Scientific principles of engineering 9781774696590, 9781774694732

Table of contents :
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
List of Abbreviations
Preface
Chapter 1 Basics of Scientific Principles
1.1. Introduction
1.2. Goals of Scientific Principles
1.3. Assumptions of Scientific Principles
1.4. The Scientific Principles
1.5. Systematic Nature of Scientific Principles
1.6. Scientific Principles and Technology
References
Chapter 2 Creativity and Scientific Approach to Engineering Problems
2.1. Introduction
2.2. Engineering Problem Solving Processes
2.3. Factors Influencing Problem Solving
2.4. Creative Approach to Engineering Problems
2.5. Scientific Approach to Engineering Problems
References
Chapter 3 Scientific Design and Modeling in Engineering
3.1. Introduction
3.2. Models in Science and Engineering
3.3. Models and Representation
3.4. Resemblances Between Model and Target Systems
3.5. Fictionalism About Models
3.6. Modeling And Design
3.7. Modeling Paradigms And Languages
3.8. Single-Domain Simulation
3.9. Interleaving Design And Simulation
3.10. Collaborative Modeling
3.11. Modeling At The Component Level
3.12. Integration With Design Tools
3.13. Future Modeling And Simulation
References
Chapter 4 Scientific Approach on Engineering Failures
4.1. Introduction
4.2. The “Traditional Approach” to Failure
4.3. Basic Assumptions in Traditional Approach
4.4. Beyond the Traditional Approach
4.5. One Customer to Many Stakeholders
4.6. A Scientific Definition of Failure
4.7. The Scientific Approach to Action
References
Chapter 5 Scientific Optimization of Engineering Systems
5.1. Introduction
5.2. Design Variables and Parameters
5.3. Objectives of Scientific Optimization
5.4. Constraints and Bounds
5.5. Optimization Problems And Methods
5.6. Design and Structural Optimization Methods
5.7. Optimization Examples in Science and Engineering
5.8. Graphical Optimization
5.9. Mathematical Optimization
5.10. Discrete Optimization
5.11. Numerical Optimization Methods
5.12. Optimization Case Studies
References
Chapter 6 Engineering Communications
6.1. Introduction
6.2. Communications and Information Resources
6.3. The Engineer as a Writer
6.4. Graphical Communications
6.5. The Engineer as a Speaker
References
Chapter 7 Scientific Principles of Management
7.1. Introduction
7.2. Fundamentals of Scientific Management
7.3. Replacement of Old Rule of Thumb Method
7.4. Scientific Selection and Training of Workers
7.5. Co-Operation Between Labor and Management
7.6. Maximum Output
7.7. Equal Division of Responsibility
7.8. Mental Revolution
7.9. Examples of Scientific Management
7.10. Criticism of Scientific Management
References
Chapter 8 Ethics in Science and Engineering
8.1. Introduction
8.2. The Part of Morals in Engineering and Science
8.3. Ethical Principles in Science
8.4. Ethics of Techniques and Process
8.5. Morals of Topics and Findings
8.6. Faults Versus Misconduct
8.7. Everyday Moral Decisions
8.8. Enforcing Moral Standards
References
Index
Back Cover

Citation preview

Scientific Principles of Engineering

SCIENTIFIC PRINCIPLES OF ENGINEERING

Lokesh Pandey

ARCLER

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www.arclerpress.com

Scientific Principles of Engineering Lokesh Pandey

Arcler Press 224 Shoreacres Road Burlington, ON L7L 2H2 Canada www.arclerpress.com Email: [email protected]

e-book Edition 2023 ISBN: 978-1-77469-659-0 (e-book)

This book contains information obtained from highly regarded resources. Reprinted material sources are indicated and copyright remains with the original owners. Copyright for images and other graphics remains with the original owners as indicated. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data. Authors or Editors or Publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The authors or editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify.

Notice: Registered trademark of products or corporate names are used only for explanation and identification without intent of infringement.

© 2023 Arcler Press ISBN: 978-1-77469-473-2 (Hardcover)

Arcler Press publishes wide variety of books and eBooks. For more information about Arcler Press and its products, visit our website at www.arclerpress.com

ABOUT THE AUTHOR

Lokesh Pandey is currently pursuing his PhD in Mechanical Engineeing from Uttarakhand Technical University, India, where he also completed his M.Tech in Thermal Engineeing. He has more than 3 years teaching experience as a Faculty for Mechanical Engineering. He has published articles in reputed Journals and has also chaired many conferences and workshops.

TABLE OF CONTENTS

List of Figures.........................................................................................................xi List of Abbreviations.............................................................................................xv Preface........................................................................................................... ....xvii Chapter 1

Basics of Scientific Principles..................................................................... 1 1.1. Introduction......................................................................................... 2 1.2. Goals of Scientific Principles............................................................... 2 1.3. Assumptions of Scientific Principles..................................................... 3 1.4. The Scientific Principles....................................................................... 6 1.5. Systematic Nature of Scientific Principles.......................................... 12 1.6. Scientific Principles and Technology.................................................. 18 References................................................................................................ 20

Chapter 2

Creativity and Scientific Approach to Engineering Problems................... 29 2.1. Introduction....................................................................................... 30 2.2. Engineering Problem Solving Processes............................................. 31 2.3. Factors Influencing Problem Solving.................................................. 33 2.4. Creative Approach to Engineering Problems...................................... 34 2.5. Scientific Approach to Engineering Problems..................................... 37 References................................................................................................ 43

Chapter 3

Scientific Design and Modeling in Engineering........................................ 47 3.1. Introduction....................................................................................... 48 3.2. Models in Science and Engineering................................................... 50 3.3. Models and Representation................................................................ 51 3.4. Resemblances Between Model and Target Systems............................ 55 3.5. Fictionalism About Models................................................................ 56

3.6. Modeling And Design........................................................................ 57 3.7. Modeling Paradigms And Languages.................................................. 59 3.8. Single-Domain Simulation................................................................. 65 3.9. Interleaving Design And Simulation................................................... 67 3.10. Collaborative Modeling................................................................... 69 3.11. Modeling At The Component Level.................................................. 71 3.12. Integration With Design Tools.......................................................... 73 3.13. Future Modeling And Simulation..................................................... 74 References................................................................................................ 78 Chapter 4

Scientific Approach on Engineering Failures............................................ 89 4.1. Introduction....................................................................................... 90 4.2. The “Traditional Approach” to Failure................................................ 93 4.3. Basic Assumptions in Traditional Approach........................................ 97 4.4. Beyond the Traditional Approach..................................................... 100 4.5. One Customer to Many Stakeholders............................................... 106 4.6. A Scientific Definition of Failure...................................................... 110 4.7. The Scientific Approach to Action.................................................... 115 References.............................................................................................. 120

Chapter 5

Scientific Optimization of Engineering Systems..................................... 131 5.1. Introduction..................................................................................... 132 5.2. Design Variables and Parameters...................................................... 132 5.3. Objectives of Scientific Optimization.............................................. 133 5.4. Constraints and Bounds................................................................... 134 5.5. Optimization Problems And Methods.............................................. 135 5.6. Design and Structural Optimization Methods.................................. 139 5.7. Optimization Examples in Science and Engineering......................... 143 5.8. Graphical Optimization................................................................... 151 5.9. Mathematical Optimization............................................................. 151 5.10. Discrete Optimization................................................................... 152 5.11. Numerical Optimization Methods................................................. 153 5.12. Optimization Case Studies............................................................. 153 References.............................................................................................. 159

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Chapter 6

Engineering Communications................................................................. 167 6.1. Introduction..................................................................................... 168 6.2. Communications and Information Resources................................... 168 6.3. The Engineer as a Writer.................................................................. 171 6.4. Graphical Communications............................................................. 177 6.5. The Engineer as a Speaker................................................................ 188 References.............................................................................................. 192

Chapter 7

Scientific Principles of Management...................................................... 201 7.1. Introduction..................................................................................... 202 7.2. Fundamentals of Scientific Management.......................................... 203 7.3. Replacement of Old Rule of Thumb Method.................................... 205 7.4. Scientific Selection and Training of Workers..................................... 206 7.5. Co-Operation Between Labor and Management.............................. 207 7.6. Maximum Output............................................................................ 208 7.7. Equal Division of Responsibility....................................................... 208 7.8. Mental Revolution........................................................................... 209 7.9. Examples of Scientific Management................................................. 210 7.10. Criticism of Scientific Management................................................ 212 References.............................................................................................. 214

Chapter 8

Ethics in Science and Engineering.......................................................... 219 8.1. Introduction..................................................................................... 220 8.2. The Part of Morals in Engineering and Science................................. 221 8.3. Ethical Principles in Science............................................................ 222 8.4. Ethics of Techniques and Process..................................................... 223 8.5. Morals of Topics and Findings.......................................................... 225 8.6. Faults Versus Misconduct................................................................. 226 8.7. Everyday Moral Decisions................................................................ 228 8.8. Enforcing Moral Standards............................................................... 229 References.............................................................................................. 231

Index...................................................................................................... 235

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LIST OF FIGURES Figure 1.1. The scientific principles as well as techniques Figure 1.2. Similarity grouping Figure 1.3. Parsimony rule Figure 1.4. The orientation of rationale for deductive and inductive research techniques Figure 1.5. Basic research plan Figure 2.1. Five problem-solving processes Figure 2.2. Design innovation via knowledge clustering as well as case-based reasoning Figure 2.3. Altshuller’s 7 TRIZ philosophical foundations Figure 2.4. Analysis of the problem Figure 2.5. A problem-solving flowchart Figure 2.6. Solution analysis based on problem criticality Figure 3.1. The importance of models in the scientific and technological fields Figure 3.2. A DNA engineering model example Figure 3.3. Technique for representing knowledge Figure 3.4. Method of engineering design Figure 3.5. A general description of our graph-based model’s network infrastructure Figure 3.6. Cognition mechanisms: the descriptive model, which is utilized during creation and re-enters the generated utterance into the comprehension systems, and also the H&H design, which is employed during creation Figure 3.7. Object-oriented data model for simple cases Figure 3.8. Two Integrated memory architecture Figure 3.9. Modeling in collaboration Figure 3.10. Component-level modeling for content coupling Figure 4.1. Ways to blunder through the science-policy engagement process more wisely Figure 4.2. The IEC vocabulary’s visual portrayal of the concepts of failures and fault Figure 4.3. Standardization and simplification principles Figure 4.4. Bearing on empty axle end (a) has a tight fit, while the loaded sector (b) of the failed swing bridge has significant wear

Figure 4.5. Diagram demonstrating the maximum permissible bearing pressure and surface velocity Figure 4.6. Module for thick film ignition Figure 4.7. A methodical approach to product development Figure 4.8. A comprehensive analysis of alliance failure Figure 4.9. User experience criteria Figure 4.10. According to the conventional method, these failure paths are acceptable: Faults in design, manufacture, and use are shown by the arrows (A, B, and C, respectively) Figure 4.11. Product failure trajectories that occur throughout the production process Figure 4.12. An electronic appliance’s retirement stage-related product failure trajectories Figure 4.13. The life cycle model of tuna can failure is outlined in Barella et al., (2011) Figure 5.1. Outcomes of the GLCE-ATS approach for detecting breast cancer, with varying results depending on the optimizing settings utilized Figure 5.2. Major phases in a typical optimization product’s process Figure 5.3. The exterior covering of the aircraft’s standard structural layout Figure 5.4. Design flow for topographical optimization Figure 5.5. The process flowchart includes data from the streams and processes Figure 5.6. System of control points for a front coil spring suspension Figure 5.7. Curves for the goal, the basic design, and the improved design’s toe and camber Figure 5.8. Rubber mounting baseline design with highlighted design variations Figure 5.9. For rubber bushings, desirable piecewise rigidity curves Figure 5.10. Improved bushing construction Figure 5.11. Target and optimum design stiffness curves Figure 6.1. Sample memorandum Figure 6.2. An illustration of a business letter Figure 6.3. Example of bibliographies and reference lists Figure 6.4. Illustration of engineering specs Figure 6.5. ANSI standard line practice. (Reprinted from ASME) Figure 6.6. (a) ANSI lettering standard procedure. (b) Letters that are angled: Lateral letters (Reproduced with permission from the American Society of Mechanical Engineers from ASME Y14.2M-1979.) Every right is reserved.) Figure 6.7. A drawing demonstrating a structural feature of a cored pilaster block is shown (Courtesy of Dr. W. Rodriguez-Ramos.) xii

Figure 6.8. A one-point perspective drawing of an item Figure 6.9. A two-point perspective depiction of an item Figure 6.10. The primary orthographic drawing lines. (Reference: James H. Earle, Engineering Design Graphics, 5th Edition, Addison-Wesley Publishing Co., Boston, MA, 1987.) Reprinted with Pearson Education’s permission Figure 6.11. A contrast between an orthographic perspective and a cross-sectional picture of a similar item. (Source: James H. Earle, Engineering Design Graphics, Fifth Edition, Addison-Wesley Publishing Company, Boston, Massachusetts, 1987 Figure 6.12. An example of a supplementary viewpoint. (Reference: James H. Earle, Engineering Design Graphics, 5th Edition, Addison-Wesley Publishing Co., Boston, MA, 1987.) With the permission of Pearson Education, Inc., Upper Saddle River Figure 7.1. Scientific management principles Figure 7.2. Process of scientific hiring and selection Figure 7.3. Management and labor working together Figure 7.4. The process of sustainable management Figure 7.5. Several useful techniques for coaching team members by leaders Figure 8.1. Smith Woodward in center and Charles Dawson on right digging the Piltdown gravels Figure 8.2. Jan Hendrick Schön asserted to have found a molecular-scale replacement for a conventional transistor Figure 8.3. The chamber of judges from the Nuremberg trials Figure 8.4. A nuclear research center cell for cold fusion. The field’s legitimate research efforts were harmed by pons and Fleischmann’s hasty declaration

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LIST OF ABBREVIATIONS

ANSI

American National Standards Institute

CAD

computer-aided design

CAE

computer-assisted engineering

CC

control chart

CSSL

continuous system simulation language

DAEs

differentially algebraic equations

DCM

dichloromethane

DEVS

discrete-time subsystems specification

DMAIC

define, measure, analyze, optimize, control

HLA

high level architectural

HWIL

hardware-in-the-loop

IEC

International Electrotechnical Commission

IGES

initial graphics exchange specification

IRBs

institutional review boards

ISO

International Organization for Standardization

MAU

multi-attribute utility

MDT

multidisciplinary teamwork

MEMS

micro electromechanical systems

PDEs

product data exchange using STEP

PSO

particle swarm optimization

QFD

quality function deployment

RMS

roots means squared

STEP

standard for the exchange of product

PREFACE The scientific principles of engineering are the foundation on which all engineering disciplines are built. These principles provide a common language and understanding for engineers of all specialties and enable them to work together to solve complex problems. Without these shared principles, the engineering profession would be far less effective. There are many different scientific principles that are important to engineering, but some of the most fundamental include the laws of physics, chemistry, and mathematics. These laws describe how the world around us works, and provide a basis for designing everything from bridges to cell phones. Without a strong understanding of these laws, it would be impossible to create reliable and safe engineering designs. In addition to the basic laws of science, there are also many practical principles that engineers use to design and build devices and systems. These principles are based on years of experience and experimentation, and provide guidance on everything from the best way to lay out a circuit board to the most efficient way to construct a building. By following these principles, engineers can create designs that are safe, reliable, and efficient. The scientific principles of engineering are constantly evolving as our understanding of the world around us grows. As new discoveries are made and new technologies are developed, the principles that guide our work will continue to change. But by working together and sharing our knowledge, we can continue to build a better future for all. As an engineer, it is important to be familiar with the scientific principles that underpin your work. This will help you to design and build structures that are safe and efficient. In this book, we will explore some of the key scientific principles that are relevant to engineering. We will learn about the properties of materials and how they can be used to create strong and durable structures. We will also look at the principles of mechanics and how they can be applied to the design of machines and other devices. By understanding these principles, you will be able to apply them in your own work as an engineer. The book has eight different chapters that are divided from one another. The first chapter introduces the reader to the Basics of Scientific Principles. Major emphasis and effort are devoted in Chapter 2 to discussing the Creativity and Scientific Approach to Engineering Problems. Engineering’s scientific design and modeling is explored in full in Chapter 3, which may be read here. In Chapter 4 of the aforementioned book, readers are given an overview of the scientific approach to engineering failures. Significant focus is placed on the scientific optimization of engineering systems in Chapter 5. The engineering communications are investigated

and given in further depth in Chapter 6. Scientific management principles are discussed in Chapter 7. In the last chapter, “Chapter 8, Ethics in Science and Engineering,” blood illnesses are discussed in considerable detail. —Author

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CHAPTER

BASICS OF SCIENTIFIC PRINCIPLES

CONTENTS 1.1. Introduction......................................................................................... 2 1.2. Goals of Scientific Principles............................................................... 2 1.3. Assumptions of Scientific Principles..................................................... 3 1.4. The Scientific Principles....................................................................... 6 1.5. Systematic Nature of Scientific Principles.......................................... 12 1.6. Scientific Principles and Technology.................................................. 18 References................................................................................................ 20

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1.1. INTRODUCTION Psychology is a scientific discipline. However, what is science? It is hard for the majority of individuals, even scientists, to solve this issue since there is no basic, obvious explanation. We may attempt to break the ice by describing science as an ordered collection of information gathered using scientific methods. Next, we must define what we understand by scientific principles, taking care to specify the underlying considerations and objectives. Thus, to assess the phrase scientific principles, we must specify the desired outcomes, the underlying assumptions, and the technique’s features (Kindstedt, 2014).

1.2. GOALS OF SCIENTIFIC PRINCIPLES Most, though not all, scientists are concerned with three objectives: comprehension, forecast, and command. Two of such objectives, comprehension, and prediction, are pursued by all scientists. Control is the third objective pursued by only certain scientists who can alter the things they investigate. Astronomy is among the most strict and accurate fields in terms of forecasting, yet it is doubtful that astronomers would ever have significant control over their field to affect happenings (Wadsak & Mitterhauser, 2010). Knowing is often used considered synonymous with observation and description when describing the objectives of science. Even though there is semantic overlap between the three notions, there are also significant distinctions. Initially, descriptions of items and occurrences are provided. The “what” of what it is we are researching must be known (Fong, 1995). It is essential to provide an accurate representation that identifies the elements and situations that occur and their extent. As the explanation grows more comprehensive—as we discover additional causes or situations that influence the events we are investigating—our comprehension of the event improves. The extra explanation could consist of a comprehensive account of the occurrence. Then, we will be capable of correctly and precisely redescribing circumstances whereby a phenomenon happens (Ololube et al., 2014). A few have stated that the overall purpose of science is forecasting. If we can forecast the recurrence of an event, we understand (to a few degrees) that we have a little understanding of it. The prediction may also allow for considerable control (Allen Jr, 2016). Whenever events could be reliably anticipated, preparations may be made in advance. However, we should not completely combine forecast with comprehension. We could be able to properly anticipate, depending on previous experience, that some

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persons with serious depression would have a recovery of signs after electroconvulsive shock. Therefore, we may not fully comprehend why this is the case (Shchedrin & Vasil’ev, 2019) (Figure 1.1).

Figure 1.1. The scientific principles as well as techniques. Source: https://en.wikipedia.org/wiki/Scientific_method.

Countries across the globe have conducted an extensive studies on natural catastrophes like earthquakes, storms, droughts, and diseases. Imagine the effect on human well-being of obtaining adequate knowledge to forecast such natural calamities. Preparedness on time might save people and significantly decrease injuries, death, and suffering (Williams et al., 2008). And then the next stage, gaining control over the atmospheric factors that contribute to such catastrophes, would allow us to adjust the time, location, and severity of their incidence or even avoid them. The possibility of gaining control over aberrant behavior is equally fascinating to consider. Whenever adequate information is obtained, we may be capable of removing or lessening the signs of several psychophysiological problems, increasing a feeling of well-being, improving learning and memory, and eradicating AIDS (Bandiera et al., 2018). Eventually, scientific principles strive to describe the universe’s events via the creation of theory. Scientists attempt to formulate generalizations that relate to the occurrences being investigated. If this is achieved, comprehension, prediction, and command will follow (Getz & Dellaire, 2020).

1.3. ASSUMPTIONS OF SCIENTIFIC PRINCIPLES Two essential ideas are shared by all scientists. The first is determinism, which holds that all occurrences in the cosmos, including conduct, are legal or ordered. The second possibility is that its legality can be discovered. It is

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important to note that the first possibility does not always entail the second. In other terms, we might presume that action is legal without assuming that we will find it (Jospe & Forbes, 1996). To state that conduct is legal is to suggest that development is a result of previous occurrences. We may argue, more broadly, that there is a causeeffect link between the old and the new, and that there is consistency in between. Humans do not act arbitrarily or capriciously, per this point of view. Even seemingly random action is required to observe certain underlying lawfulness (Crenshaw et al., 2014). Daily experiences support the belief that activity is legal. When we get behind the wheel of an automobile, we intuitively believe that the conduct of dozens of other drivers in the area will be organized. They would not abruptly swerve off the highway into our direction, stop without reason, or attempt to collide with us. Likewise, while flying, we expect that the pilots would pick a route that reduces air turbulence while maximizing passenger satisfaction. We are certain that they would not do any action on a whim, like a loop that loops at 30,000 feet (Ilo, 2019). For various purposes, the premise of legality is critical. One compelling cause is that it influences our conduct as scientists. It might make little sense to examine action if we assumed it was devoid of reasons or quantifiers. By theory, there is no rule of law if a person’s action is devoid of reasons. There is no structure to it, no link to the previous. It simply does not make sense to investigate phenomena that are thought to be illegal (Chernyaev et al., 2020). So if the premise of legality is valid, we should not be led to believe that it could lead to exact forecasts of human nature. We should recognize the vast heterogeneity in behavior that emerges from the large number of factors that have influenced a person up until that point in life. Such factors like the person’s genetic makeup as well as every encounter he or she ever had. Knowing all of such factors as well as their complicated relationships to create accurate forecasts seems to be an impossible objective. Even so, our forecasts in the behavioral sciences have improved over time, and experts expect that this tendency will maintain as cognitive science advances (Entel et al., 2011). Chaos theory, a relatively new idea that has been used in research, particularly in the behavioral sciences, is one endeavor to better comprehend the unpredictability of occurrences (Kirbas & Gürsoy, 2010). Chaos theory is an effort to use computational models to explain complicated, nonlinear, system dynamics. The theory seeks to describe

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a system’s general behavior with the lack of trying to forecast particular conditions at any one time (Shokaliuk et al., 2020). Chaos theory is sometimes misconstrued to indicate that there exist nondeterministic systems. This is not correct. In actuality, the theory implies randomness but admits that full prediction might not even be possible due to the enormous number of factors operating to affect the system at the same time. As a result, you might picture that our current behavior and ideas are influenced by a vast array of natural occurrences, such as our genetic make-up, the whole of our prior experiences, our present situation of physiology, as well as the existing environmental circumstances. Although that determinism is conceivable, it is hard to envisage having a thorough grasp of all such factors and their interconnections, which would allow us to make flawless predictions about our actions and opinions. Moreover, we should recognize that if something hasn’t been performed yet doesn’t imply it can’t be done (Tehrani et al., 2014). It is vital to highlight that scientific idea are not considered true or wrong, verifiable or unverifiable. As scientists, we establish predictions to see how they lead us to reach our objectives. We sense more confidence in our ideas if we meet our aims of forecast, command, and knowledge. However, we do not claim to have shown predictability or that independence does not occur. Such ideas might be considered the ground rules for the games that scientists play. We will follow these guidelines as soon as they are beneficial. We reject them whenever they are no longer helpful and replace them with others that agree to take us farther in our search for knowledge (Kobzar-Frolova, 2009). Major advancements in science only happened whenever one set of factors was substituted by another in many cases throughout history. This is sometimes referred to as a fundamental shift. For instance, astronomy is now regarded as among the most precise disciplines. Meanwhile, astronomy was in disarray a few hundred years ago. The idea that the sun rotates all around the earth was one that astronomers struggled with (Ptolemy) (Taheri et al., 2014). Although this presumption well-matched observations of the natural world (the sun does seem to rotate all around the earth, and the earth doesn’t seem to be changing), astronomy didn’t advance much till it was disproved. The Ptolemaic framework was just unable to reconcile many contradictory data. Ironically, astronomy didn’t become a lively study until it started operating on a presumption that was at odds with common observation. The shocking theory that the earth rotates all around the sun was put out by Copernicus. Many ambiguous findings on the movement of the stars

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as well as planets only made sense with this supposition. Ultimately, the Copernican hypothesis won out because it was more effective at forecasting and comprehending cosmic occurrences (Radchenko, 2015).

