added value in predicting the relative performance deterioration or improvements of the natural fibers with fewer errors than that of the experimental works. Also, MCC can accurately predict the stability and the reliability of the NFCs performances for various industrial applications under different environmental conditions.

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149

In consequence, it can be deduced that the MCC method can: 1. Utilize the moisture content of natural fibers to predict their behaviors under the effect of water absorption. 2. Accurately predict the relative deterioration and/or improvement in the performance of the natural fibers under the water aging effect. 3. Verify and correct the reported conflicted results regarding the variation in the performance of the natural fibers under the water absorption effect. 4. Be considered as an added-value step due to its capabilities in predicting the performance of the natural fibers under the water absorption effect systematically with less cumulative errors comparable to the experimental works measurement procedures. 5. Be utilized as a new evaluation tool for better assessing the natural fibers and improving their selection methods, particularly for the wet environment conditions. 6. Reveal the potential of new, uncommonly used fibers for wet condition applications like that of DPFs through a systematic investigations and predictions.

5.6 5.6.1

Issues and challenges Estimating the elastic properties of composites

NFCs are heterogeneous materials composed of fibers and a matrix. Fibers usually carry the load, as well as provide the stiffness for the composite, whereas matrices transfer the external load to the fibers, as well as protect the fibers. The benefit of coupling the fibers with the matrix is primarily to exploit the high stiffness and strength of fibers to produce materials with superior properties to meet the design requirements. Therefore, various well-known parameters usually influence the general performance and the macro-mechanical behavior of the NFCs, such as: 1. 2. 3. 4. 5. 6.

Fibers properties. The fiber loading, matrix type, and voids. The size and geometry of fibers. The interfacial adhesion and compatibility between the constituents (fibers and polymers). The distribution and orientation of the natural fibers. The characteristics of the matrix.

Thus it is very complicated to predict the performance of the composites without considering some principal assumptions, due to the influence of such large numbers of parameters on composite properties. Some of these assumptions are: 1. Fibers geometries, as well as properties, are similar for all fibers from a certain fiber type. 2. Fibers distribution is considered uniform and homogenous over the whole matrix. 3. The produced composites satisfy the isostrain conditions. 4. An ideal interface between the fibers and the matrix is considered. 5. Deformations of the constituents are within the elastic region. 6. No lateral deformations occur (assume Poisson’s ratio is zero). 7. The maximum tensile stress is at the middle of the fiber and zero at both ends. 8. Composite properties will not be affected by voids, but decreasing the volumetric fractions of the fibers and the matrix.

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Materials Selection for Natural Fiber Composites

The effect of such large numbers of parameters is elegantly demonstrated by the fundamental composites engineering model called the generalized rule-of-mixtures (ROM) model for predicting the tensile moduli and strengths of discontinuous fiber composites. To derive the longitudinal modulus for a unidirectional ply, the sign and nomenclature convention used here is demonstrated in Fig. 5.32. The orthogonal axes 1, 2, and 3 are corresponding to the fiber direction, in-plane transverse, and through-thickness transverse directions, respectively. It can be seen that the unidirectional ply has two different in-plane tensile moduli (E1 and E2). As an approximation, one can say that, E3  E2. The required two values of Poisson’s ratio that define the lateral contraction as a result from in-plane tension are demonstrated in Fig. 5.33. The conventional notation is that νij designates the reduction in the j-direction when stress is applied in the i-direction. From Fig. 6.2, it is noted that the lateral strain (ε2) appears due to a stress applied in the fiber direction (“1”) and it is much larger than that of longitudinal strain (ε1) subsequent from the transverse (“2”) applied stress. Therefore, ν12 > ν21. However, the Poisson’s ratios related to moduli can be expressed as: ν12 E1 ¼ ν21 E2

(5.1)

Considering the previously mentioned constraints, applying a longitudinal load F1 (in the longitudinal direction) will be equally shared between filler and the matrix, so that F1 ¼ Ff + Fm. Expressing loads in terms of stresses and cross-sectional areas to obtain:

3

1 2

Fiber cross-section

Matrix material

Fig. 5.32 Orthogonal directions in a schematic unidirectional ply.

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151

e1

Fig. 5.33 The in-plane Poisson’s ratios of an orthotropic material.

Fiber direction (“1”) e2 = –n12 e1

e2 Fiber direction (“2”) e1 = –n21 e2

σ 1 A ¼ σ f Af + σ m A m

(5.2)

where A ¼ Af + Am is the cross-sectional area of the ply. Utilizing Hooke’s law to express stress in terms of the strain multiplied by the modulus: E1 ε 1 A ¼ E f ε f A f + E m ε m A m

(5.3)

But as equal strain has been assumed (ε1 ¼ εf ¼ εm), this leads to: E1 A ¼ E f Af + E m Am

(5.4)

or E1 ¼ E f

Af Am + Em A A

(5.5)

The terms Af/A and Am/A are the area fractions of both fiber and matrix, respectively. For unidirectional composites, these area fractions are equivalent to the constituent volume fractions, thus we can write: E1 ¼ E f V f + E m V m ¼ E f V f + E m ð 1  V f Þ

(5.6)

Another similar rule of mixtures is usually used for Poisson’s ratio as follows: ν12 ¼ νf Vf + νm Vm

(5.7)

If a reasonable approximation is considered as Ef ≫ Em, then E1 ¼ Ef  Vf. For the transverse modulus in a unidirectional ply, transverse loading in the transverse direction (“2”) makes the state of stress in the relatively flexible matrix more complicated, where the transverse modulus E2 becomes a matrix-dominated property. Due to this, the rules of mixture are generally established based on simple assumptions

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Materials Selection for Natural Fiber Composites

of stress distribution, thus, they are much less reliable than those for longitudinal properties. The simplest model considers any Poisson’s contraction is ignored, and the stress in each of the composite constituents is similar to the other. As a result, the transverse modulus in a unidirectional ply can be expressed as: 1 Vf Vm ¼ + E2 Ef Em or E2 ¼

Ef Em V f Em + V m Ef

(5.8)

If Ef ≫ Em and Vf  Vm then we may write the transverse modulus in a unidirectional ply as E2 

Em ð 1  Vf Þ

(5.9)

which is an independent form of the modulus of the filler, but a matrix dominant one. However, several improvements were found in the literature to enhance a more accurate prediction for such properties of the composites. One modification that allows for fibers restricting the Poisson contraction is generated simply by replacing the matrix modulus in Eq. (5.8) to have E0m ¼

Em 1  ν2m

(5.10)

The Halpin-Tsai model is a commonly used alternative for the transverse modulus. It can be expressed as: E2 ¼

Em ð1 + ξηVf Þ ð1  ηVf Þ

(5.11)

where η¼

ð E f  Em Þ ðEf + ξEm Þ

(5.12)

The adjustable parameter ξ is usually close to unity. Eq. (5.10) is generally more reliable than the simpler alternative. For shear modulus in unidirectional ply, the shear modulus is well-defined as the τij ratio of shear stress to shear strain Gij ¼ , where the subscripts “ij” refer to the plane γ ij in which the shear modulus is defined. The rule of mixtures for shear modulus is

Material selection of natural fiber composites

153

usually based on similar assumptions as that in the case of the transverse tensile modulus, thus they are treated similarly. This can be written as: 1 Vf Vm ¼ + G12 Gf Gm

(5.13)

The Halpin-Tsai equations are also applicable to shear modulus as: G12 ¼

Gm ð1 + ξηVf Þ ð1  ηVf Þ

(5.14)

with η¼

ðGf  Gm Þ ðGf + ξGm Þ

(5.15)

Again, the parameter ξ is approximately equal to 1. Assuming transverse isotropy, we expect that G13 ¼ G12. However, the third shear modulus can be obtained from: G23 ¼

E2 2ð1 + v23 Þ

(5.16)

The Poisson’s ratio for a unidirectional ply that has transverse isotropy, it is then expected that ν13 ¼ ν12 (see Eq. 5.7). Then the expression in terms of the bulk modulus (K) for the other out-of-plane Poisson’s ratio is: ν23 ¼ 1  ν21 

E2 3K

(5.17)

where 1 Vf Vm ¼ + K Kf Km

(5.18)

with Kf ¼

Ef
3 1  2νf

(5.19)

Em 3ð1  2νm Þ

(5.20)

and Km ¼

However, for routine calculations, three-dimensional elastic constants are rarely required, but they may well be needed for finite element analysis or other numerical

154

Materials Selection for Natural Fiber Composites

approaches to predict the damage mechanism in the composites for better design purposes due to the large parameters involved in the process. Although fiber reinforced polymer composites do not firmly satisfy many of the principal assumptions, the ROM model has proved to be a good approximate in predicting the properties of the synthetic fiber composites. The ROM model can also determine the potential reinforcement of fibers. Its simplicity extends to its implementations to include the NFCs as well. As plant fibers are discontinuous in nature, the ROM model can still be utilized to predict the properties of their composites, even if continuous reinforcements are enrolled. However, for better estimation/ prediction, a modified ROM model has been introduced and verified as a better and more suitable estimator for the NFCs (Shah, 2013). This modified model included: a factor to investigate the porosity effect on the tensile properties, another factor to account for the effect of the fiber diameter distribution on the tensile modulus, as well as other factors to account for the fibers area inaccuracy (due to the discrepancy between the actual noncircular cross-sectional area of the fibers), and the reinforcement orientation due to the yarn twist effect on the tensile strength of the composites (Shah, 2013). However, the modified ROM model has been validated experimentally for limited data sets of NFCs only. Accordingly, further investigations are still required, particularly because the mechanical properties of the plant fibers are dramatically affected by several main micro-structural parameters, such as the cellulose content, the micro-fibril angle, the cellulose crystallinity, and the aspect ratio of the fibers.

5.6.2 5.6.2.1

Plant fiber processing Plant growth and fiber extraction

Natural fibers have great variations in their properties, even if they are of the same type. This is due to the wide variability in the microstructural parameters of the fibers. For a given fiber, the microstructural parameters are themselves altered by many factors, some are: (1) the growth conditions of the plants such as climate, soil, geographic location, (2) the extraction and the preparation of the fibers (plant age, location of the fibers in the plant, carding processes, methods of retting), (3) fiber processing (sliverroving-yarn-textile). Several studies have remarked the effect of these factors on the fibers, and substantially, on the composite properties (AL-Oqla et al., 2014a, 2015f, 2015g; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015e; AL-Oqla & Sapuan, 2015b, 2014a; Ardanuy, Antunes, & Velasco, 2012; Ashori & Nourbakhsh, 2010). In order to ensure the consistency in the products (i.e., the diversity of the fibers properties are within the lower limits), “batch-mixing” is typically used across several crops. In order to optimize the extraction and the preparation processes of the fibers, studies remarked that increasing the number of the processing steps will lead to an increase in defects, and a decrease in the polymerization degree of the cellulose chains, and subsequently, a reduction in the mechanical properties of the fibers (H€anninen, Thygesen, Mehmood, Madsen, & Hughes, 2012; Hughes, 2012). Better quality fibers have been produced from those fibers that have undergone retting and hackling with a minimum preparation processes (Miao & Finn, 2008; Van de Weyenberg et al., 2003).

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However, in order to attain a high benefit of the fibers properties in a composite, an aligned and continuous reinforcement product is essential. Remarkably, for twisted fibers, the twist level in the produced fibers increases at each stage of twisting and usually many unwanted effects on the properties of the composites occur with the increase of twisting, some are: a reduction in the wettability, an increase of the void formation, hindering the resin impregnation, and a severe drop in the tensile properties due to an increment in the miss-oriented fibers (Goutianos, Peijs, Nystrom, & Skrifvars, 2006; Shah, 2013). It was reported that for the same level of twist in yarns, fine-count ones (low linear density yarns) have smaller diameters than higher density ones (Shah, Schubel, Clifford, & Licence, 2013b). Hence, the twist angle in fine-count yarns, and the induced miss-oriented fibers are reduced. Consequently, the reduction in the composite properties is small. Therefore, to achieve a good compromise for minimizing the fibers processing, while at the same time using aligned/continuous reinforcements, and limiting the severe effects of the yarn twist, the preferred order for the fiber reinforced products is slivers, rovings, and then, fine-count yarns (Shah, 2013; Shah, Schubel, & Clifford, 2013a). It was also found that minimal processing is becoming attractive with respect to the trend of minimizing the environmental impact of plant fibers reinforcements, as less energy is required (Shah et al., 2013a).

5.6.2.2 Fiber surface modification Due to the hydrophilic nature of plant fibers, researchers have developed a general view regarding the plant fibers and their composites; the plant fibers are vulnerable to moisture absorption, and a poor compatibility between the polar plant fibers and the nonpolar matrices is highly expected (AL-Oqla et al., 2014c). While the vulnerability to moisture absorption is a concern for the long-life durability, the poor compatibility is a concern for the NFCs overall performance. Thus, an extensive study was carried out in this field to explore any possible techniques in order to improve the fiber-matrix compatibility (AL-Oqla et al., 2014c; George, Joseph, Nagarajan, Tomlal Jose, & George, 2013; Masseteau, Michaud, Irle, Roy, & Alise, 2014; Pan & Zhong, 2014; Symington et al., 2009; Wang et al., 2006). Major work falls into two main streams; fiber surface chemical and/or physical modifications, and matrix modifications. However, the fiber modification is more preferred. The physical modifications of the fibers as the mercerization or the plasma treatments are aimed to roughen the surface topography and to remove any possible surface impurities. This is supposed to improve the mechanical adhesion with the matrix. On the other side, in the chemical modification techniques, a coupling agent is added as a compatibilizer between the fibers and the matrix. However, a question floats to the top: is it necessary for all NFCs to apply the surface modifications on the fibers in order to improve their mechanical properties? In fact, it has been noticed that the fibers will not carry the maximum load if their length is below a critical value, called the ineffective fiber length. The fibers contribution in the composite reinforcement is determined by the ineffective length/the reinforcing length ratio. The ineffective length is directly proportional to the fiber tensile strength/fiber-matrix interfacial strength ratio. These

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Materials Selection for Natural Fiber Composites

relationships have an interesting inference described in the following: assuming that for the same diameter for both plant fibers (flax) and E-glass, NFCs and glass fiber reinforced composites (GFRP) are to be produced with the same length of fibers. Then, for the critical fiber length, it has to be similar in both natural composites and GFRP, and the fiber tensile strength/fiber-matrix interfacial strength ratio also has to be identical for both products. In fact, as plant fibers possess lower tensile strength, the NFCs, as a consequence, require a proportionally lower interfacial strength. Therefore, the common idea that NFCs possess less interfacial strength is rather trivial (Shah, 2013). In fact, improving the interfacial strength of NFCs, as well as applying physical treatment techniques on the fibers surfaces becomes important when the fibers are relatively short in comparison to the critical fiber length. For a 1 mm constant fiber length, an increase in the interfacial strength (from 15 to 30 MPa) with a consequent decrease in the critical fiber length (from 0.667 to 0.333 mm) significantly increases the length efficiency factor (from 0.667 to 0.833) (Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a, 2016b; Shah, 2013). On the other hand, if the fibers lengths are 10 times greater than the critical fiber length, the fibers are considered “long,” and no noticeable effect on the length efficiency factor will be achieved if the interfacial strength is improved by means of the surface pre-treatment techniques (see Fig. 5.34). In essence, as the fibers are exposed to the maximum load along most of their lengths, a decrease in the ineffective fiber length will have no significant impact on the fibers contribution in reinforcing the composite. Therefore, it can be recommended that when natural fibers are considered for structural applications: long fibers are used, and the fiber surface modifications are unnecessary (Shah, 2013). Indeed, NFCs can be produced with impressive mechanical properties with no need for any surface treatment, only by using high-fiber volume fractions and an optimized reinforcement form (i.e., rovings or slivers) (Shah, 2013). Considering the facts that (1) the fibers surface treatments may require toxic and/or expensive chemical substances that distort the eco-friendly and the low-cost images of the plant fibers, (2) miss-idealizing the surface treatments conditions is observed to severely drop the tensile strength of the raw fibers, (3) the lack of information on the ideal parameters of the surface treatments (treatment time and temperature,

0

200

300

Fiber aspect ratio, If / df 400

500

0.8 0.6 0.4 0.2 0.0 0

(A)

100

Fiber length efficiency factor for strength

Fiber length efficiency factor for stiffness

Fiber aspect ratio, If / df 1.0

2

4

6

Fiber length, If (mm)

8

10

1.0

0

200

0.8

300

400

500

t = 30 MPa, lc = 0.333 mm t = 15 MPa, lc = 0.667 mm

0.6 0.4 0.2 0.0 0

(B)

100

2

4

6

8

10

Fiber length, If (mm)

Fig. 5.34 Estimating of the fiber length efficiency factors (A) for stiffness and (B) for strength (Shah, 2013).

Material selection of natural fiber composites

157

concentration of the chemical agent) hinder the improvements of the mechanical properties of NFCs (AL-Oqla et al., 2015b), and (4) the improvement of the interfacial strength often lead to a decrease in the impact performance (AL-Oqla et al., 2015e). To conclude, applying fiber surface modifications in order to enhance the mechanical properties of the NFCs that are intended for use in structural applications is highly discouraged (Shah, 2013).

5.6.2.3 Fiber volume fraction Beside the properties of the constituents, the mechanical properties of the composites are also influenced by their volumetric compositions. Among all factors in the ROM model, the fiber volume fraction is considered as the most important. Shah (2014) and Shah et al. (2013b) have noticed the strong linear relationship between the tensile modulus and the fiber volume fraction in yarn reinforced unidirectional NFCs (R2 ¼ 0.99) (Fig. 5.35). This is in good agreement with the modified ROM model supposing no porosity when calculating the volume fraction (i.e, Vm ¼ 1  Vf). As understood from the composite theory, when brittle fibers are reinforcing a ductile matrix in a composite, such as NFCs, the direct proportion between the strength and the fibers contents is well justified (see Fig. 4.4 in the previous chapter). The fiber volume fraction leading to lowest strength in the composite is called the minimum fiber volume fraction. It determines the critical point at which the failure mechanism changes to another. When only a few fibers exist in the matrix of a composite, the stress may reach enough of a level to break the fibers. However, as the strain failure of the matrix is high, it would be able to withstand the load in composite up to its tensile strength. As no load can be carried by the broken fibers, they can be dealt with as holes or impurities in the matrix. Their effect will be lowering the composite tensile strength below the tensile strength of the matrix. This concept determines the critical

45

180

40

R2 = 0.94 with sf,eff = 502.7 MPa

140

35

(s m = 70 MPa, s ⬘m = 6.2 MPa)

120

30

100

25

80

20

60

15

40

10

R2 = 0.99 with Ef,eff = 44.3 GPa (Em = 3.7 GPa)

20 0 0

5

10

20 25 15 Fiber volume fraction (%)

30

Tensile modulus (GPa)

Tensile strength (MPa)

160

5 0 35

Fig. 5.35 Influence of fiber volume fraction effect on the tensile properties of a unidirectional flax/polyester composite (Shah et al., 2013b).

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Materials Selection for Natural Fiber Composites

point, that is, the minimum fiber volume fraction, below which the fibers will weaken the composite instead of strengthen it, and the composite properties and failure are determined by the matrix properties (Harris, 1986). However, when the fiber volume fraction is greater than its minimum, there will be a plenty of fibers to carry some of the loads when the failure strength in the matrix is reached. To improve the tensile strength of the composite beyond the tensile strength of the matrix, the fiber volume fraction should exceed a critical value (Vf > Vf_crit). Improving most of the mechanical properties of the NFCs (tensile stiffness, flexural stiffness, tensile strength, flexural strength, etc.) can be achieved by simply increasing the volume fraction of fibers. However, increasing the volume fraction of fibers cannot be unrestricted as a maximum limit theoretically exists. This is due to the fact that the fiber packing arrangements could not be exceeded. In addition, due to the many limitations in the manufacturing processes, the fiber volume fraction has a practical limit beyond which the properties of the composites deteriorate. This is due to an increase in the porosity, or due to insufficient fiber-matrix interactions to transfer the stress (Aridi et al., 2016a, 2016b). For example, when a research work was carried on fabricating aligned hemp/polypropylene laminates with a fiber volume fraction of 61%, the actual fraction was only 51% with a magnificent porosity content of 17% (Madsen, Hoffmeyer, & Lilholt, 2007). Besides, not only the narrow usable span of the fiber volume fraction of NFCs (from critical fiber volume fraction up to the maximum fiber volume fraction) limits the span of the mechanical properties of plant fibers, but it is also reported that NFCs fabricated in an identical manner to glass fiber composites yield lower volume fractions of fibers (Shah, 2013). This is justified by comparing the packing ability of the plant fibers (poor) with the synthetic fibers. In order to produce NFCs with high fiber volume fractions, it is suggested to apply external forces to compact the plant fibers preforms.

5.6.3 5.6.3.1

Reinforcement geometry and orientation Length efficiency factors

For optimal exploitation of the natural fiber reinforcements, the reinforcements geometry (fibers length and fibers aspect ratio) affecting the composite strength and stiffness should cautiously be specified. This can be achieved using a high aspect ratio with a fibers length significantly greater than their critical length. In fact, for an aspect ratio of 50 at a fiber length >1 mm, the obtained length efficiency factor for stiffness is 0.93. This length for the bast fiber reinforced composites was reported in the range 0.2–3 mm (Awal, Cescutti, Ghosh, & M€ ussig, 2011; Bos, M€ussig, & van den Oever, 2006; Pickering, 2008). While most bast fibers are 30 mm in length (Dittenber & Gangarao, 2011) with a great aspect ratio (100  2000), the exploited aspect ratio and length can be much lower depending on the manufacturing routes followed for the composites. Thus, the length efficiency factors are important to be determined for various types of fibers to optimize the desired beneficial performance of the NFCs.

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159

5.6.3.2 Orientation distribution factors As most of fibers are anisotropic in nature, their orientations have a great impact on the composite properties. The anisotropy may come from the natural structure of the fibers as in cellulose-based fibers. It can also come from the high aspect ratio along the fibers axial directions in comparison to the lateral directions. The manufacturing route of the composite usually determines the fibers orientation distribution. Depending upon the orientation of the ply, multi-axial fabric reinforcement composites may have a wide variety of orientation distribution factors. For composites reinforced with balanced biaxial fabrics in stacking sequence of [0, 90], and [45], the orientation distribution factors yielded are ζ o ¼ 0.5, and ζ o ¼ 0.25, respectively. At last, to ensure that ζ o is close to 1, unidirectional fibers are required. It is remarkable that using yarns reinforcements can induce obliquity to fibers, misalignments, and waviness to the loading direction of the composite; causing severe drops in the composite properties (Shah, 2013). A mathematical model has been developed by Shah (2013) based on (i) the modified ROM, (ii) the orientation distribution factor, and (iii) the relationship between the structure of an ideal fiber yarn and its properties, in order to predict the effect of the yarns twist on the tensile strength of the NFCs. Thus, controlling the orientation of fibers is crucial in determining and achieving better NFC characteristics and still needs extra efforts.

5.6.4

Natural fiber/nanoclay hybrid composites

Combining different types of reinforcements together in a matrix will generate a hybrid composite (Almaadeed, Kahraman, Noorunnisa Khanam, & Madi, 2012; Atiqah, Maleque, Jawaid, & Iqbal, 2014; Thakur, Ding, Ma, Lee, & Lu, 2012). Several reinforcements of fibers and/or fillers can be included into the hybrid composite. However, including only two types into the matrix would be more useful (AL-Oqla & Omari, 2017). By careful selection of the proper reinforcements, the composites can be produced with significant improvements for their properties, and with notable reduction in their costs (AL-Oqla & Sapuan, 2015b). The Hybrid composites are considered very attractive especially when one type of reinforcement is not able to significantly improve the performance properties without a remarkable increase in the cost (Thwe & Liao, 2002). Extensive research work has been conducted on combining the natural fibers with other natural ones, carbon fibers, glass fiber, as well as others to produce hybrid composites (Atiqah et al., 2014; Cz
el & Wisnom, 2013; Davoodi et al., 2011; Jawaid & Abdul Khalil, 2011; Majeed et al., 2013; Mansor, Sapuan, Zainudin, Nuraini, & Hambali, 2013; Mokhtar, Yahya, Kadir, & Kambali, 2013) and results were very promising. As the regular composites, the properties of the hybrid composites are influenced by the fibers contents, length, orientation, and fiber-fiber and fiber-matrix interfaces (Miwa & Horiba, 1994; Sreekala, George, Kumaran, & Thomas, 2002). An improvement in the mechanical and the physical properties of the composites were achieved because of the effect of the coupling agents on the properties of the

160

Materials Selection for Natural Fiber Composites

hybrid composites (Najafi, Kord, Abdi, & Ranaee, 2012). The influence of the fibers lengths and the fibers loading on the mechanical properties were investigated and it was deduced that there is an optimal fiber loading beyond which the properties of the composites begin to decline (Majeed et al., 2013; Miwa & Horiba, 1994). Combining nanoclay to the high density polyethylene/rice husk has a positive influence on increasing both the tensile strength, and the tensile modulus as reported in some studies (Kord, 2011). It was also found from the same studies that a specific content of the nanoclay (2 parts per hundred contents) leads to the optimal morphology among other formulations. Furthermore, the nanoclay was found to increase the crystallization temperature, crystallinity level, and the crystallization enthalpy. It was also suggested that improving the compatibilizer loading would produce a fully exfoliated morphology. It was also found that the effect of the nanoparticles in the polypropylene-based hybrid composites not only improved the stiffness, but also the flexibility of the composites. Moreover, a remarkable reduction in the water absorption with increasing the nanoparticles loading was reported. However, an improvement was obtained in the dynamic behavior, fire retardancy, and dimensional stability of the hybrid composites (Majeed et al., 2013). Other researchers investigated the effect of organoclay and compatibilization incorporation on the mechanical and thermal properties of wood flour/ HDPE hybrid composite (Majeed et al., 2013). The compatibilizer was found to enhance the mechanical properties and decrease the thermal expansion coefficient of the composite. It was also observed that incorporation of organoclay in the composite will further enhance the mechanical properties and decrease the thermal expansion coefficient. It was noticed that more compatibilizer will be required in the presence of nanoclay in order to improve the mechanical properties. For instance, adding 5 wt% of nanoclay to micro-crystalline cellulose reinforced ethylene-propylene (EP) co-polymer will increase the modulus of elasticity from 1.040 to 1.240 GPa (Pratheep Kumar & Pal Singh, 2007). The water absorption on the other hand, decreased by 15% when nanoclay was added to cellulose containing composites. Also, the composite thermal stability was enhanced. The transmission electron microscopy and the X-ray diffraction results showed intercalated structures for incorporating nanoclay composites. Another study reported the influence of nanoclay dispersion on the mechanical and the physical properties of wood flour-plastic composites incorporated with nanoclay (Kord, 2011), where Wood-Polypropylene composites were reinforced with nanoclay and glass fibers, and PP-g-MA as a coupling agent. Increasing the glass fiber contents was found to lead to an increase in the tensile modulus and strength of the composite. The best modulus and strength were obtained at an optimal content of the nanoclay of 4 phc. Incorporating the glass fibers in the wood flour reduces the water absorption, and the X-ray diffraction revealed that the dispersion of the nanoclay is better with 4 parts per hundred contents (phc) than 6 phc. For reed flour/PP biocomposite with MAPP as a compatibilizer, adding 4 phc of nanoclay will cause an increase in the modulus of elasticity from 1390 to 2630 MPa, and the tensile strength from 14.6 to 28.7 MPa (Najafi et al., 2012) and the water absorption was also decreased due to the hybridization of nanoclay in the composite. Therefore, hybrid natural composites are considered a new challenge in the field of green bio-composites to be controlled and properly selected for successful design products.

Material selection of natural fiber composites

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Unfortunately, systematic selections for such precise types of composites were not found in the literature as many parameters are involved. However, such parameters are almost similar to that of single-fiber-type composite up to certain limits and thus, similar ways of composites constituents’ selection may be applied for the hybrid composites. Moreover, new theoretical, as well as empirical, models are still required for the hybrid composites to precisely determine and predict their performance in various conditions to enhance their implementations for wider industrial applications. Furthermore, establishing a database through proper systematic selections via reliable tools, such those approved for single-type-NFCs (AL-Oqla & Omari, 2017; AL-Oqla et al., 2014b, 2014c, 2015b, 2015e, 2015f, 2015g, 2016b; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015a; AL-Oqla & Sapuan, 2015a, 2014b) would also enhance their selection for potential future sustainable applications.

5.6.5

Bio-based resins as matrices

Bio-resins are defined as resins obtained from biological sources. They are compostable and bio-degradable and can be decomposed and disposed after use. Due to the decomposition nature, their use on the exterior surfaces where perfect finishing is required is quite challenging, especially when considering applications of long life without treatments or coating. The same may happen for natural fibers, as they may have the potential to degrade even if the resin was synthetic. This is due to the composite inevitable voids. Besides, the high cost of the bio-resins is another major drawback making them nonfeasible even for mass productions. An example of this is the cost of polylactic acid (PLA) resin (the least expensive biopolymers) that exceeds 1.5 times the cost of the extensively used synthetic polypropylene resin (Koronis, Silva, & Fontul, 2013). Other drawbacks of the bio-resins are: the low heat distortion temperature, the brittleness, the low melting viscosity, and the high gas permeability. At last, there are large debates for whether these materials should or should not be considered as sustainable alternatives to the conventional plastics (Koronis et al., 2013). Considering the future trend of shifting toward the bio-based plastics; it is highly possible to alter the relations of the economic stability among societies. Such a trend will demand substitutions of many raw materials (mineral or petrochemical) by biobased plastics (van Dam, de Klerk-Engels, Struik, & Rabbinge, 2005). A very human and sensitive point that should be considered in the adequate selection of substituting materials is that they are not from edible sources. Edible materials can reduce a part of the human food supply, leading to an increase in the food prices, and undermining the stability of the food supply among societies. Also, the shifting toward the bio-based plastics will likely create the potential to decrease the fertile lands reserved for food, or to create new lands by cutting down forested areas. One suggested solution to such problems is to produce those materials in labs through microbial productions (van Dam et al., 2005). Bio-technological fermentation processes of renewable polyesters have succeeded, and currently, they are introduced to the markets. Moreover, about 90% of the literature on production of lactic acids focused on the same process (van Dam et al., 2005). However, an improvement should be achieved on the cheap raw materials to make them competitive ( John, Nampoothiri, & Pandey, 2007).

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Moreover, extra efforts are required to investigate the compatibility between the bioresins and bio fibers, as well as enhance their proper selections integrated with suitable manufacturing techniques to enhance achieving better natural and fully degradable composites for various required applications.

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Further Reading AL-Oqla, F. M., & Omar, A. A. (2015). An expert-based model for selecting the most suitable substrate material type for antenna circuits. International Journal of Electronics, 102, 1044–1055. Al-Widyan, M. I., & AL-Oqla, F. M. (2014). Selecting the most appropriate corrective actions for energy saving in existing buildings A/C in hot arid regions. Building Simulation, 7, 537–545.

Material selection of natural fiber composites using the analytical hierarchy process 6.1

6

Background of analytical hierarchy process

The Analytical Hierarchy Process (AHP) is a popular Multi Criteria Decision-Making tool created and designed to solve sophisticated compound problems. The AHP in its rank order weighing scheme is attaining popularity due to its understandability, as well as its implementation simplicity. In addition to material selection systems, the AHP has found wide utility in various divers’ domains like that of energy systems, electrical, agricultural, social, biomedical, and industrial applications (Ahmad & Tahar, 2014; AL-Oqla & Sapuan, 2014a; AL-Oqla, Sapuan, Ishak, & Aziz, 2014b; Al-Widyan & AL-Oqla, 2014; Sola & Mota, 2012). The AHP method was created to help in optimizing and selecting the best solutions out of a set of alternatives, especially when dealing with a problem governed by mixed quantitative and qualitative factors. It also minimizes the bias in the decision-making by monitoring subjective and objective evaluations, as well as examining the consistencies of the evaluations and alternatives by means of a useful mechanism when alternatives are being studied. Unlike other similar conventional operational research methods, AHP uses pairwise comparisons that enable verbal judgments to improve the precision of findings, leading to more accurate ratio and scale priorities. The AHP method was created by (Saaty, 1980) to help in making decisions for multi-criteria problems. It deals with complicated problems through dividing them into smaller, but simpler problems using the tree leaves arrangement. The following are the main three steps in the AHP method: Step 1: Model the complex problem as a hierarchy. The complicated MCDM problems are decomposing into sub-problems having criteria, goal, sub-criteria, and decision options. The main objective will be on the first level, the second level will put several governing criteria, then a third level will have sub-criteria, whereas the decision options are located at a lower level (Dweiri & AL-Oqla, 2006; Saaty & Vargas, 2012). A typical AHP structure is demonstrated in Fig. 6.1. Step 2: Compare between the alternatives and the criteria. After the hierarchy has been constructed, the relative importance of each factor should be determined within the specific level. To determine the importance of every single criterion relative to the other with regardsto the weights in the AHP method, perform several pairwise evaluations using Saaty’s comparison scale, as in Table 6.1, but the value of (1) is always given to any criterion when compared with itself (AL-Oqla & Omar, 2012; Dağdeviren, Yavuz, & Kılınc¸, 2009; Dweiri & AL-Oqla, 2006). Doing so yields to a square matrix with (ones) on diagonal. Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00006-2 Copyright © 2017 Elsevier Ltd. All rights reserved.

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Fig. 6.1 Typical AHP structure.

Table 6.1

The AHP comparative scale

For any pair of objectives i, j: Score

Relative significance

1 3 5 7 9

Objectives i and j are of equal significance Objective i is weakly more significant than j Objective i is strongly more significant than j Objective i is very strongly more significant than j Objective i is absolutely more significant than j

2, 4, 6, 8 are intermediate scores.

Such comparisons can be hosted in an (nxn) judgment (A) matrix, as in Eq. (6.1). Then, normalize the matrix elements to find out the relative weights of every single element. For each criterion relative normalized weight (Wi) can be found by calculating the geometric mean of (ith) row, i 2 [1, …, n], then normalize these geometric means, considering all rows in the (A) matrix as in Eq. (6.2). The relative normalized weight column (W) -Eigen vector- can then be obtained using Eq. (6.3). 2

3 2 3 w1 =w1 ⋯ w1 =wn a11 ⋯ a1n ⋮ ⋮ 5 A¼4 ⋮ ⋮ ⋮ 5¼4 ⋮ an1 ⋯ ann wn =w1 ⋯ wn =wn

(6.1)

Material selection of natural fiber composites using the analytical hierarchy process

vffiffiffiffiffiffiffiffiffiffiffiffi uY n u n GMi ¼ t aij

171

(6.2)

j¼1

0

1 n X ¼ GM = GM W 1 iC B 1 B C i¼1 B C n B C X B C W ¼ GM = GM B C 2 2 i W ¼B C i¼1 B C … B C B C n X @ A Wn ¼ GMn = GMi

(6.3)

i¼1

The standard form is in Eq. (6.4) as: 2

3 2 3 2 3 w1 =w1 ⋯ w1 =wn w1 w1 4 ⋮ ⋮ ⋮ 5  4 ⋮ 5 ¼ n4 ⋮ 5 wn =w1 ⋯ wn =wn wn wn

(6.4)

This can be summarized in a vectors form as: A  w ¼ nw

(6.5)

where A is the judgment matrix, w: Eigen vector and n is the matrix size. Note that Eq. (6.5) is an eigenvalue problem, and the largest Eigenvalue equals n, equals the number of comparisons for a consistent matrix, i.e., (λmax ¼ n). After that, the consistency index (CI) has to be determined by calculating λmax and applying Eq. (6.6) below. CI ¼

λmax  n n1

(6.6)

Note that the consistency indicator is vital for the judgment validation in addition, as CI becomes smaller; the consistency of judgment becomes better. The judgment has to be firmly consistent so it can be used in the AHP model, if not, the judgment should be revised to satisfy the improved level of consistency comparable to a proposed Random Index (RI) as in Table 6.2 (Saaty & Vargas, 2012). Such comparability of the matrix consistency indicator with the random index is called Consistency Ratio (CR) that has the ability to determine the acceptance of the particular judgment. If the value of CR is equal to 10% or smaller means acceptable inconsistency for the judgment. But if more than 10%, revised judgments are required. The value of CR can be found as in Eq. (6.7). CR ¼

CI RI

(6.7)

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Table 6.2 n

The standard random index (RI) for the AHP method

1 2 3

4

5

6

7

8

9

10

11

12

13

14

15

RI 0 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 1.51 1.54 1.56 1.57 1.59

Step 3: after ensuring that all captured judgments are suitable via the consistency check, the alternatives are to be ranked concerning the measured criteria in the hierarchy model (Dağdeviren et al., 2009; Rathod & Kanzaria, 2011).

6.2

Appropriateness of analytical hierarchy process for natural fiber composite selection

It is necessary to properly select materials for engineering products to attain a successful design with low costs to enhance both sustainability and customer satisfaction (AL-Oqla & Sapuan, 2014a; Dicker et al., 2014). Moreover, decisions have momentous environmental impact, such as green products receiving much consideration in modern economic planning to maximize profits, along with managing the industrial sustainability. Nowadays, the number of customers interested in the environmental performance of products is continuously increasing (Ahmed, Ahmed, & Robinson, 1995). Green products are usually more expensive than others, to keep higher compatibility among the product’s features and its environmental performance (AL-Oqla & Sapuan, 2014a). Portions of this high cost are due to the costly research investments along with the high cost of green input items and technologies (Ahmed et al., 1995). Hence, there is a growing need to enhance and assist making green products available in order to achieve real sustainable societies. This requires achieving desirable eco-friendly features to be able to achieve functional requirements of such products, where various performance parameters are experimentally, as well as numerically examined to ensure their suitability. Consequently, selecting the best material type for a specific application is deemed as a MCDM problem, as precise and keen decisions have to be made to ensure their technical suitability via proper decision-making tools (Dalalah, AL-Oqla, & Hayajneh, 2010; Dweiri & AL-Oqla, 2006; Jahan, Ismail, Mustapha, & Sapuan, 2010; Rao & Patel, 2010). Although various methods of multi-criteria decision-making have been utilized for material selections as well other purposes, however, differences between such tools usually leads to different solutions. Such decision-making tools include the analytic hierarchy process (AHP) technique for order preference by similarity to ideal solution (TOPSIS), Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, weighted product method (WPM), graph theory, Fuzzy-AHP technique, simple additive weighted (SAW) method, and others (Dweiri & AL-Oqla, 2006; Jahan et al., 2010; Opricovic & Tzeng, 2004; Rao & Patel, 2010; Rathod & Kanzaria, 2011; Sapuan et al., 2011). For instance Dweiri and AL-Oqla (2006) developed the

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173

Analytical Hierarchy Process for material selection where the choice confidence was increased by applying the sensitivity analysis. Rao and Davim (2008) illustrated a logical method by operating combined TOPSIS and AHP techniques to enrich the material selection process. Moreover, Sapuan et al. (2011) used the analytical hierarchy process to enrich the selection of the natural fiber reinforced polymer composites materials. Jahanshahloo, Lotfi, and Izadikhah (2006) have also extended the TOPSIS method to deal with decision-making problems with interval data. In addition, Dağdeviren et al.(Dağdeviren et al., 2009) built an evaluation model based on the AHP and TOPSIS methods to select weapons. Moreover, TOPSIS method under fuzzy environment was developed by Wang and Chang (2007) to evaluate the initial training aircraft. Although, there is no absolute distinguished superiority of one multi-criteria decision-making tool over the others, on one hand, it is very hard to determine the best decision-making scheme for a given problem (Mela, Tiainen, & Heinisuo, 2012; Rao, 2013), on the other, the analytical hierarchy process has various remarkable benefits like the ability to simply capture the quantitative, as well as qualitative attributes, its popularity, and its implementation simplicity. In addition, the capability of the analytical hierarchy process was validated in several examples where output decisions and ranking priorities close to the corresponding real-life answers were produced (Dweiri & AL-Oqla, 2006; Saaty & Vargas, 2012). Furthermore, the analytical hierarchy process is much preferable over many other techniques, particularly the fuzzy-AHP or any combination of fuzzy-MCDM methods, mainly when the data is precisely known. That is, when no subjectivity is involved in the decision. This is because changing the crisp data into a fuzzy format would increase the complexity of the problem along with expanding the computational requirements, and rob the simple original data of their elegance, resulting in less desirable outcomes (Rao, 2013). The analytical hierarch process is considered as one of the most powerful, direct, and flexible decision-making methods capable of achieving the best decisions considering the precise wisdom of the experts’ knowledge by utilizing various tangible and intangible aspects and attributes (Dweiri & AL-Oqla, 2006; Rao & Patel, 2010; Rathod & Kanzaria, 2011). In addition, AHP is capable of dealing with real-life complex problems under uncertain environments in a more efficient manner than fuzzy judgments (Saaty & Tran, 2007). Besides, it was proved that the fuzzy-AHP’s arithmetic operation evidently violates the AHP reciprocal and continuity axioms, as well as the operational rule of consistency. This makes The Fuzzy AHP method questionable for the decision-making process (Zh€ u, 2013). A systematic comparison between various MCDM approaches that are usually used for material selections is illustrated in Table 6.3. In addition, the competencies of AHP method were validated in plentiful examples that enabled them to be widely applied in diverse applications worldwide such as engineering design, benchmarking, health care, resource allocation, material selection, quality management, public policy, as well as others. For instance, AHP was utilized in different industries and agencies such as Xerox, NASA, and General Motors (Saaty & Shang, 2011). This is because of the solid mathematical background the AHP is based upon, due to the restricted consistency test it has that allows only high

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Table 6.3 Comparisons between various Multi-criteria decision-making approaches MCDM method

Description

Advantages

Disadvantages

Analytic Hierarchy Process (AHP)

It employs the pair-wise comparison scheme for a set of various alternatives concerning various criteria

1. Irregularities in ranking may occur if similar alternatives are added to the problem (but usually overcome in ideal model approach) 2. Some minor, but may be important information may be lost (depending on the aggregation process) 3. Several pairwise comparisons are required

Fuzzy-AHP

It depends on the AHP technique, but linguistic terms and crisp data are changed into their corresponding fuzzy numbers

1. Flexible and suitable for group decision matrix 2. Capable of examining inconsistencies within the judgment matrices 3. Provides a particular weight for each item involved in the problem due to its tree—leaf like structure 4. Capable of capturing both subjective and objective decisions 5. Only a few experts’ feedback is required to reach convergence in opinions Utilized in problems with high subjectivity

1. Its arithmetic operation violates the AHP axioms, and consistency 2. Difficult and long computational requirements 3. Often leads to less desirable results 4. Questionable to be used in any decisionmaking process

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.3

175

Continued

MCDM method

Description

Advantages

Disadvantages

Technique for order preference by similarity to ideal solution (TOPSIS)

Utilized the concept of maximizing distance from the negative-ideal solution and minimizing the distance from the positive-ideal solution

1. Has no comparative index as an indicator 2. It requires preassumed weight for each evaluation criterion

Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR)

It arranges alternatives with respect to a compromised solution that is the closest to the ideal

1. Deals with large numbers of alternatives and criteria due to the simple mathematical calculations involved 2. Can find acceptable solutions between positive ideal and negative ideal ones in a simple manner The best solution is chosen by maximizing utility group and minimizing regret group

Weighted Product Method (WPM)

It matches alternatives by the weights and ratio of one for each factor

No solution will result with equal weight of decision matrices

Weighted sum method (WSM) or simple additive weighting (SAW)

It assesses different alternatives with respect to different factors expressed in the same unit

1. Can get rid of any unit of measure 2. Relative values are utilized rather than actual ones Strong in single dimensional problems

1. The performance rating is quantified as crisp values 2. The decisionmaker has to consider imprecise or ambiguous data

1. Struggle appears on multidimensional problems 2. No consistency for the judgment

consistent findings to be utilized in the calculations for producing decisions. All such advantages and strengths of the AHP have, in fact, increased both its reliability and validity in addition to eliminating the bias in gained judgments. Thus, adopting such a reliable and appropriate method to develop the natural fiber composite selections would evidently enhance the reliability of the composite selection process.

176

6.3

Materials Selection for Natural Fiber Composites

Pairwise comparison under uncertainty environment for bio-based materials

It is well known that the properties of the natural fiber composites are strongly related to the characteristics of their constituents, as well as the interfacial characteristics between the fibers and the matrix. Hence, the characteristics and capabilities of the final product of the NFCs depend not only on the mechanical, physical, environmental, and the chemical composition of the inherited materials, but also on other crucial factors. Unfortunately, a recent belief that the long-overlooked time of not utilizing the NFCs in different industrial applications is due to the lack of sufficient material selection databases and techniques (AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Omari, 2017; Shah, 2014), and this has led to ignoring the bio-based materials from being a potential for many possible applications. Thus, it is of a great importance to develop and efficiently use systematic material selection processes for the NFCs. However, no significant utilization of the MCDM selection tools were conducted in the literature to select the appropriate NFCs regarding various combined evaluation criteria. Therefore, integrated evaluation schemes are also of paramount importance for conducting pairwise comparisons of bio-materials and their constituents in relation to integrated evaluation criteria. That is, the integrated evaluation schemes that combine various simultaneous evaluation criteria must be revealed and encouraged to be utilized through the pairwise comparisons within MCDM methods to proper evaluative the natural fiber composites and their constituents. For example, instead of conducting a pairwise comparison regarding a mechanical evaluation criterion like the tensile strength, it is recommended that a comparison is made between various bio-materials regarding simultaneous integrated mechanical-economic-environmental evaluation criterion to demonstrate a more realistic date that can be used as a “stand-alone” date in a certain database or to be utilized in AHP method to conduct a single judgment matrix. Utilizing such a novel approach, AL-Oqla, Sapuan, and Jawaid (2016b) have conducted an investigation regarding economic-environmental integrated evaluation standpoint to demonstrate the superiority of some natural fibers among others like date palm fiber. Such an investigation through pairwise comparisons has been performed in terms of the cost of one cubic meter of fiber waste to establish a novel evaluation indicator as cost per volume ratio (CPVR). That is, the cost of one meter cube of waste fibers illustrates how much waste can be utilized and eliminated if properly utilized in a beneficial application per one USD ($1). In other words, the cost per volume ratio (CPVR) can be utilized as an important direct environmental indicator for natural fibers to specify how much wastes-volume of a certain fiber type can be prevented from being burned or being an environmental waste problem per one dollar (AL-Oqla & Sapuan, 2014a). Fig. 6.2 clearly demonstrates that preventing one cubic meter of date palm fibers from being a waste costs only $2, however, preventing hemp waste will cost about $1885, which is about 90 times more costly than date palm. This evidently indicates that date palm fiber is more eco-friendly than other types of fiber. This conclusion can be made from the fact that date palm fibers can be converted from an environmental waste issue

Material selection of natural fiber composites using the analytical hierarchy process

177

Cost per volum ( USD/m3) USD/KG*kg/m3 2000 1800 1600 1400 1200 1000 800 600 400 200 0

Date palm

Jute

Hemp

Oil palm

Fig. 6.2 Pairwise comparison of natural fibers regarding CPVR.

into the opposite more efficiently than other fiber types from economic standpoint. Therefore, for a certain industry, with a specific budget, more date palm fibers can be consumed in producing cleaner biomaterials along with more waste volume can be reduced to improve the environmental performance indices. In such a way the sustainability of the cleaner production can be dramatically strengthened by ensuring a continuous flow of eco-friendly raw materials from renewable material sources. It can be noted that despite the importance and competence of the biomaterials and natural fiber composites for both the environment and industrial sustainability, they are not optimally exploited in industry. The improper evaluations with respect to the various criteria, as well as the lack of databases for biomaterial and their individual constituents’ selection, can lead to ignoring the natural fiber composites as comparable alternative materials, especially when the selection processes are shallow. Therefore, using systematic informative decisions to appropriately assess and select the natural fiber composites and their individual constituents under an uncertain environment is of a great importance for expanding the sustainable design possibilities. Alternatively, due to the rising demands on using the bio-composites in diverse industrial applications because of the awareness of the scarcity of non-renewable natural resources, as well as stresses on the environmental sustainability, little work was done on comparing the bio composites and their constituents with the synthetic-based ones to select the proper type of materials for certain applications. As a consequence, the selection of a certain fiber, type, matrix type, and particular bio-composite among diverse available alternatives is still subject to the researchers’ estimations and limited assessment standards. Therefore, to achieve the desirable bio-composites’ characteristics and performance, keen decisions under an uncertain environment are still required through proper pairwise comparisons. The uncertainenvironment in selecting the best bio-composite materials and their constituents through pairwise comparisons and utilizing integrated evaluation schemes for various desired characteristics is explained in Fig. 6.3.

178

Materials Selection for Natural Fiber Composites

Polymers

Fibers

.........

Biocomposites

Uncertainty Pairwise comparisons

Mechanical properties

Physical properties Integrated evaluation scheme Chemical/technical properties

Environmental and other properties

Best biocomposites

Fig. 6.3 The uncertainty environment in selecting the best bio-composites utilizing integrated evaluation schemes.

Regarding pairwise comparisons, Sapuan et al. (2011) were able to enhance the selection of the polymeric-based natural fiber reinforced composites for automotive dashboard panels, where 29 distinct types of natural composites were considered for the comparisons. However, this was performed by using only three criteria (tensile strength, Young’s modulus, and density). Other work performed by Mansor, Sapuan, Zainudin, Nuraini, and Hambali (2013) where the pairwise comparisons inside the expert choice© software was utilized to compare and rank potential natural fiber composites, as well as synthetic composites for automotive brake levers regarding their mechanical and physical properties. Another work applied TOPSIS method to compare and select the best design criteria for a car bumper beam (Davoodi et al., 2011). However, their work focused on optimizing the design of the bumper for a pre-selected hybrid bio-composite material. More recently, throughout their review on the biobased natural fiber reinforced composites, Koronis et al. proposed the use of the ternary diagrams (Koronis, Silva, & Fontul, 2013) as comparisons for the bio-composite materials. Such diagrams can represent three weighted bi-dimensional properties for the materials (like the specific strength, the specific stiffness, and the cost per weight), where each material will be shown as a balloon in some region of the triangle’s area. Some other researchers produced Ashby-type charts in order to perform the

Material selection of natural fiber composites using the analytical hierarchy process

179

comparison among the different types of the NFCs regarding the mechanical properties (Dicker et al., 2014; Dittenber & Gangarao, 2011; Shah, 2014). Little work was conducted for selecting the appropriate grade fibers (cotton in particular) for the textile industry considering decision models (Majumdar, 2010; Majumdar, Sarkar, & Majumdar, 2004, 2005). However, only limited comparison criteria were considered. Cheung, Ho, Lau, Cardona, and Hui (2009) have also presented some factors and criteria to be considered in comparing and selecting general materials for biomedical applications purposes. Consequently, it can be mentioned that AL-Oqla and his team have recently established the integrated evaluation schemes for conducting pairwise comparisons and selecting the natural fiber composites and their constituents through a series of systematic works, selecting the natural fibers, polymers, composites, reinforcement conditions, moisture contents, and performance deteriorations (Almagableh, AL-Oqla, & Omari, 2017; AL-Oqla, Alothman, Jawaid, Sapuan, & Es-Saheb, 2014a; AL-Oqla & Omar, 2015; AL-Oqla & Omari, 2017; AL-Oqla, Salit, Ishak, & Nuraini, 2015a; AL-Oqla & Sapuan, 2014a,2014b,2014c, 2015a,2015b; AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015e; AL-Oqla, Sapuan, Ishak, & Nuraini, 2014c, 2015c, 2015d, 2015f, 2015g, 2016a; AL-Oqla et al., 2014b, 2016b; Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a,2016b; Sapuan, Haniffah, & AL-Oqla, 2016; Sapuan, Pua, El-Shekeil, & AL-Oqla, 2013).