1.4. THE SCIENTIFIC PRINCIPLES In behavioral science, dreams are an interesting topic. A few people, including Sigmund Freud, think that dreams have a lot of symbolic content that needs to be decoded. Some individuals think that dreams are merely a neurobiological by-product of the brain’s physiological functions throughout the REM phase of sleep. Due to the obvious visually attractive material of most imagination, scientists suspected for a long time that visual facilities in the brain will be powered up throughout human dreaming. But there was no functional way to capture the movement of the person’s brain locally while someone was dreaming. A response to the research theory was therefore impossible given the development of science. The development of functional MRIs and PET scans, even so, has allowed researchers to more recently show that the visual institutes of the brain are active while dreaming (Willis et al., 2012). This example can be used to illustrate a few points. Not all occurrences are amenable to scientific study. A few are unavailable owing to technical restrictions, as was the instance with dreaming-related activity in the brain. Everyone else is unreachable because the presumptive incident has no observational referent (like ghosts or evil spirits). When we say something is empirical, we imply that it can be encountered; the event will arouse one of our diverse senses (Sadler, 2011). We have to be capable of comprehending it through touch, taste, sight, smell, hearing, or by being able to detect the sound it makes. In other sayings, an event has to be explicitly or implicitly noticeable or measurably. One subatomic particle, for instance, has never been seen, but certain scientists have measured and assessed the detail it tends to leave on a photographic plate. Although gravity has never been observed, its effects may be seen and measured everywhere. Corresponding to this, the concept of training has never been directly seen in psychology; instead, it is evaluated using how it affects a certain behavior (Somogyi et al., 2011). A public event, not a personal one, is implied by the need that an event has an actual referent. Additionally, it suggests that the findings are unbiased and not biased. As was said, certain occurrences cannot be researched since there is no empirical reference point for them. For instance, it is impossible

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to provide a logical response to the question “Is there a God?” The topic is not empirical; hence this could not be the focus of a scientific investigation (Fischer et al., 2003). This kind of question requires trust on the side of the believer, and so this faith is obtained from authorities and relevant authoritative sources (like the clergy or the Bible). We may examine religious views by asking a comparable question, though. What impact do religious convictions have on conduct, for example? Because the existence or lack of religious beliefs in an individual may be experimentally ascertained (via descriptions or surveys, for example), as well as the consequences of such views on behavior, we might research such effects objectively. Beliefs, as well as behavior, may both be seen explicitly or implicitly. These things happen empirically (Johnson & Graber, 2002). Science also demands that observations be reproducible and that research should be self-correcting, in addition to the need that events be observed. One researcher can validate the findings of others thanks to the need that data to be reproducible. The self-correcting characteristic, another crucial aspect of science, is made possible by focusing on reproducibility. Maybe only the scientific approach has a developed self-correcting process. Research undertaken in one location may often be reproduced in some other region of the globe to either validate or refute the validity of published results since occurrences are empirical and reproducible (Weymouth & Hartz-Karp, 2018). In a nutshell, they don’t. The issue is widely known to scientists. Dealing with categories of occurrences is the answer. Your birth may be special, but births, in general, are not. For other unusual occurrences, the same is often true. We research the category of events—births, deaths, personalities, and so forth—and then apply our knowledge to specific instances (Waage et al., 2010). However, there are times when certain significant events (like specific solar system planet alignments) may happen so infrequently that we are unable to study a group of such events. This issue cannot have a good ending. Having several observers present when something happens is frequently the best we can hope for. Even though the actual event might not be reproducible, several distinct observational data can be decided to make, and the outcomes can be especially in comparison. Luckily, the rare, significant event does not occur frequently enough to present a potential issue for science (Gaskins et al., 1994).

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1.4.1. The Similarity Principle As per Albert Einstein, the ultimate goal of science is to describe the largest amount of empirical events by deductive logic from the fewest amount of assumptions or axioms (Millstein et al., 1993). Defined as the scientific study of repeated events. It reveals or interprets the informal link between matched occurrences. One is known as the source, while the other is known as the impact or result. The purpose of studying history is to build or uncover the rule that may be utilized to foresee the future if the underlying cause is repeated. An event associated with a causal link may be a compound occurrence. Therefore, science doesn’t completely ignore all isolated events. Instead, unique occurrences are clustered together depending on their commonalities. In this manner, we create a fake recurrence of similar events (Litvinenko et al., 2022). Karl Popper said that any theory which does not produce accurate hypotheses is not science. A scientific hypothesis is continually evaluated in light of fresh findings. A paradigm shift might well be adopted since it solves scientific issues more effectively than the previous one. Therefore, it is not feasible for scientists to examine each instance of activity and identify the corresponding response. You may believe that the brain is so remarkable since it can do scientific tasks, but this is owing to our brains’ struggling to cope with every experience separately (Xinmin, 2019). A cause-effect pairing is the repeated occurrence of specific occurrences for which we hypothesize a causal connection (causal relationship). In this philosophical meaning, linearity is a view, an understanding of what is occurring, and not a reality; it is, at most, something that could not be confirmed or whose confirmation depends on another belief. There is a continuous, progressively intricate history (Paquet et al., 2011). People attempt to make the process easier by identifying patterns or repeating conjunctions of specific occurrences and deducing rules or causal linkages from them. The word ‘cause’ rests on the repetition of occurrences and the notion of similar or same. Recurrence refers to the repetition of either the causes and the consequence, both together now and independently. Examine the rule of component solitude: If components A and B occur, then fact C occurs, and if factor B is removed, then fact C ceases to occur, then component B is a source of fact C. Therefore, no such precise repetition exists. In the actual world, the reoccurrence of an event is simply an estimate; we overlook certain specific aspects, so it is still somewhat biased (Chen et al., 2020).

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Forecasting is based on the concept of coincidence, while causality is based on the resemblance principle: comparable events are probable to occur in a similar outcome. Whenever we assert the similar, we disregard the distinctions that may be concealed and we defy the accepted scientific rule. Once this conflict is discovered, a legal exception is recognized, which needs a change to the law. Both Science and Statistics recognize the presence of exceptions; however, Science doesn’t act till an exception happens, while Statistics reacts whenever the unexpected is anticipated (Makhmudov, 2020) (Figure 1.2).

Figure 1.2. Similarity grouping. Source: https://www.researchgate.net/publication/281098579_Principles_of_ Scientific_Methods.

Most people would probably say that the purpose of scientific inference is to find facts or rules that approach causal links so that our brain could deal with them. Because each set of things (events) is equivalent in some way, we could see reoccurrences of the similar event by grouping looking for similarities as well as disregarding differences. Significantly, this categorization is required for our brains to deal with reality effectively. Such grouping allows us to observe the recurrence of items (both causes and results) (Rahmanpoor et al., 2016). Only when similar events occur

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repeatedly can a causal relation be established. In scientific implication, those very implicit clustering of similar stuff frequently creates intangible scandals. Only the commonalities are recognized as well as stated, but the variations are unexplained or disguised. The overlooked differences produce a variation in describing sets of the same items, which is referred to as the causal space. In all other terms, determining whether objects are comparable is fairly subjective. Diverse individuals have different perspectives on many issues. If you are a big guy, what of the four main categories do you derive your qualities (e.g., life duration, probability of success on the job, or risk of cancer) from males, tall humans, tall men, or even the total population (Jona & Adsit, 2008)? We all employ the resemblance principle almost every day of our lives. It states that a comparable circumstance will result in a similar outcome. There’ll be no comparison and no scientific progress if the resemblance principle is not followed. But how can we determine the similarity between two things? If two things have comparable outcomes, we might say they are identical. However, this is just an inverted statement of the likeness principle (Malkiewich & Chase, 2019).

1.4.2. The Parsimony Principle William of Occam (1284–1347) was a philosopher and theologian from England. His contributions to the acquisition of science, logic, as well as scientific discovery, were instrumental in the transformation from the middle ages to scientific thinking. Occam emphasized the Aristotelian principle that excessive multiplication of units should be avoided (Shi et al., 2019). This concept had become recognized as Occam’s razor or the principle of simplicity. The theory that best reflects the reality of an issue should be the easiest one. Even so, Occam’s razor is not regarded as an indisputable logical principle and is not regarded as a scientific outcome. As per Albert Einstein, the supreme objective of any system is to understand the indefinable basic components as easily and little in amount as possible without sacrificing the accurate reflection of a specific item of empirical data (Pedretti & Iannini, 2020). As per Douglas Hofstadter, understanding the idea of reductionism is the easiest way. It is easily the perception that an entire thing could be fully comprehended if its components and their total amount are comprehended. Since the 1600s, reductionism is the higher approach. Rene Descartes, one of the best early campaigners of reductionism, characterized his scientific

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method as follows:.…to divide all the problems under investigation through as many components as possible, as much as are needed to solve them most effectively.’ …. to perform my opinions in a specified sequence, starting with the easiest and most readily understood items and moving up, as it were, small steps to the understanding of the most complicated (Kallmann, 1961). According to Gauch, there are two explanations for why parsimony is a crucial physical law. First and foremost, it is essential because the whole scientific organization has never generated and will never make a single conclusion that does not adhere to the principle of parsimony. Minimalism is indispensable and widespread. Second, and more effectively, parsimonious representations of scientific information may enhance comprehension, increase precision, and boost productivity. Surprisingly, parsimonious theories may be more precise than their corresponding facts (Hayes, 2015) (Figure 1.3).

Figure 1.3. Parsimony rule. Source: https://www.researchgate.net/publication/281098579_Principles_of_ Scientific_Methods.

The Parsimony principle in mathematics and data techniques may be expressed as follows: amongst models that fit the data similarly well, the simpler model must be selected. Fitting is evaluated based on its prediction accuracy, explaining capacity, trialability, capability to generate new experience and understanding, consistency with other philosophical and scientific views, and reproducibility of findings. Frequently, a simpler paradigm is less exact for specific situations but relevant to a wider array of issues (Temple, 2019).

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1.5. SYSTEMATIC NATURE OF SCIENTIFIC PRINCIPLES A significant distinction separates scientifically-based information from knowledge gathered via everyday experiences. Science is methodical. For instance, in psychology, scientists, and non-scientists alike are conversant with the topic. We spend the majority of the day and then conversing with others, noticing them, assessing them, and contemplating our actions (Near & Martin, 2007). Everybody has gained knowledge about human conduct without doing scientific research. In addition, philosophers, poets, as well as literary individuals often have more insights into human behavior than psychologists. From our everyday encounters, we draw several inferences. Regrettably, not every conclusion we draw from our everyday experiences is correct. Many are false. A systematic study and analysis of behavior are required to avoid making conclusions that show up innately accurate but are wrong. A conceptual framework permits the data collection under clear and specific as well as constrained circumstances that are repeatable, measurable, and valuable. Substantial importance is put on assessing and rejecting alternative hypotheses (explanations) for the phenomenon under investigation. In addition, special efforts are made to establish connections between occurrences (Wiedenhoft et al., 2017).

1.5.1. Inductive and Deductive Research Strategies Carried out in a systematic process of science, both deductive and inductive research methods are utilized. Inductive thinking is the process of deriving a basic rule or theory from a list of specific findings. In contrast, deductive reasoning entails the development of particular observational estimates dependent on a basic principle or theory. Figure 1.4 illustrates the line of reasoning. Observe that inductive reasoning leads different analyzes to a single theory. One uses deductive reasoning (Bastian, 1998).

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Multiple predictions are derived from theory (Derstine, 2002).

Figure 1.4. The orientation of rationale for deductive and inductive research techniques. Source: https://uca.edu/psychology/files/2013/08/Ch3-Fundamentals-of-theScientific-Approach.pdf.

Imagine the dopamine theory for schizophrenia as an instance. Schizophrenia is a severe mental condition with signs including delusional ideas, hallucinations, emotional instability, as well as social disengagement. As you would expect, one of the earliest “theories” of the condition was demonic possession. Several French psychiatrists gave a novel anesthetic medicine (later dubbed chlorpromazine) to a set of psychiatric patients throughout the middle of the 20th century. Patients with schizophrenia recovered (Werbos, 2004). Other substances, like amphetamines and cocaine, were seen to exacerbate the signs. Research on animals revealed that chlorpromazine decreases the action of a specific chemical in the brain (dopamine), while amphetamines as well as cocaine boost dopamine neural activity. Such precise data, among others, lead to the dopamine theory of schizophrenia via inductive logic. The hypothesis then suggested, via deductive logic, that additional medication that inhibits dopamine action ought to be effective in treating schizophrenia. Numerous of these medications have been examined and are already in use (Szybiński et al., 1993).

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1.5.2. Common Sense and Scientific Principles A few have tried to argue that if it relates to interpersonal behavior, rational thinking yields similar results as psychological research. The above statement implies that current studies are a waste of money and resources but simple logic provides the very same responses. What does common sense imply? It is commonly understood to refer to the buildup of experience-based knowledge that enables us to formulate generalized statements (declarations, findings, speculations) about the current world. Such generalizations ease complicated situations by trying to draw ultimate findings, i.e., without skills (DeLancey, 2005). It is not uncommon for rational thinking and scientific conclusions to coincide, but the two can also disagree. As previously stated, scientific methods can be viewed from the cautious, direct observations of empirical occurrences, frequently under controlled conditions. The findings are then thoroughly considered and exactly tried to communicate to others, who could then conduct additional evaluations. The concept (generalized statement, outcome) obtained from this study continuously forecasts actions. If not, extra study is funded and additional precepts are deduced. Typically, derived principles are expressed in an eligible type, like “Provided such situations, it is predicted that this behavior will take place” (Haase & Myers, 1988). It’s not the situation with common sense, especially as it appears in proverbs containing general “truths.” Common sense sayings frequently contradict each other. Contradicting the proverb “Look before you leap” is the proverb “He who hesitates is lost.” Yet, given the appropriate conditions (which are not indicated by the proverb), all proverbs may be accurate. Additional examples exist. The phrase “Two heads are better than one” contradicts “Too many cooks spoil the broth.” Is it true that “absence causes the heart to feel full” or does “out of sight, out of mind” apply? How commonly have you did hear the phrases “You’re still not too old to learn” and “You can’t learn an old dog new tricks”? Must the family adhere to the adage “Spare the pipe as well as completely ruin the child” or “Honey attracts more mosquitos than citric acid” (Reijnders, 1988)? The proverbs have seemed incompatible and conflicting whenever stated in real numbers, as in the things listed. It is possible that “Out of sight, out of mind” and “Absence makes the heart grow fonder” are accurate conclusions under certain conditions, but these conditions are not defined. Unlike proverbs of common sense, modern science sets out the requirements under which the concepts can be decided to be applied (Davis, 1987).

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Nonscientists, like poets, playwrights, novelists, as well as philosophers, have contributed to the study of human behavior, despite our recognition of the deep shortcomings of a rigidly commonsense view of knowledge. These people can give us profound knowledge about human actions that act as a source of inspiration for our studies (Rodriguez‐Gonzalez et al., 2015).

1.5.3. Basic Research and Scientific Principles Basic research is complex to describe and is often underappreciated among those who control large amounts of money for studies. Basic research could appear frivolous to a certain. We certainly indulge researchers by funding their pet projects, like research into moth sexual behavior, bee interaction, as well as insect sexual attractants (Gibbons et al., 2014). However, as is often the situation with basic research, the findings of such studies have already had significant impacts on agricultural practices, the world’s energy supply line, as well as the economy. Insects, for example, end up causing multimillion-dollar damage to crops each year, but control of such pests has formed its series of issues. Basic research on the physiology and behavior of pests has enabled the creation of new and secure biocontrol methods. Even so, of that kind research is frequently mocked or critiqued (Hollis, 2021) (Figure 1.5).

Figure 1.5. Basic research plan. Source: https://www.edef860.net/basic-vs-applied-research.html.

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Before actually delving deeper into basic research, one final point to consider: political leaders in charge of research grants could underestimate the importance of basic research for several reasons. They dismiss it at moments since it is insignificant, but in other instances even though of their very own partiality, prejudice, or moral values, instead of scientific support. Or their reasons for not acknowledging its worth, the scientists are sometimes to blame (Pedersen, 2003; Brouwer, 1985). Researchers, all too often, change the cost of basic research for granted but have not every time managed to make a compelling case for it. As a result, scientists, as well as educated laypeople, as well as educated laypeople must devote more attention to trying to present a defensive system of basic research (Jacobs, 1992). It is hard seeing the connection between both years of basic research and the implementation of advanced software to problems today. Basic research lays the groundwork (database) for the settlement of current and future issues, the technical progress, and a greater sense of all elements of our world (Hollis, 1955). Basic research is focused on recognizing nature, all features of the world, and natural phenomena. It is not a study aimed at resolving specific social issues. It is not the task, in the sense that it is not implicated in the task of trying to cure or trying to correct a particular disease or issue or continuing to develop a particular device (Russo & Stolterman, 2000). Basic research has no instant direct implementation; even so, it is likely to be the most efficient approach to solving most of our present and future difficulties. It has played an important role in almost all of the strategies for our huge issues. The National Science Foundation published the findings of a study (TRACES) emphasizing the significance of basic research in 1969. They investigated technological innovations of broad significance and diverse application, dating back to 1850. It was determined that roughly 70% of the key and critical events were derived from basic research (Rocca & Andersen, 2017). A few instances may help us better understand the benefits of basic research. X-ray photos were not invented by doctors to assist in the diagnosis of diseases. Somewhat more, medical applications of X-rays accompanied the pioneering studies of Wilhelm Roentgen, who was “only” involved in basic ray physics troubles. Poliomyelitis (infantile paralysis) was a dreadful disorder that left most of its people who survived paralyzed for life a few decades earlier (Patriota, 2017). We are all relatable with Drs. Salk and Sabin’s research activities resulted in vaccines that were vaccinated against

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the disease. And though many of us have heard of John Enders’ basic research, in which he was “merely” useful in learning viruses? To reach his dream, he was required to develop a method for increasing viruses in societies. While he did succeed, he unleashed a torrent of practical uses of his methods. The Salk and Sabin vaccines are just two of several momentous developments that had one’s roots in John’s laboratory (Pontis & Van der Waarde, 2020). Enders. Although George Cotzias was not looking for a cure for Parkinson’s disease, the drug L-Dopa was developed as a result of his involvement in metals and metabolic health. Correspondingly, streptomycin, the painkiller that has well almost exterminated tuberculosis, was found by a soil biochemist. The majority of AIDS therapeutic interventions now accessible are dependent on basic research in fields such as molecular virology, immunology, biochemistry, as well as genetics (Dreyfus, 1987). Take into account the animal testing of psychology today like B. F. Skinner as a further instance. Skinner noted pigeons pecking at a location in a chamber for hours at a time. Several more people may wonder (as we did) what pecking pigeons have something to do with interpreting. Skinner was well aware that the basic theories of learning disclosed in his studies can have a far impact on understanding active knowledge and can be utilized in therapeutic areas to make patients with mental problems and disorders. The widely spread utilization of behavior modification methods testifies to the importance of basic behavioral surveys undertaken numerous years ago as well as continues to be conducted presently (de França Sá & Marsico, 2022). It is difficult to understand the significance of basic research while it is being conducted. How significant was the effect of current flow on magnetic needles during Faraday’s time? Induction coils are extremely important in the field of transportation today. Induction coils were not discovered by people who were interested in transportation (this would have been missionoriented research) (Brunk et al., 1991). The invention of induction coils gave birth to the transportation industry. IBM, Control Data, and other computer behemoths did not set out to create basic computer circuits. They were discovered in the 1930s by physicists interested in nuclear physics. How important were vacuum tube properties during the time of Boyle (gas laws)? Who would have predicted transistors, printed circuit boards, and computer chips? Who could have predicted the electronics industry with the discovery of the atom (Maeyer & Talanquer, 2013)? Basic research is still producing interesting and promising results today. Recombinant DNA study has enabled the production of relatively pure

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types of insulin, a marked enhancement over the insulin presently animalderived. The finding and subsequent formation of the substance interferon retain the potential for the treatment of some diseases. The recognition and generation of monoclonal antibodies have piqued the interest of both scientists and engineers. This finding should enable the development of monoclonal antibodies that damage directly aimed at bacteria, viruses, or other foreign substances in the body. Important breakthroughs in behavioral science have also taken place. One example is the utilization of biofeedback processes to facilitate learning how to regulate their heart rate and brain activity (Prilleltensky, 1997).

1.6. SCIENTIFIC PRINCIPLES AND TECHNOLOGY Science is commonly thought of as systematically seeking information or discovering basic phenomena and then organizing this information into general explanatory principles. The application of scientific discoveries and principles to existing practical problems is commonly referred to as technology. As previously stated, scientists may discover fundamental principles many years before they are applied in the form of technology. Computers are one example, and medical immunization techniques are another. Principles must be available before the technology can be developed; however, technology must sometimes be developed before the principles can be applied. Examples of the latter include the space program and nuclear weapons (Greene & Sadowski, 1984). Scientists are frequently blamed for the problems caused by the technology that results from scientific discoveries. Although we believe that knowledge in and of itself is beneficial, the application of that knowledge can be beneficial or detrimental. The “mad scientist” stereotype might be better applied to the “mad technologist” (Rudolph & Kröplin, 1997). We’ll use computers as an example once more. Scientists are not to blame for today’s societal abuses (invasion of privacy, identity theft). Similarly, the automobile is a technological achievement; the problems it causes (pollution) are not the fault of scientists. Genetic discoveries are leading to genetic engineering (and even cloning) technologies over which scientists may have little control (Blazejczyk-Okolewska et al., 2004). What we are attempting to do here is to encourage everyone to think critically about the distinction between science and technology as we consider society’s problems. However, we must acknowledge the interplay between science and technology, and that the line between the two may

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become blurred (Weinstein et al., 2003). It is said that scientists are often blamed for issues but are rarely appreciated for their contributions to society. These accomplishments are often attributed to technologists rather than scientists. For example, medical technology is founded on the sciences of physiology and chemistry; engineering technology is founded on the science of physics, and educational technology is founded on the science of learning. Most people mistakenly credit advancements in medical, engineering, and education to technologists rather than scientists. Both organizations should be credited for such accomplishments (Akhavan, 2013). The last example will show how science and technology may work in tandem. In various Latin American nations, vampire bats are a severe concern. During the night, these bats discreetly attack sleeping mammals, scooping off a piece of skin and then sucking part of their blood. Some cattle are bitten by up to 15 bats in a single night (Hodges, 1974). The blood flows freely most of the night due to an anticoagulant in the bat’s saliva. Some of the affected cattle had rabies, which is spread by vampire bats. In certain situations, the wounds get infected, leading to decreased weight increase and milk output. The answer to this challenge exemplifies the complementary nature of science and technology in problem resolution (Fisher, 1964). Several prior efforts have been undertaken to exterminate the vampire bats. Shooting, netting, and electrocuting bats in flight were among them. One significant issue with these treatments was that they killed useful insecteating bats while not lowering vampire losses (Ferraro et al., 2005). In 1968, biologists from the Denver Wildlife Research Center started investigating the situation. They intended to design a mechanism that would control just vampires that attacked agricultural animals since only some of these bats harmed livestock. The researchers took bats into their Denver laboratory and found that adding an anticoagulant to their blood caused the vampires to bleed to death (Pall, 2015). How can one get more anticoagulants into freeflying bats? Several strategies were attempted and failed by the scientists. They next attempted to inject the anticoagulant into the first stomach of cattle at amounts that were innocuous to the livestock but not to the vampires attacking the cattle. Bats that fed on the blood of treated cattle acquired enough anticoagulants to kill them. Cattle might be treated twice a year for $30 to $40 per animal. The strategy reduced vampire bat attacks by 91% while increasing milk and meat output (Patil & Giordano, 2010).