6.4

Selecting natural fibers for a particular application under conflicting criteria using analytical hierarchy process

Natural fibers are very promising for various applications, particularly the automotive industry. The reduction in weight that can be attained due to the use of natural fibers reduces both the CO2 emissions and the fuel consumptions in vehicles. Consequently, integrating the natural fiber composites in industrial applications can enhance the sustainability and the environmental performance, as well as assist in reducing the waste problems and the pollution obtained from their disposal. NFCs are increasingly considered as competitive alternatives to the glass/carbon reinforced polymer composites due to the many advantages they have. Some of these advantages are the good thermal and acoustical insulations, the low cost, the reduction in the CO2 emissions, and the availability (sustainable source) (AL-Oqla et al., 2014b, 2016b; Dicker et al., 2014; Symington, Banks, West, & Pethrick, 2009). However, there were very few studies concerning precise decisions in choosing the proper natural fiber type for certain applications, although it is believed that selecting such types of fibers is a multiple criteria decision-making problem as several factors can affect such a selection process. Thus, it is essential to make pairwise comparisons of the natural fibers regarding a wide range of desired criteria that affect their selections to result in informative selection decisions that would help designers and decision-makers reach the best choice of natural fiber composites according to their design criteria and limitations. This would enhance establishing sufficient database for the selection of natural fibers and their composites to be

180

Materials Selection for Natural Fiber Composites

utilized as a primary selection tool for both designers and decision-makers in in the field of green composites (AL-Oqla & Sapuan, 2014a; AL-Oqla et al., 2014a). Moreover, the proper selections and implementations of natural fibers in composites can be an advantage in the economic growth, environmental performance, as well as the industrial sustainability where available low-cost natural materials would be integrated with the industry to solve an environmental waste problem (AL-Oqla & Sapuan, 2014a). This would also result in achieving low-weight and cheap products with low energy consumptions (Blume & Walther, 2013; Subramoniam, Huisingh, Chinnam, & Subramoniam, 2013). The availability of the natural fibers worldwide would also develop the productivity of the industry, as no shortage of raw materials can occur (AL-Oqla & Sapuan, 2014a; AL-Oqla et al., 2014b). Furthermore, the desired degradability features of the natural fibers would develop the environmental performance and remanufacturing them by stimulating the end life products’ recyclability (Blume & Walther, 2013; Subramoniam et al., 2013) which would broaden the economic and ecological rewards of such adoption. Accordingly, adapting natural fibers in industry has optimistic social, economic, and environmental impacts that make it one of the most approachable solutions for the industrial sustainability at the current time. AL-Oqla et al. (2015g) were able to build up an informative decision-making model to properly compare and select the best natural fiber type for the automotive sector by means of the Analytical Hierarchy Process (AHP). They were successfully able to rank the appropriateness of various fibers and predict the potential of new uncommonly used fibers like date palm as a new, reasonably cheap alternative for the automotive sector in order to reduce the weights of their products. For their model, a pilot study was utilized to ensure the appropriateness of the model used by sending it to various experts worldwide. In order to apply the AHP to draw complex decisions for selecting the most appropriate fiber considering multiple factors, main three phases have to be executed (i) building the structure of the model, including a goal and evaluation criteria, (ii) carrying out pairwise comparisons to assess the decision-makers’ evaluations, (iii) Yielding weights for criteria has to be prepared by means of Eigenvector method. Factors and their sub-factors that have influence on this decisionmaking (selecting the best natural fiber type) have been wisely proposed for the evaluations and comparisons. The factors and their sub-factors that were utilized for the evaluation process and building the decision-making model are illustrated in Table 6.4. The comparability of the model factors was done by means of experts’ feedback through a questionnaire to assign the weights of the evaluation criteria and factors. However, consistence is a must within the expert judgments to be accepted. The AHP model of the case is demonstrated in Fig. 6.4. In that work, the alternative natural fiber types were coir, flax, date palm, sisal, and hemp. All of them are widely used in the automotive industry except the date palm fiber. A set of matrices were developed to represent pairwise comparisons for all the levels of the hierarchy. This led to a square matrix of judgments as in Eq. (6.4). Such a comparison was performed based upon how a certain factor dominates the others getting benefit of Saaty’s 1 to 9 scale (Saaty, 1980). To help experts fully understand the problem and to summarize their knowledge efficiently, verbal assessment was used. For illustration, considering the Specific Performance for Automotive

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.4

181

Factors for the AHP model (AL-Oqla et al., 2015g)

Main factor

Sub-factor

Physical

Fiber’s diameter (FD) Fiber’s length (FL) Length to diameter ratio (L/D) Density (D) Fiber’s cellulose content (FCC) Fiber’s hemicellulose content (FHC) Fiber’s lignin content (FLC) Fiber’s tensile strength (FTS) Fiber’s tensile modulus (FTM) Fiber’s specific modulus (FSM) Fiber’s elongation to break percentage (FEB) Fiber’s specific strength to cost ratio (FSSCR) Fiber’s thermal conductivity (FTC) Fiber’s moisture absorption (FMA)

Chemical/biological

Mechanical

Specific performance (automotive application)

Application main factor, typical forms of required question to be answered by experts may be expressed as: l

l

l

How much more significant is the Fiber’s Specific Strength to Cost Ratio compared to Fiber’s Thermal Conductivity considering the Specific Performance for Automotive Application standpoint? How much more significant is the Fiber’s Specific Strength to Cost Ratio compared to Fiber’s Moisture Absorption considering the Specific Performance for Automotive Application standpoint? How much more significant is the Fiber’s Thermal Conductivity comparative to Fiber’s Moisture Absorption considering the Specific Performance for Automotive Application standpoint?

The answer for such pairwise comparisons is captured in Table 6.5. After performing the consistency and finding the weights or contributions of these criteria, the judgment was accepted as shown in Fig. 6.5 where expert choice® was utilized to do so. The comparability between the natural fibers as alternatives with respect to each sub-criterion has been performed in the model utilizing experimental published data in the literature. The required information for the comparability is mentioned in Table 6.6. This method was adopted because of: 1—the difficulty of consulting experts who can reliably compare the whole fibers simultaneously regarding widely different evaluation criteria, 2—The privilege of this way of integrating the expert knowledge with the published work, which usually enhances achieving confident informative decisions in the selection process of the most appropriate natural fiber type for automotive. Also, for a given specific criterion in the pairwise comparisons, average values were used. The relative importance of each of these alternatives toward a specific criterion was calculated relative to these average values.

Materials Selection for Natural Fiber Composites

Level 2: subcriteria

FD FL L/D D

FHC

FLC

FTS FTM

FEB

Hemp

FSM

FSSCR

FTC

Sisal

Specific performance for automotive application

Mechanical properties of fiber

Flax

Chemical/technical properties of fibers

FCC Predicting the potential of date palm fibers for sustainable automotive industry

Level 3: alternatives

Coir

Level 1: main criteria

Physical properties of fibers

Goal

Date palm

182

FMA

Fig. 6.4 The AHP model of selecting the best natural fiber type (AL-Oqla et al., 2015g).

The total importance or weights of the main factors are revealed in Fig. 6.6. Such weights clearly demonstrate that the specific performance for the automotive applications, as well as the mechanical characteristics of the natural fibers are the most important evaluation factors in selecting the appropriate natural fiber type for the

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.5

183

A case of pairwise comparisons

Criterion Specific strength to cost ratio Thermal conductivity Moisture absorption

Specific strength to cost ratio

Thermal conductivity

Moisture absorption

1

1.44

1

1/1.44 1

1 1.44

1/1.44 1

0.371 0.258 0.371

Fiber’s specific strength to cost ratio Thermal conductivity Fiber’s moisture absorption Inconsistency = 0. with 0 missing judgments

Fig. 6.5 Contributions of sub-factors to their main one.

Table 6.6 Data used for the pairwise comparisons of natural fibers in the AHP method Fiber type

Coir

Date palm

Flax

Hemp

Sisal

Density (g/cm3) Length (mm) Diameter (μm) Tensile strength (Mpa) Tensile modulus (Gpa) Specific modulus (approx.) Elongation to break (%) Cellulose (wt %) Hemicellulose (wt %) Lignin (wt %) Moisture content (wt %)

1.15–1.46 20–150 10–460 95–230

0.9–1.2 20–250 100–1000 97–275

1.4–1.5 5–900 12–600 343–2000

1.4–1.5 5–55 25–500 270–900

1.33–1.5 900 8–200 363–700

2.8–6

2.5–12

27.6–103

23.5–90

9–38

4

7

45

40

17

15–51.4

2–19

1.2–3.3

1–3.5

2–7

32–43.8 0.15–20

46 18

62–72 18.6–20.6

68–74.4 15–22.4

60–78 10–14.2

40–45 8

20 5–10.5

2.3 8–12

3.7–10 6.2–12

8–14 10–22 Continued

184

Materials Selection for Natural Fiber Composites

Table 6.6

Continued

Fiber type

Coir

Date palm

Flax

Hemp

Sisal

Cost per weight (USD/kg) Thermal conductivity (W/m K)

0.3

0.02

9

1.2

1

0.047

0.083

0.119

0.115

0.07

Adapted from Majeed, K., Jawaid, M., Hassan, A., Abu Bakar, A., Abdul Khalil, H., Salema, A., et al. (2013). Potential materials for food packaging from nanoclay/natural fibres filled hybrid composites. Materials & Design, 46, 391–410; Lewin, M. (2007). Handbook of fiber chemistry, Boca Raton, FL: CRC Press, Taylor & Francis Group; Li, X., Tabil, L. G., Oguocha, I. N., & Panigrahi, S. (2008). Thermal diffusivity, thermal conductivity, and specific heat of flax fiber–HDPE biocomposites at processing temperatures. Composites Science and Technology, 68, 1753–1758; Agoudjil, B., Benchabane, A., Boudenne, A., Ibos, L., & Fois, M. (2011). Renewable materials to reduce building heat loss: Characterization of date palm wood. Energy and Buildings, 43, 491–497; Pilla, S. (2011). Handbook of bioplastics and biocomposites engineering applications, Salem: Scrivener Publishing; AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Aziz, N. A. (2014b). Combined multi-criteria evaluation stage technique as an agro waste evaluation indicator for polymeric composites: Date palm fibers as a case study. BioResources, 9, 4608–4621; AL-Oqla, F. M., & Sapuan, S. M. (2014a). Natural fiber reinforced polymer composites in industrial applications: Feasibility of date palm fibers for sustainable automotive industry. Journal of Cleaner Production, 66, 347–354, but data for date palm were adapted from John, M. J., & Anandjiwala, R. D. (2007). Recent developments in chemical modification and characterization of natural fiber-reinforced composites. Polymer Composites, 29, 187–207; Al-Khanbashi, A., Al-Kaabi, K., & Hammami, A. (2005). Date palm fibers as polymeric matrix reinforcement: Fiber characterization. Polymer Composites, 26, 486–497; Abdal-Hay, A., Suardana, N. P. G., Jung, D. Y., Choi, K.-S., & Lim, J. K. (2012). Effect of diameters and alkali treatment on the tensile properties of date palm fiber reinforced epoxy composites. International Journal of Precision Engineering and Manufacturing, 13, 1199–1206; Kriker, A., Bali, A., Debicki, G., Bouziane, M., & Chabannet, M. (2008). Durability of date palm fibres and their use as reinforcement in hot dry climates. Cement and Concrete Composites, 30, 639–648; Ghosh, S., Nayak, L., Day, A., & Bhattacharyya, S. (2007). Manufacture of particle board from date-palm leaves—A new technology product. Indian Journal of Agricultural Research, 41, 132–136; Agoudjil, B., Benchabane, A., Boudenne, A., Ibos, L., & Fois, M. (2011). Renewable materials to reduce building heat loss: Characterization of date palm wood. Energy and Buildings, 43, 491–497; AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Aziz, N. A. (2014b). Combined multi-criteria evaluation stage technique as an agro waste evaluation indicator for polymeric composites: Date palm fibers as a case study. BioResources, 9, 4608–4621; AL-Oqla, F. M., & Sapuan, S. M. (2014a). Natural fiber reinforced polymer composites in industrial applications: Feasibility of date palm fibers for sustainable automotive industry. Journal of Cleaner Production, 66, 347–354.

Weights of main factors Specific performance for automotive applications

0.295

Mechanical properties

0.295

0.172

Chemical/bilological properties

0.238

Physical properties 0

0.05 0.1 0.15 0.2 0.25 0.3 0.35

Fig. 6.6 weights of importance of the evaluation criteria in selecting the natural fibers for automotive sectors.

Material selection of natural fiber composites using the analytical hierarchy process

Coir 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Sisal

Hemp

185

FSSCR FTC FMA

Date palm

Flax

Fig. 6.7 Comparisons of the natural fiber alternatives regarding the specific performance for the automotive application factor (AL-Oqla et al., 2015g).

automotive sectors according, to the experts’ feedback. These weights will then be utilized in determining the attributes of each fiber type regarding the sub-factors inside each of these main ones as in Table 6.4. Moreover, the importance of the natural fibers alternatives regarding all sub-factors in a particular criterion, like that of Specific Performance for Automotive Application, is explained in Fig. 6.7. The largest priority regarding both Fiber’s Specific Strength to Cost Ratio (FSSCR) and Fiber’s Moisture Absorption (FMA) criterion can be noted to be in favor of date palm fiber, but coir fiber has the largest importance in terms of Thermal Conductivity (FTC) factor. Such detailed investigations can evidently lead to a proper decision for the most accurate type of fibers for a particular application, such as in automotive. The investigations of the priorities of natural fiber composites for physical, Chemical/Biological Properties, and Mechanical Properties factors are illustrated in Figs. 6.8–6.10, respectively. The overall priories of the whole alternatives regarding all factors and sub-factors in the model are demonstrated in Fig. 6.11. It is seen that both flax and date palm fibers are the most appropriate types according to the whole evaluation criteria simultaneously. Moreover, these two types of fiber are very close in their importance (flax fiber with a score of 22.7%, and date palm with 21.0%). This closeness in priorities obviously indicates the difficulty of selecting the best fiber type with all considered evaluation criteria without utilizing a multi-criteria decision-making tool like that of the AHP method, as no dominant fiber type exists in the selection model to be directly

186

Materials Selection for Natural Fiber Composites

Coir 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Sisal

Date palm FD FL L/D D

Hemp

Flax

Fig. 6.8 Comparisons of the natural fiber alternatives regarding the physical properties factor.

Coir 1 0.8 0.6 0.4 Date palm

Sisal 0.2

FCC 0 FHC FLC

Hemp

Flax

Fig. 6.9 Comparisons of the natural fiber alternatives regarding the chemical/biological properties factor.

selected by human estimations. There is no dominant fiber type in the model to be directly selected under all situations. This closeness in priorities makes the selection of the best fiber type a very difficult process without using such decision-making techniques.

Material selection of natural fiber composites using the analytical hierarchy process

187

Coir 1 0.8 0.6 0.4 Date palm

Sisal 0.2

FTS 0

FTM FSM FEB

Hemp

Flax

Fig. 6.10 Comparisons of the natural fiber alternatives regarding the mechanical properties factor.

Sisal

Priority

Hemp Flax Date palm Coir 0 Coir Priority

0.185

0.05

0.1

0.15

0.2

0.25

Date palm

Flax

Hemp

Sisal

0.21

0.227

0.184

0.194

Fig. 6.11 The overall priorities of the alternative natural fibers for automotive applications.

6.5

Selecting polymers for bio-based materials under conflicting criteria using analytical hierarchy process

As the properties of the natural fiber composites strongly depend on the matrix type, fiber type, and their interfacial bonding, studying the physiochemical, mechanical, and electrical along with other behaviors is a must for their performance optimization (Almagableh et al., 2017; AL-Oqla & Sapuan, 2015a). The mechanical properties and

188

Materials Selection for Natural Fiber Composites

those of the composite are seriously determined by the adhesion between the matrix and the reinforcing natural fibers. This is mainly because the stress transfer between the matrix and the reinforcement fibers determines the reinforcement efficiency and compatibility of the composite (Alawar, Hamed, & Al-Kaabi, 2009; AL-Oqla & Sapuan, 2014a; Arbelaiz et al., 2005). Unfortunately, to the best of our knowledge, researchers usually select polymer matrix types according to very limited criteria and/or their own estimations. Accordingly, a lack of evidence to support the selection process of polymers for bio-composites corresponding to wide, usually conflicting, evaluation criteria and comprehensive standpoints is found. But, designers must be careful of several factors in selecting the best material types for their products to guarantee the compatibility and synergy of the material utilized and the corresponding performance, along with attaining improved environmental performance indices. Accordingly, determining the best composite components, particularly the fibers and polymers became a complex matter with the nature of a multi-criteria decision-making problem (MCMD) (AL-Oqla & Sapuan, 2015b; AL-Oqla et al., 2016a; Dweiri & AL-Oqla, 2006), where keen decisions are required to develop the tendency of cleaner production and to save both money and effort. According to Bonilla (Bonilla, Almeida, Giannetti, & Huisingh, 2010), to achieve more sustainable societies, carrying out decision-making models for future perspective green products are of rising importance. However, no systematic decision-making model capable of utilizing experts’ knowledge to properly select and predict the suitability of a particular polymer type for a given natural fiber was found before AL-Oqla et al. (ALOqla & Sapuan, 2015b; AL-Oqla et al., 2015f) to form a preferred natural fiber reinforced polymeric-based material. Due to the availability of a wide range of polymers with diverse characteristics and credentials, and capabilities, the analytical hierarchy process was utilized by AL-Oqla et al. (AL-Oqla & Sapuan, 2015b; AL-Oqla et al., 2016a) to predict the appropriateness of various available polymer types for the date palm fibers (DPF), as well as other commonly used natural types in the automotive industry. For the first time they have introduced a decision-making selection model capable of applying an efficient rational base method to quantify experts’ knowledge in the field of natural fiber composites to qualitative analysis in order to solve and explain the multi-criteria decision-making problem concerning evaluating and predicting the most applicable polymer matrix type for the natural fibers for the automotive applications. By their efforts, keen decisions in selecting the proper polymer matrix type for a specific natural fiber was successfully performed to help avoid major drawbacks of the natural fibers through the manufacturing process, mainly the low permissible processing temperature. Such a way of thinking introduced and drawing decisions can also help achieve better interaction bonding between the fiber and the matrix and thus meeting the desired optimized characteristics and performance of the natural fiber composites. A schematic illustration of the role of analytical hierarchy process in selecting the best polymer types is shown in Fig. 6.12. In that way of selecting the most appropriate polymer matrix type, factors that affect the selection process have been carefully explored and suggested to create the model involving various physical, chemical, environmental, mechanical, and technical aspects, as well as other features of polymers.

Material selection of natural fiber composites using the analytical hierarchy process

Low density polyethylene

189

High density polyethylene

Polypropylene

Epoxy

Polyester

AHP

Physical properties of the polymer matrix

Chemical/technical properties of the polymer matrix

Mechanical properties of the polymer matrix Environmental and other properties of the polymer

Proper polymer for DPF

Fig. 6.12 Illustration of AHP role in polymer selection process (AL-Oqla et al., 2015f).

Moreover, to ensure adopting a comprehensive and representative model, a pilot study was performed to enhance getting better judgments in selecting the polymer matrices for the natural fiber composites. The considered criteria are tabulated in Table 6.7. As well, the polymer alternatives were proposed to be technically appropriate for the date palm fiber to ensure that they can be safely utilized with date palm to avoid causing thermal degradation due to the processing heat (Abu-Sharkh & Hamid, 2004; Al-Khanbashi, Al-Kaabi, & Hammami, 2005). The polymer alternatives were Polypropylene (PP), High density polyethylene (HDPE), Polyester, Epoxy, and the Low density polyethylene (LDPE). The polymer selection model is illustrated in Fig. 6.13, and the detailed flow of steps used in constructing the polymer selection model is illustrated in Fig. 6.14. The pairwise comparisons of all items in the model were attained by experts’ feedback via questionnaires. Here, two types of pairwise comparisons were made. One for the model factors to display the experts’ priorities, and the other was done for the alternative pairs to determine their relative merits. Some experts’ feedback regarding pairwise comparisons or judgments were omitted after being examined by the inconsistency test, as they showed an inconsistency ratio of more than 0.1. The results consistency tests for experts’ judgments regarding the model factors are demonstrated in

190

Materials Selection for Natural Fiber Composites

Table 6.7 Factors of the polymer selection model (AL-Oqla et al., 2015f) Main criterion

Sub-criteria

Physical properties of the polymer matrix (PPPM)

Density (D) Thermal conductivity (TC) Coefficient of thermal expansion (CTE) Glass transition temperature (GTT) Acoustic insulation properties (AIP) Elastic modulus (EM) Fracture toughness (FT) Elongation to break (EB) Yield strength (YS) Impact strength (IS) Curing temperature (CT) Curing pressure (CP) Curing time (CM) Resistance of chemicals (RC) Level of hydrophobic nature (LHN) Weather resistance (WR) Service temperature (ST) Sunlight and UV resistance (SR) Wettability (water resistance) (W) Cost (C)

Mechanical properties of the polymer matrix (MPPM)

Chemical/technical properties of the polymer matrix (CTPPM)

Environmental and other properties of the polymer matrix (EOPPM)

Table 6.8, and those for alternative comparisons in Table 6.9, where underlined values denote those that were rejected (more than the acceptable consistency ratio (0.1)). To illustrate a model factor pairwise comparison example, the judgment matrices of the “PPPM” main factor for the date palm fiber case is shown in Table 6.10 from experts’ feedback with matrix size of 5. Thus, the expert should answer ten questions to fill the judgment matrix. A typical form of questions for this case can be stated as “How much more significant is the P1 comparative to P2 regarding the ‘PPPM’ standpoint?” Normalizing columns and then averaging the resulting rows of Table 6.10 would result in the matching weights of each factor as stated below: 0

0:568 B 0:142 B B 0:095 B @ 0:081 0:114

0:571 0:143 0:143 0:071 0:071

0:632 0:105 0:105 0:053 0:105

0:466 0:133 0:133 0:066 0:200

1 0:536 0:214 C C 0:107 C C 0:036 A 0:107

The row averages vector is called Eigen vector or priority vector and can be calculated to be as: (0.56 0.15 0.12 0.06 0.12)T. The same results were accomplished by the

Material selection of natural fiber composites using the analytical hierarchy process

191

P2 P3 P4

Polymer (N)

Physical properties

P1

M2 M3

M5

C1

Polymer 3

M4

C3 C4

Polymer 2

C2

C5

Environmental and other properties

.....................

M1

E1 E2 E3

Polymer 1

Mechanical properties Chemical / technical properties

Selecting polymers under uncertainty

P5

E4 E5

P1, density; P2, thermal conductivity; P3, coefficient of thermal expansion; P4, glass transition temperature; P5, acoustic insulation properties; M1, elastic modulus; M2, fracture toughness; M3, elongation to break; M4, yield strength; M5, impact strength; C1, curing temperature; C2, curing pressure; C3, curing time; C4, resistance of chemicals; C5, level of hydrophobic nature; E1, weather resistance; E2, service temperature; E3, sunlight and UV resistance; E4, wettability; E5, level of hydrophobic

Fig. 6.13 The polymer selection model (AL-Oqla & Sapuan, 2015b).

192

Materials Selection for Natural Fiber Composites

A

Start

Initiate the overall objective of the problem (selecting the best polymer for natural fiber composites) utilizing commonly and noncommonly used fibers (Date palm and Flax fibers as case studies)

Identify potential polymers

Develop pairwise comparison matrices for the criteria and their subs for each fiber type

Send experts for their judgments Check experts’ judgments consistency for each judgment matrix using the consistency ratio (CR) NO CR ≤ 0.1

Identify model items Yes

Conduct an initial (pilot) questionnaire to ensure adopting representative model

Accept judgments till have 6 to 9 consistent judgments for each judgment matrix Develop pairwise comparison matrices for the alternatives w.r.t. model items

Review coherence of the proposed items in the model

Send other experts for their judgments Check experts’ judgments consistency using (CR) NO

No

Is the model representative and conprehensive?

CR < 0.1 Yes Accept 6 to 9 judgments for each matrix Calculate the overall weights of the polymer alternatives

Yes A

Finish

Select the most appropriate polymer type for natural fiber composites

Rank the appropriateness of polymer types for date palm and flax fibers Apply the sensitivity analysis for the results

Fig. 6.14 Steps of the polymer selection model.

Expert Choice® software as in Fig. 6.15. Moreover, the matrix captured in Table 6.10 has the largest (principle) eigenvalue λmax that can be calculated by finding the summation of products among each element of priority vector and the sum of columns in the judgment matrix.

Material selection of natural fiber composites using the analytical hierarchy process

193

Table 6.8 Consistency results of experts’ feedback regarding the model factors (AL-Oqla & Sapuan, 2015b) Expert Matrix size (n) 1 2 3 4 5 6 7 8

Main

(PPPM)

(MPPM)

(CTPPM)

(EOPPM)

(4  4)

(5  5)

(5  5)

(5  5)

(5  5)

0.02 0.00 0.04 0.01 0.03 0.04 0.07 0.03

0.01 0.01 0.05 0.06 0.07 0.02 0.13 0.03

0.01 0.01 0.00 0.07 0.03 0.00 0.02 0.01

0.02 0.03 0.09 0.04 0.04 0.01 0.05 0.03

0.00 0.00 0.01 0.01 0.03 0.00 0.00 0.01

Table 6.9 Consistency results of experts’ feedback regarding the model alternatives (AL-Oqla & Sapuan, 2015b) Expert

P1 P2 P3 P4 P5 M1 M2 M3 M4 M5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5

Matrix size (n) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5) (5  5)

1

2

3

4

5

6

7

8

9

10

0.02 0.00 0.04 0.01 0.03 0.00 0.07 0.03 0.04 0.07 0.04 0.04 0.05 0.06 0.00 0.11 0.08 0.04 0.04 0.04

0.01 0.01 0.05 0.06 0.07 0.02 0.13 0.03 0.03 0.09 0.04 0.07 0.00 0.01 0.09 0.00 0.05 0.05 0.12 0.05

0.01 0.01 0.00 0.11 0.03 0.00 0.02 0.01 0.06 0.06 0.01 0.01 0.14 0.01 0.03 0.00 0.11 0.10 0.00 0.12

0.02 0.03 0.09 0.04 0.04 0.01 0.05 0.03 0.02 0.00 0.04 0.11 0.13 0.15 0.07 0.06 0.08 0.08 0.12 0.09

0.03 0.00 0.03 0.12 0.13 0.00 0.14 0.04 0.01 0.11 0.05 0.06 0.07 0.02 0.13 0.11 0.12 0.01 0.04 0.04

0.00 0.05 0.01 0.01 0.06 0.00 0.01 0.03 0.04 0.01 0.00 0.01 0.03 0.00 0.02 0.04 0.02 0.14 0.05 0.11

0.00 0.05 0.12 0.03 0.04 0.02 0.00 0.06 0.02 0.03 0.09 0.14 0.04 0.01 0.05 0.06 0.00 0.06 0.13 0.00

0.00 0.03 0.01 0.12 0.05 0.01 0.13 0.03 0.00 0.05 0.01 0.01 0.13 0.16 0.00 0.01 0.00 0.01 0.09 0.09

0.00 0.07 0.01 0.02 0.00 0.01 0.01 0.04 0.02 0.00 0.04 0.12 0.03 0.00 0.07 0.14 0.00 0.12 0.04 0.04

0.00 0.00 0.02 0.03 0.09 0.02 0.03 0.03 0.03 0.03 0.00 0.00 0.01 0.01 0.01 0.05 0.00 0.06 0.05 0.05

194

Materials Selection for Natural Fiber Composites

Table 6.10 A sub-factor judgment matrix for the physical properties criterion in the case of date palm fiber type Factor

P1

P2

P3

P4

P5

P1 P2 P3 P4 P5 Sum

1 1/4 1/6 1/7 1/5 1.76

4 1 1 1/2 1/2 7.00

6 1 1 1/2 1 9.50

7 2 2 1 3 15.00

5 2 1 1/3 1 9.33

Density Thermal conductivity Coefficient of thermal expansion Glass transition temperature, Tg Acoustic insulation properties Inconsistency = 0.037 with 0 missing judgments

0.558 0.148 0.116 0.061 0.117

Fig. 6.15 Priorities and inconsistency of sub factors to their main one.

That is: lmax ¼ 1:76ð0:56Þ + 7:00ð0:15Þ + 9:5ð0:12Þ + 15:00ð0:06Þ + 9:33ð0:12Þ ¼ 5:195 From Table 6.2, the value of RI for a 5-factor matrix is 1.12, thus, the value of CI can be found from Eq. (6.6) to be 0.048. Utilizing Eq. (6.7), the value of CR can be found to be 0.04, which is less than 10%. Therefore it is acceptable, and the experts’ judgments are said to be consistent. Accordingly, the contributions of the model factors for both commonly used fibers like flax and uncommonly used ones like that of date palm are demonstrated in Fig. 6.16. Results demonstrated that the MPPM occupies the most model stacks, as the key criterion in the model with a priority of 47.4% in the case of date palm fiber. Then, the second important factor in selecting the polymer matrix for the natural fiber composites in automotive sectors is PPPM with a weight of 31.5%. Conversely, the CTPPM is revealed to be the least significant factor in the selection process with a weight of only 9.5%. Similarly, the relative merits of alternatives were determined by calculating their priorities and thus, their potential ranking regarding all of the main factors in the model. Fig. 6.17 displays the alternatives priorities regarding the Physical Properties factor. It can be observed that Polypropylene polymer type has the highest significance in the case of both commonly and uncommonly used fibers, demonstrating that it is the best from this criterion standpoint. However, Polyester has the least priority value from the physical properties point of view. Furthermore, Imports of the polymer

Material selection of natural fiber composites using the analytical hierarchy process

195

0.5 0.45 DPF

0.4

Flax

Weight

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Physical properties

Mechanical properties

Chemical/ technical properties

Environmental and other properties

Fig. 6.16 The weights of the main factors of the polymer selection model for various natural fibers.

Priority w.r.t. physical properties

0.25

DPF Flax

0.2

0.15

0.1

0.05

0 Polypropylene

Polyester

Epoxy

High density Low density polyethylene polyethylene (HDPE) (LDPE)

Fig. 6.17 Polymer priorities regarding the physical properties factor.

alternatives with respect to the Mechanical Properties factor are illustrated in Fig. 6.18. It is clear that Epoxy is the best alternative from the Mechanical Properties standpoint for both types of fibers and Polypropylene and Polyester are the second and third choices for the commonly and uncommonly used fiber types, respectively. On the other hand, Low Density Polyethylene was shown to be the last choice in its

196

Materials Selection for Natural Fiber Composites

DPF

0.3

Priority w.r.t. mechanical properties

Flax 0.25

0.2

0.15

0.1

0.05

0 Polypropylene

Polyester

Epoxy

High density Low density polyethylene polyethylene (HDPE) (LDPE)

Fig. 6.18 Polymer priorities regarding the mechanical properties factor.

potential to be a matrix base for both of flax and date palm fibers from the Mechanical Properties point of view. Likewise, the polymer types’ priorities regarding Chemical/Technical Properties and Environmental and Other Properties factors are demonstrated in Figs. 6.19 and 6.20, respectively. Results demonstrate that the best choice to make a matrix for both

Priority w.r.t. chemical/technical properties

0.35

0.314

0.33

DPF Flax

0.3 0.227

0.25

0.224 0.195

0.2

0.188

0.153

0.15

0.112

0.123 0.134

0.1 0.05 0 Polypropylene

Polyester

Epoxy

High density Low density polyethylene polyethylene (LDPE) (HDPE)

Fig. 6.19 Polymer priorities regarding the chemical/technical properties factor.

Priority w.r.t. environmental and other properties

Material selection of natural fiber composites using the analytical hierarchy process

0.35

0.3140.305

197

DPF Flax

0.3 0.25 0.203 0.173 0.162

0.2 0.149

0.188

0.188 0.19

0.135

0.15 0.1 0.05 0 Polypropylene

Polyester

Epoxy

High density Low density polyethylene polyethylene (LDPE) (HDPE)

Fig. 6.20 Polymer priorities regarding the environmental and other properties factor.

date palm and flax was in favor of the Polypropylene for both considered factors. Conversely, Polyester type was found to be the worst regarding both factors in the case of date palm fiber with a weight of 11.2% regarding Chemical/Technical Properties and 14.9% regarding Environmental and Other Properties. But it was the worst for flax regarding Environmental and Other Properties factor, whereas Epoxy type was the worst choice for flax regarding Chemical/Technical Properties consideration with a weight of only 13.4%. Priorities of polymer alternatives can be found for every considered criterion in the model in a similar manner. These priorities are all sub-factors in the Mechanical properties of the polymer matrix case of date palm fibers are demonstrated Fig. 6.21. It is clear that Epoxy has the highest priorities regarding elastic modulus (M1) and yield strength (M4) (see Fig. 6.13 for the notation), but the least in others. Polypropylene is the best regarding M2 criterion, but not the worst in in any. Furthermore, HDPE is the best concerning M5, but the worst regarding M4. LDPE on the other hand, has the highest priority in M3, but the lowest in both M1 and M4 criteria. However, Polyester has neither the highest priority nor the least regarding any of the considered criteria within the MPPM category. It is believed that such types of detailed assessments for the polymer alternatives would evidently lead to better decisions regarding their proper selection for green composites, and would enhance establishing clearer guidelines for the polymer selection process for numerous engineering applications. In addition, these proper types of assessments and evaluations of polymers would enhance their selection with minimum human errors results in reducing bias in the selection decisions. As a consequence, this properly detailed assessment capable of enhancing the reliability of the polymer selection procedure under an uncertainy environment, along with expanding their applications in further sustainable products. Priorities of the rest of alternatives

198

Materials Selection for Natural Fiber Composites

Fig. 6.21 Comparison of priorities of the polymer alternatives with respect to the mechanical properties main factor.

M1 1.000 0.800 0.600 0.400

M5

M2

0.200 0.000

M4 PP

Pol

M3 HDPE

Epo

LDPE

with respect to the whole of considered factors are illustrated in Fig. 6.22 for the physical properties main factor, Fig. 6.23 for the chemical/ technical properties, and Fig. 6.24 for environmental and other properties. On the other hand, the significances of all polymer types with respect to the whole evaluation criteria in the model (twenty criteria simultaneously) are illustrated and discussed in Fig. 6.25, where Polypropylene polymer type has the highest overall priorities for both date palm and flax fibers and thus, it is capable of forming a desirable P1 1 0.8 0.6 0.4

P5

P2

0.2

Polypropylene Polyester Epoxy

0

HDPE LDPE

P4

P3

Fig. 6.22 Comparison of priorities of the polymer alternatives with respect to the physical properties main factor.

Material selection of natural fiber composites using the analytical hierarchy process

199

C1 1 0.8 0.6 0.4

C2

C5

Polypropylene Polyester

0.2

Epoxy

0

HDPE LDPE

C4

C3

Fig. 6.23 Comparison of priorities of the polymer alternatives with respect to the chemical/technical properties main factor. E1 1 0.8 0.6 0.4

E5

E2

0.2

Polypropylene Polyester Epoxy

0

HDPE LDPE

E4

E3

Fig. 6.24 Comparison of priorities of the polymer alternatives with respect to the environmental and other properties main factor.

composite with such natural fibers in a more reliable way regarding the considered evaluation criteria along with the up-to-date experts’ knowledge. Moreover, it is a worth noting here to say that the closeness in the imports of polymers certainly indicate the absence of a dominant polymer type that can be always selected as the most appropriate choice under all conditions, and thus decision-making methods have to be utilized the selection process.

200

Materials Selection for Natural Fiber Composites

0.3 DPF

0.25 Total priority

Flax

0.2 0.15 0.1 0.05 0 Polypropylene DPF Flax

High density polyethylene (HDPE)

Low density polyethylene (LDPE)

0.217

0.181

0.179

0.22

0.195

0.18

Polyester

Epoxy

0.244

0.179

0.236

0.168

Fig. 6.25 The overall significance of polymers with respect to the whole evaluation criteria in the selection model.

6.6

Evaluating various natural fiber composites under conflicting criteria using analytical hierarchy process

In order to enhance producing better green products, special care should be taken to ensure excellent compatibility as well as performance synergy between the available natural resources and the industry. This would guarantee the industrial sustainability which will lead to better environmental indices (Almagableh et al., 2017; AL-Oqla et al., 2014a, 2015e). The sustainability of the modern industry becomes seriously dependent on the sustainability of the material itself, its performance, and the recycling possibilities. These factors impose several limitations and concerns to properly choosing of the best material type for a particular industrial application (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Oqla & Omari, 2017; Dweiri & AL-Oqla, 2006). This, in fact, will complicate the selection process of the desirable material toward optimum decision-making (Abdollah, Shuhimi, Ismail, Amiruddin, & Umehara, 2015; AL-Oqla et al., 2015a; Al-Widyan & AL-Oqla, 2011, 2014; Dalalah et al., 2010; Khalili & Duecker, 2012; Subramoniam et al., 2013). As natural fiber composites have emerged as one competitive choice to be used for modern industries, various types of natural fibers of plant origin have been used as reinforcing fillers for a wide range of applications. In such reinforced materials, the final characteristics are subject to the features of the individual ingredients, as well as the integrated characteristics of the fillers and matrices. Therefore, selecting the best natural composite for a specific application becomes a complex procedure with

Material selection of natural fiber composites using the analytical hierarchy process

201

many interactive properties and criteria to be taken into account. This also leads to a multi-criteria decision-making problem where proper features of the composites have to be optimized in a fair manner. Such proper selection and optimization of the natural composites could maximize the desirable performance of the composites while keeping costs to a minimum and shortening the design and production cycle time (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omari, 2017; AL-Oqla & Sapuan, 2015b; Edwards, 2005; Pickering, Beckermann, Alam, & Foreman, 2007; Shah, 2013; Toupe, Trokourey, & Rodrigue, 2014). Unfortunately, natural fibers have poor properties that could limit their use in certain industrial products. An example of such poor properties is the hygroscopic characteristic. This characteristic is crucial for the overall mechanical properties of composite materials. This is mainly due to the fact that poor hygroscopic characteristics can cause a dramatic reduction in the bonding between the matrix and the filler in the natural fiber composites, leading to significant mechanical performance deterioration, which in turn may lead to shattering effects in the final product. These effects include cracking in the composites, acceleration of fiber degradation, loss of mechanical properties, swelling, as well as inviting decay fungi (AL-Oqla, Salit, Ishak, & Nuraini, 2015b). Hence, studies for deeper and more proper evaluations of the natural fiber composites have to be conducted before using in a given application. Due to the attractive features and characteristics it has, Polypropylene (PP) based natural fiber composites received a wide acceptance in different industries such as automotive, where it has been used extensively in the panel manufacturing. The attractive properties of PP include its excellent recycling behavior, price, mechanical properties, low density, and superior electrical properties (Almaadeed, Kahraman, Noorunnisa Khanam, & Madi, 2012; AL-Oqla et al., 2015e; Awal, Cescutti, Ghosh, & M€ ussig, 2011). This is in addition to the range of methods that can be used to process PP, like molding, extrusion, film, and fiber manufacturing with higher thermal stability (AL-Oqla et al., 2016a; Zampaloni et al., 2007). Therefore, polypropylene was commonly used in the automotive industry with natural fibers to make suitable components. A thorough reviewof the literature reveals that plenty of research work has been performed to address the capabilities and potential of natural fiber composite materials in a wide range of applications, including the automotive industry. However, such studies were conducted to only improve the composites’ performance, where researchers extensively studied the mechanical properties, manufacturing processes, constituents’ compatibilities, as well as the chemical modifications of the natural composites. But very few studies can be found addressing the comparisons among different types of natural composites and how to select the best one for a specific application (AL-Oqla et al., 2015a, 2015c, 2016a; Shah, 2013, 2014). Unfortunately, the information available on how to select the best natural fiber composite material for the automotive industry is very limited and does not represent enough support or reliable evidence for the decision-makers. To the best of the authors’ knowledge, no previous published research was found considering this issue systematically or tried ranking different natural composites in terms of their suitability for the automotive industry before AL-Oqla et al. (2016a) neither to use informative decision-making methods in the selection process of such natural composites. In fact,

202

Materials Selection for Natural Fiber Composites

in order to fill this gap, AL-Oqla et al. (2016a) present an informative decision-making selection model for the first time to improve the selection process of the natural fiber composites especially from the automotive industry point of view. To be more precise, the aim of that work was to give intense evaluation and selection process of the best natural fiber/ PP base-composites for the interior products in the automotive sector. This was achieved using the analytical hierarchy process when considering different simultaneous evaluating criteria. The expected outcomes of this multi-evaluationcriteria selection process is the optimization of the desired properties of the new natural composites, together with the expansion of the sustainable design possibilities that contribute to the automotive industry and directly lead to lower cost, less effort, and shorter manufacturing time processes. To provide designers with very helpful information for proper evaluation of the PP-natural fiber composites toward achieving better environmental and sustainability standards in the automotive industry, numerous integrated evaluation criteria capable of leading to the optimization of the overall performance should be introduced. Therefore, AL-Oqla et al. (2016a) have suggested all of the tensile strength, impact strength, flexural strength, and the maximum water absorption as four initial evaluation criteria to assess the performance of the PP-based natural fiber composites, as these criteria were successfully adopted and examined in evaluating various natural fiber composites for automotive applications (AL-Oqla & Sapuan, 2014a). To support the appropriateness of these selected evaluation criteria, a pilot questionnaire was prepared and sent to twelve international experts in the field of natural fiber composites. In this questionnaire, experts were kindly asked to modify, suggest, and add any reasonable criterion for evaluation according to their best knowledge (with reasonable justifications) to expedite better assessment of the natural composites for the interior automotive products. After experts’ feedback, the final evaluation criteria in the AHP selection model were considered after introducing two new criteria to have a total of six criteria as: the flexural strength (FS), the tensile strength (TS), the flexural modulus (FM), the tensile modulus (TM), the impact strength (IS), and the maximum water absorption (MWA) of the composites. This would enhance the expediency of the built model ensuring that it is a comprehensive one. It must be noted here that good design requires high values in all these criteria except for the maximum water absorption criterion, where the lowest possible values are required. In that work, comprehensive comparisons among different commonly used PP-based natural fiber composites for the automotive industry were conducted throughout the model. The considered fibers were namely jute, coir, flax, kenaf, and sisal. Additionally, a comparison with the newly emerged date palm fibers with all previous composites was extensively conducted to investigate its possibilities and capabilities for the automotive industry. Bearing in mind such natural fibers will result in a wide range of potential composites to be investigated. Such composites are varied in their performances due to the differences in the reinforcement conditions, fiber loadings, and treatments. However, the composites considered to be alternatives are those found to exhibit relatively high mechanical properties. After further screening and investigations, only 15 potential alternatives were considered to be assessed concerning the various evaluation criteria simultaneously. These alternatives are demonstrated in Table 6.11 (Arbelaiz et al., 2005;

Potential PP/ natural fiber composites (AL-Oqla et al., 2016a)

Alternative

Name

TSa (MPa)

TMb (MPa)

FSc (MPa)

FMd (MPa)

ISe (J m21)

MWAf (%)

Coir 15 wt%/PP treated*

A1

2210* 2400*

42.7* 26*

1830* 900*

22* 64*

0.52* 2.6*

Coir 20 wt%/PP treated*

A2

3000*

33*

1150*

65*

3.9*

Date palm 30 wt%/PP

A3

1572 2100

47

3.2

A4

55*

2400*

53.5*

3.2*

Flax 30 wt%/PP

A5

48

4500

67g

6.5

Flax 30 wt%/PP treated*

A6

68*

4600*

78*, g

4.5*

Flax 50 wt%/PP Flax 50 wt%/PP treated*

A7 A8

700 1550 1610 650 800* 1350* 800* 5178 1700 5701.4* 2000* 2500 2700*

27.8 51

Date palm 30 wt%/PP treated*

42 83*

700 650*

45g 65*, g

10 12*

Jute 20 wt%/PP Jute 20 wt%/PP treated*

A9 A10

26.1* 28* 29* 26* 29.5* 24 18 21 17 29* 25* 28* 40.1 26 44.1* 43* 24 50* 53* 25.8 29.8*

45 53.8*

1900 2500*

31 44*

0.8 0.7*

1680 2490* 1880*

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.11

Continued 203

204

Table 6.11

Continued Name

TSa (MPa)

TMb (MPa)

FSc (MPa)

FMd (MPa)

ISe (J m21)

MWAf (%)

Jute 30 wt%/PP treated* Kenaf 30 wt%/PP treated*

A11 A12

2250* 2600* 6000* 1774*

51* 29*

0.7* 11*

A13

20

A14

6000* 1778*

28*

16*

Sisal 15 wt%/PP treated*

A15

44* 19.84* 55* 28*

5900 5000 1492 5500* 1648*

28

Kenaf 40 wt%/PP treated*

54* 58* 58* 65* 36* 60 19.67 24.01 71* 80*

2900* 2700* 1678*

Kenaf 40 wt%/PP

28.7* 46* 23.18* 57* 50* 35 10.33

2640*

48.8*

1430*

26*

0.72*

a

TS: tensile strength. TM: tensile modulus. FS: flexural strength. d FM: flexural modulus. e IS: impact strength. f MWA: maximum water absorption. g Values converted from ISO 180 to ASTM D 256. *Treated composites. b c

8000 1282

Materials Selection for Natural Fiber Composites

Alternative

Material selection of natural fiber composites using the analytical hierarchy process

205

Asadzadeh, 2013; Asumani, Reid, & Paskaramoorthy, 2012; Bendahou et al., 2008; Haque, Hasan, Islam, & Ali, 2009; Joseph, Rabello, Mattoso, Joseph, & Thomas, 2002; Kahraman, Abbasi, & Abu-Sharkh, 2005; Law & Ishak, 2011; Mahmoudi & Hebbar, 2014; Mir, Nafsin, Hasan, Hasan, & Hassan, 2013; Rahman, Huque, Islam, & Hasan, 2008; Sudhakara et al., 2013; Zampaloni et al., 2007). One can notice here that every examined alternative has its distinguished behavior under each particular evaluation criterion. Moreover, the polypropylene matrix exhibits dissimilar behaviors with respect to the considered criteria as captured from the literature (Arbelaiz et al., 2005; Asadzadeh, 2013; Asumani et al., 2012; Bendahou et al., 2008; Haque et al., 2009; Joseph et al., 2002; Kahraman et al., 2005; Law & Ishak, 2011; Mahmoudi & Hebbar, 2014; Mir et al., 2013; Rahman et al., 2008; Sudhakara et al., 2013; Zampaloni et al., 2007). Fig. 6.26 demonstrates the variations of the PP behavior under different evaluation criteria. As a consequence of the nonuniform samples and the different experimental conditions, wide deviation in the measured values of the properties of composites under study is clearly noticed. To minimize such variations in the results, the average of the reported values of the properties of each alternative is considered. This averaging method is usually considered to be reasonable in the studies of the natural fiber composites (AL-Oqla & Sapuan, 2014a; AL-Oqla et al., 2014b, 2016a; Rowell, Sanadi, Caulfield, & Jacobson, 1600.00

50.00 Tensile modulus (MPa) Flexural modulus (MPa) Tensile strength (MPa) Flexural strength ( MPa)

Left axis 45.00 1400.00 40.00 1200.00

30.00

1000.00

MPa

MPa

35.00

25.00 800.00 20.00 600.00 15.00 Right 400.00 1

2

3

4 5 6 7 Sample number

8

9

10.00 10

Fig. 6.26 Various responses of PP matrix regarding some evaluation criteria (AL-Oqla et al., 2016a).

206

Materials Selection for Natural Fiber Composites

1997). Table 6.12 represents the judgment matrix of the composites alternatives and their merits regarding each evaluation criteria. In fact, one key factor that increases the degree of difficulty and complexity for designers to assign the best composite material for a specific application is the wide variation in the composites’ behavior under the given evaluation criteria. As an example of this wide variation, the average responses of all alternatives regarding both the tensile and flexural strength is represented in Fig. 6.27. The tensile modulus and the flexural modulus of these composites are also illustrated in Fig. 6.28, whereas Figs. 6.29 and 6.30 give illustrations of the composites impact strength criterion and maximum water absorption, respectively. As can be seen from these figures, it is almost certain that there is no absolute best composite in terms of all required design criteria in one hand, and the selection process of composites should be performed according to a well-organized comprehensive selection model to be able to integrate the assessment of each single alternative regarding its overall behavior from different design considerations. Consequently, one critical point in the selection process is that selecting an optimum composite regarding only one or two evaluation criteria while ignoring others would evidently lead to a false selection for the automotive applications. To illustrate, it can be realized in Fig. 6.27 that composites (A8) and (A14) are the best desired alternatives in terms of flexural strength criterion, however, composite (A14) is not desired in terms of tensile strength. Extra inspections can prove that candidate (A8) and (A14) are not preferred at all concerning the tensile and flexural moduli as realized from Fig. 6.28, but (A13) is the most appropriate one with respect to these moduli. Moreover, impact strength factor eliminates the candidate (A13) from being the most desirable, but supports candidates (A6) and (A5) as displayed in Fig. 6.29. Finally, Fig. 6.30 demonstrates that the maximum water absorption criterion contradicts and disregards all pervious possible candidates, but supports composites (A10), (A11), and (A15). Thus, as a direct result of the above discussion, one can come to conclude that to minimize such conflicting behavior of the composites and to make the selection process more accurate and reliable, implementing a comprehensive unbiased decision-making selection model is a must. To estimate the weights of the evaluation criteria— two options are available, either to assign an equal weight for all criteria, or to adopt a more realistic manner to enhance better assessment of the composites. In this way, the weights of evaluation criteria are to be determined by means of experts via the relative importance scale of the AHP. Thus, a questionnaire for this purpose was conducted by AL-Oqla et al. (2016a) and sent to a group of eleven experts worldwide to determine the relative importance of the adopted evaluation criteria. However, only six judgment matrices were approved for calculating the aggregated weight using the geometrical mean method, as their levels of consistency were acceptable. It is a worth noting here that only a small and limited number of experts’ feedback is required for the AHP method to ensure the convergence in the decision (Saaty, 2013). Utilizing Eqs. (6.1)–(6.5), the weights captured from experts were analyzed to obtain the influence of each evaluation criterion in the mode. These obtained weights are shown in Fig. 6.31. Consequently, the impact strength criterion was found to have the most contribution to

The judgment matrix of PP/natural fiber composite alternatives (AL-Oqla et al., 2016a)

Name

Tensile strength (MPa)

Tensile modulus (MPa)

Flexural strength (MPa)

Flexural modulus (MPa)

Impact strength (J/m)

Maximum water absorption (%)

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

27.7 27.75 20 25.5 33.05 43.55 24 51.5 25.8 29.8 28.7 44.05 22.67 39.61 28

2305 3000 1127.5 983.33 3439 3850.7 2500 2700 1680 2185 2250 3458 4641 3889 2640

34.35 33 39.4 55 48 68 42 83 45 53.8 54 54.25 39.84 75.5 48.8

1365 1150 1836 2400 4500 4600 700 650 1900 2500 2900 2189 5450 3574 1430

43 65 47 53 67 78 45 65 31 44 51 29 28 28 26

1.56 3.9 3.8 3.8 6.5 4.5 10 12 0.8 0.7 0.7 11 20 16 0.72

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.12

207

208

Materials Selection for Natural Fiber Composites

90

Tensile strength (MPa)

80

Flexural strength (MPa)

70 60 50 40 30 20 10 0 A1

A2

A3

A4

A5

A6

A7

A8

A9 A10 A11 A12 A13 A14 A15

Fig. 6.27 Average values of the composites with respect to the tensile and flexural strengths properties.