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REFERENCES 1.

Akhavan, Q., (2013). Criticism of principles and assumptions of the functionalist approach to religion. Jostarha-ye Falsafe-ye Din, 2(2), 21–34. 2. Allen, Jr. L. V., (2016). Basics of compounding: Clinical pharmaceutics, part 2. International Journal of Pharmaceutical Compounding, 20(6), 485–493. 3. Bandiera, G., Kuper, A., Mylopoulos, M., Whitehead, C., Ruetalo, M., Kulasegaram, K., & Woods, N. N., (2018). Back from basics: Integration of science and practice in medical education. Medical Education, 52(1), 78–85. 4. Bastian, O. L. A. F., (1998). Landscape-ecological goals as guiding principles to maintain biodiversity at different planning scales. Ekologia (Bratislava)/Ecology (Bratislava), 17(1), 49–61. 5. Blazejczyk-Okolewska, B., Czolczynski, K., & Kapitaniak, T., (2004). Classification principles of types of mechanical systems with impacts– fundamental assumptions and rules. European Journal of Mechanics-A/ Solids, 23(3), 517–537. 6. Brouwer, F. J., (1985). On the principles, assumptions and methods of geodetic very long baseline interferometry. Publications on Geodesy, 7(4), 3–9. 7. Brunk, C., Haworth, L., & Lee, B., (1991). Is a scientific assessment of risk possible? Value assumptions in the Canadian alachlor controversy1. Dialogue: Canadian Philosophical Review/Revue Canadienne de Philosophie, 30(3), 235–247. 8. Chen, T. L., Kim, H., Pan, S. Y., Tseng, P. C., Lin, Y. P., & Chiang, P. C., (2020). Implementation of green chemistry principles in circular economy system towards sustainable development goals: Challenges and perspectives. Science of the Total Environment, 716, 136998. 9. Chernyaev, A. A., Serdobintsev, D. V., & Kudryashova, E. V., (2020). Scientific basics and prospects of development of production of organic animal products in Russia. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development, 20(2), 117. 10. Crenshaw, T. D., Rortvedt-Amundson, L. A., Cuarón, J. A., Bergstrom, J. R., & Litta, G., (2014). Triennial growth symposium: Vitamin

Basics of Scientific Principles

11. 12.

13.

14.

15.

16.

17.

18.

19. 20. 21.

21

D-establishing the basics to dispel the hype. Journal of Animal Science, 92(3), 883–886. Davis, B. D., (1987). Bacterial domestication: Underlying assumptions. Science, 235(4794), 1329–1335. De França Sá, A. L., & Marsico, G., (2022). Decoloniality and disruption of the scientific status quo: Dissemination of universal theoretical assumptions in international research. Review of General Psychology, 1, 3–9). DeLancey, J. O., (2005). The hidden epidemic of pelvic floor dysfunction: Achievable goals for improved prevention and treatment. American Journal of Obstetrics and Gynecology, 192(5), 1488–1495. Derstine, P. L., (2002). Implementing goals for non-cognitive outcomes within a basic science course. Academic Medicine: Journal of the Association of American Medical Colleges, 77(9), 931–932. Dreyfus, A., (1987). The validation of developers’ assumptions about a technology‐minded biological curriculum. Research in Science & Technological Education, 5(2), 173–183. Entel, P., Dannenberg, A., Siewert, M., Herper, H. C., Gruner, M. E., Buchelnikov, V. D., & Chernenko, V. A., (2011). Compositiondependent basics of smart Heusler materials from first-principles calculations. In: Materials Science Forum (Vol. 684, pp. 1–29). Trans Tech Publications Ltd. Ferraro, F., Pfeffer, J., & Sutton, R. I., (2005). Economics language and assumptions: How theories can become self-fulfilling. Academy of Management Review, 30(1), 8–24. Fischer, F., Bouillion, L., Mandl, H., & Gomez, L., (2003). Scientific principles in Pasteur’s quadrant: Integrating goals of understanding and use in learning environment research. In: Designing for Change in Networked Learning Environments (Vol. 1, pp. 493–502). Springer, Dordrecht. Fisher, K. A., (1964). The assumptions in scientific therapy. Psychoanalytic Review, 51(2), 85–105. Fong, C. T., (1995). Back to basics: Principles of inborn errors of metabolism: An exercise. Pediatrics in Review, 16(10), 390–395. Gaskins, I. W., Guthrie, J. T., Satlow, E., Ostertag, J., Six, L., Byrne, J., & Connor, B., (1994). Integrating instruction of science, reading, and writing: Goals, teacher development, and assessment. Journal of

22

22.

23.

24.

25.

26. 27.

28. 29.

30.

31.

32.

Scientific Principles of Engineering

Research in Science Teaching, 31(9), 1039–1056. Getz, L. J., & Dellaire, G., (2020). Back to basics: Application of the principles of bioethics to heritable genome interventions. Science and Engineering Ethics, 26(5), 2735–2748. Gibbons, M. C., Lowry, S. Z., & Patterson, E. S., (2014). Applying human factors principles to mitigate usability issues related to embedded assumptions in health information technology design. JMIR Human Factors, 1(1), e3524. Greene, T. J., & Sadowski, R. P., (1984). A review of cellular manufacturing assumptions, advantages and design techniques. Journal of Operations Management, 4(2), 85–97. Haase, J. E., & Myers, S. T., (1988). Reconciling paradigm assumptions of qualitative and quantitative research. Western Journal of Nursing Research, 10(2), 128–137. Hayes, S. C., (2015). Analytic goals and the varieties of scientific contextualism. In: The act in Context (Vol. 1, pp. 126–142). Routledge. Hodges, D. L., (1974). Breaking a scientific taboo: Putting assumptions about the supernatural into scientific theories of religion. Journal for the Scientific Study of Religion, 1, 393–408. Hollis, F., (1955). Principles and assumptions underlying casework practice. Social Work (1939–1970), 12(2), 41–55. Hollis, F., (2021). Principles and assumptions underlying casework practice. In: Social Work and Social Values (Vol. 1, pp. 22–38). Routledge. Ilo, A., (2019). Design of the smart grid architecture according to fractal principles and the basics of corresponding market structure. Energies, 12(21), 4153. Jacobs, M. S., (1992). Testing the assumptions underlying the debate about scientific evidence: A closer look at juror incompetence and scientific objectivity. Conn. L. Rev., 25, 1083. Johnson, S. E., & Graber, B. E., (2002). Enlisting the social sciences in decisions about dam removal: The application of social science concepts and principles to public decision making about whether to keep or remove dams may help achieve outcomes leading to sustainable ecosystems and other goals in the public interest. BioScience, 52(8), 731–738.

Basics of Scientific Principles

23

33. Jona, K., & Adsit, J., (2008). Goals, Guidelines, and Standards for Student Scientific Investigations (Vol. 1, pp. 4–8). North American Council for Online Learning. 34. Jospe, N., & Forbes, G., (1996). Back to basics: Fluids and electrolytes— Clinical aspects. Pediatrics in Review, 17(11), 395–403. 35. Kallmann, F. J., (1961). New goals and perspectives in human genetics. Acta Geneticae Medicae et Gemellologiae: Twin Research, 10(4), 377–388. 36. Kindstedt, P. S., (2014). The basics of cheesemaking. Cheese and Microbes, 1, 17–38. 37. Kirbas, U., & Gürsoy, M., (2010). Developing the basics of pavement management system in Besiktas district and evaluation of the selected sections. Scientific Research and Essays, 5, 2–6. 38. Kobzar-Frolova, M. N., (2009). The basics (main principles) of tax delictology. Taxes and Taxation, (11), 1. 39. Litvinenko, V., Bowbrik, I., Naumov, I., & Zaitseva, Z., (2022). Global guidelines and requirements for professional competencies of natural resource extraction engineers: Implications for ESG principles and sustainable development goals. Journal of Cleaner Production, 130530. 40. Maeyer, J., & Talanquer, V., (2013). Making predictions about chemical reactivity: Assumptions and heuristics. Journal of Research in Science Teaching, 50(6), 748–767. 41. Makhmudov, K., (2020). Innovative cluster of pedagogical education: Common goals and specific interests. Academic Research in Educational Sciences, (2), 182–187. 42. Malkiewich, L. J., & Chase, C. C., (2019). What’s your goal? The importance of shaping the goals of engineering tasks to focus learners on the underlying science. Instructional Science, 47(5), 551–588. 43. Millstein, S. G., Petersen, A. C., & Nightingale, E. O., (1993). Adolescent health promotion: Rationale, goals, and objectives. Promoting the Health of Adolescents: New Directions for the TwentyFirst Century, 1, 3–10. 44. Near, J. A., & Martin, B. J., (2007). Expanding course goals beyond disciplinary boundaries: Physiology education in an undergraduate course on psychoactive drugs. Advances in Physiology Education, 31(2), 161–166.

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45. Ololube, N. P., Ingiabuna, E. T., & Agbor, C. N., (2014). Universal concepts, nature, and basics principles of educational management: Implication for present day school management. International Journal of Educational Foundations and Management, 2(1), 43–62. 46. Pall, M. L., (2015). Scientific evidence contradicts findings and assumptions of Canadian safety panel 6: Microwaves act through voltage-gated calcium channel activation to induce biological impacts at non-thermal levels, supporting a paradigm shift for microwave/lower frequency electromagnetic field action. Reviews on Environmental Health, 30(2), 99–116. 47. Paquet, P. J., Flagg, T., Appleby, A., Barr, J., Blankenship, L., Campton, D., & Smith, S., (2011). Hatcheries, conservation, and sustainable fisheries—achieving multiple goals: Results of the hatchery scientific review group’s Columbia River basin review. Fisheries, 36(11), 547– 561. 48. Patil, T., & Giordano, J., (2010). On the ontological assumptions of the medical model of psychiatry: Philosophical considerations and pragmatic tasks. Philosophy, Ethics, and Humanities in Medicine, 5(1), 1–7. 49. Patriota, A. G., (2017). On some assumptions of the null hypothesis statistical testing. Educational and Psychological Measurement, 77(3), 507–528. 50. Pedersen, P. B., (2003). Culturally biased assumptions in counseling psychology. The Counseling Psychologist, 31(4), 396–403. 51. Pedretti, E., & Iannini, A. M. N., (2020). Towards fourth-generation science museums: Changing goals, changing roles. Canadian Journal of Science, Mathematics and Technology Education, 20(4), 700–714. 52. Pontis, S., & Van, D. W. K., (2020). Looking for alternatives: Challenging assumptions in design education. She Ji: The Journal of Design, Economics, and Innovation, 6(2), 228–253. 53. Prilleltensky, I., (1997). Values, assumptions, and practices: Assessing the moral implications of psychological discourse and action. American Psychologist, 52(5), 517. 54. Radchenko, A. V., (2015). Professional self-assessment of future health basics teachers as professionally important quality. Pedagogics, Psychology, Medical-Biological Problems of Physical Training and Sports, 19(12), 87–90.

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55. Rahmanpoor, M., Hosayni, M., & Nasr, E. A., (2016). The amount of observance the scientific principles in codification the goals of curriculum in graduate studies (case study: Isfahan university). Research in Curriculum Planning, 13(48), 56–69. 56. Reijnders, P. J., (1988). Ecotoxicological perspectives in marine mammalogy: Research principles and goals for a conservation policy. Marine Mammal Science, 4(2), 91–102. 57. Rocca, E., & Andersen, F., (2017). How biological background assumptions influence scientific risk evaluation of stacked genetically modified plants: An analysis of research hypotheses and argumentations. Life sciences, society and policy, 13(1), 1–20. 58. Rodriguez‐Gonzalez, O., Buckow, R., Koutchma, T., & Balasubramaniam, V. M., (2015). Energy requirements for alternative food processing technologies—Principles, assumptions, and evaluation of efficiency. Comprehensive Reviews in Food Science and Food Safety, 14(5), 536–554. 59. Rudolph, S., & Kröplin, B. H., (1997). On modeling assumptions and artificial neural networks. In: Workshop Proceedings “Selbstorganisation Von Adaptivem Verhalten (SOAVE’97)” (Vol. 23, p. 24). Universität von Ilmenau. 60. Russo, N. L., & Stolterman, E., (2000). Exploring the assumptions underlying information systems methodologies: Their impact on past, present and future ISM research. Information Technology & People, 1, 4–8. 61. Sadler, T. D., (2011). Situating socio-scientific issues in classrooms as a means of achieving goals of science education. In: Socio-scientific Issues in the Classroom (Vol. 1, pp. 1–9). Springer, Dordrecht. 62. Shchedrin, V. N., & Vasil’ev, S. M., (2019). Conceptual and methodological principles (basics) of development strategies for land reclamation as a national treasure of Russia. Scientific Journal of Russian RDI of Melioration Problems, 1, 4–7. 63. Shi, L., Han, L., Yang, F., & Gao, L., (2019). The evolution of sustainable development theory: Types, goals, and research prospects. Sustainability, 11(24), 7158. 64. Shokaliuk, S. V., Bohunenko, Y., Lovianova, I., & Shyshkina, M., (2020). Technologies of distance learning for programming basics on the principles of integrated development of key competences. In: CEUR Workshop Proceedings (Vol. 2643, pp. 548–562).

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65. Somogyi, A., Hathcock, J., Biesalski, H. K., Blumberg, J. B., Antoine, J. M., Edwards, G., & Prock, P., (2011). Scientific issues related to codex Alimentarius goals: A review of principles, with examples. Regulatory Toxicology and Pharmacology, 60(1), 161–164. 66. Szybiński, Z., Nauman, J., Gembicki, M., Rybakowa, M., Huszno, B., Gołkowski, F., & Skalski, M., (1993). Principles, main goals and methods of the nationwide program: “investigations on iodine deficiency and model of iodine prophylaxis in Poland”. Endokrynologia Polska, 44(3), 235–248. 67. Taheri, A., Mansoori, P., Sandoval, L. F., Feldman, S. R., Pearce, D., & Williford, P. M., (2014). Electrosurgery: Part I. Basics and principles. Journal of the American Academy of Dermatology, 70(4), 591-e1. 68. Tehrani, M., Maleki, M., & Ghofrani, F., (2014). Act on Civil Service Management (Principles, Basics and Proposed Strategies), 1, 4–7. 69. Temple, S. A., (2019). Conservation biology: New goals and new partners for managers of biological resources. In: Challenges in the Conservation of Biological Resources (Vol. 1, pp. 45–54). Routledge. 70. Waage, J., Banerji, R., Campbell, O., Chirwa, E., Collender, G., Dieltiens, V., & Unterhalter, E., (2010). The millennium development goals: A cross-sectoral analysis and principles for goal setting after 2015: Lancet and London international development center commission. The Lancet, 376(9745), 991–1023. 71. Wadsak, W., & Mitterhauser, M., (2010). Basics and principles of radiopharmaceuticals for PET/CT. European Journal of Radiology, 73(3), 461–469. 72. Weinstein, M. C., O’Brien, B., Hornberger, J., Jackson, J., Johannesson, M., McCabe, C., & Luce, B. R., (2003). Principles of good practice for decision analytic modeling in health-care evaluation: Report of the ISPOR task force on good research practices—Modeling studies. Value in Health, 6(1), 9–17. 73. Werbos, P., (2004). ADP: Goals, opportunities and principles. Handbook of Learning and Approximate Dynamic Programming, 2, 3–44. 74. Weymouth, R., & Hartz-Karp, J., (2018). Principles for integrating the implementation of the sustainable development goals in cities. Urban Science, 2(3), 77.

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75. Wiedenhoft, G. C., Luciano, E. M., & Magnagnagno, O. A., (2017). Information technology governance in public organizations: Identifying mechanisms that meet its goals while respecting principles. JISTEMJournal of Information Systems and Technology Management, 14, 69–87. 76. Williams, C., James, D., & Wilson, C., (2008). Mathematics and Science for Exercise and Sport: The Basics. Routledge. 77. Willis, C., Greenberg, J., & White, H., (2012). Analysis and synthesis of metadata goals for scientific data. Journal of the American Society for Information Science and Technology, 63(8), 1505–1520. 78. Xinmin, M. A., (2019). China’s arctic policy on the basis of international law: Identification, goals, principles and positions. Marine Policy, 100, 265–276.

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CREATIVITY AND SCIENTIFIC APPROACH TO ENGINEERING PROBLEMS

CONTENTS 2.1. Introduction....................................................................................... 30 2.2. Engineering Problem Solving Processes............................................. 31 2.3. Factors Influencing Problem Solving.................................................. 33 2.4. Creative Approach to Engineering Problems...................................... 34 2.5. Scientific Approach to Engineering Problems..................................... 37 References................................................................................................ 43

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2.1. INTRODUCTION Customers’ requirements fluctuate, the physical world changes and technical knowledge grows swiftly. The inability of technical systems to adjust to such changes creates both possibilities and difficulties (Schank & Cleary, 1995). To recognize possibilities and address issues in a constantly changing world, imagination is required. The arsenal of instruments at an engineer’s discretion today consists mostly of techniques for optimization procedures and proposed solutions, but it misses features that enable the generation of new ideas that take benefit of the specific (and occasionally novel) scenario at hand (Pal & Poyen, 2017). Engineers are supposed to be innovative, yet the vast majority are seldom so. Innovative engineering items develop practically every day as a consequence of the way that corporations employ a small number of extremely innovative engineers and innovators who “think” whilst others are engaged in regular engineering (Gruszka & Tang, 2017; Segal, 2004). The creation of organized instruments for innovative design processes will allow for broader participation in the generation of ideas. The creation of ideas and methods for design concepts would also add to the curricula of engineering schools that now focus mostly on analysis, while synthesis in general and creative design, in particular, are seen as subjects that must be learned on the job (Abkevich et al., 2012). In the majority of studies on problem-solving, the efficacy of techniques is characterized in terms of expert vs beginner performance. Experts’ problem-solving tactics are not generally transferrable to beginners owing to the cognitive demands of using these techniques (Williams et al., 2004). For instance, beginners may have less information from which to draw conclusions and may face cognitive resource overload when using expert approaches. The inability to solve the issue without gaining further knowledge, the lack of awareness of performance faults, and the reluctance to alter a chosen approach or representation are some of the key obstacles to reaching expert performance (Wang & Chiew, 2010). While it has been suggested that problem-solving may be utilized to accomplish instructional objectives like acquiring facts, ideas, and processes

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(Wilson et al., 1993), a study has demonstrated that low cognitive load ability may impede understanding all across the problem-solving process (Sweller, 1988). If a student’s load capacity is poor, (s)he may not have the extra capacity to store new information because lower-level activities have not been completed effectively (Hickey, 2001). Low cognitive load capability is considered to be associated with the Einstellung effect, which occurs when a person maintains to adopt an ineffective but successful strategy without realizing there is a more effective one (Bowden & Jung-Beeman, 2003). Better cognitive workload capability is indicative of higher success while breaking obstacles in problem-solving efforts since it enables simultaneous evaluation of several tries retained in working memory (Hambrick & Engle, 2003).

2.2. ENGINEERING PROBLEM SOLVING PROCESSES In the realm of physics, the differences between professional as well as beginner problem solvers’ approaches were investigated, and two concepts were built to show the diverse procedure kinds. The Means-Ends novice model explains how novices advance via several phases of (1) picking relevant knowledge, (2) linking it to other data, and (3) applying it (Charlton & Bakan, 1989). Means-end is a sort of selection method in which, given a present state and the desired state, an activity is selected that is thought to narrow the gap between the two states. The Knowledge Development expert model exemplified how specialist methods were often “decomposed” into smaller phases using bigger data processing pieces (Larkin, Mcdermott, Simon, & Simon, 1980b). In addition, it was discovered that experts had more effective models that contain less unnecessary data and answer important characteristics required for analysis. Whenever requested to sort a set of physics issues, professionals categorized them depending on the fundamental theory required to solve the difficulties, but novices categorized them due to the surface aspects of the issues, like inclined planes (Chi, et al., 1988).

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Figure 2.1. Five problem-solving processes. Source: ing/.

https://www.humorthatworks.com/learning/5-steps-of-problem-solv-

The amount of metacognition utilized to control the problem-solving procedure is a further aspect of problem-solving effectiveness. Progress Monitoring theory by MacGregor, Ormerod, and Chronicle presents two kinds of monitoring tools strategies: (1) maximization heuristic, in which issue integrators try to advance as far as potential on every try; or (2) progress monitoring, in which critical thinkers evaluate goal progress thru the iterative monitoring all across the implementation phase as well as deflect the strategy when they realize this would give rise to an inaccurate solution (Calderon et al., 2017). If critical thinkers do not possess the ability to execute the resource-intensive processes of the progress monitoring approach, they might resort to the maximizing heuristic. People use methods and heuristics to lessen the cognitive burden when cognitive resources are finite. Figure 2.1 provides a sampling of reported procedures for solving mathematical tasks, albeit it is by no means exhaustive (Crews, 2000; Nation & Siderman, 2004). Although such techniques are effective for lowering cognitive burden, they are not applicable in all

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circumstances and may result in an inappropriate strategy for tackling the issue. Also, individuals might become too dependent on tactics or misuse them, resulting in a decline in effectiveness (Matlin, 2001).

2.3. FACTORS INFLUENCING PROBLEM SOLVING Research on types of errors and techniques has demonstrated that features of the issue, including its difficulty or framework, the individual, like previous experiences (Kirton, 2003), and thinking ability (Jonassen & Hung, 2008), the procedure, like cognitive and metacognitive acts (Greeno & Riley, 1987; Sternberg, 1985) and techniques (Nickerson, 1994), and the environment, such as the social context (Woods, In the browse for promoting positive problem-solving proficiency, a significant amount of research has centered on categorizing differences in ability among both specialist and beginner hard workers (Hutchinson, 1988), presumably but since expert, a method to demonstrate a more successful utilization of problem-solving skills. Numerous performance disparities among problem-solvers have been attributed to varying degrees of experience. For instance, a study has demonstrated that beginners incur more mistakes and use distinct strategies than professionals. Experts are four times quicker than beginners at identifying a solution, despite pausing between obtaining formulas or data chunks (Chi et al., 1981) and spending a lot of time than beginners in the issue description stage of the problemsolving method (Pretz et al., 2003). Additionally, experts arrange their knowledge differently than beginners, exhibiting bigger data chunks (Larkin et al., 1980a). Experts’ problem-solving procedures are not generally transferrable to beginners owing to the cognitive skills required to be using them. Cognitive overload might contribute to the incapacity to solve issues without obtaining additional knowledge, the lack of awareness of performance failures, and the reluctance to alter a chosen approach or presentation (Wang & Chiew, 2010). Various problem-solving methods, like issue deconstruction and subgoals, may be utilized to ease a few of the cognitive strain of the issue (Nickerson, 1994); nevertheless, individuals might become too dependent on tactics or utilize them improperly, leading to a decline in efficiency (Matlin, 2001).