Tensile modulus (MPa) 6000

Flexural modulus (MPa)

5000

4000

3000

2000

1000

0

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

Fig. 6.28 Average values of the composites with respect to the tensile and flexural moduli properties.

the evaluation process composites. This result might be reasonable according to the fact that such composites generally have a lack in their capabilities to resist the impact loads and experts have realized that composites with higher impact resistance are more appropriate for the overall performance. Moreover, flexural strength has another high influence in the overall performance, as it is a bending and shear strengths

Material selection of natural fiber composites using the analytical hierarchy process

209

80

Impact strength (J/m)

70 60 50 40 30 20 10 0 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

Fig. 6.29 Average values of composites with respect to the impact strength criterion.

20

Maximum water absorption (%)

18 16 14 12 10 8 6 4 2 0 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

Fig. 6.30 Average values of composites with respect to the maximum water absorption property.

combination, which are together signify the ability of the composite to resist deformations under loads. Therefore, it is very beneficial for the automotive applications. It is believed now that such significance of criteria is recommended to be considered in the design stage of components made from green composites, as it is consistent experts’ feedback. Moreover, flexural modulus has been found to have higher impact

210

Materials Selection for Natural Fiber Composites

0.45 0.45 0.40 0.35 Weight

0.30 0.24 0.25 0.20 0.14

0.15 0.10

0.09

0.05 0.03

0.05 0.00 Tensile strength

Tensile modulus

Flexural strength

Flexural modulus

Impact Maximum strength water absorption

Fig. 6.31 Weights of evaluation criteria for the selection model form experts’ points of view.

in the evaluation process than that of the tensile strength, tensile modulus, and maximum water absorption criteria. It is also supposed that revealing such importance of various evaluation criteria is an added-value step of adopting green composites material selection method, which would help designers to better evaluate such types of materials for the automotive applications. In a similar manner, the relative imports of the whole composite alternatives were obtained regarding each evaluation criterion by making use of the weights tabulated in Table 6.12 to make the judgment matrices for each item in the model. A detailed calculation example for the upper part tensile strength judgment matrix with an inconsistency value of 0.04 is presented in Table 6.13 as the lower part is just the reciprocal of the upper one. Consequently, the priorities of candidate composites regarding the most evaluation criteria are demonstrated in Fig. 6.32. It can be detected that the inherent variations in the composites behavior regarding the considered criterion make it very difficult to rate the best type that maximizes the whole desired criteria simultaneously unless adopt such a systematic selection model. Moreover, the final imports of the evaluated candidates regarding all the considered criteria simultaneously are shown in Fig. 6.33 where the closeness in proprieties of some alternatives also designates the importance and need for utilizing more appropriate decision-making selection models in the field of natural fiber composites to attain better industrial sustainability. In addition, the final ranking of natural composite alternatives according to the ability to maximize the overall desired properties is revealed in Table 6.14. It is now evidently revealed that although there are some alternatives with high values of flexural and/or tensile strengths (like A14 and A8), the best composite was A6, which has much lower tensile strength and flexural values. This evidently proves and supports the value and rewards of using a wide range of simultaneous

Material selection of natural fiber composites using the analytical hierarchy process

211

A detailed judgment matrix of composite alternatives regarding the tensile strength criterion (AL-Oqla et al., 2016a)

Table 6.13

A1

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A11

A12

A13

A14

A15

1.000

1.002

0.722

0.987

1.193

1.572

0.866

1.859

0.931

1.076

1.036

1.590

0.818

1.430

1.011

0.721

0.985

1.191

1.569

0.865

1.856

0.930

1.074

1.034

1.587

0.817

1.427

1.009

1.000

1.367

1.653

2.178

1.200

2.575

1.290

1.490

1.435

2.203

1.134

1.981

1.400

1.000

1.296

1.708

0.941

2.020

1.012

1.169

1.125

1.727

0.889

1.553

1.098

1.000

1.318

0.726

1.558

0.781

0.902

0.868

1.333

0.686

1.198

0.847

1.000

0.551

1.183

0.592

0.684

0.659

1.011

0.521

0.910

0.643

1.000

2.146

1.075

1.242

1.196

1.835

0.945

1.650

1.167

1.000

0.501

0.579

0.557

0.855

0.440

0.769

0.544

1.000

1.155

1.112

1.707

0.879

1.535

1.085

1.000

0.963

1.478

0.761

1.329

0.940

1.000

1.535

0.790

1.380

0.976

1.000

0.515

0.899

0.636

1.000

1.747

1.235

1.000

1.415

A2

1.000

A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14

1.000

A15

1 0.16

15

2

0.14 0.12

14

3

0.1 0.08 0.06

13

4

0.04

Priority FS

0.02

Priority TS

0

Priority TM

12

5

Priority FM Priority IS

11

6

10

7 9

8

Fig. 6.32 Candidate composite priorities regarding the model criteria.

212

Materials Selection for Natural Fiber Composites

0.12

0.1

Priority

0.08

0.06

0.04

0.02

0 A1

A2

A3

A4

A5

A6

A7

A8

A9 A10 A11 A12 A13 A14 A15

Priority 0.06 0.07 0.06 0.07 0.08 0.1 0.05 0.09 0.05 0.07 0.08 0.06 0.06 0.07 0.05

Fig. 6.33 Priorities of the considered natural fiber composite alternatives regarding the main goal of the model.

Table 6.14 Rank

The overall ranking of the considered natural composites 1

2

3

4

5

6

7

8

9

10 11

12 13 14 15

Alternative A6 A8 A5 A11 A4 A10 A2 A14 A13 A3 A12 A1 A7 A9 A15

evaluation criteria in a systematic decision-making selection model in an integrated manner to properly determining the best green composite type for the automotive industry with minimum human errors.

6.7

Optimizing the reinforcement conditions of natural fiber composites using analytical hierarchy process

It is necessary to rate the correct material type for engineering components to be able to attain successful low cost design to develop sustainability along with customer satisfaction attributes in order to achieve more real sustainable societies (AL-Oqla & Sapuan, 2014a; Dicker et al., 2014). Technically speaking, the performance and properties of natural fiber composites intensely depend on the features of their individual interfacial adhesions (Nurwaha, Han, & Wang, 2013; Ojha, Raghavendra, & Acharya, 2014; Sapuan et al., 2013). That is, the overall attributes of the natural fiber composites,

Material selection of natural fiber composites using the analytical hierarchy process

213

as well as their capabilities depend on the physical, chemical composition, and mechanical properties of the inherent material where technical aspects including fiber length, fiber orientation, fiber diameter, as well as fiber’s surface treatment are crucial in determining the overall performance of the composites (Alawar et al., 2009; Kaddami et al., 2006; Ojha et al., 2014; Sapuan et al., 2013). Regarding this issue, AL-Oqla and Sapuan (2014a) have presented extensive factors to be seriously considered when selecting the appropriate natural composites for a given application. On the other hand, reaching the optimal reinforcement condition for the natural fiber composites to obtain the exact desired characteristics is still challenging for both designers and the industry. It is believed that selecting the most appropriate reinforcement condition for green composites would affectedly enhance achieving better ecofriendly, low-cost materials capable of expanding the sustainable design possibilities. However, numerous factors have roles in acquiring such reinforcement conditions that brand it a multi-criteria decision-making (MCDM) problem. For example, several studies had examined date palm/ Epoxy composite to enhance its mechanical performance for wider applications (Abdal-Hay, Suardana, Jung, Choi, & Lim, 2012; Alsaeed, Yousif, & Ku, 2012; Kaddami et al., 2006; Shalwan & Yousif, 2014). However, it was verified that there was no precise distinguished reinforcement condition capable of maximizing all the distinct aspects of the tensile properties of this composite. In more detail, although some particular reinforcement conditions have the capability of increasing the maximum tensile strength, they usually reduce the shear stress and/or the ductility of the date palm/ Epoxy composite and vice versa. Thus, it is recommended to optimize all the reinforcement conditions and to select the most suitable reinforcement conditions of the composites to achieve the best overall tensile properties and performance. To fill this gap, as well as to evidently verify the possibility of achieving the best reinforcement conditions of natural fiber composites, AL-Oqla et al. (2015c) had introduced and built for the first time a decisionmaking selection model to optimize the reinforcement conditions for a particular natural fiber composite type. In that model, the Analytic Hierarchy Process was integrated with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to increase its reliability. The considered composite was date palm/Epoxy and the work objective was to maximize the overall tensile property of the composites considering combined evaluation criteria. In that model, eleven potential reinforcement conditions were considered as alternatives to be evaluated regarding the maximum tensile strength (MTS) property, the elongation to break (EL) property and the maximum shear stress (MSS) criterion simultaneously as they were found decisive for the overall tensile property of the composites (AL-Oqla et al., 2015c). To determine the suitability of the evaluation criteria and their consistent weights, experts’ feedback was surveyed and the final weights of criteria were demonstrated in Fig. 6.34. In fact, this model presents a guideline, as well as a roadmap for employing proper decision-making models for green composites in general to optimize their desired characteristics in a more reliable way. The flow of steps considered to reach the performance ranking of the reinforcement condition alternatives to maximize the overall performance of the composites via the integration of AHP and TOPSIS methods is demonstrated in Fig. 6.35.

214

Materials Selection for Natural Fiber Composites

0.45 0.4 0.35

Priority

0.3 0.25 0.2 0.15 0.1 0.05 0 Priority

MTS

MSS

EL

0.31

0.39

0.29

Fig. 6.34 Weights of the evaluation criteria.

To practically reach the optimal reinforcement condition of the date palm/Epoxy that is capable of providing reasonably high tensile properties, various potential reinforcement conditions of composites with reasonable tensile features were considered. Such composites were formed from combinations of various composite parameters like those of fiber lengths, fiber diameter, and various NaOH concentration treatments, as this solution was found to be suitable for date palm fibers to enhance its mechanical properties (Abdal-Hay et al., 2012; Shalwan & Yousif, 2014). The eleven candidates of composite alternatives are tabulated in Table 6.15 (AL-Oqla et al., 2015c). According to the TOPSIS procedure, the best choice is the closest one to the positive ideal answer, or the farthest one from the negative ideal solution. Its main principle suggested by Tavana and Hatami-Marbini (2011) and Wang and Chang (2007) is to recommend a solution that has the shortest distance from the hypothetical one. It happens sometimes that the proposed solution has the shortest distance from the ideal and negative ideal solutions. TOPSIS technique attempts to find the closest solutions to the ideal one however, farthest away from the worst solution. For instance, suppose that there is a multi-criteria decision problem, this problem has (n) alternatives (A1, A2, A3, …, An) And (m) criteria (C1, C2, C3, …, Cm). Then a matrix A (aij)nxm can be created to calculate the values of different alternatives according to each criterion. Let ¼ (w1, w2, w3, …, wm) be the vector of criteria should satisfy the summation XW m w ¼ 1. Then, (A) matrix for the ranking can be built as: j¼1 j 2

3 a11 a12 … a1n 6 a21 a22 … a2n 7 6 7 6 : : : : 7 6 7 where, i ¼ 1, …,m, and j ¼ 1,…, n A¼6 : : : 7 6 : 7 4 : : : : 5 am1 am2 … amn

(6.8)

Material selection of natural fiber composites using the analytical hierarchy process

215

Goal: determining the optimal reinforcement conditions of the Date palm/Epoxy composite for maximum tensile properties

Suggesting initial evaluation criteria for the composite tensile property

Modifying criteria accordingly and conducting a questionnaire for experts to determine the weight of each criterion in the evaluation process

Conducting a pilot questionnaire and sending experts to explore the appropriateness of the used criteria

Check the consistency of the experts’ feedback

Have atleast six consistent experts’ feedback?

No

Contact more experts

Yes

Determining the reinforcement condition alternatives of the composite and their corresponding value regarding each single evaluation criterion

Calculating the weights of the criteria and selecting the highest three ones to be used simultaneously in the evaluation process in the decision models

Building the AHP model and calculate the alternative judgment matrices regarding each single criterion

Applying the AHP procedure to rank the alternatives’ tensile property regarding the whole evaluation criteria simultaneously

Ranking the alternatives’ tensile property regarding the whole evaluation criteria simultaneously

Implementing TOPSIS method and finding the relative closeness to ideal solution for each alternative

Comparing AHP and TOPSIS results and determining the optimal reinforcement conditions that maximize the composite’s tensile property

Fig. 6.35 The integrated steps to select the best reinforcement of green composites.

Then TOPSIS steps will be as following: Step 1: Build the normalized judgment matrix to calculate the normalized score rij as: rij ¼

Xij i ¼ 1, …, m, j ¼ 1, …, n X  1 2 2 Xij i

(6.9)

216

Materials Selection for Natural Fiber Composites

Candidates of the date palm/epoxy reinforcement conditions

Table 6.15

Candidate composite specification

DPF/epoxy with 5 (mm) FLd, 0.2 (mm) FDe via 9% NaOHf DPF/epoxy with 15 (mm) FL, 0.2 (mm) FD via 6% NaOH DPF/epoxy with 20 (mm) FL, 0.2 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD, via 0% NaOH (untreated fiber) DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 3% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 9% NaOH DPF/epoxy with 40 (mm) FL, 0.5 (mm) FD via 3% NaOH DPF/epoxy with 40 (mm) FL, 0.5 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.7 (mm) FD via 3% NaOH DPF/epoxy with 40 (mm) FL, 0.7 (mm) FD via 6% NaOH

Composite name

Material selection criteria

C1

MTSa (MPa) 115

MSSb (MPa) 3.1

ELc (%) 5

C2

320

4.2

16

C3

270

3.8

18

C4

115

10

8

C5

150

10

16

C6

145

10

15

C7

135

10

10

C8

130

6

12

C9

120

6

10

C10

120

5

9

C11

85

4

14

a

MTS: maximum tensile strength. MSS: maximum shear stress. c EL: elongation to break. d FL: fiber length. e FD: fiber diameter. f Date palm fiber/epoxy composite with 5 mm fiber length, 0.2 mm fiber diameter, treated with 9% NaOH solution. b

Step 2: Use the AHP technique to calculate the weighted normalized judgment matrix, by integrating AHP and TOPSIS, the weighted normalized score Vij is expressed as: Vij ¼ Wj  rij

(6.10)

Step 3: Find both positive and negative ideal solutions (V+) and (V), respectively. This can be calculated by:   V + ¼ V1+ , …, Vn+ where Vj+       ¼ max i νij if j 2 J; min i νij if j 2 J 0

(6.11)

Material selection of natural fiber composites using the analytical hierarchy process

  V  ¼ V1 , …, Vn where Vj       ¼ min i νij if j 2 J; max i νij if j 2 J 0

217

(6.12)

where J ¼ (j ¼ 1, 2, …, n)/j is the set of beneficial factors, and J0 ¼ (j ¼ 1,2, …,n)/j is the set of nonbeneficial factors. Step 4: Find the separation measures for each different choice by using the n-dimensional Euclidean distance. One can use the following formulas to find the separation from the ideal alternative: " #1 2 X  2 Vj +  Vij ; i ¼ 1, …,m Si + ¼

(6.13)

j

" Si  ¼

X

Vj   Vij

2

#1 2

; i ¼ 1, …, m

(6.14)

j

Step 5: Calculate the relative closeness to the optimal solution (Ci *) and the corresponding rank. Simply, choose the alternative with largest (Ci *). This relative closeness Aij can be expressed as: Ci * ¼

Si  ; 0 < Ci * < 1, Si  0, Si +  0 Si + S i 

(6.15)

+

Accordingly, the normalized weights using TOPSIS analysis of the decision matrix based upon values in Table 6.15 are presented in Table 6.16. As a result, the rank of the options was found. The results of analysis are summarized in Table 6.17. Thus, each candidate was ranked according to its relative

Table 6.16

Weights using TOPSIS methods

Alternatives

MTS

MSS

EL

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 Weight

0.20 0.57 0.48 0.20 0.27 0.26 0.24 0.23 0.21 0.21 0.15 0.31

0.13 0.18 0.16 0.42 0.42 0.44 0.42 0.25 0.25 0.21 0.17 0.39

0.12 0.38 0.43 0.19 0.38 0.36 0.24 0.29 0.24 0.21 0.33 0.29

218

Materials Selection for Natural Fiber Composites

Table 6.17

Results from TOPSIS

Alternatives

MTS

MSS

EL

Si +

Si 

Ci *

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 V +j Vj

0.063 0.176 0.148 0.063 0.085 0.080 0.074 0.072 0.066 0.066 0.047 0.176 0.047

0.051 0.070 0.061 0.166 0.166 0.166 0.166 0.099 0.099 0.083 0.066 0.166 0.051

0.035 0.110 0.117 0.055 0.117 0.103 0.069 0.083 0.069 0.062 0.097 0.117 0.035

0.181 0.096 0.108 0.129 0.091 0.097 0.113 0.128 0.137 0.148 0.164

0.016 0.151 0.131 0.117 0.146 0.138 0.123 0.072 0.062 0.046 0.064

0.084 0.610 0.549 0.477 0.617 0.586 0.521 0.361 0.312 0.237 0.280

The final ranking of the alternatives according to TOPSIS method

Table 6.18 Rank

1

2

3

4

5

6

7

8

9

10

11

Alternative

C5

C2

C6

C3

C7

C4

C8

C9

C11

C10

C1

closeness to the ideal solution (Ci *) and the descending order ranking of the alternatives is shown in Table 6.18. To assess the alternatives using AHP method, weights tabulated in Table 6.15 regarding each criterion were utilized to build up their judgment matrices and to calculate the relative priorities of the whole candidates. The upper parts of the judgment matrices are shown in Fig. 6.36 and red values represent the reciprocals of their values (a value of 1.3 in red means 0.7692), keeping in mind that the judgment matrices are consistent. Utilizing Eqs. (6.2)–(6.5) to every single judgment matrix leading to results shown in Table 6.19. In the same way, the final priorities of the considered alternatives regarding the entire criteria that maximize the tensile properties of the composites are shown in Fig. 6.37. The priority closeness of some alternatives indicated that selecting the optimal reinforcement condition without building such selection model is very difficult task. Accordingly, the output ranking of the alternatives to maximize the tensile properties is presented in Table 6.20. According to this ranking, it can be demonstrated that despite the high values of maximum tensile stress for C2 and C3, the most appropriate reinforcement condition was C5 that has much lower tensile stress property.

Material selection of natural fiber composites using the analytical hierarchy process

C3

C2

C1 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

2.78

C4 2.34 1.18

C5 1.0 2.78 2.34

C6 1.3 2.13 1.8 1.3

C7 1.26 2.2 1.86 1.26 1.03

C8 1.17 2.37 2.0 1.17 1.11 1.07

C9 1.13 2.46 2.07 1.13 1.15 1.11 1.03

219

C10 1.04 2.66 2.25 1.04 1.25 1.2 1.12 1.08

C11 1.04 2.66 2.25 1.04 1.25 1.2 1.12 1.08 1.0

1.35 3.76 3.17 1.35 1.76 1.7 1.58 1.52 1.41 1.41

Incon: 0.00

(A) C1 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

C3

C2 1.35

C4 1.22 1.1

C5 3.22 2.38 2.85

C6 3.22 2.38 2.85 1.0

C7 3.22 2.38 2.85 1.1 1.1

C8 3.22 2.38 2.85 1.0 1.0 1.1

C9 1.93 1.42 1.57 1.66 1.66 1.65 1.66

C10 1.93 1.42 1.57 1.66 1.66 1.66 1.66 1.0

C11 1.61 1.19 1.31 2.0 2.0 2.0 2.0 1.2 1.2

1.29 1.05 1.05 2.5 2.5 2.5 2.5 1.5 1.5 1.25

Incon: 0.00

(B) C1 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

C3

C2 3.0

C4 3.7 1.3

C5 1.6 1.8 2.25

C6 3.2 1.2 1.12 2.0

C7 3.0 1.15 1.2 1.87 1.06

C8 2.0 1.4 1.8 1.25 1.6 1.5

C9 2.4 1.1 1.5 1.5 1.33 1.25 1.2

C10 2.0 1.4 1.8 1.25 1.66 1.5 1.0 1.2

C11 1.8 1.5 2.0 1.12 1.77 1.66 1.11 1.33 1.11

2.8 1.0 1.28 1.75 1.14 1.07 1.4 1.16 1.4 1.55

Incon: 0.00

(C) Fig. 6.36 Detailed judgment matrices of candidates regarding (A) maximum tensile strength, (B) maximum shear strength, and (C) elongation to break.

This evidently proves the advantage of using the combined evaluation criteria (more than one property at once) as well as implementing decision-making models to evaluate and determine the best reinforcement conditions in green composites to reduce human errors and bias in the selection process. Furthermore, it can be seen that the untreated fiber with suitable diameter and length has the capability to demonstrate a better overall tensile strength than others treated, but with improper reinforcement conditions. So, fiber treatment is not always recommended if unsuitable reinforcement conditions are used and most treatments have to be avoided to save time, cost, and effort. It can be noticed that the final ranking is not similar for both methods, which are frequently found while considering a problem with various decision-making tools,

220

Materials Selection for Natural Fiber Composites

Table 6.19 AHP- relative priorities of the reinforcement condition alternatives regarding the model’s evaluation criteria (AL-Oqla et al., 2015c) Criteria Alternatives

MTS

MSS

EL

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

0.068 0.187 0.158 0.068 0.088 0.085 0.079 0.076 0.07 0.07 0.05

0.043 0.058 0.052 0.138 0.138 0.143 0.138 0.083 0.083 0.069 0.055

0.038 0.106 0.138 0.061 0.123 0.115 0.076 0.092 0.076 0.069 0.107

0.14 0.12

Priority

0.1 0.08 0.06 0.04 0.02 0

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

C11

Priority 0.048 0.107 0.107 0.095 0.12 0.119 0.103 0.084 0.078 0.069 0.071

Fig. 6.37 Priorities regarding the whole evaluation criteria simultaneously.

i.e., different tools usually lead to a different ranking (Dağdeviren et al., 2009; Opricovic & Tzeng, 2004). However, both tools ended up with a similar best alternative (C5) to confidently concluded that DPF/Epoxy with 40 (mm) FL, 0.3 (mm) FD via 3% NaOH is the best reinforcement condition capable of maximizing the overall tensile property, considering combined evaluation criteria. In more detail, both AHP and TOPSIS methods revealed the same first four alternative ranking, namely C5, C6, C2

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.20

Final ranking using AHP method

Rank

Alternative

1 2 3 4 5 6

DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 3% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 6% NaOH DPF/epoxy with 15 (mm) FL, 0.2 (mm) FD via 6% NaOH DPF/epoxy with 20 (mm) FL, 0.2 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD via 9% NaOH DPF/epoxy with 40 (mm) FL, 0.3 (mm) FD, via 0% NaOH (Untreated Fiber) DPF/epoxy with 40 (mm) FL, 0.5 (mm) FD via 3% NaOH DPF/epoxy with 40 (mm) FL, 0.5 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.7 (mm) FD via 6% NaOH DPF/epoxy with 40 (mm) FL, 0.7 (mm) FD via 3% NaOH DPF/epoxy with 5 (mm) FL, 0.2 (mm) FD via 9% NaOH

7 8 9 10 11

221

C5 C6 C2 C3 C7 C4 C8 C9 C11 C10 C1

AHP TOPSIS

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10 C11

Fig. 6.38 Trend of ranking in both methods.

and C3. This can enhance the reliability of the decision-making model. A comparison of the ranking trend for both tools is demonstrated in Fig. 6.38, where clear similarity is revealed. Moreover, the relative priorities of the first five candidates (C5, C6, C2, C3, and C7) are shown with appropriate scales in Fig. 6.39, where the first potential alternative (C5) doesn’t demonstrate the largest value either in MTS or EL. Besides, composites designated as C7 comes in the fifth position though it exhibits equal value of MSS with alternative C5. This detailed investigation obviously demonstrates that utilizing combined evaluation criteria evidently results in better assessments of green composites and has the ability to improve finding the optimal reinforcement condition. In addition, TOPSIS method’s reliability is verified in Fig. 6.40, where the alternatives normalized values with respect to the entire evaluation criteria are within the required limits of the positive and negative ideal solutions.

C5 0.12 0.1 0.08 0.06 0.04

C7

C6

0.02 0 AHP priority OPSIS priority MTS MSS EL C3

C2

Fig. 6.39 Comparison of the first five alternatives. C1 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

C1 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

C11

C10

C11

C2

C10

C3

C7

C4

C5

C8

C5

C8

C3

C9

C4

C9

C2

C7

C6

(A)

C6

(B) Positive ideal solution Negative ideal solutions Normalized MTS

C11

C10

C1 0.12 0.1 0.08 0.06 0.04 0.02 0

Positive ideal solution Negative ideal solutions Normalized MSS

C2

C3

C4

C9

C5

C8 C7

C6

Positive ideal solution Negative ideal solutions Normalized EL

(C) Fig. 6.40 Arrangement limits of the evaluation criteria in TOPSIS method (A) MTS (B) MSS, and (C) EL.

Material selection of natural fiber composites using the analytical hierarchy process

6.8

223

Sensitivity analysis of the selection models using analytical hierarchy process

The various comprehensive decision-making selection models built for various engineering fields by including the composite selection ones AL-Oqla et al. (AL-Oqla & Hayajneh, 2007; AL-Oqla & Omar, 2012, 2015; AL-Oqla & Sapuan, 2015a,2015b; AL-Oqla et al., 2015a, 2015c, 2015f, 2015g, 2016a) demonstrate strong reliability of their results by means of studying the sensitivity analysis offered by the AHP method. To demonstrate the reliability of the drawn decisions as a consequence of altering various parameters of the selection models, a sensitivity analysis has to be performed. In such studies, the models’ output weights first have to be calculated. For instance, regarding the model generated for predicting the potential and selection the available natural fibers, the model output is demonstrated in Fig. 6.41 where flax and date palm fibers are very close in their priorities and there is no dominant fiber that can be always selected. Now, a question about how the model would respond if some weights of criteria are changed is still valid. In other words, how sensitive is the model for a small change in the factor’s weight? To clearly answer this question, the sensitivity analysis has to be implemented. Such analysis is usually performed by changing main factors’ weights to unexpected or exaggerated levels. This is in fact, to demonstrate an unreasoning change under normal circumstances and to examine the dominancy of the alternatives in the altered model, i.e., making a particular evaluation criterion to be dominant (increasing its weight to more than 50%) and checking the existence of any dominant alternative. If no dominant alternative appears, it will be an indication that the models’ drawn decisions will be stable and insensitive to a small change in the weights of the main factor. Accordingly, the decision will be confident and stable. This will be repeated in all factors in the model to ensure the robustness of the model.

Sisal

Priority

Hemp Flax Date palm Coir 0

Priority

Coir 0.185

0.05 Date palm 0.21

0.1

0.15 Flax 0.227

0.2 Hemp 0.184

Fig. 6.41 Outputs of the model used for selecting the natural fibers.

0.25 Sisal 0.194

224

Materials Selection for Natural Fiber Composites Obj%

Alt% 0.40

0.90 0.80 0.30 0.70

Flax Date Palme

0.60

Sisal

0.50

0.20

Coir Hemp

0.40 0.30 0.10 0.20 0.10 0.00 Physical pro

0.00 Chemical/Bio

Mechanical P

Specific Per

Overall

Fig. 6.42 The sensitivity graph of the model concerning selecting the natural fibers for green composites (AL-Oqla et al., 2015g).

As a result, the sensitivity graph of the main factors of the abovementioned model regarding the goal is presented in Fig. 6.42. A close examination of this graph shows that the two competing fibers (flax and date palm) are close in their sensitivity regarding Physical Properties criterion, but far in their sensitivity regarding the Mechanical Properties criterion. In other words, Fig. 6.42 reveals that flax is positively sensitive to the Mechanical Properties criterion, but negatively sensitive to the Specific Performance for Automotive Applications. On the contrary, date palm is positively sensitive to the Specific Performance for Automotive Applications factor, but negatively sensitive to the Mechanical Properties. Such sensitivity behavior evidently indicates that any reasonable increment in the Mechanical Properties weight would significantly expand the gap in favor of flax as the best choice, whereas a reasonable decrease in this factor’s weight would shrink the gap and may change the priority to be in favor of date palm fiber as the best choice. The same conclusions can be drawn if the weight of Specific Performance for Automotive Applications is changed where raising its weight makes date palm fiber the best solution. Furthermore, to examine the model response, the weights Mechanical Properties and Specific Performance for Automotive Applications were almost doubled and the new rankings are demonstrated in Figs. 6.43 and 6.44, respectively. Even though the newly assigned weights are hard to occur under normal conditions, Fig. 6.43 still designates flax as the best choice with a score of 27.1% without dominating the other alternatives. Similarly, the sensitivity of the selection model regarding selecting the best polypropylene/ natural fiber composites was examined with different scenarios. It was demonstrated that altering the weights of criteria (increasing or decreasing) by 10% did not significantly change the ranking of the alternatives, which represents the robustness of the model. One scenario is revealed in Fig. 6.45, where tensile

Material selection of natural fiber composites using the analytical hierarchy process 15.2% Physical properties

17.3% coir

15.2% Chemical / biological properties

16.6% date palm

54.2% Mechanical properties

27.1% flax

15.3% Specific performance for automotive application

20.6% hemp

225

18.3% sisal

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0

0.1

0.2

0.3

0.4

Fig. 6.43 The sensitivity regarding the mechanical properties factor, the new weights (left), and corresponding alternatives new scores (right) (AL-Oqla et al., 2015g).

15.2% physical properties

20.7% coir

15.2% chemical / biological properties

25.3% date palm

15.2% mechanical properties

19.1% flax

54.4% Specific performance for automotive application

17.3% hemp 17.5% sisal

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0

0.1

0.2

0.3

0.4

Fig. 6.44 The sensitivity regarding the specific performance for automotive applications factor, new weights (left), and the corresponding new scores (right) (AL-Oqla et al., 2015g).

strength weight was enlarged from 8.68% to about 9.54% without altering the candidates’ ranking. Such analysis reveals the reliability in the drawn decisions in selecting the appropriate natural fiber composites. Moreover, various scenarios of altering the main factors of the model of selecting the most appropriate polymer matrix type for the date palm fibers have been performed and the corresponding scores of the alternatives were obtained and tabulated in Table 6.21 to demonstrate the robustness of the model and the reliability of the drawn decisions.

226

Materials Selection for Natural Fiber Composites

5.5% A1

9.5% tensile strength

6.7% A2

3.3% tensile modulus

5.6% A3

23.9% flexural strength

6.9% A4 8.4% A5

13.5% flexural modulus

10.1% A6 44.6% impact strength

5.4% A7

5.2% maximum water absorption

8.7% A8 5.3% A9 6.9% A10 7.4% A11 5.6% A12 5.7% A13 6.6% A14 5.2% A15

0

0.1

0.2

0.3

0.4

0

0.1

Fig. 6.45 One sensitivity scenario of changing the weights of factors by 10% (AL-Oqla et al., 2016a).

The weights of both Physical Properties and Mechanical Properties were changed by 10%, but the rest weights were changed by only 5% because they only have less than 12% contributions to the model. A close study of Table 6.21 reveals that in all scenarios, the ranking of alternatives are almost the same and similar to the original model output ranking, but the order of Polyester and Low Density Polyethylene types fluctuates in the fourth and fifth locations subjected to the altered criteria, which is strongly expected because they have almost similar priorities in the selection model. Therefore, sensitivity analysis definitely demonstrates the robustness of the obtained selection results and that the drawn decisions were confident, stabile, and reliable. In advance, the overall aggregated local (with respect to the main criterion) and global (with respect to the main goal of the model) weights or contributions of the developed selection model items are revealed in Table 6.22. This demonstrates that the largest global contribution among all items in the model of selecting the best polymer type for the commonly and uncommonly used fibers (in the case of flax and date palm) was coming from the Density factor (score of 17.6%), then by both Elastic Modulus and Yield Strength items (score of 15.7% for each). Conversely, the least globally contributive factor was the Curing Pressure with a score of only 0.8%. Here it is woth investigating the role of the contributive criteria in the selection models in order to directly indicate their importance. Therefore, it can be detected that all of Density, Yield Strength, and Elastic Modulus are the three main important

Ranking of polymer candidates corresponding to a change in the weights of the model criteria Physical properties

Rank (priority) 1 2 3 4 5

10% Increase PP (24.3%) Epoxy (21.3%) HDPE (18.3%) LDPE (18.2%) Polyester (17.9%)

10% Decrease PP (24.5%) Epoxy (22.0%) HDPE (18.0%) Polyester (17.9%) LDPE (17.5%)

Mechanical properties 10% Increase PP (23.9%) Epoxy (22.8%) Polyester (18.5%) HDPE (17.7%) LDPE (17.2%)

10% Decrease PP (25.0%) Epoxy (20.6%) HDPE (18.6%) LDPE (18.5%) Polyester (17.4%)

Chemical/technical properties 5% Increase PP (24.8%) Epoxy (21.1%) HDPE (18.4%) LDPE (18.1%) Polyester (17.6%)

5% Decrease PP (24.0%) Epoxy (22.2%) Polyester (18.3%) HDPE (17.9%) LDPE (17.6%)

Environmental/other properties 5% Increase PP (24.8%) Epoxy (21.4%) HDPE (18.2%) LDPE (17.9%) Polyester (17.8%)

5% Decrease PP (24.0%) Epoxy (22.0%) HDPE (18.1%) Polyester (18.1%) LDPE (17.8%)

Material selection of natural fiber composites using the analytical hierarchy process

Table 6.21

227

228

Materials Selection for Natural Fiber Composites

Local and global priorities of the model items of selecting the best polymer type

Table 6.22

Main criterion

Sub-criterion

Physical properties D TC CTE GTT AIP Mechanical properties EM FT EB YS IS Chemical/technical properties CT CP CM RC LHN Environmental and other properties

WR ST SR W C

Local priority

Global priority

0.315 0.558 0.148 0.116 0.061 0.117 0.474 0.331 0.110 0.098 0.331 0.130 0.095 0.301 0.089 0.138 0.259 0.212 0.117 0.237 0.266 0.245 0.146 0.106

0.315 0.176 0.047 0.037 0.019 0.037 0.474 0.157 0.052 0.046 0.157 0.061 0.095 0.028 0.008 0.013 0.025 0.020 0.117 0.028 0.031 0.029 0.017 0.012

factors in the model, however, Curing Time, Curing Pressure, Glass Transient Temperature, Polymer Cost, and Wettability are not as important for the selection process of polymers from the experts’ knowledge standpoint. This would improve the future perspectives of selecting polymers for various industrial applications in a more practical manner.

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Rahman, M. R., Huque, M. M., Islam, M. N., & Hasan, M. (2008). Improvement of physicomechanical properties of jute fiber reinforced polypropylene composites by posttreatment. Composites Part A: Applied Science and Manufacturing, 39, 1739–1747. Rao, R. V. (2013). Decision making in the manufacturing environment: Using graph theory and fuzzy multiple attribute decision making methods. Springer. Rao, R., & Davim, J. (2008). A decision-making framework model for material selection using a combined multiple attribute decision-making method. International Journal of Advanced Manufacturing Technology, 35, 751–760. Rao, R., & Patel, B. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31, 4738–4747. Rathod, M. K., & Kanzaria, H. V. (2011). A methodological concept for phase change material selection based on multiple criteria decision analysis with and without fuzzy environment. Materials & Design, 32, 3578–3585. Rowell, R. M., Sanadi, A. R., Caulfield, D. F., & Jacobson, R. E. (1997). Utilization of natural fibers in plastic composites: problems and opportunities. Lignocellulosic-Plastics Composites, 23–51. Saaty, T. (1980). The analytic hierarchy process. New York: McGrawHill. Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: the AHP/ANP approach. Operations Research, 61, 1101–1118. Saaty, T. L., & Shang, J. S. (2011). An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts. European Journal of Operational Research, 214, 703–715. Saaty, T. L., & Tran, L. T. (2007). On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process. Mathematical and Computer Modelling, 46, 962–975. Saaty, T. L., & Vargas, L. G. (2012). Models, methods, concepts & applications of the analytic hierarchy process. New York: Springer. Sapuan, S. M., Haniffah, W., & AL-Oqla, F. M. (2016). Effects of reinforcing elements on the performance of laser transmission welding process in polymer composites: A systematic review. International Journal of Performability Engineering, 12, 553. Sapuan, S. M., Kho, J. Y., Zainudin, E. S., Leman, Z., Ali, B., & Hambali, A. (2011). Materials selection for natural fiber reinforced polymer composites using analytical hierarchy process. Indian Journal of Engineering & Materials Sciences, 18, 255–267. Sapuan, S. M., Pua, F. -L., El-Shekeil, Y., & AL-Oqla, F. M. (2013). Mechanical properties of soil buried kenaf fibre reinforced thermoplastic polyurethane composites. Materials & Design, 50, 467–470. Shah, D. U. (2013). Developing plant fibre composites for structural applications by optimising composite parameters: A critical review. Journal of Materials Science, 48, 6083–6107. Shah, D. U. (2014). Natural fibre composites: Comprehensive Ashby-type materials selection charts. Materials & Design, 62, 21–31. Shalwan, A., & Yousif, B. (2014). Investigation on interfacial adhesion of date palm/epoxy using fragmentation technique. Materials & Design, 53, 928–937. Sola, A. V. H., & Mota, C. M. D. M. (2012). A multi-attribute decision model for portfolio selection aiming to replace technologies in industrial motor systems. Energy Conversion and Management, 57, 97–106. Subramoniam, R., Huisingh, D., Chinnam, R. B., & Subramoniam, S. (2013). Remanufacturing decision-making framework (RDMF): Research validation using the analytical hierarchical process. Journal of Cleaner Production, 40, 212–220.

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Sudhakara, P., Jagadeesh, D., Wang, Y., Prasad, C. V., Devi, A., Balakrishnan, G., et al. (2013). Fabrication of (Borassus) fruit lignocellulose fiber/PP composites and comparison with jute, sisal and coir fibers. Carbohydrate Polymers, 98, 1002–1010. Symington, M. C., Banks, W. M., West, O. D., & Pethrick, R. (2009). Tensile testing of cellulose based natural fibers for structural composite applications. Journal of Composite Materials, 43, 1083–1108. Tavana, M., & Hatami-Marbini, A. (2011). A group AHP-TOPSIS framework for human spaceflight mission planning at NASA. Expert Systems with Applications, 38, 13588–13603. Toupe, J. L., Trokourey, A., & Rodrigue, D. (2014). Simultaneous optimization of the mechanical properties of postconsumer natural fiber/plastic composites: Phase compatibilization and quality/cost ratio. Polymer Composites, 35, 730–746. Wang, T. -C., & Chang, T. -H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33, 870–880. Zampaloni, M., Pourboghrat, F., Yankovich, S., Rodgers, B., Moore, J., Drzal, L., et al. (2007). Kenaf natural fiber reinforced polypropylene composites: A discussion on manufacturing problems and solutions. Composites Part A: Applied Science and Manufacturing, 38, 1569–1580. Zh€ u, K. (2013). Fuzzy analytic hierarchy process: Fallacy of the popular methods. European Journal of Operational Research, 236, 209–217.

Further Reading Agoudjil, B., Benchabane, A., Boudenne, A., Ibos, L., & Fois, M. (2011). Renewable materials to reduce building heat loss: Characterization of date palm wood. Energy and Buildings, 43, 491–497. Ghosh, S., Nayak, L., Day, A., & Bhattacharyya, S. (2007). Manufacture of particle board from date-palm leaves—A new technology product. Indian Journal of Agricultural Research, 41, 132–136. John, M. J., & Anandjiwala, R. D. (2007). Recent developments in chemical modification and characterization of natural fiber-reinforced composites. Polymer Composites, 29, 187–207. Kriker, A., Bali, A., Debicki, G., Bouziane, M., & Chabannet, M. (2008). Durability of date palm fibres and their use as reinforcement in hot dry climates. Cement and Concrete Composites, 30, 639–648. Lewin, M. (2007). Handbook of fiber chemistry. Boca Raton, FL: CRC Press, Taylor & Francis Group. Li, X., Tabil, L. G., Oguocha, I. N., & Panigrahi, S. (2008). Thermal diffusivity, thermal conductivity, and specific heat of flax fiber–HDPE biocomposites at processing temperatures. Composites Science and Technology, 68, 1753–1758. Majeed, K., Jawaid, M., Hassan, A., Abu Bakar, A., Abdul Khalil, H., Salema, A., et al. (2013). Potential materials for food packaging from nanoclay/natural fibres filled hybrid composites. Materials & Design, 46, 391–410. Pilla, S. (2011). Handbook of bioplastics and biocomposites engineering applications. Salem: Scrivener Publishing.

Material selection of natural fiber composites using other methods 7.1

7

Java-based materials selection

Various research has considered the material selection process for the design of polymer composite materials using numerous methods. But minimal considerations have been given for material selection of green composites. Several procedures and methods were offered in this field. Knowledge-based expert systems and computer oriented materials selection ones and are of the main method in materials selection. The use of a computer in engineering has been practiced since its invention. Because computer systems offer fast processing and accurate results, it is employed for selection systems. Thousands of material types are accessible for the design and manufacturing process in engineering, where new materials and innovative products are the key factors in the successful engineering industries (AL-Oqla & Omar, 2015; Aridi, Sapuan, Zainudin, & AL-Oqla, 2016b; Dweiri & AL-Oqla, 2006; Sapuan, 2001; Sapuan, Haniffah, & AL-Oqla, 2016). It is known that designers usually not rely on a single material type for their designs, but do think about composites to enhance their material integrity (Almagableh, AL-Oqla, & Omari, 2017; Alves et al., 2010). As result of the continuous research and development, numerous new composites are emerging in the world. Thus, the concept of selecting the optimum functional material type for various components is always in the minds of engineers and decision makers and has been for the past few decades. As a result, various procedures, algorithms, and software tools have been established for proper materials selection (Ashby, 2005; Jahan, Ismail, Sapuan, & Mustapha, 2010). In recent times, computer-based material selection has been highly emphasized and deployed for materials section and various commercial software systems have been utilized, such as the Cambridge Material Selector (CMS) software. Moreover, Computer-Aided Design (CAD) structural optimization, Computer-Aided Process Planning (CAPP), and Multipoint Approximation (MARS) method are also used in the selection process (Ali, Sapuan, Zainudin, & Othman, 2013; Chiner, 1988; Khalili & Duecker, 2012). Such methods have enabled designers to examine the possibility of substituting traditional materials with natural fiber composites (Agoudjil, Benchabane, Boudenne, Ibos, & Fois, 2011; AL-Oqla, Almagableh, & Omari, 2017; AL-Oqla & Hayajneh, 2007). For developing the Java-based natural composite material selection process, the physical and mechanical properties of natural fiber composites are primarily considered. Thus, limited criteria are usually considered for selecting such types of composites(Alkaabneh, Barghash, & Mishael, 2013; AL-Oqla, Alothman, Jawaid, Sapuan, & Es-Saheb, 2014; AL-Oqla & Omari, 2017; AL-Oqla & Sapuan, 2015a; AL-Oqla, Sapuan, Ishak, & Aziz, 2014; AL-Oqla, Sapuan, Ishak, & Nuraini, Materials Selection for Natural Fiber Composites. http://dx.doi.org/10.1016/B978-0-08-100958-1.00007-4 Copyright © 2017 Elsevier Ltd. All rights reserved.

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Selection modules

Java

Rule-based expert system

User interface software

Database User list Material property Case study Weight-age Result-set

Fig. 7.1 A schematic diagram of a Java based expert material selector (Ali et al., 2013).

2015a; Aridi, Sapuan, Zainudin, & AL-Oqla, 2016a). The selection criteria for the natural fiber composites based on Java-based systems were found to be in most cases the density, the tensile strength, and Young’s modulus. This is because the natural fiber composites database did not usually meet the recently advanced engineering requirements comparable to other traditional and commercial types of materials, like metals and plastics (AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015; AL-Oqla, Sapuan, Ishak, & Nuraini, 2015b, 2015c, 2015d, 2016). In addition, the data of natural fiber composites are usually collected from the literature, particularly the experimental published results. A schematic block diagram of Java-based expert material selector is demonstrated in Fig. 7.1. For instance, a Java-based selection system for natural fiber composites was demonstrated by Ali et al. (2013) and developed using the software programming language NetBean JAVATM. The Java was used because it is considered as an independent platform capable of moving from one platform to another easily. In addition Java can work on distributed environment and it is easy to organize as hypertext transport protocol (http) for World Wide Web usage. Such a selection system can also offer a user-friendly access interface. Moreover, when it executes the system, it can display a login screen as presented in Fig. 7.2, which represents a security feature providing the required authentication via validating the username and password. Such Java-based selector systems for natural fiber reinforced polymer composites consist of four modules with four different interface screens for more user-friendly access. These modules are: 1. 2. 3. 4.

Material knowledge-based module Case study selection unit Selection scheme module The process module

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Fig. 7.2 The login screen for the natural composite selection system (Ali et al., 2013).

The material database was oriented for the natural fiber reinforced composites, which contains enormous types of composite materials data, which was managed with the relational database management system (RDBMS) software MySQL server database. Such databases can be updated with expanding features to select optimum composite materials according to certain requirements where connections with five types of tables within the database, such as user list, material properties, case study, weight-age, and result set. Fig. 7.3 illustrates the natural material database

Fig. 7.3 The screen manipulation of the natural material database (Ali et al., 2013).

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Fig. 7.4 A case study menu (Ali et al., 2013).

manipulation screen. Such screen enables the user to show all the available material data with a facility for data management like adding new material, editing, and searching the available materials in the database. The case study that considered the automotive components was implemented to demonstrate the feasibility of the selection system where three automotive components were considered, namely, the door panel, the dashboard component, and rear panel. In such case study verification, the user can choose a drop-down from a specific component required to be explored as shown in Fig. 7.4. In the selection process, users can enter the range of data desired for the component and then execute the selection result. Rigorous advances of computer-oriented software and hardware has recently allowed the creation of distinct software packages—expert systems that have the capabilities of including various databases, as well as offering opportunities for interactive material selection systems for wide engineering applications (I˙pek et al., 2013; Md Hassan, 2014). Such expert systems in fact allow the integration of utilizing the expert-knowledge of materials with fast informative decision-making for product development and materials selection processes. The available computer databases are becoming more commercially available, resulting in materials properties and capabilities being rapidly transferred into various engineering activities vital to such information technologies. Furthermore, data obtained from primary and secondary sources are usually considered complex with various levels of reliability and comparability as a result of the standard or non-standardized testing methods(Sanyang & Sapuan, 2015). Therefore, the available databases are different in terms of both types

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and numbers of the collected data, accuracy levels, structure of the recorded information, search capabilities, in addition to the type of treated materials such as ceramics, polymers, metals, and composites and in properties (chemical, physical, mechanical, optical, etc.) and material applications such as automotive, biocompatible, packaging, aircraft, etc. Here, some of the available databases are mentioned with their main features: l

l

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l

l

l

l

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l

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Material Index database: it is from manufacturer MEMSnet. It contains general material properties, and is free of charge. Principal Metals Online: contains the basic properties of metal alloys. It can be found free of charge. Steel Spec Web Edition: it is a web page of the printed edition of Steel Specification of the UK Steel Association. Prospector: it is of manufacturer IDES-Integrated Design Engineering Systems, contains about 49,000 pages of plastic materials from about 400 suppliers. Campus Plastics: offers high quality and comparable data regarding polymeric materials including brief descriptions of materials, thermal properties, mechanical properties, electrical characteristics, diagrams, resistance to chemical influence, etc., based on unique international standards, like ISO 10350, ISO 11403-1. Alu SELECT: it is an internet database containing technical data regarding aluminum alloys such as the mechanical, chemical, and physical. Win Steel: is an electronic database containing data about a very large number of steel grades with information about the chemical composition and characteristics based on various international standards. Key to metals: it is a universal database for both ferrous and non-ferrous materials. It is considered as a comprehensive electronic database in the field of materials. Users may have the ability either to install it on a personal computer from a CD or to use it online with appropriate compensation. Steel Selector-Metal Ravne: is an electronic database that provides the ability to explore steel from the production program of steelworks Metal Ravne. Matweb: is internet-based and contains data about a large number of materials such as ceramics, polymers, iron alloys, pure elements (metals), super alloys, non-iron, and other engineering materials. Stahl Wissen Navimat-Online: it is multimedia software containing various features of the steel.

Expert systems on the other hand, were developed in the mid-twentieth century based on expert knowledge integrated with the practical experience, by means of artificial intelligence (Chakraborty, Chakraborty, Prasad, & Prasad, 2016). Such systems try to reproduce the methodologies used in experts’ problem-solving via various reasoning methods of assessment and decision-making (Prasad, Mahanty, Maity, & Chakraborty, 2014). However, the major limitations of the expert systems are the proper collection, evaluation, and shaping the knowledge for later processing for a particular situation. In addition, finding experts capable of adequately quantifying the required knowledge and into consistent judgment is another problem for such expert systems. Modern expert systems have the ability to fill out the questionnaire, selection criteria, as well as presenting results by means of tabular and graphical display of all information. It is also capable of communicating with other related

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Fig. 7.5 Cambridge Engineering Selector.

activities, including supply of material, manufacturing, and control processes. Cambridge Engineering Selector (CES) is a commonly used expert system for material selection, as in Fig. 7.5. It is a personal computer-oriented application that enables engineers and material experts to find proper optimum materials for their product. This product is helpful for reaching proper decisions about materials in the early stage of product design, or replacement of the existing materials with new ones (Prasad et al., 2014). It also offers tools to enhance rational selection methodologies where software and database modules are properly connected in packages. Each package is oriented to permit the complete selection according to a specific CAD requirement. The material selection in Cambridge Engineering Selector is directed in a way that input data are exactly the design requirements. Such requirements are then properly conveyed into guidelines for material selection as well as properties of machining process. A program then reveals proper selection charts as a matter of visual representation for the important properties or combinations to enhance the material selection process as in Fig. 7.6. Such a screening way for ranking material properties is the first step in such system for selecting materials. The resulting charts and graphs give a ranking list for the potential material alternatives and use combined multiple charts which can significantly narrow the range of possible alternatives. However, the resulting alternatives after performing a series of such stages are large numbers as this system lackss the simultaneous combined evaluation methodology presented by AL-Oqla, Sapuan, Ishak, and Aziz (2014). After CES demonstrates the potential group of satisfactory materials that passes the series of “passing limitation stage schemes,” it enables direct

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Fig. 7.6 A scheme of setting limitations for material selection in Cambridge Engineering Selector software.

access to a huge source of additional information regarding these materials by means of diagrams, texts, data, and figures to let the user know the characteristics of such final alternatives.