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2.4. CREATIVE APPROACH TO ENGINEERING PROBLEMS The ideas of engineering innovation are grounded on theories of innovation in nature. Therefore, restricting the scope to engineering areas allowed engineering creativity scientists to convey their ideas in less abstract and more precise language (Seitz, 2003). Even though several engineering design theorists approach originality from the perspective of its computational automation, we provide their fundamental concepts about engineering imagination without no addressing computational difficulties in this part (Calderon et al., 2017).

2.4.1. Creative Design The design phase relies heavily on the retrieval of earlier designs or design concepts, which are subsequently modified to meet contemporary needs. Pieces of intricate design solutions may be saved at a low granularity level, whilst initial principles can be stored at a high granularity level (Osgood et al., 1957). Both granularity levels have the potential to inspire new creative concepts. Using complex design elements to justify a new design is known as case-based reasoning. Case-based reasoning is the study of reusing answers to past issues to solve new problems, building, and searching case libraries, and merging and adapting instances (Chan et al., 2013) (Figure 2.2).

Figure 2.2. Design innovation via knowledge clustering as well as case-based reasoning. Source: https://link.springer.com/article/10.1007/s00366–019–00712–5.

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As per Kolodner (Kolodner, 1993), creative design in the creative stage entails combining recognizable design elements, and utilizing or changing a well-known design element in unconventional methods. Case-based reasoning may be used to describe the aforementioned design processes, given they all depend substantially on past design experiences (Frojmovic & Karkov, 2017). Design based on initial principles is contrasted with designing based on design component collections. According to Williams (Wiliams, 1990), innovation is the capacity to develop nonobvious answers that are qualitatively distinct from some of those previously observed. He contends that designing libraries utilize but does not generate innovation. They are incapable of using initial principles or focusing their use on the creation of novel gadgets. To achieve innovation, one must think from the underlying physical principles that define technology. Cagan & Agogino designing from the first concepts depends on causal, subjective, or quantitative knowledge utilized abductively to connect purpose to perceptions and attitude to form without the need for compiled information (Cagan & Agogino, 1987).

2.4.2. Increasing Dimensionality Cagan and Agogino (Cagan & Agogino, 1987) convey an idea of a design concept that is comparable to Boden’s view of the inventive procedures in general: “Non-routine Design differs from routine models in that the latter are deduced from a fixed space, whereas the previous is described by an enlarged design process” (page 95) (Newell & Simon, 1972). Cagan and Agogino propose, based on their concept of non-routine layout, a computing process that leverages optimization data to determine how to change and extend the layout space by adding new variables, hence expanding its complexity. In the process of building an ideal beam, for instance, their software may add a third variable – the beam’s taper angle – to the original design space consisting of two factors (dimensions of rectangle crosssectional area) (Klarman et al., 2012).

2.4.3. Altshuller’s Theory As per Altshuller, a layout is innovative whenever it handles a dilemma without sacrifice or concession (Altshuller, 1985). Altshuller expanded his hypothesis by comparing a large number of technical breakthroughs with common or routine solutions proposed for the same issue (Fernando & Jackson, 2006) (Figure 2.3).

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Figure 2.3. Altshuller’s 7 TRIZ philosophical foundations. Source: https://www.sciencedirect.com/science/article/abs/pii/ S0360835220303065.

Because Altshuller’s theory is the foundation of this study, explain how the theory of adequate circumstances evolved from Altshuller’s concept of conflict resolution (Sutherland & Legasto, 1978).

2.4.4. Ulrich’s Theory As per Ulrich [Ulrich, 1988], functional share in mechanical systems is the concurrent realization of many functions in an item by a particular structural part. Ulrich cites three primary explanations for the significance of features wanting to share in structural engineering: first, models that showcase function exchange are superior in most ways to others not (lesser energy, easier association, less needed maintenance, higher results due to reduced size and weight, etc.) (Mednick, 1962); second, the consciousness of the procedure of function sharing enables the developer to believe in a modular, decayed manner with the choice of consequently utilizing function sharing; and third, function sharing enables the developer to believe in a modular, dissolved manner Ulrich recommends the following method for exchanging functions: (1) a structural element is removed from the physical description; (2) replacement characteristics that might perform the function of the removed part are found, and (3) the discovered characteristics are changed to emphasize their desired secondary attributes (Joshi et al., 2014).

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2.5. SCIENTIFIC APPROACH TO ENGINEERING PROBLEMS There are several technical problem-solving strategies, the most common of which are Define, Measure, Analyze, Optimize, Control (DMAIC) as well as PDCA (Plan, Do, Check, Act) (Joshi et al., 2014). P plays an important role in the PDCA paradigm since it highlights what, why, how, or when. All phases in the DMAIC paradigm are equally vital, from describing the issue to managing it. All of such strategies depend on various problemsolving tools. We shall discuss a few of them here. Such methods are useful when attempting to identify the underlying reasons for an issue (Williams, 1991; Sternberg, 2009). The following tools are widely used in this realm: MultiDisciplinary Teamwork (MDT), Brainstorming, Pareto analysis (80/20 rule), Fishbone Diagram (cause-effect analysis), 5W tool, Check sheet, as well as Control Chart (CC). MDT includes major by going “outside the box,” as a heterogenic group of people examines the issue from many perspectives (Wallach & Kogan, 1965). In its application domain, the brainstorming device is rather common. Whenever a judgment is driven by pure numbers as well as their interpretations, a check sheet and a chart are more realistic approaches. Pareto’s principle, sometimes known as “vital very little and trivial numerous,” holds that 20% of anything is always accountable for 80% of the result (Verma et al., 2019). The 5W technique assumes that the fifth Why is the fundamental cause of the issue and hence the one that should be improved. The five whys are also included in the fishbone structure. The fishbone model, by far the most desired and popular technique, is based on cause-effect analysis (Ward et al., 1999). The fish structure’s head represents the result, while the skeleton bone examines the cause-effect correlations. The vertices of the fishbone may be made up of 6 Ms or 6 Ps, where the 6 Ms stand for Man, Machine, Method, Material, Mother Nature, and Measurement and the 6 Ps stand for Procedure, Policy, Plant, Person, Planet, and Programs (Kaur et al., 2010).

2.5.1. Initiating a Scientific Solution Whenever a challenging scenario develops, we should act in the manner shown in the flowchart below. We select our tool of choice for preference in analysing these steps. Here, we may look at the problem in two ways (Allen, 1962; Feigenbaum, 1977). First, we can use the Deductive Analysis Approach, which collects a large amount of data, analyzes it, and then draws findings and suggestions. The second way is the Inductive Analysis

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Approach, in which we begin with a hypothesis, examine its validity, and then make a conclusion with some suggestions. It is important to note that there is no such thing as the optimal tool for any specific issue scenario. It is dependent on and tailored to the individual’s intuition and capacity (Hillier & Lieberman, 1990). • • • • • •

Step I: What must occur‼! Step II: What is going on‼! Step III: What is going on‼! Step IV: Identifying and prioritize the root sources of the issue. Step V: What must be done!!! Finding a solution to the problems. Step VI: What has been accomplished!!! Using the solution to resolve the issue. Again, in step I, when we predict what should happen, it contains some features that contribute to its determination (Altshuller, 1984). We anticipate events based on (Arciszewski et al., 1995): • • • •

What plans have we made; Assumption; Experience; and Perceiving.

2.5.2. Operations Research Operations research was created to tackle problems when the nature of the issue is complicated and there is no clear cut or, in fact, no solution. It is a mathematical analytical approach that provides a quantitative foundation for difficult administrative choices. (Hillier and Lieberman, among others) (Arciszewski, 1997). The problem-defining stage is distinguished by research, data analysis, and knowledge application. Goals and boundary conditions are clarified and organized using needs analysis, input-output analysis, and objective trees. Value system design uses techniques such as weighted criterion trees, flow charts, and causal loop diagrams to arrange goals into a hierarchy tree (Szczepanik et al., 1996; Weisberg, 1993). The Quality Function Deployment (QFD) matrix facilitates the generation of alternatives based on these tools. Different options are forecasted in the synthesis of alternatives to meet the needs. In this stage, methods such as nominal group methodology, computer simulation, and Zwicky’s morphological box are used. The process of

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systems modeling and analysis creates models for analyzing and comparing options (Arciszewski et al., 1992; Girosi et al., 1995). Data analysis, probability theory, econometric modeling, regression, forecasting, queuing, networks, reliability analysis, and mathematical programming are some of the techniques used here. Numerical iteration, derivative calculus, calculus of variations, and graphical approaches are utilized in the optimization of alternatives, and the impact of modifications is quantified using parametric sensitivity analysis. The assessment of alternatives provides the operator with the final result to pick from when making a choice (Wnek & Michalski, 1994) (Figure 2.4).

Figure 2.4. Analysis of the problem. Source: http://ijaers.com/uploads/issue_files/29%20IJAERS-MAY-2017–42Problem%20Solving%20Approach.pdf.

Multi-attribute utility (MAU) theory, game theory, risk analysis, impact diagram, decision analysis, data analysis, and statistical approaches are utilized as tools (Arciszewski et al., 1992) (Figure 2.5).

Figure 2.5. A problem-solving flowchart. Source: http://ijaers.com/uploads/issue_files/29%20IJAERS-MAY-2017–42Problem%20Solving%20Approach.pdf.

A value system sensitivity study measures the impact of changes in judgment. The last phase is action planning, in which the implementation is carried out and recorded via resource planning and scheduling (Szczepanik et al., 1996).

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2.5.3. Analyzing the Problem and Solution This section discusses analyzing the domain and depth of an issue before attempting to solve it. Any collection of problems may be represented in a set of four quadrants, with the axes spanning from basic issue too complicated problem and solution accessible to solution unavailable (Touretzky et al., 1996). The graphics below represent the quadrants involved in issue solving. We must first determine which quadrant a given issue is located in, and then continue to solve it based on that information. So we begin in the first quadrant and work our way to the fourth (Arciszewski, 1997). Quadrant I: The problem is straightforward, and the solution is readily accessible. Here, we implement the remedy quickly before the situation becomes difficult (Ward et al., 1999). Quadrant II: Although the situation is complicated, solutions are accessible. In this scenario, if there are many solutions, we implement them one after the other. We are genuinely using the hit and trial process to get the finest viable answer (Wnek & Michalski, 1994). Quadrant III: The structure of the issue in this sector is complicated, and no remedies are accessible. This quadrant provides birth to a highly significant statistical method, operations analysis, to address as well as remedy such circumstances (Cheng & Simon, 1995). It is divided into three parts: formulation, analysis, and interpretation. Inside this quadrant, we divide the issue into little areas and attempt to match them into the other three quadrants. In this manner, we could mitigate the problem’s impact. After this is completed, there might still be portions in the third quadrant. We continue the procedure until the issue is as little as possible (Arciszewski, 1995). Quadrant IV: The essence of the issue is easy in this quadrant, but no answers are accessible to address it. Here, one must be innovative via invention, either on an individual or collective scale. We will be using a well-known test case to demonstrate the problem (Cagan & Agogino, 1987). A man possesses 17 gold coins and three heirs, A, B, and C. On his deathbed, he states that A would get 50% of the coins, B would get one-third, and C would take one-ninth. The issue is well-known and straightforward; however, the answer is unavailable since 17 is not divisible by 3 (Kryssanov et al., 2001) (Figure 2.6).

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Figure 2.6. Solution analysis based on problem criticality. Source: http://ijaers.com/uploads/issue_files/29%20IJAERS-MAY-2017–42Problem%20Solving%20Approach.pdf.

Therefore, we may solve difficulties by tackling them using the approaches described above. As a result, it is correct to say that by analyzing the reason and then implementing the necessary treatments, we may decrease the divergence and thus resolve or lessen the issue (Arnon & Kreitler, 1984; Byrne, 1986).

2.5.4. Decision Making Making the appropriate judgments is an important part of addressing difficulties. Making decisions is a critical ability to have if you want to lead a team or operate a project successfully and efficiently. The process of examining potentials, equating them, and then choosing a plan of action is known as decision making (Wiggins, 2003; Moxey, 1998). Gathering a multitude of facts about the circumstance at hand is the backbone of excellent decision-making. Then analyzing and dissecting them in great detail to have a thorough knowledge of the issue. Vision, mission, perception, priority, acceptability, risk, resources, objectives, values, expectations, styles, and judgment are all factors that impact decision-making (Boden, 1994). The DECIDE model is a powerful tool for making more rational decisions. What the term DECIDE stands for (Bilton & Leary, 2002). • •

D= Detect change E= Estimate the change’s significance

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• • •

C = Select a result I = Identify possibilities D = Decide on the best course of action. E = Evaluate the outcomes. The six C’s of decision-making rule (Maiden & Gizikis, 2001). • • • • • •

Create a clear image of what has to be accomplished. Make a list of the prerequisites. Gather information about potential options. Compare and contrast alternatives. Think about what may go wrong. Commitment is the sixth step.

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REFERENCES 1.

2.

3.

4. 5. 6.

7. 8.

9.

10. 11.

12. 13.

Abkevich, V., Timms, K. M., Hennessy, B. T., Potter, J., Carey, M. S., Meyer, L. A., & Lanchbury, J. S., (2012). Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. British Journal of Cancer, 107(10), 1776– 1782. Allen, M. S., (1962). Morphological Creativity: The Miracle of Your Hidden Brain Power: A Practical Guide to the Utilization of Your Creative Potential (Vol. 1, pp. 3–9). Prentice-Hall. Altshuller, G. S., (1984). Creativity as an Exact Science: The Theory of the Solution of Inventive Problems (Vol. 1, pp. 3–6). Gordon and Breach Science Publishers. Arciszewski, T., (1995). Empirical Performance Comparison of Selective and Constructive Induction, 1, 3–5. Arciszewski, T., (1997). Engineering semantic evaluation of decision rules. Journal of Intelligent & Fuzzy Systems, 5(3), 285–295. Arciszewski, T., Bloedorn, E., Michalski, R., Mustafa, M., & Wnek, J., (1992). Constructive induction in structural design. Report of Machine Learning and Inference Laboratory, 1051, 92–97. Arciszewski, T., Michalski, R. S., & Wnek, J., (1995). Constructive Induction: The Key to Design Creativity.(pp. 1-25) Arnon, R., & Kreitler, S., (1984). Effects of meaning training on overcoming functional fixedness. Current Psychological Research & Reviews, 3(4), 11–24. Bilton, C., & Leary, R., (2002). What can managers do for creativity? Brokering creativity in the creative industries. International Journal of Cultural Policy, 8(1), 49–64. Boden, M. A., (1994). Précis of the creative mind: Myths and mechanisms. Behavioral and Brain Sciences, 17(3), 519–531. Bowden, E. M., & Jung-Beeman, M., (2003). Aha! Insight experience correlates with solution activation in the right hemisphere. Psychonomic Bulletin & Review, 10(3), 730–737. Byrne, D. M., (1986). The Taguchi approach to parameter design. ASQ’s Annu. Qual. Congr. Proc., 40, 168. Cagan, J., & Agogino, A. M., (1987). Innovative design of mechanical structures from first principles. AI EDAM, 1(3), 169–189.

44

Scientific Principles of Engineering

14. Calderon, T. M., Williams, D. W., Lopez, L., Eugenin, E. A., Cheney, L., Gaskill, P. J., & Berman, J. W., (2017). Dopamine increases CD14+ CD16+ monocyte transmigration across the blood–brain barrier: Implications for substance abuse and HIV neuropathogenesis. Journal of Neuroimmune Pharmacology, 12(2), 353–370. 15. Chan, C. Y., Lam, J. W., Jim, C. K., Sung, H. H., Williams, I. D., & Tang, B. Z., (2013). Polycyclotrimerization of dinitriles: A new polymerization route for the construction of soluble nitrogen-rich polytriazines with hyperbranched structures and functional properties. Macromolecules, 46(24), 9494–9506. 16. Charlton, S., & Bakan, P., (1989). Cognitive complexity and creativity. Imagination, Cognition and Personality, 8(4), 315–322. 17. Cheng, P. C., & Simon, H. A., (1995). Scientific discovery and creative reasoning with diagrams. The Creative Cognition Approach, 205–228. 18. Feigenbaum, E. A., (1977). The art of artificial intelligence: Themes and case studies of knowledge engineering. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (Vol. 2, pp. 2–7). Boston. 19. Fernando, M., & Jackson, B., (2006). The influence of religion-based workplace spirituality on business leaders’ decision-making: An interfaith study. Journal of Management & Organization, 12(1), 23–39. 20. Frojmovic, E., & Karkov, C. E., (2017). Post Colonizing the Medieval Image (Vol. 1, p. 241). Routledge. 21. Girosi, F., Jones, M., & Poggio, T., (1995). Regularization theory and neural networks architectures. Neural Computation, 7(2), 219–269. 22. Gruszka, A., & Tang, M., (2017). The 4P’s creativity model and its application in different fields. In: Handbook of the Management of Creativity and Innovation: Theory and Practice (Vol. 1, pp. 51–71). 23. Hickey, M., (2001). An application of Amabile’s consensual assessment technique for rating the creativity of children’s musical compositions. Journal of Research in Music Education, 49(3), 234–244. 24. Hillier, F. S., & Lieberman, G. J., (1990). Introduction to Stochastic Models in Operations Research (Vol. 1, pp. 3–7). McGraw-Hill Companies. 25. Joshi, S. R., Anjana, R. M., Deepa, M., Pradeepa, R., Bhansali, A., Dhandania, V. K., & ICMR–INDIAB Collaborative Study Group,

Creativity and Scientific Approach to Engineering Problems

26.

27.

28. 29.

30. 31. 32. 33. 34. 35.

36. 37. 38.

45

(2014). Prevalence of dyslipidemia in urban and rural India: The ICMR–INDIAB study. PloS One, 9(5), e96808. Kaur, G., Srivastav, J., Jain, S., Chawla, D., Chavan, B. S., Atwal, R., & Prasad, R., (2010). Preliminary report on neonatal screening for congenital hypothyroidism, congenital adrenal hyperplasia and glucose6-phosphate dehydrogenase deficiency: A Chandigarh experience. The Indian Journal of Pediatrics, 77(9), 969–973. Klarman, M., Skeel, D., & Steiker, C., (2012). The Political Heart of Criminal Procedure: Essays on Themes of William J. Stuntz (Vol. 1, pp. 3–8). Cambridge University Press. Kreitler, S., & Kreitler, H.(1986) Individuality in Planning: Meaning Patterns of Planning, 1, 3–9. Kryssanov, V. V., Tamaki, H., & Kitamura, S., (2001). Understanding design fundamentals: How synthesis and analysis drive creativity, resulting in emergence. Artificial Intelligence in Engineering, 15(4), 329–342. Maiden, N., & Gizikis, A., (2001). Where do requirements come from?. IEEE Software, 18(5), 10–12. Mednick, S., (1962). The associative basis of the creative process. Psychological Review, 69(3), 220. Moxey, J., (1998). A creative methodology for idea generation in printed textile design. Journal of the Textile Institute, 89(3), 35–43. Newell, A., & Simon, H. A., (1972). Human Problem Solving (Vol. 104, p. 9). Englewood Cliffs, NJ: Prentice-hall. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H., (1957). The Measurement of Meaning (Vol. 1, p. 47). University of Illinois Press. Pal, A., & Poyen, E. F. B., (2017). Problem solving approach. International Journal of Advanced Engineering Research and Science, 4(5), 237180. Schank, R. C., & Cleary, C., (1995). Making machines creative. The Creative Cognition Approach, 229–247. Segal, E., (2004). Incubation in insight problem solving. Creativity Research Journal, 16(1), 141–148. Seitz, J. A., (2003). The political economy of creativity. Creativity Research Journal, 15(4), 385–392.

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39. Sternberg, R. J., (2009). The nature of creativity. The Essential Sternberg: Essays on Intelligence, Psychology and Education, 103– 118. 40. Sutherland, J. W., & Legasto, A., (1978). Management Handbook for Public Administrators (Vol. 1, pp. 3–9). New York: Van Nostrand Reinhold. 41. Szczepanik, W., Arciszewski, T., & Wnek, J., (1996). Empirical performance comparison of selective and constructive induction. Engineering Applications of Artificial Intelligence, 9(6), 627–637. 42. Touretzky, D. S., Mozer, M. C., & Hasselmo, M. E., (1996). Advances in Neural Information Processing Systems 8: Proceedings of the 1995 Conference (Vol. 8, pp. 4–8). MIT Press. 43. Verma, M., Shafiq, N., Tripathy, J. P., Nagaraja, S. B., Kathirvel, S., Chouhan, D. K., & Dhillon, M. S., (2019). Antimicrobial stewardship program in a trauma center of a tertiary care hospital in North India: Effects and implementation challenges. Journal of Global Antimicrobial Resistance, 17, 283–290. 44. Wallach, M. A., & Kogan, N., (1965). A new look at the creativity‐ intelligence distinction. Journal of Personality, 33(3), 348–369. 45. Ward, T. B., Smith, S. M., & Finke, R. A., (1999). Creative cognition. Handbook of Creativity, 189, 212. 46. Weisberg, R., (1993). Creativity: Beyond the Myth of Genius (Vol. 1, pp. 3–8). WH Freeman. 47. Wiggins, G., (2003). Categorizing creative systems. In: Proc. of the 3rd Workshop on Creative Systems, IJCAI (Vol. 3, pp. 1–5). 48. Williams, B. C., (1991). Invention from first principles: An overview. Artificial Intelligence at MIT Expanding Frontiers, 1, 430–463. 49. Williams, D., Jung, D. W., Khersonsky, S. M., Heidary, N., Chang, Y. T., & Orlow, S. J., (2004). Identification of compounds that bind mitochondrial F1F0 ATPase by screening a triazine library for correction of albinism. Chemistry & Biology, 11(9), 1251–1259. 50. Wnek, J., & Michalski, R. S., (1994). Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules, 1, 2–8. 51. Wnek, J., & Michalski, R. S., (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning, 14(2), 139–168.

3

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SCIENTIFIC DESIGN AND MODELING IN ENGINEERING

CONTENTS 3.1. Introduction....................................................................................... 48 3.2. Models in Science and Engineering................................................... 50 3.3. Models and Representation................................................................ 51 3.4. Resemblances Between Model and Target Systems............................ 55 3.5. Fictionalism About Models................................................................ 56 3.6. Modeling And Design........................................................................ 57 3.7. Modeling Paradigms And Languages.................................................. 59 3.8. Single-Domain Simulation................................................................. 65 3.9. Interleaving Design And Simulation................................................... 67 3.10. Collaborative Modeling................................................................... 69 3.11. Modeling At The Component Level.................................................. 71 3.12. Integration With Design Tools.......................................................... 73 3.13. Future Modeling And Simulation..................................................... 74 References................................................................................................ 78

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3.1. INTRODUCTION The use of simulation and modeling gives designers the ability to check whether or not design criteria are satisfied by using virtual instead of actual tests. The designing cycle may be greatly shortened and design costs can be significantly reduced when virtual prototyping is used. In addition to this, it gives the designer instant feedback on design choices, that, in turn, promised a more thorough examination of design options and a final presentation that is more effective. The designing of multi-disciplinary networks, wherein the elements from a variety of fields (mechanics, electric, integrated management, etc.) are closely connected to produce an optimum performance of the system, is one of the most significant areas during which simulations play an especially essential role (Ascher & Petzold, 1998). By focusing on modeling just at the network level, we can restrict the range of the research. Just at the level of the process, elements, and subsystems are treated as “black boxes” that communicate with one another through distinct interfaces. In general, differentially algebra equations (DAEs) and/or discrete-time subsystems specification (DEVS) may be used to represent the behavior of these kinds of systems. We are not going to take into consideration any systems whose modeling of parts of the system or constituent interactions necessitates the use of differential equations and numerical simulation models (Fishwick, 1998). The components of simulation and modeling that are especially useful in the context of designing are where we spend the majority of our work going forward. To be more specific, we analyze the present advancements in model expression, model recycling, interaction within helps to portray, and cooperative modeling (Hill, 2002). When it comes to simulation studies in specific situations of design, one of the most fundamental requirements would be that the programming language is satisfactorily expressive to simulate the non-linear, multidisciplinary, combination constant phenomena that are seen in design rapid prototyping. That was one of the requirements laid down. A great number of simulation and experimental languages have been created so over years; however, only a select fraction of such languages are suitable for the modeling of multi-disciplinary organizations (Alciatore, 2007). The early simulation languages, which were operational and then founded on Continuous System Simulation Language (CSSL), gave a low-level model of the system in terms of the finite difference method. These languages were developed in the 1970s. Declarative modeling, often known as equation-based modeling,

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and object-oriented modeling are two significant breakthroughs that evolved as a direct result of these languages. Recent research expands significantly on these advancements by going toward element modeling and by assisting hybrids (mixed constant event) networks. These are also examples of how recent research develops on previous discoveries (Reed, 1961) (Figure 3.1).

Figure 3.1. The importance of models in the scientific and technological fields. https://www.researchgate.net/figure/A-schematic-representation-of-the-role-ofmodels-in-science-and-engineering-that_fig1_279666103.

Another need is that it should not be difficult to construct and recycle the simulations. Developing simulators with a high level of accuracy is a difficult process that often takes a significant amount of time. Particle languages provide several distinct benefits, particularly concerning the creation, management, and reusing of models. In addition, to make the most of simulation within the specific situation of design, it is vital to building a model-based framework which is incorporated with both the design phase offers a straightforward and user-friendly interaction and necessitates only a minimal amount of analysis expert knowledge. This is required to reap the benefits of simulation within the frame of reference of design (Tiernego & Bos, 1985). In the last part of our discussion, we focus on the topic of collaboration modeling. To design sophisticated multi-disciplinary networks, it is necessary to enlist the knowledge of a group of professionals who work together. Collaborating alongside analysts, production engineers, marketing professionals, and business executives are designers who come from a variety of academic and professional backgrounds. This is necessary to critically record the designs, and collect their semantic meaning, but rather make the designs obtainable in well-organized archives that are compatible with both the context of the internet to support the cooperative aspect of the

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simulated world and also design. In addition, it is worth making sure that these registries are compatible with the internet (Paynter & Juarez, 1999).

3.2. MODELS IN SCIENCE AND ENGINEERING Over the last 30 years or more, the word “model” has always been used in technical and philosophical discussions. It is well known that engineers and scientists use modeling in their work. There are animal studies, mathematical analysis, scale models of automobiles or structures, and climatic models. The Bohr atomic model, with the Schelling model of segregating, the LotkaVolterra system of carnivores interaction, and the billiards ball model of a gaseous, or double-helical version of DNA are just a few instances of models. What all these frameworks in engineering and science do is the fundamental question just at the start of this thesis (Finger & Dixon, 1989) (Figure 3.2).

Figure 3.2. A DNA engineering model example. https://www2.nau.edu/lrm22/lessons/dna_model/dna_model.html.

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Models are used by researchers and technologists, and much is known. However, whether something can be said regarding modeling that is both generic and instructive is still up for debate. Because there are various kinds of scientific models, this problem could not have a unified solution, claim Frigg and Hartmann (2012). Additionally, there appear to be several applications for models in both engineering and science. For instance, it is sometimes said that while engineers want to change the world, researchers simply want to comprehend it (Franssen et al., 2015). Therefore, it stands to reason that architects use models to change the world and researchers use models to comprehend. It is debatable if such a clear distinction between engineering and science could be made, though. Giving a broad explanation of what modeling appears to be particularly challenging. This article aims to provide such a modeling account which looks at specific model applications rather than just general model use. Here, the approach is to start with models which are utilized as representations. Since many models serve as representations of their subjects, the idea of representations is closely related to the activity of modeling. So, I start by separating the two different methods for explaining representations. One strategy starts by asking what a representational simpliciter is. The alternative strategy focuses on issues of what constitutes a true, faithful, or sufficient portrayal (Paynter & Longoria, 1997).

3.3. MODELS AND REPRESENTATION Researchers who study philosophy concur that the majority of modeling is representational. Whereas if a majority of modeling are representatives, therefore by reviewing the definition of representations, one may discover what characteristics modeling in research (and possibly also technology) share in general. That shifting from the difficulty of defining models to that same problem of defining manifestations is the basis for the current thesis. The issue of scientific participation is often brought up by academics (Sinha et al., 2001). Unfortunately, it is not quite obvious just what the issue is. Most philosophers believe that the issue refers to two or more questions: Firstly, what qualifies modeling as a representative of another system, often known as a target network? What, secondly, qualifies a model as just an adequate consideration of such a target network? Today, the preferred method of presentation in the scientific method is to divide the topic into two separate questions (Callender & Cohen, 2006; Frigg, 2006). In the first problem, the term “representations simpliciter” is defined, and in the

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following, the topic of “adequate consideration” is discussed. People who have a perspective on representations that emphasizes the description part of the idea of representations begins by addressing the very first question. Some people don’t even answer those questions question (Contessa, 2013; Nguyen, 2016). The emphasis of a perspective that examines the normative side of the idea of representation, in comparison, is on determining what qualifies a representation vehicle as an accurate depiction of another object. Resolving this issue is the major challenge in comprehending representation, claims a perspective that emphasizes the normative component. Furthermore, such a viewpoint contends that representation modeling necessitates model assessments by behavior as opposed to a solely descriptive viewpoint for models. That’s so while models and other representation tools are utilized in epistemology. There is also an epistemic purpose for representing models (Toon, 2012) (Figure 3.3).

Figure 3.3. Technique for representing knowledge. https://www.javatpoint.com/ai-techniques-of-knowledge-representation.

Modeling is a tool which is used as an “inquiry technique for knowing something” (Morrison & Morgan, 1999). A prescriptive view of representation’s key concern may thus be stated as follows: by consequence what does modeling sufficiently reflect target systems throughout order to promote knowledge about such target network? Inside this research, I make a distinction between models that have been recognized as inadequate representations and modeling that have been recognized as attempting representations. As a model-based concept,

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“representing” may refer to at least two different things. I thus suggest the etymological difference below: A representation modeling has as its goal accurately resembling a certain target server. An effort at an acceptable representation is what a perceptual model should be first and foremost in connection to. (ii) representational modeling is a representation paradigm that accurately captures the behavior of the target systems, making this a relatum in the relationship of sufficient representations (Poznic, 2017). Therefore, it is stated that determining what qualifies a represented vehicle as a representational vehicle, that is, an appropriate depiction of anything else is the key challenge in comprehending depiction (Rosenberg & Karnopp, 1983).

3.3.1. Indirect Representation The idea of indirectly representations, initially put out by Ron Giere in 1988 and specifically associated with that word by Michael Weisberg, serves as the broad basis for this argument (2007). The indirect representations point of view describes modeling as a two-step procedure (Edström, 1998). First, model users specify “model systems” with the help of “model descriptions.” Such model representations may take the form of mathematical formulas, statements written in scientific and technical language, or simple phrases. Networks which consists, which might be different sorts of things, are described in these modeling definitions. Model networks may be computationally structured, those that are at the core of computerized simulators, mathematical concepts entities including settheoretic constructions, or they might be actual artifacts like scale models (Weisberg, 2013). Model systems seek to accurately depict access to the target system, with such a relationship between the model system and the target application to be formed (Poznic, 2016b). Roman Frigg’s fictionalist explanation of modeling and representations is a key viewpoint just on the indirect approach of representations. The relationship between model specifications and model systems is known as “p-representation” by Frigg (2010a). Model specifications provide instructions on how certain premises should be represented. The “universe of the model,” that defines the model system, is made up of these ideas. The relationship among experimental models with targeted systems is known as the “t-representation” according to Frigg. Part of the information about just the target network is built on the connection of both the t-representation. Since specific target systems are represented by models, conclusions about

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just the target network may be drawn from information about just the experimental systems (cf. Frigg, 2010a, 2010b). The indirect representations framework indicates that representations should be at the very minimum a relationship between such a model system as well as system hardware. This relationship may also be thought of as including other relata in addition to these two. Following is further information about this (Ferris & Stein, 1994).

3.3.2. Relation of Representation The primary way that representations are understood in academic papers would be as a connection that, at the very least, combines modeling and goals Conversely, other academics disagree with a perspective that views representations as a connection. This approach has among its justifications the conceptual exclusion of models that have no existent objectives, including such models of the ethereal. For instance, whether representations are indeed a connection is discussed by Tarja Knuuttila (2011), Adam Toon (2012), and Mauricio Suárez (2015). It is important to think about this, although one might just state categorically that not all simulations are representational. One may differentiate between representational models and those that are not by using the terms of representing and representing models. Certain models could be representative models without actually representing anything; in other words, they are merely attempts at representation (Breunese & Broenink, 1997). The goals of a behavior influence the relationship of representations since models are employed with specific intentions. As a result, many academics agreed that representations involve at minimum a triadic relationship among modeling, goals, and consumers. According to Giere (2004), the relationship among models, objectives, consumers, and objectives should be seen as multiple connections (Giere, 2004). The strategy obtained using the proposed places a strong emphasis on users and objectives. The consumers and aims, however, are often left unstated. For instance, users, and aims were not mentioned directly in the illustration of indirect representations. It could be possible to talk as if the presentation were just a dyadic connection among transporters and targets if indeed the relation of consumers and goals are regarded to just be fixed components across multiple contexts (Trent, 1955). Within my definition, a representational model is a relative in connection of representations that accurately depicts a specific goal for a specific user base following a specific aim. For all potential users or purposes, a representational model doesn’t effectively reflect the relevant goal in any way. The discussions around

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representations may teach us that interpretations are often imperfect and only partially accurate (Teller, 2001). The objective of this thesis would be to build upon the research done by Giere, Weisberg, as well as other academics who take an indirect approach to representations. I too believe that a representation is mainly a connection. Models are only referred to be “representations” in such a derived understanding as they are not connections in and of themselves, but more the relevant type of model is merely a single relatum in such a hypothetical relationship of representations (Branin, 1966). Since it is vital to understand representations as just a connection and also as a successful word, I distinguish between the terms “model” with “representation” since not even all modeling techniques are a representation. For instance, so because ether doesn’t exist, depictions of the flexible, solid ether aren’t reconstructions of both the ether (Durfee et al., 1991). There is also no connection as well as the model would not be representational if such relatum of a stated connection between both the model’s reality ether doesn’t exist. There are also toy modeling or exploring models which are built without the intention of modeling target systems and are used just to examine certain theoretical techniques. An instance of this kind of model within quantum field theory is then the “4-model” (Frigg & Hartmann, 2012). Other academics emphasize that modeling is utilized for several other things than reflecting actual goals. Models may be used for other things, and there are connections among targets and vehicles which go beyond simple representations, but perhaps the most typical use for modeling would be to describe something which goes beyond the model themselves (McPhee, 1996). As was already noted, representations are often unfinished and insufficient. Invoking the idea of resemblance is one technique to cope with the reality that neither system is flawless. Neither model system could be a replica of such a target, but that can at minimum be comparable to the target in some ways and to some extents (Baciu & Kesavan, 1997).

3.4. RESEMBLANCES BETWEEN MODEL AND TARGET SYSTEMS According to the similarities perspective of modeling and presentation, it is acceptable to believe that even a model system’s destination node must be similar to this in any manner for somebody to understand it by using it. For statements about just the target based just on a theoretical model to be true, it

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would seem that the system and also the target must share certain important traits (Diaz-Calderon et al., 1999). In the philosophy of science, there are numerous perspectives on modeling and similarity. The idea of similarity is used in earlier similarity perspectives of modeling (Hesse, 1963; Leatherdale, 1974). According to Mary Hesse, the “positive similarity” refers to features that such a model and goal share, the “negative analogy” refers to model characteristics that a target lacks, and also the “neutrality analogy” refers to model characteristics whose target compatibility has not yet been determined (Giere, 1988, 2006), who employs this idea directly, is the most well-known supporter of the similarity approach. Weisberg (2013) creates a similarity theory that mostly utilizes research on similarity assessments conducted in psychology. He also takes into account the potential of similarities could not be symmetrical. There have been several structuralist viewpoints that may be viewed as similar viewpoints in addition to these techniques. By using the idea of such a mapping connection among specimens manufactured and concepts of homomorphism, generalization, or incomplete mimetic, several social constructionist viewpoints provide a rigorous mathematical description of known structure (da Costa & French, 2003). A structuralist perspective on representations which is not dedicated to a particular mapping connection among structures is defended by Christopher (Pincock, 2012). Bas van Fraassen supports a different structuralist viewpoint (2008). This viewpoint claims that the incorporation of data structures throughout theory-building structural components is just a model of consumer accomplishment that could be explained by the concept of morphism. Van Fraassen also recognizes selected similarity as a representational standard for just a measurement’s result. He doesn’t expressly support the similarity viewpoint. He opposes simplistic similarity interpretations of representations and supports one user account for representation. He may be advocating a weakened version of the similarity perspective, however, if he insists on selected resemblance (Elmqvist et al., 1998). Serious arguments to similarity perspectives of representations are considered and eventually refuted when the topic of similarities and representations is addressed see also (Poznic, 2016a).

3.5. FICTIONALISM ABOUT MODELS Most system definitions found in sciences do not exactly correspond to realworld physical and social processes. And so many more, depictions of ideal gas law, smooth planes, as well as the behaviors of ideally rational beings are a

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few examples. If such descriptions don’t have a counterpart within the social or physical world, whatever must they be (Zeigler, 2014)? asks a fictionalist view of modeling. Waltonian fictionalist interpretations of modeling often respond that the preceding descriptions aren’t accurate representations but instead instructions for envisioning certain ideas. Such “characterizations” are prescriptions for envisioning claims about theoretical systems which do not exist in our reality, according to one certain response. Frigg’s indirect perspective should be in this position (Frigg, 2010a). Another particular stimulus is that such “representations” are guidelines for conceptualizing relative to competitors that are focused on actual target organizations rather than hypothetical ones. That’s the stance taken in Toon’s (2012) straight perspective. Facial expression practice seems to be the act of speaking and reasoning about this kind of hypothetical, imaginary system as if they were real. I adopt this definition of the word as a name for fictionalism’s driving motivation: whenever scientists talk as well as think regarding hypothetical processes as if they are real, they engage in facial landmark practice. The goal of fictionalist modeling accounts is to provide a reason for the habit of taking people at face value (Poznic, 2016c; Leão et al., 2011). Waltonian fictionalism holds that imaginative activities must adhere to certain guidelines in specific situations. These situations are referred described as “games of end up making” and are considered to just be games. Those games incorporate “objects,” or instruments, and rules that, when combined with both the props, dictate how particular propositions should be imagined. These visions are not merely subjective and changeable if the guiding concepts are broadly accepted there are reliable norms (Seabra & Machado, 2009). Practitioners with in-game of start making my sense of experience recognize the quality of the premises which need to be imagined. The supporters of fictionalism may explain why imagined model frameworks can play a crucial part in epistemological processes in science without undermining the validity of the research for this position of objective visions. There is a detailed discussion of Waltonian fictionalism. This comprises a thorough critique of two specific fictionalist viewpoints that Frigg and Toon support. The main argument for the critique is an epistemic one (Borutzky, 1999).

3.6. MODELING AND DESIGN Another assertion made by philosophical technologies would be that engineering design attempts to create technological objects (cf. Franssen

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et al., 2015). This procedure includes means-end analysis and examination of the functional specifications of the intended products (cf. Meijers, 2009, Part III). But in this area of philosophy, there hasn’t been much discussion of the relationship between modeling and representations. There aren’t many academics that combine the philosophy of science with the philosophy of technologies (Sterrett, 2014; Knuuttila & Boon, 2011). The discussions in the philosophy of scientific and philosophical technologies are often separated, even though there is research on modeling within industrial engineering. It is unclear how modeling for the goal of developing engineering objects compares to or relates to the technique of representative modeling mostly in sciences. It also is unclear if engineering models reflect aims in the same manner that scientific models do (Bastogne, 2004). On either side, the philosophy of science seldom discusses appears to mean logic. Additionally, there is no equivalent within the scientific method for arguments regarding technological functions that are found in technological philosophy. Whether the epistemological responsibilities of scientific principles should be understood is still up for debate. There is disagreement over what this view on modeling as “epistemology instruments” means. Some academics refer to modeling as “epistemological tools” (Knuuttila, 2011) (Figure 3.4).

Figure 3.4. Method of engineering design. https://www.twi-global.com/technical-knowledge/faqs/engineering-designprocess.

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3.7. MODELING PARADIGMS AND LANGUAGES There are several frameworks and languages for overall numerical simulations. The following requirements may be used to categorize them: functional vs entity paradigm, operational versus declaratory systems, multidomain against base classifiers, and continuous against discrete models (Barker et al., 2020). With the help of the following, we would show how the different modeling paradigms vary. The reading heads of a storage device are controlled by a streamlined control method with one rotating level of flexibility. A source of energy, an Electric motor, a huge pile that reflects the try driving head, a compression codec which measures the load’s angular acceleration and incorporates this to extract an angular displacement, and just a PWM control system (Pulse Width Modulation) that regulates the head’s positioning make up the five main parts of this technical system (Maffezzoni & Girelli, 1998).

3.7.1. Graph-Based Modeling In so many various simulation domains, linked processes have indeed been represented using graphs. Bonded graphs, straight graphs, and schematic diagrams are the three graph-based frameworks that have received the most attention in unified modeling studies (Kloas et al., 1995). The foundation of bonded graph modeling involves power connections that link components that store and transform energy. As just a result of circulation and exertion variables, the bonds indicate the flow of energy between both modeling pieces. Kirchhoff’s voltage and current rules, represented by the 0- and 1-junctions, however, link the elements to one another. Bond graphs are generally domain neutral, however, they are not particularly useful for modeling constant systems or 3D dynamics (Mattsson et al., 1998) (Figure 3.5).

Figure 3.5. A general description of our graph-based model’s network infrastructure.

https://www.researchgate.net/figure/Overview-of-our-the-system-architecture-of-the-graph-based-model_fig1_226649605.

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Bonding graphs are used to represent the servo mechanism that was previously described. The fact that now the bonding graph’s topology differs significantly first from the topology of an equivalent physical process may seem confusing to novice consumers, even with this simple system. For instance, the encoding component in conceptual is expressed as a connection between such a 1-junction as well as the controllers rather than mapping to a specific bond graph node (Piela et al., 1991). Additionally, that model’s causality was predetermined just at the moment of model construction. For systems where the causality varies dynamically, this might provide issues (e.g., at zero speed for Coulomb resistance models). Even though the basic building blocks of bond graphs are often linear, several programming languages, including CAMP-G or SIDOPS+, offer irregular multivariate relationship systems that may include combined continuous-time as well as special devices (Piela, 1989). Building on regular graph theory, this second graph-based modeling paradigm. Trent and Branin were the first to notice the connection between regular graphs and the physical world. These straight graphs, which resemble bond graphs, display the program’s energy flow as indicated by through it and over variables (also called terminal variables). The terminal of such a component of the system corresponds to a graph’s points, and a side with a line chart denotes the presence of such a flow of energy in that element (Cellier & Greifeneder, 2013). There’s also a terminal equation for every edge that expresses the relationship among its terminating variables. The dynamics of such a component are defined by one or even more lines and the related terminal equation. By joining the nodes that have connection points, such terminals graphs of separate components may be combined into systems graphs. The system topology is explicitly reflected by linear network models, as opposed to bond network models. They could be readily expanded to simulate 3D mechanics including hybrid electric vehicles and thus are domain agnostic (Banks, 1998). The base format again for VHDL-AMS simulating languages is a straight graph. Connecting two electric terminals and a b is edge e1, which stands for the source of power. The DC motor engages in both electromechanical power domains. In electromechanical realms, it is characterized by two edges, e2 and e3, correspondingly. The solitary edge e4 linking nodes d and e in the mechanical domain serves

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as a representation of the load because it is a purely mechanical element. Blocks icons linked to one another and the most energetic portion of a linear curve serve as representations of the signal portion. The encoder calculates the angular location by integrating the load’s angular acceleration. The controller receives this value and delivers power to a DC motor (Glynn, 1989). The third group of graph-based modeling paradigms is based on block diagrams, as in SimuLink or Easy Here, basic models like integrator, multiplication, or adders are connected at their outputs and inputs to specify the models. Models of components at lower levels that are enclosed are used to represent complicated systems (Koenig et al., 1967). With block diagrams, the design variables are often specified programmatically. The customer must manually restructure the system equations since the solutions are unable to break potential algebraic looping. To prevent an algebraic loop, both the loading and motors model are merged within the control scheme block diagrams model. The loading and motors inertias are added together and allocated to a single signal generator through make the system manageable (Barton & Pantelides, 1994). Modules discontinuous events modeling and declaratory objectorientated modeling are both using similar block structures. The terminal model represents every component of the conceptual framework as a separate component. A hierarchy composition comprising inertia models and a viscous dissipation model is used to describe the load (Fishwick, 1997).

3.7.2. Declarative and Procedural Modeling The Continuous System Simulation Language (CSSL) served as the foundation for several early modeling languages. These were procedural, which means because, as is typical in most computer languages, characteristics were specified by assignment. Projects must be assessed in the sequence specified by the user that describes a response variable as just a purpose of the self-governing variables (fixed causality). Symbolic modification is forbidden and the reuse of generative models is constrained (Cellier, 1996) (Figure 3.6).

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Figure 3.6. Cognition mechanisms: the descriptive model, which is utilized during creation and re-enters the generated utterance into the comprehension systems, and also the H&H design, which is employed during creation. https://www.researchgate.net/figure/Cognitive-mechanisms-the-declarativeprocedural-model-used-during-comprehension-and_fig2_347033275.

But from the other side, declaratory or equation-based language doesn’t enforce fixed causation just on the model. In such languages, the relationship between both the variables and their variations, with time is established by a series of calculations that serve as the model’s definition (Zeigler et al., 1991). Those equations must be transformed into computer-evaluable software methods by the simulation platform. Declarative language provides the benefit that consumers will not have to specify the equation’s theoretical causation. As a result, the very same model may be used for whatever cause and effect are enforced by other parts of the system (Diaz-Calderon et al., 2000). There were several declaratory languages created, including VHDLAMS, MOSES, Smile, ObjectMath, SIDOPS+, as well as Modelica. Several programming languages, such as ASCEND, extend the declaratory paradigm by enabling models to just be non-causal even within their variables, suggesting that modeling inputs and outputs may be used to answer for design variables (Zhang & Zeigler, 1989). Several declarative technologies have features for generative models too though, including Modelica as well as VHDL-AMS. This helps add embedded systems programs or discrete-time models that seem to be easier to specify as a procedure (e.g., the PWM organizer in the instance) (Fritzson & Engelson, 1998).