7.2

Knowledge-based system

A knowledge-based system (KBS) is a decision-making system based on knowledge of a particular task and the logical procedures to take advantage of such knowledge. It is easy to build such a system, simply, like a tree diagram that has branches; each of these branches describes a criterion of selection that when combined, helps in making the decision. This kind of tree structure can be computerized to be automated systems, which are sometimes called expert systems. Such knowledge-based systems can answer why data is required and how conclusions are revealed. A system can be interactive with users, i.e., asking the user questions to be more precise, or embedded, where all input come from another program (Anojkumar, Ilangkumaran, & Sasirekha, 2014; Sapuan, 2001; Sapuan, Jacob, Mustapha, & Ismail, 2002). KBS have the ability to deal with a huge number of problems. The development of such KBS software is easy, but time-consuming. New materials can be defined and inserted into such software, thus, the database expanded with experience. In fact, to effectively implement a knowledge-based system with a low cost, one needs to adopt (easy-to-use)

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improvement tools readily offered to the experts (Chiner, 1988). As more problems for complicated applications require solving, more advanced level tools have to be used along with advanced features to be integrated with other programs. A knowledge-based system is an artificial intelligence tool that works in a narrow domain to offer intelligent decisions with explanation (Islam, Beg, & Mina, 2013). The first advantage gained by such a system is documentation of knowledge. Knowledge is the most respected resource that must be taken, stored, used, and continuously improved, in the same way database systems have been used in the previous decades. However, efficient knowledge-based systems applications may be a significant market. Knowledge can be expressed in different forms and it is vital to express all types of knowledge in a single knowledge-based system. The most well-known form of such knowledge base systems is the rule-based expert type that usually offers many desired features like: l

l

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l

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Provides user defined interface. Provides intelligent system. Knowledge is expressed to present the information. Knowledge is used based on preference. Contains some of the data of the interest to the system. Connected to public database. Suitable for human users. Provides general problem-solving information methods. Understand, analyze, and process the rules. Offers exploratory information.

Therefore, the benefits provided by knowledge-based systems are many, for instance, they can: l

l

l

l

l

Save energy and time. Obtain instant results. Improve the results easily. Be applied effectively for any design or application. Give recommendations about the optimal solutions.

Practically, for a material selection problem, knowledge is represented as assigning weights for material desired characteristics, then by recommending suitable types or alternatives of materials and linking the relationships between such properties with the production processes’ parameters and the final expected performance. As a result of such roles, the process of reasoning and decision-making would be significantly accelerated and revealed. The extended knowledge engineering process if represented in Fig. 7.7. A typical knowledge based system has the following components: l

l

l

Rule-Based Reasoning: The most common form of such systems is the rule-based expert system. It usually offers the user defined interface, and considered as the intelligent system that can express knowledge to demonstrate the required data. Database: it contains some of the required data to be used by the system as well as connecting the user, either to an online user profile or to a public database. Inference Engine: it offers the problem-solving knowledge methods, interprets, and processes the rules as it determines the sequence of the rules.

Material selection of natural fiber composites using other methods

User interface

Knowledge validation (test cases)

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Source of knowledge

Knowledge acquisition Knowledge base

Knowledge representation

Evaluation implementation maintenance

Explanation, justification

Inferencing

Fig. 7.7 The extended knowledge engineering process.

In fact, various material selection case studies were performed utilizing the knowledge based systems. For instance, a case study of selecting an optimum material for engine components including a piston, connecting material, and piston ring from ceramic matrix composites was performed using Kappa-PC tool-kit for the utilized knowledge-based system (Sapuan et al., 2002). The proposed hierarchy structure is demonstrated in Fig. 7.8. In addition, the rule-based system was utilized for selecting several engine components, thus numerous rules were generated. They were chained by using forward chaining technique. However, the constraints or limiting values were assigned according to the product design specification. The simple utilized rule was expressed as: If (condition), Then (conclusion) or If the condition of a rule is satisfied, then the conclusion is set as the result. Moreover, if more than one material type satisfies the constraint values, then all such materials are selected as results, and then ordered with respect to the properties and stored in the record images and the best material is presented separately. Another case study of selecting the optimum material for a drinking container was achieved via knowledge-based system by manipulating multi-dimensional array function utilizing MATLAB (Maleque & Salit, 2013). In such a case, the scale property values were calculated for various candidate materials including steel, aluminum, polyethylene (PE), glass, and polyethanol (PET), and the syntax “for” was utilized to perform one or more MATLAB statements in a loop manner. Accordingly, the system provided the optimum performance index value. Such material was then selected as the best candidate material as shown in Fig. 7.9. Results of the KBS in MATLAB program had revealed that aluminum type was validated according to the performance index to be the best among other compared material types for a drinking container.

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Fiber.reinforcement

SiC.fiber

SiCf.Si3N4 SiCf.SiC

Carbon.fiber

SiCf.Alumina Cf.SiC

ZrO2.fiber Nextel312.fiber

ZrO2f Al2O3 ZrO2f.Al2O3.SiO2 Temrok SiCw.Al2O3 SiCw.Al2O3.ZrO2 SiCw.Carbon

SiC.whisker

SiCw.Mullite.ZrO2 SiCw.Si3N4

Whisker.reinforcement

CMC

Particle.reinforcement

SiCw.Cordierite SiCw.Mullite

Si3N4.whisker

SiCw.ZeO2 Si3N4w.Si3N4

ZrO2.whisker

ZrO2w.Al2O3

B4C.whisker

B4Cw.Al2O3 SiCp.Al2O3

SiC.particle

SiCpSi3N4 SiCp.Sialon

Al2O3.particle

SiCp.YTZP.Mullite Al2O3p.Si3N4.Y2O3

TiB2.particle ZrB2.particle

TiB2p.SiC ZrB2p.SiC

BN.particle AIN.particle

BNp.SiC AINp.SiC

ZrO2.particle

ZrO2p.Al2O3 ZrO2p.Cordierite ZrO2p.Mosi2 ZrO2p.Mullite

Fig. 7.8 The hierarchy of the KBS for selecting engine components from ceramic matrix composites (CMC) (Sapuan et al., 2002).

7.3

Digital logic technique

Unlike the exact sciences, materials selection has various conflicting advantages and limitations requiring compromises and trade-offs. Therefore, many satisfactory solutions are possible as similar components performing similar functions, even if they are manufactured from different materials via different manufacturing processes. For selecting the most appropriate materials for a given application, comparing material characteristics is a must to determine the compromised ones. In the case where several material characteristics are identified, but their distinguished relative importance with respect to each other is not clear, a digital logic technique is utilized to determine their relative weights by means of a weighting factor to enhance the reliability of selection (Tulevski et al., 2014). The digital logic method is utilized as a systematic tool where assessments are set according to a pairwise comparison (i.e., only two characteristics are considered at a time). Then every possible combination of assessing the characteristic is compared, but there is no need for making a decision, only a yes or no result for each assessment is required (Rao & Patel, 2010). To decide the relative significance of each material characteristic, a table is usually created as shown in

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Fig. 7.9 The KBS for selecting drinking container by MATLAB (Maleque & Salit, 2013). Number of positive decisions N = n(n – 1)/2 Characteristic # 1

1 2 3 4 5

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Fig. 7.10 Illustrative example of obtaining weighting factors of various characteristics using the digital logic method.

Fig. 7.10, where the more significant property is given numerical one (1) and the less important is given zero (0). Consequently, there is N total number of possible judgements as N ¼ n(n  1)/2, where n is the number of compared characteristics under consideration. Furthermore, a relative weighting factor is obtained for each judgment

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matrix by dividing the number of positive choices (m) into the total number of possible decisions (N) considering that the summation of these weighting factors equals unity. To enhance the accuracy of judgment using the digital logic method, the yes-no assessments can be improved by allocating shade marks ranging from 0 (no difference in significance) to 3 (large dissimilarity and importance). In such a way, the total shade marks for each selection criterion are obtained by adding up the individual shade marks, where the weighting factors are then obtained by dividing the total shade marks by their total.

7.4

Quality function deployment for environment

Materials selection can be carried out involving multiple objectives, such as cost reduction, weight reduction, satisfying new service conditions, or new materials, and esthetic design. The wrong choice of material for certain objectives commonly involves high costs and may result in product failure. So, designers have to detect and select the right material for products to have successful lowest cost ones with customer satisfaction attribute (AL-Oqla & Sapuan, 2014a, 2014b, 2014c; AL-Oqla, Sapuan, Ishak, & Nuraini, 2014, 2015e; AL-Oqla, Sapuan, & Jawaid, 2016; AL-Oqla et al., 2015d; AL-Oqla, Sapuan, Ishak, et al., 2016). The quality function deployment (QFD) method is a popular widely used technique for selecting materials according to certain criteria. QFD is a systematic process considered in the product development as a complementary way for defining how and where priorities are to be given. Therefore, the QFD is a tool that can be applied mainly at the conceptual design stage (Dursun & Karsak, 2013; Mayyas, Qattawi, Mayyas, & Omar, 2013). However, a limited number of researches are found in the literature using QFD for materials selection. The process of the QFD can be achieved in more than one operational unit with different levels of complexity. They may be performed in the form of: l

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Table: as the simplest operational unit utilized to understand the problem. It can be performed through a tree diagram; Matrix: it is usually a correlation of two tables. The house of quality is just a pattern of a matrix that compares the customers’ requirements with the service or product’s quality requirements. Conceptual Model: it is a mapping for arranging or organizing several tables and matrices in order to achieve the completion of the desired tables. Standard: it is the unfolding information created for communicating various zones of an organization.

Therefore, the house of quality (HoQ) is usually the first matrix constructed in the QFD method and it is considered as the heart of the Quality Deployment, as it is directly connected to the customer requirements (Chakraborty et al., 2016). That is; the HoQ is the matrix that translates the customer requirements into technical requirement as illustrated in Fig. 7.11. This process is called extraction and could be made several times through the design process according to a particular conceptual model. The QFD has been utilized for environmental application where customer needs were usually related to the performance of the products or services including cost,

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Technical correlation matrix Primary Secondary

Tertiary

Technical requirements

Secondary

Primary

Interrelationship matrix

Planning matrix

Tertiary

Prioritized technical requirements

“House of quality’’

Technical requirements

Customer requirements

Fig. 7.11 House of quality.

durability, time delivery, availability and quality (Bhattacharya, Geraghty, & Young, 2010; Zaim et al., 2014). For qualitative research, the customer needs must be properly identified via various way including brainstorming, questionnaire, discussion, and historical data. Thus the major concern of the QFD is how to build the house of quality.

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Fig. 7.12 Detailed of structure HOQ.

F B A

C

D

E

The way of detecting the relationship between customer needs is called “Whats,” whereas the engineering characteristics are called “Hows.” Accordingly, establishing the HOQ requires cross-functional teams to be correctly achieved. Generally, the structure of HOQ consists of six main parts to properly convert the customer needs into product characteristics. A detail of the structure of HOQ is illustrated in Fig. 7.12 and consists of several parts: Part Part Part Part Part Part

A: The customer requirements (Whats) B: The engineering characteristic (Hows) C: The connection between Whats and Hows D: The planning matrix E: Ranking the engineering characteristic and target value F: The correlations between the engineering characteristics

The conceptual model may be considered as a collection of tables and matrices that demonstrates the way that must be followed to achieve the design goals, as in Fig. 7.13. It can be defined as a graphic form for representing cause-effect relationships of required quality, or other effects, with cause factors that contribute to the formation of the product, according to a desired logic. The QFD Method is widely known tool in the field of product development and used for material selection, but it involves human intervention and bias. To improve the reliability of the materials selection method by QFD, as well as reducing its uncertainty, data contained from this method Fig. 7.13 Illustration of QFD conceptual model.

“House of quality”

Customer requirements

Systems sub-systems parts (SSP)

Technical requirements

Material selection of natural fiber composites using other methods

Assessment matrix

Selection matrix

Materials properties

Weight

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Materials list

Correlation matrix

Materials properties values

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Fig. 7.14 Conceptual model of QFD materials selector.

are usually standardized or provided by experts or handbooks. As an example, a materials selector based on the principles of QFD can be built with a conceptual model as illustrated in Fig. 7.14 with two matrices an assessment matrix and a selection one. Furthermore, QFD method is utilized for the design of eco-friendly products in which environmental requirements are considered at the early stage of product design. Utilizing QFD method with the environmental considerations would dramatically support designers identifying the priorities of environmental indicators capable of improving the eco-friendly products. The procedure of the QFD for environment can be designated as follows: 1. Identifying Customer as well as Environmental Requirements. The environmental requirements for eco-friendly products may include the energy consumption, easy disposal, less material use, reduction of waste, easy to transport and retain, easy to clean, harmless to the living environment, safe emissions, and high durability. 2. Identifying Technical Attributes. This is, in fact, how to answer the customer environmental requirements, which are usually wide, complex, and uncertain. Thus, a systematic approach is required to perform this step. A life-cycle analysis that involves raw material, design, manufacturing, end life cycle, distribution, and use may be considered as an appropriate framework for this step. 3. Performing QFD with Environmental Concern. This is how to assign weights to the customer environmental requirements and how to recognize the relationship matrix between Whats and Hows. Therefore, this stage would involve participation of some experts to assign such properties and relationships. For this issue, the AHP decision matrix or fuzzy methods may be applied. 4. Prioritize Objective Improvement Model. Technical attributes values have to be considered by the multiplication of the weight of each customer-environmental requirement with the corresponding relationship matrix (the technical attributes). Finally, the environmental indicator with the most priority can be recognized based on the largest value of technical attributes provided. Such indicators can be considered as a target for developing eco-friendly products or materials.

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Sustainability

Environmental factors

Economical factors

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Fig. 7.15 Factors affecting sustainability in automotive industry (Mayyas et al., 2011).

As an example of applying QFD method for environment, material selection has been performed for automotive application considering various terms including the environmental, economic, social, and technical ones for evaluating different body-in-white (BIW) structures designs in terms of such sustainability issues as mentioned in Fig. 7.15. The considered alternatives of material included Bake Harden-able steel (BH), High Strength Low Alloy steel (HSLA), Aluminum 5xxx and Aluminum 6xxx sheets, Dual Phase steel (DP), Martenistic steel, Magnesium sheets, Titanium sheets, and composite materials commonly used for such applications like the Carbon Fiber Reinforced Plastic (CFRP) and High Density Polyethylene (HDPE) (Mayyas et al., 2011). In such a case study, it was considered that selecting new materials for automobile bodies is driven by several techno-economic issues. Thus, significant challenges still lie ahead for the advanced materials industry, as well as the automotive industry and its design to attain the sustainability goals. However, society has to take its role in a long-term basis for driving the industry toward sustainable product design. For purposes of material selection utilizing the QFD method, the total life cycle energy analysis and the total life cycle CO2 emission analysis for all material alternatives were performed in Fig. 7.16 to make the real comparisons of such alternatives in the QFD regarding such sustainability factors. In the material selection process utilizing the QFD, a set of objective functions was used for each panel material option in order to optimize the overall design. Thus, QFD was utilized to set all constraints for the optimization process where the following constraints were considered: l

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(B) Fig. 7.16 Total life cycle energy (A) and CO2 emission (B) analysis material alternatives (Mayyas et al., 2011).

Among these constraints, the interrelationship matrix that demonstrates the relationship between the engineering metrics was developed. Such a relationship usually shows a complete view of how an increase in score of one metric can be reflected in the others. The house of quality was also established to demonstrate the design needs where the engineering metrics were identified. For such purpose, scores were

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assigned for each design need and every design need-engineering metric entity. As an example, dent resistance had a score of 10 as a high desired design need for the panels that are prone to dent, such as front or rear fenders, roof, and quarter panels. However, lower dent resistance values were assigned to the A, and B pillars, as they are structural panels that are not prone to dent. Accordingly, the house of quality for such a case study in selecting materials for panels in the automotive industry using QFD and considering the environmental issues is represented in Fig. 7.17 as a case of roof. This figure demonstrates the customer needs and ranking of other engineering metrics for the roof. As a result, the technical rank obtained can help designers reach the relative importance for all engineering metrics. As a consequence, the first three options

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Part

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Roof Hood (inner) Hood (outer) Trunk (inner) Trunk (outer) Trunk pan Engine cradle Shock towers Quarter panel Front fender Door (inner) Door (outer) Wheel house A, B pillars Floor pan

CFRP CFRP CFRP CFRP CFRP CFRP Steel-DP Steel-DP CFRP CFRP CFRP CFRP CFRP Steel-DP CFRP

Steel-martensite Steel-martensite GFRP Steel-martensite Steel-martensite Steel-martensite Steel-BH Steel-BH Steel-martensite GFRP Steel-martensite Steel-martensite Steel-martensite Steel-BH Steel-martensite

Steel-DP Steel-DP Al-6xxx Steel-DP Steel-DP Steel-DP Steel-HSLA Steel-HSLA Steel-DP Al-6xxx Steel-DP Steel-DP Magnesium Steel-HSLA Steel-DP

Fig. 7.18 The first three options of materials using QFD for the considered panels in the automotive (Mayyas et al., 2011).

of materials for each considered panels for automotive applications based on the QFD method are demonstrated in Fig. 7.18 where composite materials were found to have high potential and ranked the first for most of the considered panels.

7.5

Pugh selection method

The selection of appropriate technological aspects, including materials, is an important step in the design process. In selecting the right technological alternative aspect, there is not always a single evaluation criterion for selection, but a large number of criteria have to be considered, including ethical, technological, economic, political, legal, and social. In the outcome, the aim of any selection procedure is to recognize the suitable selection criteria, and obtain the most suitable arrangement of criteria in coincidence with the real requirement. Therefore, efforts need to be extended to recognize such evaluation criteria that have influence in detecting the most appropriate alternatives for a given problem, by means of simple and logical approaches (AL-Oqla & Sapuan, 2015b; AL-Oqla, Sapuan, & Jawaid, 2016). The Pugh Matrix (PM) is one type of matrix diagram that helps compare a number of design alternatives or materials to achieve the best that meet a set of criteria through a qualitative optimization (Renzi, Leali, Pellicciari, Andrisano, & Berselli, 2015). This method is easy to use by utilizing a series of pairwise comparisons between alternatives using a set of large evaluation criteria. Moreover, Pugh Matrix offers a simple approach to make a pairwise comparison to capture subjective decisions. It also allows for performing a simple sensitivity analysis to enhance the robustness of the selection decision. Fig. 7.19 displays a completed Pugh Matrix utilized for evaluating and

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Materials Selection for Natural Fiber Composites

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selecting four candidate alternatives A, B, C, and D and some combinations as E and F. Such alternatives are evaluated against 10 criteria. To construct the Pugh Matrix for such alternatives, one alternative “A” is selected to be “baseline” and have a score as “S” against all criteria. Then all other alternatives are compared in a pairwise manner against alternative A for each of the evaluation criteria. If an alternative is found to be better than the baseline, a “+” sine is given in the appropriate cell, whereas a worse alternative is given a “-” sign and the similar alternative is given the “S” sign. For instance, alternative B is better than the baseline (A) for criteria 1, but alternative B is worse than the baseline regarding criteria 2. The overall evaluation is prepared by adding the “+” signs and “-” signs for each candidate alternatives. The Pugh Matrix also has the ability to conduct a qualitative optimization via combining some alternatives to form hybrid one like Alternative AB to be alternative E or BC to be alternative F, etc. Generally, constructing a Pugh Matrix includes five steps: Step 1: recognize and define the evaluation criteria where the design requirements have to reflect the user-customer attributes. Step 2: Using one alternative as a baseline with an assumption that some people prefer to use this alternative. Usually the traditional option is set to be the baseline, as its performance is reasonably well known.

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Step 3: Compare all alternatives with the baseline regarding all the considered evaluation criteria and assign a pairwise score as S ¼ same, + ¼ better, - ¼ worse. However, it is possible for some cases to add extra levels of judgment utilizing either numerical values (as a scale of 1–5) or using signs like: ++ ¼ much better, – ¼ much worse. Step 4: Calculating the total score for each alternative by summing the number of + and -. The winner option is the one with the highest ranked score. But common sense has to be used to not just select the winner alternative. Step 5: Consider hybrid options by combining, if possible, the best features from each alternative to make a qualitative optimization. Step 6: Make the selection decision with justifications behind the decisions, particularly if the final decision is not obvious from the overall evaluation criteria.

Generally, with a Pugh Matrix there is no clear winning alternative, but there is often a clear loser. Thus, a rationality check (does the decision make sense?) is usually performed to remove the losing options by weighting the criteria to give better differentiation between alternatives (Hazelrigg, 2010). In fact, the quality of the gained results in Pugh Matrix method for material selection strongly depends upon the evaluation criteria. The quality of the evaluation criteria has three aspects—incorrect selection criteria, incomplete selection criteria, and inadequate selection criteria. The incorrect selection criteria results in absolutely wrong decisions, whereas the incomplete selection criteria will occur if either not considering all suitable stakeholders points of view, or intentionally ignoring some criteria as they don’t fit our opinions or are considered as not important ones. Moreover, the inadequate criteria occur if we attempt to use poorly defined criteria, which will lead to multiple interpretations like that of low cost criterion. This criterion may lead to a fuzzy meaning, as it may have multiple meanings, such as development costs, purchase cost, or running costs. Therefore, developing suitable criteria must be performed in a careful manner. In consequence, it is recommended that the team size that develop the criteria be between four and eight experts in the field of application to properly validate and weight the selection criteria, as well as to perform robustness checks for the consistency of the matrix.

7.6

Artificial neural network

While selecting material for a sustainable product design, several factors have to be considered in the decisions like that of mechanical properties, environmental aspects, cost, and process performance, etc., therefore, multiple objectives will exist and they are often in conflict. Generally, trusting experience in the material selection process is more common than using numerical approach due to the large number of available alternatives, as well as various influencing selection criteria. However, with the development of computers, more artificial intelligence technologies are validated for material selections (Zhou, Yin, & Hu, 2009). In fact, in developing sustainable products, various issues should be considered including (Zhang & Friedrich, 2003): l

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Developing clean production process. Avoiding hazardous materials. Using easy recycling and degradable materials. Producing materials with low energy consumption.

The chain of materials for sustainable products is demonstrated in Fig. 7.20. Moreover, selecting materials for sustainable products should consider various evaluation indicators, like those indicated in Fig. 7.21. Traditional decisions in material selection only take into account physical and mechanical characteristics, process properties, and cost. But, the new approach should integrate such criteria with the environmental aspects, which make the selection more complex. Artificial neural networks (ANNs) are nonlinear mapping structures established on the function of the human brain. They are considered as powerful tools for modeling cases with unknown underlying data relationship. They have the capability of identifying and learning the correlated patterns between input data sets and corresponding

Raw material resource

Waste management degradation

Chain of material

Reuse & recycling

Production process

Sustainability

Energy consumption

Economic cost

Fig. 7.20 The chain of materials for sustainable products. Fig. 7.21 Some indicators for sustainable material selection.

Evaluation indicators of material selection

Mechanical properties

Economic properties

Strength; stiffness; toughness; hardness; density; ......

Purchase cost; Process cost; Transportation cost; recycle / disposal cost; ......

Environmental properties Environmental pollution; energy consumption; recycle & reuse; breakdown;

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Fig. 7.22 Artificial neural network with three hidden layers.

target values. ANNs are also used to predict the outcome of new independent input data after performing a proper training. Generally, the neural network has one input layer, hidden layers, and one output layer (Cherian, Smith, & Midha, 2000). Fig. 7.22 demonstrates an example of artificial neural networks with input, three hidden layers, and an output. ANNs involve of simple computational units called neurons that are strongly interconnected. Such neurons are parallel computational models with dense interconnected-adaptive processing units that are capable of implementing nonlinear static or dynamic systems. A nonlinear neuron block is demonstrated in Fig. 7.23. The Bias bk x1

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most important features of such networks is their capability of adaptation, where “learning by example” is substituted by “programming” in solving problems. Neural networks are now being progressively utilized for classification and prediction instead of the traditional regression models or statistical techniques. A neuron is the ANN information-processing unit that is similar to the brain in human beings. It is made of three main parts: the first one is a set of synapses that link the input signal (xj) to the neuron by means of a set of weights (Wkj). The second part is the adder (uk) that usually sums up the input signals that are weighted by the relevant synapses. And the activation function [φ(°)] for regulating the output of the neuron. However, a bias (bk) can be added to the structure of the neuron either to increase or decrease the neuron’s net output. Generally, the neuron’s output range depends on the activation function type where the main four commonly used types are the hard-limit activation function, linear activation function, log-sigmoid function, and tan-sigmoid (Karnik et al., 2008). The configuration of such activation functions are demonstrated in Fig. 7.24.