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3.7.3. Discrete and Continuous Simulation Physical processes happening in time series and events taking place at distinct time and space coordinate combine to produce the behavior of interdisciplinary systems. Mixed modeling, which can describe either discrete or continuous events processes, is essential for the optimal simulated world of such organizations (Mattsson & Elmqvist, 1997). A collection of differentially algebraic equations (DAEs) is the ideal model for several physical processes since they develop essentially continuously time functions, including rigid body waves, the electrical flow of current, fluid flow, and heat transport (Franke, 1997; Murphy et al., 2011). On either side, physiological changes and digital elements provide outputs at specific times and locations; these are best described by utilizing variables or impulsive functions. Data networks, rigid body accidents, and even digital controls are a few examples. Several other fields, including logistics, transport, handling of materials, including defense simulations, also use computer simulations provide a comprehensive summary of both the precisely defined simulation’s fundamentals and commercial applications (Nilsson & Eborn, 1998). Mechatronic systems need mixed constant models even though they incorporate extracted features and continuous temporal processes. Hybrid system modeling is supported by several simulation technologies, notably Modelica as well as VHDL-AMS. Improved solvers that successfully integrate discrete event transmission and DAE solutions are also necessary for these models. Today, this feature is included in the majority of computer imitation software programs (Diaz-Calderon et al., 2000).

3.7.4. Object-Oriented Modeling Systems modeling may benefit from using the entity-programmed software design concept thanks to the advantages of streamlined model building and maintenance (Kübler & Schiehlen, 1999). Data concealing or encapsulating is a key concept in entity development; each object can only be accessed via its communication line, which is separate from the implementations used behind. Creating a separation between an object’s internal activity (implement) as well as its external interaction with its surroundings (interface) allows the same idea to be used in modeling (Lückel et al., 1993). A model interface is made up of terminals

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that seemingly disconnected the information flow, weight, or power to a limited number of places on the product’s interface. Kirchhoff’s networking regulations are applied to the port parameters while joining ports. The solvers determine the causation of energy interconnections again for equations defining the inner behavior of an object. The benefit of encapsulating would be that a system may be represented by grouping and linking the interface of its components, regardless of how they will ultimately be implemented (Otter et al., 1996) (Figure 3.7).

Figure 3.7. Object-oriented data model for simple cases. https://www.geeksforgeeks.org/basic-object-oriented-data-model/.

The connectivity of a DC motor contains two electrical connectors and two metallic contacts (stator and rotor). By enclosing the real formulas inside the motor’s construction, the systems as a whole are kept ignorant of how electric power is transformed into mechanical power (Sinha et al., 2000). Inheritance is just a second key concept in entity computing, where entities which are descended from such a base class inherited the interfaces and data elements of that class. Similar to this, in modeling, a derived model inherited the original model’s interfaces and formulas. By adding more interactions (ports) to the interfaces or more equations to the implementations, the kid model may be expanded (Crolla et al., 1993). The work of recycling, updating, and expanding family members of simulators is made simpler by the entity prototype model, which creates a hierarchical structure of models. For both linear systems and discrete-time systems, many research groups have created entity languages. Not all of these languages have the same level of instrument paradigm compatibility; Modelica has the most complete object-oriented concept assistance (Wittenburg & WOLZ, 1985).

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3.7.5. Comparison between Modelica and VHDL-AMS Modelica and VHDL-AMS are the results of two significant recent initiatives to build powerful simulation language suitable for modeling complex multi networks. Whereas VHDL-AMS has emerged as just an extension of VHDL to handle Analogue and Made by mixing elements, Modelica has indeed been developed largely by the continuous equating in Europe (Schiehlen, 1997). The richness and range of both languages are extremely comparable in general: Both allow descriptive modeling and hierarchy encapsulation, as well as the continuous duration and when few modeling in different energy domains. The fact that Modelica is an entity and supports model derivation and datatypes is another benefit (VHDL is limited to encapsulation). Additionally, it enables the design of aggregation links that may incorporate many across, between, and signal factors from several energetic domains (Shabana, 1997). But from the other side, additional modeling primitive people for distinct simulation are available in VHDL-AMS, an expansion of VHDL. It provides studies of quiet points, small-signal AC, and comparatively tiny noise in addition to rapid issues (Von Schwerin, 1999). The solutions used to analyze these models are a final significant distinction between the two languages. Just Dynasim presently offers Modeling compatibility. Their solution can handle index three issues, which are frequent in physical models, by symbol index reductions (Reckhow & Chapra, 1983). The least amount of differences that exist necessary to produce an analogous explicit system of difference equations is known as a system of DAEs’ grade. While various electronic CAD manufacturers are planning to develop solver implementation, VHDL-AMS has just been defined by IEEE and presently has relatively little support (Gillespie & Colgate, 1997).

3.8. SINGLE-DOMAIN SIMULATION Several businesses provide reliable simulation software in the mature field of numerical simulations, which is specified for a specific domain like electrical or mechanical circuits. We look at only one modeling element inside this part that affects multi-domain system design modeling (Richard & Gosselin, 1993).

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3.8.1. Multi-Body Dynamics Simulation An essential component of the behavior of robotic devices is the physical connections between several stiff bodies. A technique graph-based theories are especially applicable to element systems modeling. Two fundamental components of multi-body networks are bodies (such as the rigid body) and interconnections (e.g., joints). Standard graph methods may conceptually derive the DAEs characterizing the multi-body complex from such components, which correspond to nodes and edges inside a network graph, accordingly (Broenink et al., 1998). A second strategy is built on component, object-oriented concepts that may be assembled into whole organizations in such a hierarchy fashion. This method creates a collection of DAEs rather than closed-form expression symbols, sometimes with indexing of two or three (Rai & Jackson, 1999). Depending on the findings of this study, several software programs, such as ADAMS or DADS, provide effective multi-body analytical capabilities. The framework data can be directly transmitted from CAD models to that same simulator thanks to the integration of certain analysis methods with development tools. Rigid body simulations are reviewed in further detail in, whereas progress on modeling phases as follows systems is explored in (Boot et al., 1999) and particular numerical techniques are covered in (Le et al., 1997).

3.8.2. Electrical and Electronics Systems There are several simulation languages available again for modeling electrical components. The group of SPICE languages that’s the most often used for circuit applications, whereas VHDL and Machine language are frequently used for electronic circuits. For the modeling of hybrid analogdigital circuits for multifunctional networks, more contemporary languages, including VHDL-AMS, have indeed been created. The majority of commercial manufacturers of electrical network simulation models strongly link their modeling engines using ECAD technology. As a consequence, the designer is given access to a full modeling and simulation environment where he or she may specify the circuit topology, produce a physical structure, and check the behavior of the circuits (Tummescheit & Eborn, 1998).

3.8.3. Control Systems The mechanical and electrical elements of the project are heavily interacted with by embedded controls. These also often take the form of digital

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devices, necessitating the use of a hybrid constant simulator to assess how they interface with the outside world (Pirker-Fruhauf et al., 2007). Hardware-in-the-Loop (HWIL) testing is an essential component of evaluating integrated controllers. This enables both the assessment of a digital embedded device engaging with such an actual tangible system and the assessment of physically controlled prototypes engaging with such digital electro-mechanical systems. Several controlling algorithms platforms provide specialized additions for HWIL evaluation and automated code creation (Sinha et al., 2001).

3.8.4. Hydraulic and Thermal Systems Models of hydraulic as well as thermoelectric systems frequently include interactions between them and alongside mechanical parts (Sinha et al., 2001). The symmetry of the elements as well as their physiological setups have a significant impact on how thermohydraulic systems behave. Close interaction with the design software environment is important for mechanical components (Shooter et al., 2000). It is evident from the foregoing assessment that several simulation environments have a tight relationship to design software. The simulations and designing of multifunctional frameworks are now gaining in popularity (Beitz et al., 1996).

3.9. INTERLEAVING DESIGN AND SIMULATION The design may be seen as a method that links composing and breakdown. High-level activities are hierarchical and broken down into activities for the subsystem, and these components are subsequently assigned to physical parts, which would then be recomposed into a whole system (Sinha et al., 1998). The designer decides which elements are utilized and when they connect throughout the compositions (i.e., synthesizing) phase. The method used here is similar to the hierarchy model development: to describe the system behavior or subsystems, designs of elements are linked to one another through modeling techniques (depicting the dynamic of the numerous contributions). Both processes—form composing for design and behavioral models in simulation—are founded on hierarchy assembly (Sydow, 1982). As was mentioned in the preceding section, several electronic CAD suppliers provide seamless integration seen between model development of such circuit design utilizing manufacturing languages and also the circuit

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design processes. A system-level model is automatically assembled from the related models of elements and constituent relationships whenever the user determines the circuit architecture. This may be done with ease because of how readily electrics interact with one another: the voltage of two connected terminals are identical, and the total of the current flow is zero (Shah & Mäntylä, 1995) (Figure 3.8).

Figure 3.8. Two Integrated memory architecture. https://www.researchgate.net/figure/Interleaved-Memory-System_ fig1_2813175.

Although in the mechanical realm, a range of links bottom pairs, geared contact, or rolled contacts—can be used to determine how components interact with one another. Kinematical limitations on the locations of connecting pieces are produced by these linkages. The information required to instantiate these developments rapidly (the modeling organization and associated elements) may either be retrieved directly from the physical Cad system or must be supplied mostly by designers (Daberkow & Kreuzer, 1999). The combination of CAD data with behavioral modeling is possible with certain CAD programs, including such Pro/Engineer Behavioral Modelers (Lin S. & Lin J., 1998). Whenever a component or assembly is changed, the behavioral analysis may be performed automatically by incorporating assessment techniques as aspects within the features tree. For instance, using the CAD shape and the material properties, an assessment feature may determine the development’s mass. Currently, research is moving toward expanding the only integration of modeling and prototyping tools to many domains. Components models that incorporate both shape and a behavior description may help with this. When

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two components are connected, their behavioral models are configured in addition to their physical appearance. The component-based approach may be further expanded to include object-oriented intelligence elements, which connect knowledge-based platforms with design guidelines with CAD with behavioral models (Duckering et al., 2000). Micro-electromechanical systems (MEMS) is just a field where proposed system modeling has previously been shown (Susca et al., 2000). While mechanical and electrical elements typically make up MEMS technologies, the mechanical behavior is generally assumed as being restricted to two dimensions. Because of how simple the resultant physical equations are, they can be represented using the current modeling methodologies of electric CAD tools. In such simulation tests, it could be essential to simulate the MEMS devices as a more intricate finite-element simulation rather than a grouping of solid bodies. Research on the subject is underway (Sinha et al., 2001).

3.10. COLLABORATIVE MODELING A team of experts must collaborate to design complicated interdisciplinary systems: Working together with statisticians, industrial engineers, marketing experts, and business executives are designers with a variety of educational backgrounds. Several multinational corporations have benefited from computer-assisted engineering (CAE) technologies that allow distribution, visualization, documenting, and administration of product lines to organize design processes across geographically distributed and diverse teams. Collaboration simulation still is a relatively new concept (Stein & Louca, 1994) (Figure 3.9).

Figure 3.9. Modeling in collaboration. https://www.researchgate.net/figure/Collaborative-Modeling_fig1_220706606.

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Design engineers require standardized, distributed rules to achieve, a repository for managing model parts, and modeling abstractions abilities to continue providing designers access to various views of the models (Bettig et al., 2000).

3.10.1. Common Representation Designers require a standard model format to exchange simulations inside a collaboration digital model. Overall stages of contemporary extremely huge integrated (VLSI) designing, the Very Elevated Electronic Circuit Hardware Description Language (VHDL) has been utilized as a design test automation. Similarly to this, the Top Level Architectural (HLA) has been utilized by the U. S. Department of Defense as well as its subcontractors to simulate combat situations (Dewey et al., 1999; Modlo et al., 2019). HLA is indeed a set of guidelines that spells out how several simulations, referred to it as entities, may collaborate to create a bigger virtual world. By preventing the translation between forms of different analysis and simulation programs, these guidelines promote collaborative modeling. The problem of integrating and using models created in many application areas will need more study (Zhou & Dowd, 2002).

3.10.2. Model Management Product models are usually kept within a repository inside a cooperative simulation environment, which is often built as a database. Various organization methods, domain ontology, and uniform datatypes have been established by academics to handle these model collections (Romanowicz, 1998). Again for the administration of VHDL modeling, Park, and Kim presented a structural algebraic structure. Inside this architecture, a VHDL network model is used to arrange a variety of hierarchy systems. The very first step is choosing a prospective structural model from the group that satisfies design goals. After that, a VHDL collection of simple VHDL modeling is combined to create the structure’s VHDL models methodically (Mukherjee & Fedder, 1999). A methodology for modeling reusing is provided by (Breunese et al., 2000). Instrumental in improving, theoretical representations regarding physical phenomena, and mathematics linkages are indeed the three degrees of complexity for a design. There are several possible conceptual explanations connected to each component. Ideas are then further defined

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by choosing one of many possible mathematical connections. One may mix models with various degrees of complexity and validate status by saving the models put together from these basic building blocks mostly in libraries (Hofmann et al., 1996). The NIST designs archive project (Sriram et al., 1992) includes very thorough organization and storage efforts for simulators. The objective of a design repository, as opposed to a typical design database, is to collect, distribute, and recycle design information. This information comprises more than simply CAD data; it also likely includes the logic behind the design as well as behavioral patterns and functionality representations (Wilson et al., 1999).

3.10.3. Model Abstraction The ability to mimic a system at many abstraction levels is crucial when it comes to design. As the development process progresses, several degrees of analysis are needed—high-level analysis initially in the conception phase when little is known about the design, and so more comprehensive analysis as the design phase closes. Moreover, the behavior of other subsystems with which a given subsystem doesn’t come in direct contact may only need to be described at a top standard for a given subsystem’s development. Modeling abstractions aims to provide consumers with a representation of the developed program which only includes the crucial dynamics (Wang et al., 2008).

3.11. MODELING AT THE COMPONENT LEVEL Modeling By shifting away first from levels of difference calculus towards the usage of more logical and approachable building elements, simulation languages have progressed from procedurally and functioning languages through formula and entity languages (Pottmann & Wallner, 2001). The creation of simulation languages that function just at levels of system components will be the next stage in this progression. Several component models comprise a variety of performance measures that depict the element at various degrees of detail as well as from various angles. The matching modeling approach is generated from the individual parts when the designer assembles systems from their parts. The relevant model specifies each of the elements and their connections may be created dependent on the kind of simulation study (Cutkosky et al., 1996) (Figure 3.10).

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Figure 3.10. Component-level modeling for content coupling. https://ecomputernotes.com/software-engineering/component-level-design.

3.11.1. Component Interaction Modeling Whenever a developer builds a system out of parts, he also specifies how these parts work together (their virtual places, electrical links, etc.). Usually, the user must explicitly simulate the behavior of such relationships. Fortunately, the architect often previously supplied the data necessary to derive these interaction models. These context design tools which are coupled with both the simulation environment may immediately gather the data required to define the investigation of the effect (Bajaj & Cutchin, 1999).

3.11.2. Selecting an Adequate Level of Detail Different analyzes must be carried out at various phases of design. To prevent wasting computer resources, the model of such a system must be modified to be sufficiently accurate without even being unduly complicated. Simulator models cannot simply be changed to multiple degrees of abstraction given the state of the art. The best model for every element and its interactions in a network should be automatically selected, according to future studies (Iwasaki et al., 1997).

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3.11.3. Improvement of Numerical Solvers The majority of VHDL-AMS simulations are presently ineffective for simulating systems having mechanical parts. They frequently have trouble solving index-3 issues, which are ubiquitous in mechanical components and finding algebraically compatible beginning circumstances. Solvers must handle the unique challenges given by all the many application fields as interdisciplinary systems simulation becomes increasingly prevalent. Beyond only choosing the step length as well as integration technique, they need to provide the user with more precise control well over numerical solutions (Lutz et al., 1998).

3.12. INTEGRATION WITH DESIGN TOOLS 3.12.1. Integration with CAD Model generation and design confirmation are made easier by the simulation and analysis environment’s tight interaction with development tools, as was discussed in earlier sections. Therefore, just electronics CAD has had such effective integration (with some imperfect addition in other fields). To enable a design team to develop prototype models from inside that help to portray what they were used to which are specifically adapted to their requirements, additional interaction of solitary CAD tools with such a shared simulations environment is required (Park & Kim, 1998).

3.12.2. Finite-Element Modeling Because of their computing needs and the difficulties of integrating finite elements with lumped simulations, finite volume instruments are presently not connected to domain-specific environments. Future studies should concentrate on these interface problems to integrate modeling of dispersed physical processes like physical elastic deformation or sophisticated electromagnetism and thermodynamic behavior in information systems. This is because computing resources are increasing (Breunese et al., 1998).

3.12.3. Integration with Optimization and Synthesis Tools The present simulation systems only provide a limited amount of scripting functionality for interaction with optimization and synthesizing tools (e.g., to route a specific imitation for a variety of limited standards). The designer must manually alter the design requirements in light of the findings.

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This procedure needs to be mechanized in future by better integration of simulations and design technologies. First advances have previously been made in this approach in (Murdock et al., 1991).

3.13. FUTURE MODELING AND SIMULATION 3.13.1. Unified Model Representation Modelica, VHDL-AMS, and HLA are a few modeling languages and systems for simulation in many fields that have lately been produced as a consequence of standardizing and unifying initiatives. However, the number of single areas imitation surroundings continue to make use of exclusive solutions and file formats which are strongly linked with both the digital model. By integrating solutions or by utilizing standard model representation, emerging simulation systems should provide tight integration across application areas (Ozawa et al., 1998).

3.13.2. Ontologies for Modeling and Simulation Researchers began to create domain taxonomies to further enhance the interchange of model data (among agents and humanity) (Devedžić, 1999). To facilitate various degrees of information sharing among people and various modeling and simulated data agents, for example, Ozawa suggested a standard vocabulary. Integrated classifications and word networking may be created based upon that domain knowledge to assist model search and repositories administration (Schlenoff et al., 1998).

3.13.3. Lack of Knowledge about the System’s Requirements At about this top standard, the stakeholders may evaluate the defined operational behavior, determine whether they are happy with both the operations or even identify their actual wants. They may assess the original specs’ quality on their own and provide input to designers. The developers can spot minute conceptual flaws and confirm that now the system function as planned (Szykman et al., 1999). Concepts and demands validation is a process of verifying that the preliminary requirements correspond to the actual demands of the stakeholder (Devedžić, 1999). In general, interaction simulators are a great way to communicate among engineering fields as well as between professionals and stakeholders that are not particularly skilled in engineering. When determining needs and converting those into

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product details, workable requirements help close the most significant communication barriers (Morgan, 2009). Executable requirements enhance information exchange among developers and customers, either between technical experts or disciplinary engineers, by facilitating communication. Executable requirements are a supplement to existing quality control techniques such as QFD. Collectively, they aid consumers and developers in determining if the design phase is beginning and moving in the direction that will satisfy stakeholder needs (Schlenoff et al., 1999).

3.13.4. Lack of Knowledge About the System’s Environment A system’s operating environment may not always be completely understood when the design phase first begins. This is particularly relevant in situations where the system modifies or develops its very own surroundings. For instance, it is unknown how many people will use the airport rail before it is completed. No one uses the system before its implementation. As immediately as it is constructed, people start acting differently, and at minimum, some will board the train (Szykman et al., 2000). Even while designers could have a general notion of future increases in passengers, there will always be some degree of unpredictability. Designers may investigate how sensitive the system’s functioning is to these ambiguous external conditions by using simulations and modeling. Arrivals of passengers were modeled as a stochastic function. That’s an illustration of how to approximation epistemological ambiguity that used a description of ambiguously defined confusion (Lee & Fishwick, 1997; Ozawa et al., 1999). Future customer behavior is a mystery to designers. Even though the method is genuinely random, the particular characteristics of a process, like the actual mean, are unknown to the developers. We investigated rates using averages of 1, 3, and 10 seconds under the assumption that arrivals follow a Poisson process. The simulations showed that the maximum wait length and average arrival time had a nearly direct correlation (Hsieh & Levitan, 1998). Similar trials may be performed inside a formally planned experiment (perhaps depending on experimental design with resilient design) using varying values for these other system characteristics, like the time allotted for loading or the speed where the trains travel among stops. Without even a simulation study, it would also be challenging, if not unattainable, to dynamically examine the system’s conception under various situations. The program’s sensitivity to different environmental factors is shown by the simulated results. This knowledge helps in the creation of systems that are resilient to unpredictability (Horvath, 2001).

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3.13.5. Lack of Knowledge of Future Decisions There at the start of this project, a development’s explanation is necessarily lacking. In essence, designers are unable to foresee their choices before they will be made. Epistemological uncertainty takes the shape of the required insufficiency of both the system specifications at the moment the system is modeled (Finger & Dixon, 1989). These next design choices do not follow a random variable that could be modeled by such a probability density function. And what’s the likelihood, for instance, that a certain gear would be employed in the doorway method? Typically, it is impossible to know such distributions. Even though they are specifications, several finalized design specifications and characteristics are widely known (Rai, 2003). The finished version must equal the modeling, and a profitable product layout satisfies the criteria, thus a prototype which will match that finished version (with a chance close to one). Designers may utilize executable requirements in this manner to lessen ambiguity. Decomposing choices are made by designers without knowledge of the program’s final form. But since they are describing the desirable action, individuals are aware of how it will act (Bowers & Ludäscher, 2005). The system will work as intended if it is constructed following its specifications. Without having access to the system’s completely comprehensive design, this behavior may be simulated. Feedback may guarantee that the requirements are satisfied in typical use situations, presuming the described technology is realistic. For instance, feedback may guarantee that now the train won’t leave till either as the doors are shut or just after 5 seconds, whatever is longer if indeed the specs say that it requires the gates 5 seconds to shut. As a result, the bottom bound’s ambiguity is gone (Li et al., 2016).

3.13.6. Lack of Knowledge About Emergent Attributes Utilizing execution specifications, customers, and engineers may investigate how their demands and choices affect the program’s conception. There are behavioral linkages in the execution requirements that might be hard to comprehend without simulations (David et al., 2013). The following jobs, for instance, could benefit from the simulated world: Identify inconsistencies within the control system and operations, as well as any timing issues or deadlocks. Check to see whether the program’s behavior matches the nominal (planned) behavior. Investigate how the choice of design specifications and subsystem needs will affect operations (Xie et al., 2018).

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3.13.7. Revealing Problems in Operation Executable requirements may aid in determining how different system components, including timing and controlling rules, must be coordinated. For instance, the system must ensure that now the operations to take people and doors open never happen at the same time since doing so would pose a serious safety risk. Other relationships may not be as evident as this limitation, however (Moody, 2009). Demand for resources is also another issue that might arise with both the operational management of the system. A wireless connection, for instance, has a finite amount of bandwidth. Overall network requirements might be much lower than this. There might be instances, however, when many networking devices simultaneously seek more bandwidth than is available. When a calamity necessitates intensive cooperation of rescue operations, this may occur if indeed the network is a component of an emergency personnel organization. Modeling and analysis may be used to identify this resource rivalry (Radmanović & Mančić, 2004).

3.13.8. Verifying Behavior Design professionals can confirm that the existing specifications can produce the desired behavior of the system with the use of execution requirements. Though this confirmation may go further into the design phase, it is comparable to a concept verification that was previously addressed (Haber & McNabb, 1990). The simulation can show if specific design mistakes were committed. For instance, the modeling or subsystem architecture has to be modified when a subsystem requires certain feedback from the environment but that resource is not described again for the subsystem’s parental level (Drexler, 2006).