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Feed-forward back-propagation network of the ANNs usually involves of three layers: the input, output, and one or more hidden layers that have multiple neurons with tan-sigmoid or sigmoid activation function, whereas output layers usually utilized the linear activation functions. Such networks are called multilayer perceptrons, which are trained by means of algorithms called error back-propagation that are divided into two phases, forward pass, and the backward pass. In the forward pass, the input vector is applied on the network’s input nodes and then propagates through the network layers to make an actual output. During this forward pass stage, the synaptic weights are not changed but remain fixed. Once actual output is obtained, it is compared with the target output to obtain an error signal, which is then propagated back (in an opposite direction) into the network to adjust the weights of the synaptic to get closer response of the target. Properly trained back-propagation networks usually have a tendency to give more reasonable responses when offered with inputs that they have never seen. Fig. 7.25 illustrates a multilayer feed-forward back-propagation artificial network. It was found that only a few works have considered the application of neural networks in composite material field (Canakci, Ozsahin, & Varol, 2014; Hassan, Alrashdan, Hayajneh, & Mayyas, 2009; Varol, Canakci, & Ozsahin, 2014; Vassilopoulos, Georgopoulos, & Dionysopoulos, 2007; Xu, You, & Du, 2015; Yan, Lin, Wang, Azarmi, & Sobolev, 2017; Zhang & Friedrich, 2003) and very limited work has been considered on the selection of composite materials (Budan, Vijarangan, Arunachalam, & Page, 2008; Zhang, Friedrich, & Velten, 2002) or the selection of natural fiber-reinforced composites (Ali, Salit, Zainudin, & Othman, Input

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2015; Farhana et al., 2016; Li, Yan, Pan, & Zhao, 2017). Particularly, the neural network-based systems have been utilized and developed for natural fiber composite materials selection process utilizing a pre-collected database (Sapuan & Mujtaba, 2009). Datasets in such systems were collected from the literature and utilized for input and output parameters. Both tensile strength and modulus were utilized as inputs to predict the appropriate flexural strength as an output. Such a selection system with the aid of ANNs was used the horizontal shelf as a case study, where the selection process of materials involved two stages. The first one was to predict the correlation between the inputs and the output is predicted. Then the available materials with properties close to those predicted ones were selected. Further, the second stage of the selection process had utilized a multi-attribute ranking method to decide the most appropriate material type. Regarding the selection of the natural fiber-reinforced polymer composites, it is of paramount importance to obtain a reliable experimental data about various composites to be utilized in materials selection process, particularly with ANNs, as it requires various inputs. However, the possibility of making large combination of parameters in such type of composites is considered as a major problem in this field. Fig. 7.26 demonstrates the many possibilities that may exist for creating polymer composites involving several types of fibers and fillers, available natural fibers, and various types of matrices from thermoplastic and thermosetting polymers. Moreover, other main serious limitations of such work are that there is always inadequate information about the characteristics of the constituents of the natural composites on one hand, and the large variation of the collected data on the other (Abdollah, Shuhimi, Ismail, Amiruddin, & Umehara, 2015; Sapuan & Mujtaba, 2009; Sapuan, Pua,

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Fig. 7.27 Schematic of the ANNs fiber reinforced composite materials system.

El-Shekeil, & AL-Oqla, 2013). This, in fact, altered the distribution and makes it biased toward the lower end value of the data. Fig. 7.27 represent a schematic diagram of the ANNs reinforced material selection system for the horizontal shelf, where MATLAB neural network toolbox was utilized for developing such selection system. The connection between the input and output data were connected by means of a set of nonlinear functions. Four-layer feed-forward neural artificial network where fully connected by sigmoid transfer functions to make one input, two hidden, and one output layers. Fig. 7.28 illustrates the detailed flow of the ANNs composite horizontal shelf selection system. The prediction of the output criteria (flexural modulus) for the potential fiber reinforced materials is found in Fig. 7.29, where two inputs (tensile strength and tensile modulus) were utilized to predict such output criteria and thus to select materials that have such corresponding property of flexural modulus. Moreover, the considered selected composite material alternatives corresponding to ten predicted data outputs (five alternatives for each output) are shown in Fig. 7.30.

7.7

TOPSIS method

To select the best material type from a huge set of alternatives, researchers had suggested some techniques to organize the choices so to be able to pick the best among many choices, one of the techniques is Technique of Ordering Preferences by Similarity to the Ideal Solution (TOPSIS).

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Designer

Back propagation algorithm neural network 61 datasets for training 30 datasets for testing Input Tensile strength, tensile modulus Output Flexural strength For each predicted output (flexural strength), Select 5 materials that have flexural strength property close to the predicted output Check if the random input data close to input properties of the selected materials corresponding to output predicted data

Natural fibre composite data from journals stored in MS Excel 121 sets of normalized numerical data

Back propagation algorithm neural network obtain correlation between inputs and output using 121 datasets Back propagation algorithm neural network propose 10 sets of random inputs to predict the outputs using correlation equation

If yes, ok If not, find reasons

Material selected from the first stage of materials selection selection for horizontal shelf

For each set of random input data proposed, rank the 5 selected materials according to tensile strength, tensile modulus, aesthetics, manufactrability, availability and cost

Material selected form the second stage of materials selection selection for horizontal shelf

Composite horizontal shelf

Fig. 7.28 The detailed flow of the ANNs composite horizontal shelf (Sapuan & Mujtaba, 2009).

As stated by Dağdeviren, Yavuz, and Kilinc¸ (2009), Jahanshahloo, Lotfi, and Izadikhah (2006), Majumdar, Sarkar, and Majumdar (2005), Moghassem (2010), and Wang and Chang (2007) the best choice is the closest one to a positive ideal answer, or the farthest one from a negative ideal solution. The main principle suggested by (Tavana & Hatami-Marbini, 2011; Wang & Chang, 2007) is to

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Input 1 (tensile strength, MPa) 90 15 35 50 4 100 150 18 20 77

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Input 2 (tensile modulus, MPa) 7500 12,000 5700 1500 350 800 6000 390 3500 3850

Fig. 7.29 Prediction for the flexural modulus.

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Material 5 (MPa)

53

80

27.7

74

19

94

19

17.6

3

131

Alternative fiber reinforced materials

Corresponding characteristics of the composite alternatives to a given predicted value of the flexural modulus

Fig. 7.30 The selected composite materials corresponding to the predicted outputs.

264

Materials Selection for Natural Fiber Composites

recommend a solution that has the shortest distance from the hypothetical one. It happens sometimes that the proposed solution has the shortest distance from the ideal and negative ideal solutions. TOPSIS technique attempts to find the closest solutions to the ideal one however, far away from the worst solution. For instance, suppose we have a multi-criteria decision problem, this problem has (n) alternatives (A1, A2, A3, …, An) and (m) criteria (C1, C2, C3, …, Cm). Then matrix A(aij) can be created to calculate the values of different alternatives according to each criterion. W ¼ (w1, w2, w3, …, wm) be the vector of criteria should satisfy the summation XLet m w ¼ 1. Then, (A) matrix for the ranking can be built as: j¼1 j 2

3 a11 a12 … a1n 6 a21 a22 … a2n 7 6 7 6 : : : : 7 6 7 where, i ¼ 1,…, m, and j ¼ 1, …,n A¼6 : : : 7 6 : 7 4 : : : : 5 am1 am2 … amn

(7.1)

Then TOPSIS steps will be as following: Step 1: Build the normalized judgment matrix to calculate the normalized score rij as: rij ¼

Xij i ¼ 1, …,m, j ¼ 1,…, n X  1=2 2 Xij i

(7.2)

Step 2: Use the AHP technique to calculate the weighted normalized judgment matrix, by integrating AHP and TOPSIS, the weighted normalized score Vij is expressed as: Vij ¼ Wj  rij

(7.3)

Step 3: Find both positive and negative ideal solutions (V+) and (V) respectively. This can be calculated by:         V + ¼ V1+ , ...., Vn+ where Vj+ ¼ max i νij if j 2 J; min i νij if j 2 J 0

(7.4)

        V  ¼ V1 , …, Vn where Vj ¼ min i νij if j 2 J;max i νij if j 2 J 0

(7.5)

where J ¼ (j ¼ 1, 2, …, n)/j is the set of beneficial factors, and J0 ¼ (j ¼ 1, 2, …, n)/j is the set of nonbeneficial factors. Step 4: Find the separation measures for each different choices by using the n-dimensional Euclidean distance. One can use the following formulas to find the separation from the ideal alternative:

Si+

¼

" X

Vj+  Vij

2

#1=2 ; i ¼ 1,…, m

(7.6)

j

S i ¼

hX

VJ  Vij

2 i1=2

; i ¼ 1, …,m

(7.7)

Material selection of natural fiber composites using other methods

265

Step 5: Calculate the relative closeness to the optimal solution (Ci*) and the corresponding rank. Simply, choose the alternative with largest (Ci*). This relative closeness Aij can be expressed as: C∗i ¼

S + i ; 0 < C∗i < 1, S i  0, Si  0 + S i

(7.8)

Si+

In fact, several works have utilized TOPSIS method for selecting materials and recently TOPSIS method was integrated with the analytical hierarch process (AHP) to select the natural fiber reinforced polymer composites, as AHP was verified to be a powerful tool for selecting materials and assigning consistence judgments for various engineering fields (AL-Oqla & Omar, 2012, 2015; AL-Oqla & Omari, 2017; AL-Oqla et al., 2015a, 2015b, 2017; AL-Widyan & AL-Oqla, 2011, 2014; Dağdeviren et al., 2009; Dalalah, AL-Oqla, & Hayajneh, 2010; Khorshidi & Hassani, 2013; Majumdar et al., 2005; Mansor, Sapuan, Edi Syams, Abd Aziz, & Hambali, 2014; Moghassem, 2010). In the area of considering the selection of the natural fiber reinforced composites using integrated AHP-TOPSIS methods, the automotive parking brake lever form hybrid natural fiber composites, as well as selecting the best reinforcement conditions of the natural fiber composites from date palm fibers were systematically considered (AL-Oqla et al., 2015b; Mansor et al., 2014). The selection of the hybrid natural fiber composites brake lever consisted of several stages like that of the identification of the alternative materials, determining the selection criteria, analyzing the candidate attributes, and finding the best thermoplastic matrix for the hybrid natural composites, where four various thermoplastic matrices were suggested like those of the polypropylene (PP), high density, as well as low density polyethylene (HDPE and LDPE) in addition to nylon 6. The properties of the selected polymers were then tabulated and the evaluation criteria were set as shown in Fig. 7.31.

Tensile strength

Young modulus

Process melting temperature

Water absorption

Density

Elongation

Impact strength

Heat deflection temperature

Raw materials cost Coefficient of thermal expansion

Performance

Cost

Weight

Fig. 7.31 The evaluation cafeteria for the hybrid brake lever.

Service conditions

266

Materials Selection for Natural Fiber Composites

Fig. 7.32 Weights of the evaluation criteria in determining the best polymer for the hybrid composites for the brake lever (Mansor et al., 2014).

The subjective judgments were then utilized to determine the relative significance of the considered criteria regarding the goal. This was achieved utilizing the AHP method. Outcomes of local weights of the evaluation criteria in determining the best polymer for the hybrid composites for the brake lever are demonstrated in Fig. 7.32. The TOPSIS calculations, as well as their gained results regarding the polymer selection, are demonstrated in Fig. 7.33. Accordingly, the final overall ranking of the considered thermoplastics is demonstrated in Fig. 7.34 based upon their relative closeness to the ideal scores obtained from the TOPSIS technique.

TOPSIS processing

Positive ideal solution Negative ideal solution

Density

Water absorption

0.0243

Coefficient thermal expansion 0.0251

0.1159

0.0012

Heat deflection temp. 0.0771

0.0012

0.0378

0.144

0.1247

0.0584

Tensile strength

Modulus young

Elongation

Impact strength

0.0401

0.0541

0.0241

0.0173

0.0041

0.0036

Raw Process melting material cost temp. 0.0626 0.0215

0.0418

0.146

Output results of TOPSIS

Separation from positive ideal solution PP LDPE HDPE Nylon 6

0.0428 0.0602 0.0536 0.1560

Separation from negative ideal solution 0.1523 0.1517 0.1483 0.0595

Relative closeness from ideal solution 0.7805 0.7160 0.7347 0.2761

Fig. 7.33 TOPSIS calculations and results regarding the polymer selection types.

Material selection of natural fiber composites using other methods

267

0.9000 0.8000

TOPSIS score

0.7000 0.6000 0.5000 0.4000 0.3000 0.2000 0.1000 0.0000 PP

LDPE

HDPE

Nylon 6

Fig. 7.34 The overall ranking of the considered thermoplastics using TOPSIS method (Mansor et al., 2014).

It can be noted that the final ranking was in favor of polypropylene matrix type followed by HPDE, LDPE. But nylon 6 thermoplastic was ranked the lowest. Consequently, it can be concluded that polypropylene is the best thermoplastic matrix for making hybrid natural fiber composites, as it satisfies all the required design attributes and specifications for the intended application.

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Index Note: Page numbers followed by f indicate figures, and t indicate tables. A

B

Activation functions, 258–259 Agro fibers, 28 Alu SELECT, 239 Analytical hierarchy process (AHP), 17–18, 65–66, 68, 89–90, 110, 265 appropriateness, composite selection, 172–175 background of, 169–172 bio-based materials, 176–179 composite reinforcement conditions, optimizing, 212–222 composites evaluation, conflicting criteria, 200–212 factors for, 181t local and global priorities, model, 228t natural fiber selection, conflicting criteria, 179–186 polymers selection, conflicting criteria, 187–199 selection models, sensitivity analysis, 223–228 Animal fibers, 24 Aramid fibers, 31–33, 84 Artificial intelligence (AI) methods, 60–62 case based reasoning, 62 computer-aided materials selection systems, 60–61 knowledge-based systems, 61–62 neural networks, 62 Artificial neural networks (ANNs), 255–261 feed-forward back-propagation network, 259 potential fiber reinforced materials, 261 Asbestos, 24 Ashby charts, 10, 57–59, 89–90 Automotive sectors, 39, 61–62 bio-based products in, 13–14, 16f natural fibers for, 184f, 187f

Basalt fibers, 84 Bast fibers, 85 Batch-mixing, crops, 154–155 Biaxial aramid fibers, 32f Bio-based composites, 23–24 Bio-based materials pairwise comparison, 176–179 polymers selection, 187–199 Bio-based resins, 161–162 Bio-composites, 177 Biodegradable polymers, classification of, 82f Bio-resins, 161 Bio-technological fermentation process, 161–162 C Cambridge Engineering Selector (CES), 239–241 Campus plastics, 239 Carbon fibers, 31, 84 Case based reasoning (CBR), 62 Case study selection unit, 236 Cationic polymerization, 124–125 Cellulose, 26–27, 34, 84, 88–89 reinforcement, 87–88t Cellulose-hemicellulose network, 25–26 Cellulose nanofiber (CNF), 88 Ceramic matrices, 76 C-glass, 30 Coconut fibers, 37 Coir fibers, 147–148 Combined double-evaluation criterion (CDEC), 133–134 Combined multicriteria evaluation stage technique (CMCEST), 128–136 combined double-evaluation criterion in, 133–134 combined triple-evaluation criterion in, 134–136

274

Combined multicriteria evaluation stage technique (CMCEST) (Continued) physical evaluation criterion comparison in, 130–133 raw data, for evaluations, 130t specific properties and cost ratios, 131t Combined triple-evaluation criterion (CTEC), 134–136 tensile strength to cost ratio, 134–136 Component failure, 3–4 Composite characteristics level, 112 NFC selection, 114t Composite materials, 5–7 categories, 5–6 definition of, 5–6 fiber level, 113t Composite materials selection. See also Materials selection advanced techniques in, 91–99 issues in, 73–76 research work on, 89–91 Composite matrices, 76–82 ceramic matrices, 76 for general composites, 76–80 metal matrices, 76 plastic (polymers) matrices, 77 properties of, 76t Composite performance levels, 115t Composite properties, assumptions, 149–150 Composites, elastic properties, 149–154 Composites, judgment matrix of, 205–206, 207t Computer-aided materials selection systems, 60–61 Computer simulations, 64 Consistency ratio (CR), 171–172 Cost per unit property method, 55–56 Cost per volume ratio (CPVR), 176–177 Cotton, 26–27 Curing pressure, 226 D Date palm fibers (DPFs), 111–112, 116–118, 120–123 governmental support for, 123–124 modulus of elasticity per cost ratio for, 122–123, 123f social positive view, 123–124

Index

sub-factor judgment matrix, physical properties, 194t Date palm’s thermal conductivity, 119f Decision-making, 169. See also Multi-criteria decision-making (MCDM) Density factor, 226 Design process, 2–3 D-glass, 31 Digital logic technique, 244–246 Dry wood, 27 E E-glass, 30, 94 E-glass reinforced plastics, 94, 95f Eigen values, 98 Eigen vector, 170–171, 190–192 Eigen vector method, 180 Elasticity to cost ratio, 122–123 Elastic modulus, 226 ELECTRE methods, 67–68 Elementary fibrils, 26–27 Elongation to break (EL) property, 122, 140f, 143, 147t, 213 Engineering materials, 8–9 ceramics, 8 elastomers, 8 glasses, 8 hybrids, 8 metals, 8 polymers, 8 Expert systems, 239–242 F Fiber length efficiency factors, 156f Fiber’s moisture absorption (FMA) criterion, 185 Fibers selection. See also Materials selection nanocrystalline cellulose, 86–88 natural fibers, 84 synthetic fibers, 82–84 Fiber’s specific strength to cost ratio (FSSCR), 185 Fiber volume fraction, 82–83, 157–158 Flexural modulus (FM), 202–210, 263f Flexural strength (FS), 202–210 Fruit fiber, 85 Fuzzy multi-criteria decision-making (MCDM) methods, 65–66, 173

Index

275

G

L

General composite performance level, 113 Genetic algorithm, 64 Glass fibers, 30–31, 84, 116 Glass transition temperature, 77 Goal programming (GP), 62 Green composites, reinforcement, 215f, 218–219

Leaf fibers, 85 Length efficiency factors, 158 L-glass, 31 Life cycle assessments (LCAs), 10–13 Lignin, 27–28 Lignocellulosic (plant) fibers, 24 Low density polyethylene (LDPE), 197 Low-to-medium performance composites, 5–6

H Halpin-Tsai equations, 153 Halpin-Tsai model, 152 Hemicellulose, 27 Hemp fiber reinforced polypropylene composites, 110 High density polyethylene (HDPE), 197 House of quality (HoQ), 246–248, 247f Hybrid fabrics, 33–35 Hypertext transport protocol (http), 236 I Impact strength (IS), 202–205 Ineffective fiber length, 155–156 Informative decision-making model, 180, 201–202 Injection molded (IM) natural fiber composites, 92 In-plane Poisson’s ratios, 151f Interfacial bonding, 108 Interphase, 5–6 J Java-based materials selection, 235–241 modules, 236–237 NetBean JAVATM, 236 schematic diagram of, 236f Jute fiber reinforced polymer composites, 110 Jute fibers, 37 K KEE, commercial system shell, 90–91 Key to metals database, 239 Knowledge-based systems (KBS), 61–62, 241–243 benefits, 242 database, 242 inference engine, 242 rule-based reasoning, 242

M Material index database, 239 Material knowledge-based module, 236 Material performance indices, 75–76 Material screening schemes, 109 Material selection process, 2 Material selection program (MSP), 60–61 Materials in products selection (MiPS) methods, 57 Materials performance, 3–5 Materials screening techniques, 54 Materials selection, 8–14 advanced techniques, 60–68 artificial intelligence methods, 60–62 Ashby charts, 57–59 ceramics, 8 chart, 3 in composites, issues, 73–76 conventional techniques, 55–57 cost per unit property method, 55–56 and design, 49–50 elastomers, 8 factors, determining, 52–53 genetic algorithm and neural network, 62–63 glasses, 8 goal programming, 62 hybrids, 8 materials comparing and choosing techniques, 54 materials screening techniques, 54 metals, 8 MiPS methods, 57 multiple attribute decision-making methods, 64–66 multiple objective decision making methods, 66–68

276

Materials selection (Continued) needs for, 50–53 optimization methods, 63–64 polymers, 8 questionnaire method for, 56 simple additive weighting method, 63 tools and techniques of, 53–54 Mathematical programming, 63–64 MATLAB program, 243 Matrices selection. See also Composite matrices natural fiber reinforced polymeric based composites, 80–82 Matrix level, NFC selection, 114t Matweb, 239 Maximum shear stress (MSS), 213 Maximum tensile strength (MTS), 213 Maximum water absorption (MWA), 202–210 Medical disposable tools, 40–41 Mercerization, 155–156 Metal matrices, 76 Microcrystalline cellulose (MCC), 86–88 Micro-fibrillated cellulose (MFC), 110 Microfibrils, 26–27 Mineral fibers, 24 Modern expert systems, 239–240 Moisture content criterion approach, 137–149 evaluation tool, 144t MCC methodology, 140–145 natural fiber properties for, 142t tensile moduli, 146f tensile strength, flax fiber, 146f Multi-attribute utility analysis (MAUA), 67 Multi-criteria decision-making (MCDM), 54 methods, 172–173 problem, 187–188, 200–201, 213 Multiple attribute decision-making (MADM), 54, 64–66 fuzzy multi-criteria decision-making methods, 65–66 individual methods in, 66 TOPSIS method, 65 Multiple objective decision making methods (MODMM), 54, 66–68 individual methods in, 67–68 multi-attribute utility analysis, 67

Index

N Nanoclay, 6–7 Nanoclay hybrid composites, 159–161 Nanocrystalline cellulose (NC), 86–88 Natural composite selection system, login screen, 237f Natural fiber composites, 89–90. See also Natural fibers advantages and disadvantages of, 28–35 applications of, 37–43 automotive and aerospace applications, 38–40 construction and infrastructure, 42 developing, for industrial applications, 44–45 electrical and electronic market, 41 furniture and consumer goods, 42 injection molded, 92 medical applications, 40–41 nonwoven reinforcements, 92 packaging market, 42 polymer types, factors, 81t properties of, 37 sports and leisure, 43 on textile reinforcements, 92 Natural fiber properties level, 112 Natural fiber reinforced polymer composites, 9, 12–13 advantages and disadvantages of, 14t Natural fiber reinforced polymeric based composites, 80–82 Natural fibers, 23–28, 84 advantages and disadvantages of, 28–35 aramid fibers, 31–33 availability of, 121 carbon fibers, 31 cellulose, 26–27 cellulose and lignin content, 120 chemical composition of, 28–29t classification of, 24, 25f factors affecting, selection, 89t fiber types, for industrial applications, 128–149 glass fibers, 30–31 hemicellulose, 27 hybrid fabrics, 33–35 independent constituent, 23 issues for selecting, 88–89

Index

lignin, 27–28 mechanical characteristics of, 30t mechanical properties of, 35–36 physical properties of, 29t polymer matrices for, 124–127 quality of, 85 reinforcement geometry and orientation, 158–159 sources of, 85 Natural fibers capabilities density, 118 elasticity to cost ratio, 122–123 elongation to break, 122 length to diameter (L/D) aspect ratio, 118–119 potential industrial use, 116–117 proper assessment of, 113–124 thermal conductivity, 119–120 Natural material database, screen manipulation of, 237f NetBean JAVATM, 236 Neural networks, 62 Neurons, 256–258 O Optimization methods, materials selection, 63–64 computer simulations, 64 genetic algorithm, 64 mathematical programming, 63–64 Orientation distribution factors, 159 Out-of-plane Poisson’s ratio, 153 P Pairwise comparison bio-based materials, 176–179 data use, AHP method, 183–184t Plant fiber processing, 154–158 fiber surface modification, 155–157 fiber volume fraction, 157–158 plant growth and fiber extraction, 154–155 Plant fiber reinforced plastics (PFRPs), 94, 95f Plant fibers, 24–26 Plastic (polymers) matrices, 77 Plastic matrices, characteristics of, 78t Polyhydroxyalkanoates (PHA), 82 Polylactic acid (PLA), 82

277

Polymer base (matrix) properties level, 112 Polymeric based composites, applications of, 43–44t Polymer selection model, 191–192f Polymer selection process AHP role in, 187–188, 189f factors of, 190t Polypropylene (PP), 125–126, 197, 201 Polypropylene (PP) polymer matrix, 36t Polyvinyl alcohol (PVA), 82 PP-natural fiber composites, 202–205 Preference selection index (PSI), 98, 98f Pre-systematics selection, 109–110 Principal component analysis (PCA), 98, 99–100f Principal metals online, 239 Process module, 236 Production method, 87–88t Prospector database, 239 Protofibrils, 26–27 Pugh selection method, 253–255 Q Quality function deployment (QFD) method, 246–253 economic constraints, 250 for environment, 246–253 environmental constraints, 250 house of quality, 246–248, 247f material constraints, 250 materials selection, 246 procedure of, 249–250 technical constraints, 250 Questionnaire method, 56 R Raw fiber cost, 121–122 Reinforcement efficiency, 108, 124–125 Relational database management system (RDBMS) software, 237–238 Root fibers, 85 Rule-of-mixtures (ROM) model, 150, 154 S Seed fibers, 85 Selection scheme module, 236 S-glass, 30 Shear modulus, 152–153

278

Index

Specific composite performance level, 113 Specific performance for automotive application main factor, 180–181 Stahl Wissen Navimat-Online, 239 Stalk fibers, 85 Standard random index, 172t Starch, 82 Steam explosion process, 86–88 Steel Selector-Metal Ravne, 239 Steel Spec Web Edition, 239 Strengthening, composites, 5–6 Stress stain diagram, 78–79f Synthetic fibers, 82–84 cost aspects of, 84

thermoplastic vs. thermoset, 80t thermoset polymers, 124–125 Traditional approach, 109–113 Transverse modulus, 151–152 Trichoderma reesei, 86–88

T

Van der Waals bond, 31 Vegetable fibers. See Plant fibers VIsˇekriterijumsko KOmpromisno Rangiranje (VIKOR) method, 66, 89–90

Technique for order preferences by similarity to ideal solution (TOPSIS) method, 172–173, 178–179, 213–214, 217–218t, 221, 222f, 261–267 Technique of ranking preferences by similarity to ideal solution, 65, 68 Tensile modulus (TM), 202–210 Tensile strength (TS), 202–210, 211t Ternary diagrams, 94–98, 96–97f Thermal conductivity properties, 59, 59f Thermoplastics, 77 advantages, 79 disadvantages, 79–80 melting viscosity, 79–80

U Uncertainty environment, bio-based materials, 176–179 Uniaxial carbon fibers, 32f Uniaxial glass fibers, 31f Unidirectional natural fiber composites, 92 V

W Waste management, 128 Weighting factors, 244–246, 245f Win Steel, 239 Wood-polypropylene composites, 160 Y Yield strength, 226 Young’s modulus, 57–58