3.13.9. Exploring Decisions Designers may investigate how various design model parameters impact operation behavior in terms of the instance by using the execution specifications. This is crucial whenever two systems or a tradeoff’s qualities are tightly connected (Field et al., 2007). Although using executable specs won’t completely remove ambiguity, it may aid designers in more accurately estimating how choices would affect characteristics. As an illustration, we ran simulations of the two or three train systems while maintaining the same values for all other variables. According to the simulation’s findings, the third train would have cut the highest wait lengths by around 30%. Such predictions of operating behavior might help designers make better design choices (Palermo et al., 2007).

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REFERENCES Alciatore, D. G., (2007). Introduction to Mechatronics and Measurement Systems (Vol. 1, pp. 3–9). Tata McGraw-Hill Education. 2. Ascher, U. M., & Petzold, L. R., (1998). Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations (Vol. 61, pp. 2–9). Siam. 3. Baciu, G., & Kesavan, H. K., (1997). From particle-mass to multibody systems: Graph-theoretic modeling. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(2), 244–250. 4. Bajaj, C., & Cutchin, S., (1999). Web based collaborative visualization of distributed and parallel simulation. In: Proceedings of the 1999 IEEE symposium on Parallel Visualization and Graphics (Vol. 1, pp. 47–54). 5. Banks, J., (1998). Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (Vol. 1, pp. 3–7). John Wiley & Sons. 6. Barker, H. A., Chen, M., Grant, P. W., Jobling, C. P., & Townsend, P., (2020). Graphical user interfaces in computer-aided control system design. In: CAD for Control Systems (Vol. 1, pp. 561–578). CRC Press. 7. Barton, P. I., & Pantelides, C. C., (1994). Modeling of combined discrete/continuous processes. AIChE Journal, 40(6), 966–979. 8. Bastogne, T., (2004). A multiport diagram for batch system modeling. Methodology and implementation. Simulation Modelling Practice and Theory, 12(6), 425–449. 9. Beitz, W., Pahl, G., & Grote, K., (1996). Engineering design: A systematic approach. MRS Bulletin, 71. 10. Bettig, B., Summers, J. D., & Shah, J. J., (2000). Geometric exemplars. In: Workshop on Knowledge Intensive CAD (Vol. 1, pp. 45–57). Springer, Boston, MA. 11. Boot, R., Richert, J., Schutte, H., & Rukgauer, A., (1999). Automated test of ECUs in a hardware-in-the-loop simulation environment. In: Proceedings of the 1999 IEEE International Symposium on Computer Aided Control System Design (Cat. No. 99TH8404) (Vol. 1, pp. 587– 594). IEEE. 12. Borutzky, W., (1999). Bond graph modeling from an object oriented modeling point of view. Simulation Practice and Theory, 7(5, 6), 439– 461. 1.

Scientific Design and Modeling in Engineering

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13. Bowers, S., & Ludäscher, B., (2005). Actor-oriented design of scientific workflows. In: International Conference on Conceptual Modeling (Vol. 1, pp. 369–384). Springer, Berlin, Heidelberg. 14. Branin, F. H., (1966). The algebraic-topological basis for network analogies and the vector calculus. In: Symposium on Generalized Networks (Vol. 1, pp. 453–491). NY: Polytechnic Institute of Brooklyn. 15. Breunese, A. P. J., Broenink, J. F., Top, J. L., & Akkermans, J. M., (1998). Libraries of reusable models: Theory and application. Simulation, 71(1), 7–22. 16. Breunese, A. P., & Broenink, J. F., (1997). Modeling mechatronic systems using the SIDOPS+ language. Simulation Series, 29, 301–306. 17. Broenink, J. F., Hilderink, G. H., & Bakkers, A. W., (1998). Conceptual design for controller software of mechatronic systems. In: 1998 Lancaster International Workshop on Engineering Design (Vol. 1, pp. 215–229). 18. Cellier, F. E., & Greifeneder, J., (2013). Continuous System Modeling (Vol. 1, pp. 3–6). Springer Science & Business Media. 19. Cellier, F. E., (1996). Object-oriented modeling: Means for dealing with system complexity. In: Proceedings of the 15th Benelux Meeting on Systems and Control (Vol. 1, pp. 53–64). Mierlo, The Netherlands. 20. Crolla, D. A., Horton, D., & Firth, G., (1993). VDAS—a toolkit approach to vehicle system simulation. In: Advanced Multibody System Dynamics (Vol. 1, pp. 367–372). Springer, Dordrecht. 21. Cutkosky, M. R., Tenenbaum, J. M., & Glicksman, J., (1996). Madefast: Collaborative engineering over the internet. Communications of the ACM, 39(9), 78–87. 22. Daberkow, A., & Kreuzer, E. J., (1999). An integrated approach for computer aided design in multibody system dynamics. Engineering with Computers, 15(2), 155–170. 23. David, O., Ascough, II. J. C., Lloyd, W., Green, T. R., Rojas, K. W., Leavesley, G. H., & Ahuja, L. R., (2013). A software engineering perspective on environmental modeling framework design: The object modeling system. Environmental Modeling & Software, 39, 201–213. 24. Devedžić, V., (1999). A survey of modern knowledge modeling techniques. Expert Systems with Applications, 17(4), 275–294.

80

Scientific Principles of Engineering

25. Dewey, A., Srinivasan, V., & Icoz, E., (1999). Towards a visual modeling approach to designing microelectromechanical system transducers. Journal of Micromechanics and Microengineering, 9(4), 332. 26. Diaz-Calderon, A., Paredis, C. J., & Khosla, P. K., (2000). Automatic generation of system-level dynamic equations for mechatronic systems. Computer-Aided Design, 32(5, 6), 339–354. 27. Diaz-Calderon, A., Paredis, C. J., & Khosla, P. K., (2000). Reconfigurable models: A modeling paradigm to support simulationbased design. In: Summer Computer Simulation Conference (Vol. 1, pp. 388–394). Society for Computer Simulation International. 28. Diaz-Calderon, A., Paredis, C. J., & Khosla, P., (1999). A Composable Simulation Environment for Mechatronic Systems, 1, 3–5. 29. Drexler, K. E., (2006). Toward integrated nanosystems: Fundamental issues in design and modeling. Journal of Computational and Theoretical Nanoscience, 3(1), 1–10. 30. Duckering, B. C., Cugini, U., & Wozny, M., (2000). Behavioral modeling technology: Leveraging engineering knowledge in CAD models. In: The Fourth IFIP Working Group 5.2 Workshop on Knowledge Intensive CAD (KIC-4) (Vol. 1, pp. 126–135). 31. Durfee, W. K., Wall, M. B., Rowell, D., & Abbott, F. K., (1991). Interactive software for dynamic system modeling using linear graphic. IEEE Control Systems Magazine, 11(4), 60–66. 32. Edström, K., (1998). Simulation of Newtons Pendulum using Switched Bond Graphs (Vol. 1, pp. 3, 4). Linköping University Electronic Press. 33. Elmqvist, H., Mattsson, S. E., & Otter, M., (1998). Modelica: The new object-oriented modeling language. In: 12th European Simulation Multiconference (Vol. 5, pp. 4–9) Manchester, UK. 34. Ferris, J. B., & Stein, J. L., (1994). Development of proper models of hybrid systems: A bond graph formulation. Simulation Series, 27, 43. 35. Field, F., Kirchain, R., & Roth, R., (2007). Process cost modeling: Strategic engineering and economic evaluation of materials technologies. JOM, 59(10), 21–32. 36. Finger, S., & Dixon, J. R., (1989). A review of research in mechanical engineering design. Part II: Representations, analysis, and design for the life cycle. Research in Engineering Design, 1(2), 121–137. 37. Finger, S., & Dixon, J. R., (1989). A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-

Scientific Design and Modeling in Engineering

38.

39. 40. 41.

42.

43. 44.

45.

46.

47.

48.

81

based models of design processes. Research in Engineering Design, 1(1), 51–67. Fishwick, P. A., (1997). Integrating continuous and discrete models with object oriented physical modeling. In: 1997 Western Simulation Multiconference (Vol. 1, pp. 3–8). Fishwick, P. A., (1998). A taxonomy for simulation modeling based on programming language principles. IIE Transactions, 30(9), 811–820. Franke, R., (1997). Object-oriented modeling of solar heating systems. Solar Energy, 60(3, 4), 171–180. Fritzson, P., & Engelson, V., (1998). Modelica—A unified objectoriented language for system modeling and simulation. In: European Conference on Object-Oriented Programming (Vol. 1, pp. 67–90). Springer, Berlin, Heidelberg. Gillespie, R. B., & Colgate, J. E., (1997). A survey of multibody dynamics for virtual environments. In: ASME International Mechanical Engineering Congress and Exposition (Vol. 18244, pp. 45–54). American Society of Mechanical Engineers. Glynn, P. W., (1989). A GSMP formalism for discrete event systems. Proceedings of the IEEE, 77(1), 14–23. Haber, R. B., & McNabb, D. A., (1990). Visualization idioms: A conceptual model for scientific visualization systems. Visualization in Scientific Computing, 1(74), 93. Hill, D. R., (2002). In: Zeigler, B. P., Praehofer, H., & Kim, T. G., (eds.), Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (Vol. 1, pp. 3–8). Academic Press, San Diego, CA. Hofmann, K., Lambert, J., Karam, J. M., Glesner, M., & Courtois, B., (1996). Generation of a behavioral model of an acceleration sensor from its finite-element-description. In: MECHATRONICS’96, (Vol. 1, pp. 3–10). Horvath, I., (2001). A contemporary survey of scientific research into engineering design. In: Proceedings of the International Conference on Engineering Design, ICED (Vol. 1, pp. 21–23). Hsieh, Y. W., & Levitan, S. P., (1998). Model abstraction for formal verification. In: Proceedings Design, Automation and Test in Europe (Vol. 1, pp. 140–147). IEEE.

82

Scientific Principles of Engineering

49. Iwasaki, Y., Farquhar, A., Fikes, R., & Rice, J., (1997). A web-based compositional modeling system for sharing of physical knowledge. In: IJCAI (1) (Vol. 1, pp. 494–500). 50. Kloas, M., Friesen, V., & Simons, M., (1995). Smile-A simulation environment for energy systems. Systems Analysis-ModellingSimulation, 18(1), 503–506. 51. Koenig, H. E., TOKAD, Y., & Kesavan, H. K., (1967). Analysis of Discrete Physical Systems, 1, 3–7. 52. Kübler, R., & Schiehlen, W., (1999). Modular modeling and simulation of multibody systems. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19777, pp. 387–397). American Society of Mechanical Engineers. 53. Le, T., Renner, F. M., & Glesner, M., (1997). Hardware in-the-loop simulation-a rapid prototyping approach for designing mechatronics systems. In: Proceedings 8th IEEE International Workshop on Rapid System Prototyping Shortening the Path from Specification to Prototype (Vol. 1, pp. 116–121). IEEE. 54. Leão, C., Machado, J., Seabra, E., Campos, J. C., & Soares, F., (2011). Safe Controllers Design for Industrial Automation Systems, 1, 3–11). 55. Lee, K., & Fishwick, P. A., (1997). Semiautomated method for dynamic model abstraction. In: Enabling Technology for Simulation Science (Vol. 3083, pp. 31–41). SPIE. 56. Li, Y., Huang, Z., Jiang, M., & Chang, T. W., (2016). The effect on pupils’ science performance and problem-solving ability through Lego: An engineering design-based modeling approach. Journal of Educational Technology & Society, 19(3), 143–156. 57. Lin, S. T., & Lin, J. H., (1998). Computer aided dynamic analysis and simulation of multibody mechanical systems in AutoCAD. Simulation, 71(5), 328–335. 58. Lückel, J., Junker, F., & Toepper, S., (1993). Block-oriented modelling of rigid multibody systems with regard to subsystem techniques. In: Advanced Multibody System Dynamics (Vol. 1, pp. 49–66). Springer, Dordrecht. 59. Lutz, R., Scrudder, R., & Graffagnini, J., (1998). High level architecture object model development and supporting tools. Simulation, 71(6), 401–409.

Scientific Design and Modeling in Engineering

83

60. Maffezzoni, C., & Girelli, R., (1998). MOSES: Modular modeling of physical systems in an object-oriented database. Mathematical and Computer Modelling of Dynamical Systems, 4(2), 121–147. 61. Mattsson, S. E., & Elmqvist, H., (1997). Modelica-An international effort to design the next generation modeling language. IFAC Proceedings Volumes, 30(4), 151–155. 62. Mattsson, S. E., Elmqvist, H., & Otter, M., (1998). Physical system modeling with modelica. Control Engineering Practice, 6(4), 501–510. 63. McPhee, J. J., (1996). On the use of linear graph theory in multibody system dynamics. Nonlinear Dynamics, 9(1), 73–90. 64. Modlo, Y., Semerikov, S., Bondarevskyi, S., Tolmachev, S., Markova, O., & Nechypurenko, P., (2019). Methods of Using Mobile Internet Devices in the Formation of the General Scientific Component of Bachelor in Electromechanics Competency in Modeling of Technical Objects, 1, 3–9). 65. Moody, D., (2009). The “physics” of notations: Toward a scientific basis for constructing visual notations in software engineering. IEEE Transactions on Software Engineering, 35(6), 756–779. 66. Morgan, A., (2009). Solving Polynomial Systems Using Continuation for Engineering and Scientific Problems (Vol. 1, pp. 3–8). Society for Industrial and Applied Mathematics. 67. Mukherjee, T., & Fedder, G. K., (1999). Hierarchical mixed-domain circuit simulation, synthesis and extraction methodology for MEMS. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 21(3), 233–249. 68. Murdock, J. W., Szykman, S., & Sriram, R. D., (1997). An information modeling framework to support design databases and repositories. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 80463, p. V004T31A042). American Society of Mechanical Engineers. 69. Murphy, C., Gardoni, P., & Harris, C. E., (2011). Classification and moral evaluation of uncertainties in engineering modeling. Science and Engineering Ethics, 17(3), 553–570. 70. Nilsson, B., & Eborn, J., (1998). Object-oriented modelling of thermal power plants. Mathematical and Computer Modeling of Dynamical Systems, 4(3), 207–218.

84

Scientific Principles of Engineering

71. Otter, M., Elmqvist, H., & Cellier, F. E., (1996). Modeling of multibody systems with the object-oriented modeling language dymola. Nonlinear Dynamics, 9(1), 91–112. 72. Ozawa, M., Biswas, G., & Zhu, L., (1999). Task distribution and lumped parameter modeling in multi-disciplinary product development. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19715, pp. 1259–1270). American Society of Mechanical Engineers. 73. Ozawa, M., Cutkosky, M. R., & Howley, B. J., (1998). Model sharing among agents in a concurrent product development team. In: Workshop on Knowledge Intensive CAD (Vol. 1, pp. 143–165). Springer, Boston, MA. 74. Palermo, A., Pampanin, S., & Marriott, D., (2007). Design, modeling, and experimental response of seismic resistant bridge piers with posttensioned dissipating connections. Journal of Structural Engineering, 133(11), 1648–1661. 75. Park, H. C., & Kim, T. G., (1998). A relational algebraic framework for VHDL models management. Transactions of the Society for Computer Simulation, 15(2), 43–55. 76. Paynter, H. M., & Juarez, J. M., (1999). Thermodynamic treatment of tug-&-twist technology. 2. Thermodynamic twistor design. In: 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No. 99TH8399) (Vol. 1, pp. 826–829). IEEE. 77. Paynter, H. M., & Longoria, R. G., (1997). Two-port canonical bond graph models of lossy power transduction. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (Vol. 2, pp. 1533–1537). IEEE. 78. Piela, P. C., (1989). ASCEND: An Object-Oriented Computer Environment for Modeling and Analysis (Vol. 1, pp. 2–9). Carnegie Mellon University. 79. Piela, P. C., Epperly, T. G., Westerberg, K. M., & Westerberg, A. W., (1991). ASCEND: An object-oriented computer environment for modeling and analysis: The modeling language. Computers & Chemical Engineering, 15(1), 53–72. 80. Pirker-Fruhauf, A., Schonherr, K., Laroche, A., & Pelz, G., (2007). Worst-case modeling and simulation of an automotive throttle in VHDL-AMS. In: 2007 IEEE International Behavioral Modeling and Simulation Workshop (Vol. 1, pp. 148–152). IEEE.

Scientific Design and Modeling in Engineering

85

81. Pottmann, H., & Wallner, J., (2001). Computational Line Geometry (Vol. 1, pp. 3–8). Berlin-Heidelberg, New York: Springer. 82. Radmanović, M., & Mančić, D., (2004). Design and Modeling of the Power Ultrasonic Transducers (Vol. 1, pp. 3–8). NIŠ, Faculty of Electronic Engineering. 83. Rai, B., (2003). Molecular modeling and rational design of flotation reagents. International Journal of Mineral Processing, 72(1–4), 95– 110. 84. Rai, S., & Jackson, W. B., (1999). Collaborative design of modular electromechanical systems with distributed controls. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19739, pp. 369–374). American Society of Mechanical Engineers. 85. Reckhow, K. H., & Chapra, S. C., (1983). Engineering Approaches for Lake Management; Data Analysis and Empirical Modeling (Vol. 1, Pp. 340). Butterworth Publishers Boston. 86. Reed, M. B., (1961). Linear Graphs and Electrical Networks. AddisonWesley Publishing Company. 87. Richard, M. J., & Gosselin, C. M., (1993). A survey of simulation programs for the analysis of mechanical systems. Mathematics and Computers in Simulation, 35(2), 103–121. 88. Romanowicz, B. F., (1998). Methodology for the Modeling and Simulation of Microsystems (Vol. 2, pp. 4–9). Springer Science & Business Media. 89. Rosenberg, R. R., & Karnopp, D. C., (1983). Introduction to Physical System Dynamics (Vol. 1, pp. 3–9). McGraw-Hill, Inc. 90. Schiehlen, W., (1997). Multibody system dynamics: Roots and perspectives. Multibody System Dynamics, 1(2), 149–188. 91. Schlenoff, C. I., Ivester, R. W., Libes, D. E., Denno, P. O., & Szykman, S., (1998). An Analysis of Existing Ontological Systems for Applications in Manufacturing and Healthcare, 1, 3–7. 92. Schlenoff, C., Denno, P., Ivester, R., Szykman, S., & Libes, D., (1999). An analysis of existing ontological systems for applications in manufacturing. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19722, pp. 935–944). American Society of Mechanical Engineers.

86

Scientific Principles of Engineering

93. Seabra, E., & Machado, J. M., (2009). Teaching Kinematics and Dynamics of Multibody Mechanical Systems Using the Object-Oriented Language Modelica 1, 7–9. 94. Shabana, A. A., (1997). Flexible multibody dynamics: Review of past and recent developments. Multibody System Dynamics, 1(2), 189–222. 95. Shah, J. J., & Mäntylä, M., (1995). Parametric and Feature-Based CAD/CAM: Concepts, Techniques, and Applications (Vol. 1, pp. 5–15). John Wiley & Sons. 96. Shooter, S. B., Keirouz, W. T., Szykman, S., & Fenves, S. J., (2000). A model for the flow of design information in product development. Engineering with Computers, 16(3), 178–194. 97. Sinha, R., Paredis, C. J., & Khosla, P. K., (2000). Integration of mechanical CAD and behavioral modeling. In: Proceedings 2000 IEEE/ACM International Workshop on Behavioral Modeling and Simulation (Vol. 1, pp. 31–36). IEEE. 98. Sinha, R., Paredis, C. J., Gupta, S. K., & Khosla, P. K., (1998). Capturing articulation in assemblies from component geometry. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 80326, p. V002T02A027). American Society of Mechanical Engineers. 99. Sinha, R., Paredis, C. J., Liang, V. C., & Khosla, P. K., (2001). Modeling and simulation methods for design of engineering systems. J. Comput. Inf. Sci. Eng., 1(1), 84–91. 100. Sriram, D., Logcher, R. D., Groleau, N., & Cherneff, J., (1992). DICE: An object-oriented programming environment for cooperative engineering design. In: Artificial Intelligence in Engineering design (Vol. 1, pp. 303–366). Academic Press. 101. Stein, J. L., & Louca, L. S., (1994). A component-based modeling approach for system design: Theory and implementation. Simulation Series, 27, 109. 102. Susca, L., Mandorli, F., & Rizzi, C., (2000). How to represent “intelligent” components in a product model. In: Workshop on Knowledge Intensive CAD (Vol. 1, pp. 159–172). Springer, Boston, MA. 103. Sydow, A., (1982). Hierarchical concepts in modeling and simulation. Progress in Modeling and Simulation, 1, 3–9.

Scientific Design and Modeling in Engineering

87

104. Szykman, S., Senfaute, J., & Sriram, R. D., (1999). The use of XML for describing functions and taxonomies in computer-based design. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19722, pp. 945–955). American Society of Mechanical Engineers. 105. Szykman, S., Sriram, R. D., Bochenek, C., Racz, J. W., & Senfaute, J., (2000). Design repositories: Next-generation engineering design databases. IEEE Intelligent Systems, 15(3), 48–55. 106. Tiernego, M. J. L., & Bos, A. M., (1985). Modelling the dynamics and kinematics of mechanical systems with multibond graphs. Journal of the Franklin Institute, 319(1, 2), 37–50. 107. Trent, H. M., (1955). Isomorphisms between oriented linear graphs and lumped physical systems. The Journal of the Acoustical Society of America, 27(3), 500–527. 108. Tummescheit, H., & Eborn, J., (1998). Design of a thermohydraulic model library in Modelica. In: 12th European Simulation Multiconference (Vol. 1, pp. 5–7). 109. Von, S. R., (1999). Multibody System Simulation: Numerical Methods, Algorithms, and Software (Vol. 1, 7, pp. 3–9). Springer Science & Business Media. 110. Wang, X., Wang, H., & Wu, D., (2008). Interactive simulation of crawler crane’s lifting based on OpenGL. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 43277, pp. 1533–1540). 111. Wilson, N. M., Pinsky, P. M., & Dutton, R. W., (1999). Investigation of Tetrahedral Automatic Mesh Generation for Finite-Element Simulation of Micro-Electro-Mechanical Switches, 1, 3–9. 112. Wittenburg, J., & WOLZ, U., (1985). Mesa Verde-a Program for Nonlinear Articulated-Rigid-Body Dynamics (Vol. 1, No. 1, pp. 7–18). Robotersysteme. 113. Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J., (2018). Learning and teaching engineering design through modeling and simulation on a CAD platform. Computer Applications in Engineering Education, 26(4), 824–840. 114. Zeigler, B. P., (2014). Object-Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems (Vol. 1, pp. 6–9). Academic Press.

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Scientific Principles of Engineering

115. Zeigler, B. P., Luh, C. J., & Kim, T. G., (1991). Model base management for multifaceted systems. ACM Transactions on Modeling and Computer Simulation (TOMACS), 1(3), 195–218. 116. Zhang, G., & Zeigler, B. P., (1989). The system entity structure: Knowledge representation for simulation modeling and design. Artificial Intelligence, Simulation and Modeling, 1, 47–73. 117. Zhou, G., & Dowd, P., (2002). A method to include micromechanical components into the system level simulation. Sensors and Actuators A: Physical, 97(1), 386–397.

4

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SCIENTIFIC APPROACH ON ENGINEERING FAILURES

CONTENTS 4.1. Introduction....................................................................................... 90 4.2. The “Traditional Approach” to Failure................................................ 93 4.3. Basic Assumptions in Traditional Approach........................................ 97 4.4. Beyond the Traditional Approach..................................................... 100 4.5. One Customer to Many Stakeholders............................................... 106 4.6. A Scientific Definition of Failure...................................................... 110 4.7. The Scientific Approach to Action.................................................... 115 References.............................................................................................. 120

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4.1. INTRODUCTION Both the morality of engineering and the profession of engineering place a significant emphasis on the concept of failures. Failing engineering relics raises a series of moral considerations regarding the distribution of obligation, the reasonably foreseeable risks, the priority setting of safety, as well as a variety of other topics. This is true even in the case of the most inconsequential events, which only outcome in minor inconveniences. Concerns with the implementation of any new technologies stem from the fact that this may not always be possible to rule out completely the potential of designed artifacts failing to function properly (Affonso, 2013). Mostly on the engineering side of things, failures are undesirable consequences which should be battled using the most effective tools that are made available by technical knowledge and is also one of the primary sources of this same information (Petroski 1985, 2006). The analysis of accidents has been an extremely important contributor to the improved dependability and security of the aviation industry (Wood & Sweginnis, 2006). For example, the knowledge of material failure that was made possible as a result of lessons acquired from the accidents of two Comet jets that year 1954 was a significant step toward improving the design of aviation mainframes (Wanhill, 2003). In a manner analogous to that of aviation, various sectors of the economy and subfields of engineering have made significant headway by analyzing the factors that contribute to the occurrence of failures (Schlager, 1994). Computer models (Collins, 1993), Failure Modes as well as Effects And criticality analysis (Stamatis, 1995), as well as Fault Tree Analysis, are just a few examples of the types of tools that have made their way into the design phase in recent years, allowing for the comprehensive evaluation of possible failures becoming an essential component (Xing & Amari, 2008). In addition, the instinctive ideas of breakage and building collapse no longer constitute the whole of what is included by the notion of failure. For example, in the field of aerospace engineering, the concentration of studies has shifted away from merely concentrating on “technological aspects,” which have been the predominant concern in the early stages of aviation, and instead has shifted to addressing “human factors” and “organizational characteristics” to better ensure passenger safety (ICAO 2009). Simultaneously, engineers are attempting to remove themselves from the pejorative connotations associated with the idea of failing and come up with a less judgmental definition (Hendershot, 2012). It is made very

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plain by investigating organizations such as the Dutch Safety Board and the Australian Transport Safety Bureau, for example, that it is not within their “remit to attempt to prove the responsibility, culpability, or liability belonging to every party” (The Dutch Safety Board, n.d.). Rather, their job is to investigate what went wrong and what caused accidents so that they may eliminate the possibility of such situations in the future (Hendershot, 2010) (Figure 4.1).

Figure 4.1. Ways to blunder through the science-policy engagement process more wisely. https://link.springer.com/article/10.1007/s11027–021–09940-x.

It is important to note, even so, that despite improvements in understanding of failings and also the physical and institutional processes that influence them, and also rising attention to the larger implications of failings, there is not a corresponding improvement in the development of a truly united conceptual model that is got to share among some of the various disciplines (Edwards, 2011). A condition of mental and definitional segmentation has arisen, primarily along the lines of disciplinary boundaries,

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as a result of the pressing need to develop effective solutions to manage failure and the complexities of failure occurrences. Separate fields of study often place a greater emphasis on certain aspects and develop definitions that are specifically catered to a set of applications. As a consequence of this, the idea of failure may be understood in a variety of ways and could be defined in a variety of ways, even though the loss is a prevalent subject within engineering as well as regardless of the significance of the idea of failure (Eini et al., 2015). According to all these initiatives, an agreement, however incomplete, has emerged around the language issued in 1990 by the International Electrotechnical Commission (IEC) as well as later used by several foreign standards. A citation from the IEC description of failure may be found in so many technical books and publications that relate to loss in one form or the other (Shariff & Zaini, 2013): Failure is defined as the loss of a product’s capacity to carry out a task that was intended for it. This concept has the potential to be considered “the conventional definition of failures.” It has been demonstrated that engineers seem to be able to explain as breakdowns situations which do not best suit the above classic definition but besides the applicability of the term and its capacity to properly capture a broad variety of experiences. This is because the term has the opportunity to collect properly a broad variety of experiences (Qi et al., 2012). These troubling situations lend credence to the idea that some of the fundamental assumptions which underpin the conventional approach may not be supported at all. Therefore, the purpose of this work is to do a deep examination of the conventional method and to investigate the potential of conceiving a more expansive concept that can cope with difficult circumstances. As a result of this, the purpose of this study is to provide the morality of engineering with more precise and up-to-date knowledge of failures in engineering, which will enhance its examination of duty, culpability, and hazards (Leong & Shariff, 2009). More precisely, it is claimed that a definition of failure inspired by the idea of the product cycle is just an appropriate response to this search (De Rademaeker et al., 2014). It is demonstrated that it is consistent with the collection of failures comprising the foundation of the conventional method; additionally, it can explain those occurrences and situations that, albeit in contradiction to standard assumptions, engineers are ready to recognize as

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cases of failures. The use of a life cycle perspective enables the introduction of such a clearer definition of failures and permits a rich categorization of failure phenomena that reflects the responsibilities of many stakeholders participating in the different phases of a production process (Srinivasan & Natarajan, 2012).

4.2. THE “TRADITIONAL APPROACH” TO FAILURE Engineering research provides a variety of explanations and character traits of failure. Tam and Gordon (2009) note that such “fluidity of the vocabulary and frequently conflicting shades of meanings contribute to confusion and misunderstanding” in a study on failing language for industrial capital management. A similar issue is brought up by Prasad et al., (1996) form of the perspective of reliable computing, wherein “various authors and standards utilize the language […] in a non-uniform manner” (Klein & Vaughen, 2017). Let’s examine two significant sources as a model for this absence of homogeneity: The IEEE Standard Thesaurus of Software Development Glossary (IEEE 1990) and Chapter 191 Reliability and Service quality of a Global Electrotechnical Vocabulary (IEC 50(191), 1990) were both produced either by Electrical and Electronic engineers Engineers. Failing and defect are distinguished in the IEC terminology and thus are stated as follows (Mkpat et al., 2018): •

Failure: the loss of a product’s capacity to carry out a necessary function. • Fault: the condition of a thing that prevents it from carrying out a necessary function, barring situations when it can’t because of scheduled maintenance, insufficient resources, or another circumstance (Brown et al., 2021). A performing variable’s measured level over time is shown on a curve. The contains the following initially comply with the goal value before progressively starting to depart from it. Failing is the condition when the contains the following exceeds the permissible bounds, at which point the object seems to have been in a common fault. The illustration makes it obvious that the word “termination” in the IEC standard must not be understood as a complete absence of capacity to operate, but rather as the infringement of (Shariff et al., 2016) (Figure 4.2).

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Figure 4.2. The IEC vocabulary’s visual portrayal of the concepts of failures and fault. Source: semanticscholar.org/paper/Failure-of-Engineering-Artifacts%3A-ALife-Cycle-Frate/15d1602864f401684fc8b808cd279ff47e347613.

The degrees of acceptance. After a failure occurs, a product may still be able to fulfill the necessary function, although to an unsatisfactory degree (Knegtering & Pasman, 2009). Of course, just because an item is failing based on a chosen performance criteria does not indicate that it is failing regarding most of its operations. Even if the capacity to execute a secondary function has fallen below the acceptable thresholds, an object may still be able to fulfill its primary necessary function (Li et al., 2011). These events fall under the category of “partial failures” and are referred to as “small failures” inside the IEC lexicon. On the other side of the spectrum are “full” failures, which consist of the “total inability to execute all needed duties.” If the impacted functions are seen to be of considerable significance, then the incomplete failure is deemed to be a “significant” one (Wilkie & Morgan, 2009). It’s noteworthy to notice that problem gradations are not supported by the IEC terminology. This implies that the concept of defect is dichotomous for a given product and a particular activity: either as the item executes that particular (major or minor) feature within the limitations or it will not. In

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line with this, there are also no different levels for “capacity to execute.” Therefore, as long as efficiency is within acceptable bounds, an object is said to be in a working condition regardless of how near it is to breach the appropriate performance barrier (Stephanopoulos & Reklaitis, 2011). The phrase “progressive failure” is supposed to characterize the processes immediately before the failure occurrence, according to the IEC lexicon. The resulting occurrence is referred to as a “gradual failure” if indeed the difference between both the actual performance as well as the goal level develops gradually (for example, so because the object is gradually working out), whereas a “sudden failure” occurs when the difference suddenly exceeds the allowed limits (Jafari et al., 2018). The same is true of objects that serve many purposes. Imagine a product with performance measures that are on the verge of, but not yet beyond, the permissible limits. Then, despite a decrease in net efficiency, the object is still regarded as being in a functional condition. A partial failure is considered to have happened after at minimum a few of the performance characteristics crosses the border, at which time the faulty state would take over. More complete failures were likely to happen till the object is in a total failure condition if the equipment is not taken off the road and repaired (Tugnoli et al., 2012). As a result, the word “fault” has a narrower scope in IEC jargon than it does in everyday speech, where it might refer to faults, irregularities, or faults that decrease the quality and value of products and may even allow it to fulfill its intended purpose. The IEC terminology, on the other hand, mandates an essential connection between the concepts of fault and inability to fulfill one or more needed duties. Failure only happens when at most one performance metric has surpassed the permissible limits. The differences between goal level and current results might be significant and may impact numerous capabilities at once (Abidin et al., 2018). Apart from minor phrasing variations, it appears that the idea that the IEC glossary refers to as “defect” is referred to by the IEEE lexicon as “failure,” whereas the other phrase in the pair is handled somewhat variously by the two systems. Engineering jargon deviates from standards, which further confuses the issue. According to a statement in the IEEE dictionary, the word “fault” is “mainly utilized either by fault-tolerant discipline,” although “in ordinary language, the phrases “fault” and “bug” are being used to describe this concept.” The IEC also acknowledges that one’s definitions differ considerably from accepted use. The meanings of failings and mistakes are taken directly first from IEC terminology in the “Concepts and meanings” segment discussed in the following Analysis methods for system stability

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(IEC 60812, 2006), however, a notification is added stating that “for cultural reasons” these same terms would be used synonymously (Brown et al., 2020). Yellman (1999) recognizes in the conversation that consumer demands should come first, but he also points out a significant flaw in Chillarege’s description: when faced with such a product that manifestly needs to perform below preconceptions, engineers are willing to identify it as an inability, “whether or not those who [the customers] believe (correctly or incorrectly) their preconceptions have indeed been met” (Dimian et al., 2014). There seem to be two competing requirements in the technical research on failure. On only one side, there is indeed a push for uniformity and simplicity, while on the other, there is recognition of the complexity of the failure phenomenon, which defies easy categorization (Villa et al., 2016) (Figure 4.3).

Figure 4.3. Standardization and simplification principles. https://dentalblog.3m.com/dental/could-standardization-make-you-a-happierdentist/.

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As a consequence, there is a duality within the literature, with the most authorized and well-known term in the center and more specific explanations in the periphery. The IEC explanation, which is frequently referred to as “the classic definition of failures,” is in the middle of the stage (Blache and Shrivastava, 1994). Although there are many failure occurrences that such a definition could handle satisfactorily, the development of modifications and variants demonstrates there are certain situations that engineers are ready to label failures but those are difficult to include by the conventional definition (Wilson & Schwarzman, 2009).

4.3. BASIC ASSUMPTIONS IN TRADITIONAL APPROACH Four fundamental presumptions capability, usage context, identification mechanism, and negative assumptions—can be used to assess the conventional approach to failures. Whenever an event meets all four presumptions, it is considered a failure in this method (Baybutt, 2016). The idea of such an item’s purpose in the context of the conventional method is highlighted by the presumption of lacking capability. The key point is that although things may act in a variety of abnormal ways and might even depart from the requirements in a variety of ways, a failure is only considered to have occurred when the execution of a necessary function is discontinued, or when it exceeds the allowable bounds (Turton et al., 2008). The article by Rausand and Ien (1996) may help clarify what function means in the context of the conventional approach. They support the way of expressing item operations using a verb-noun combination (such as the “transmitter side”) rather than the IEC language, which expresses the link among outputs and inputs of streams of power, material, and information (Pahl et al., 2007; Stone and Wood, 2000). Functions may then be categorized into several groups, such as fundamental, supplementary, preventive, and so forth (Planas et al., 2014). Even though Rausand and I need not specifically refer to the class of necessary function, they assert that important functions were those that were “required to perform the original goal of the object” and are characterized as “the grounds for putting the item.” Additionally, the popular ones of the objects directly reflect the needed or fundamental activities; for example, the purpose of pumping is “to circulate a fluid.” Last but not least, they demand that processes of products can be broken down into sub and coordinated into properly functioning hierarchical structures

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symbolized by these so “typical block graphs,” which are explained, among many other sources, in Pahl et al., (2007) and advised by a range of technical benchmarks like IEC 60812 (IEC 2006) andMIL-STD-1629A. (1980) (Lakhe et al., 2019). Performance measures, goal levels, and permissible limitations must be established for all activities to recognize whether such a breakdown has happened. Flow and pressure percentage are common performance different factors for just a product like a compressor. As a result, a pump is considered to have had a “gradual significant failure” if its output pressure progressively went below the permitted level. A “steady minor failure” is what is referred to it as the gradual decline of a sub-function which does not affect the primary function (Anastas & Eghbali, 2010). The use of contextual assumption comes in second. Consumption is the period of a product’s existence when it performs the needed function and satisfies the demands of the user. Usage often starts when the product is set up and placed into use. For example, a pump could be built in a chemical factory to move a fluid, but for whatever reason, it ceases working. In the edition of an IEC definition provided by Features typically (2002), this condition is highlighted and rendered more obvious (Jiménez-González et al., 2011): Exploitation episodes may also happen in a different context, particularly during testing, even if the exploitation stage is the natural setting in which things are supposed to execute their needed duties. Tests are a way of vicariously using the system that they symbolize. To “test full technical systems under settings as near as feasible to real field circumstances,” certifying tests, such as those performed upon aircraft engines or mainframe computers, are quite rigorous (Sims, 1999; Yellman, 1999). After the end of the production operations or after scheduled maintenance, relatively small and much less demanding testing is often conducted. Although the customers whose demands are expected to be satisfied by the execution of both the item’s functionalities are not present, they play a part in the testing process via the employment of tools and certified individuals who serve as surrogate users. Tests may thus be considered as components of a broadly interpreted usage context that includes both the use phase and testing in terms of failure (Rogers & Jensen, 2019). The accurate inventory assumption comes in third. When examining the conventional method as reflected by the whole collection of IEC language definitions, it becomes clear that it is predicated on the notion that

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engineered products are tangible objects whose behavior can be observed and quantified (Paltrinieri et al., 2014). As was previously mentioned, failure determinations are founded on comparisons between actual physical parameters of a particular item (such as hydrostatic flow and fluid velocity of a particular pump) and the goal values and allowable levels specified by the specification. In truth, as users, we rely on them again and are surrounded by a wide variety of physical objects’ capacity to fulfill our requirements by actually carrying out their intended duties. For example, automobiles get us about, clocks keep time, printers create paper, etc. Many more of our judgments refer to the characteristics and actions of these everyday objects (Koc et al., 2012). In addition to these decisions based on such a product’s characteristics at the physically component level, engineers, and laypeople often reach decisions based on characteristics at the category level. For illustration, dependability is a quality that may be based on both a particular product and a class of products (Xing & Amari, 2008). The key distinction between the two types of judgments is that the information which can be used to establish a judgment at the attribute level may not be appropriate or enough to support a conclusion at the code level. Because of this, it is feasible to assert without disagreement that a particular Ford Fusion item has indeed been determined to be more trustworthy than a particular Volkswagen Golf just at the code level. For example, the Volkswagen Golf may be found to be more accurate than a separate but equivalent category, the Ford Focus (Paltrinieri et al., 2013). Chemical engineers have developed criteria for evaluating the category safety features of processing facilities. Chemical engineers use the idea of intrinsically safer design when making these kinds of decisions (American Institution of Chemical Engineers, 2009; Kletz, 1998). Similarly, the conclusion that a system is an inherent safety refers to a feature or set of attributes that are not completely stated just at the level of an individual object. Conversely, it is an assessment of the properties that all objects created following the same design share (Rocha-Valadez et al., 2014). Last but not least, there would be the negative assumption, which holds that failure occurrences are undesirable and need to be avoided. The idea behind this presumption is that mistakes should be avoided regardless of their results. Fortunately, only a small percentage of failures have catastrophic repercussions, while the great majority just create minor inconveniences. However, engineers are prepared to label a subpar product as just a failure

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not just whether consumers complain about that now, as shown by Yellman’s (1999) critique of Chillarege’s (1996) definition (Centi & Perathoner, 2009).

4.4. BEYOND THE TRADITIONAL APPROACH Along with its evident conformity with such a sizable class of occurrences that are significant in practical application, the failure perspective expressed by the IEC concept and various definitions founded on it has attained a prominent standing. A significant amount of engineering work is done to prevent items inside the field from ceasing to perform necessary tasks throughout the design, testing, manufacture, servicing, and other stages. Engineers are inclined to classify as failures occurrences which do not fit this understanding, that is, occurrences which do not meet the four fundamental assumptions, as evidenced by the variety of available interpretations and other information in the research (Landucci et al., 2017). Negativity is the single presumption that almost all designers will not find problematic. They may argue about what constitutes a mistake or perhaps a class of failures, and they will both accept because if it is a failure, it should’ve been prevented. The very first three premises may well not stand in certain situations that are pertinent to practical application, as will be seen in the next paragraphs of this chapter (Biswas et al., 2021).

4.4.1. Missing Functionality Assumption According to the conventional approach, the concept of failings is binary. Given a particular object and a particular (major or minor) feature, the object is either operating but is not, guess it depends upon whether normally involves lies inside or outside the reasonable parameters. This is demonstrated in “Basic Presumptions of a Conventional Methodology.” There is nothing between such a basic object performing its one needed function and total failure for simple objects (Koc et al., 2013). The factors are considered to be in a condition of full failure if the contains the following exceeds the permissible bounds. Items that fulfill many needed tasks, however, may have partial problems. Based on how important the impacted function is, these failures may be small or substantial (Misra, 2008). In any case, the distinction between a functional state and a problem state for just a particularly necessary function (minor or major) is simple and also is based just on whether the contains the following is within or outside of the permitted bounds (Tugnoli et al., 2012).

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Engineers, on the other hand, seem to adopt a more sophisticated concept of failure, one which considers both the residual functionality of the object and the pace with which the contains the following is nearing the permissible bounds. In particular, Lewis et al., (2003, p. 27) provide the exact description: A product fails when it is unable to achieve or sustain the efficiency or strength requirements in the applications for which it was intended (Lewis et al., 2003). Since its performance is declining considerably more quickly than expected, or in other words, it becomes unable “to continue to ensure,” the anticipated efficiency, an object having contains the following is still well within the reasonable parameters that may be labeled while being in a fault condition and taken out of service. If the identical circumstance were evaluated using the conventional methodology, the conclusion would be the exact opposite since the decision would be made on the basis that now the object is still working in line with the requirements (Jung et al., 2010). A case study by Gagg and Lewis (2007) involving the collapse of a swivel bridge serves as an illustration of these various outcomes (Gagg & Lewis, 2007) (Figure 4.4).

Figure 4.4. Bearing on empty axle end (a) has a tight fit, while the loaded sector (b) of the failed swing bridge has significant wear. Source: https://sci-hub.hkvisa.net/10.1016/j.engfailanal.2006.11.064.

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The bridge was supported by three metal caster legs, each of which rotated inside two bearings bushes composed of Oilon, a self-lubricating polymer substance, and ran through steel tracks set in stone. During its six months of use, the bridge seemed to be performing as intended. The initial regular check was the first time wear just on shafts among all three caster legs was discovered (Xue et al., 2021) (Figure 4.5).

Figure 4.5. Diagram demonstrating the maximum permissible bearing pressure and surface velocity. Source: https://sci-hub.hkvisa.net/10.1016/j.engfailanal.2006.11.064.

Gagg and Lewis focus on three castors as that of the “failing axle bearings arrangement” even though now the bridges were operating as anticipated before the inspections (p. 1633). In actuality, the technologies that provide support were classified as defective parts but were taken out of service after a thorough failure assessment (Wood & Sweginnis, 1995). In the end, it was found that Oilon would not be a good material for making the rollers but that the simple fix for this “fault” was to replace Oilon for Nylube, a quality of material which was far better equipped to sustain the load requirements faced by this swinging bridges castor, p. 1634 (Álvarez-Chávez et al., 2012).

4.4.2. Utilization Context Assumption Engineers are normally primarily concerned with failures that occur during use, but they are conscious that damage is possible well before the goods enter use and begin performing the needed tasks. An instance of “catastrophic failures during manufacture” (p. 35) involving bimetal

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bushings for automobile use was looked at by Sudhakar and Paredes in 2005. Just after the sintering temperature, where a coating of the copper alloy was being welded to a steel substrate, several bearings in a production facility were discovered to be fractured (Pasman et al., 2013). According to the research, the incorrect heating conditions for the sintering temperature were to blame for the structural failure. Although Sudhakar and Paredes stated that now the bearings had “catastrophic failures during manufacture” and set out to determine the “failure causes,” it is obvious that the conventional “termination of a needed function” would not be the criteria chosen by them (Erythropel et al., 2018).

4.4.3. Item Level Assumption The conventional method bases the concept of failures on the premise that engineered products are physical objects with observable, measurable features and behaviors (Liew et al., 2014). Engineers, on the other hand, are accustomed to thinking in terms of object kinds’ characteristics, which do not apply to specific items. Take the idea of field returns, which is used in the automobile sector, as an example (Womack et al., 2007). After just a service professional has identified a defect that is protected either by the manufacturer’s warranty, the equipment is delivered to that same manufacturer and is known as a fields return. In just this view, a field’s returns are a tangible object that has been unable to carry out its intended purpose. Engineers, on the other hand, extend this feature to the whole kind of an item, where at the point that will be the frequency of field responses and, as being such, doesn’t specifically belongs to any object but rather to the whole type (Gangadharan et al., 2012). The type-level attribute may be employed in engineering decisions about failure, much as its equivalent just at a component level. Ford developed all these so Thick Film Ignition Modules there in the early 1980s to outperform the dependability of Japanese automobiles that were encroaching on the Us marketplace. This objective was turned into such a condition that now the frequency of field returns shall not exceed 1.6 responses out of every 100 deployed modules (Pecht, 2006; Qiet et al., 2008). The igniting modules proved to be a significant failing after several months, with field returns substantially exceeding predictions, prompting Ford to choose to come up with a fix. Numerous factors, such as manufacturing flaws, assembling issues, and improper maintenance, contributed to the various failures (Wanhill, 2002). Unfortunately, the underestimating of the temperature present in

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the environmental conditions, which would be to explain, the engine bay in which the modules were mounted, was the primary cause of the wave of field return. The modules reacted irregularly due to their inability to tolerate the heat loads, which “may be the most pervasive periodic failure situation yet observed” (Qi et al., 2008). Indeed, several individual components did function well; they were the ones that were put in automobiles that were driven in cooler climates. The key issue is that the Thick Film Ignition modules, as just a type, would be unable to produce the anticipated velocity of field outputs (Hassim et al., 2012) (Figure 4.6).

Figure 4.6. Module for thick film ignition. http://www.ozfalcon.com.au/index.php?/topic/8765-ford-tfi-module-diagnostics-and-troubleshooting/.

This sort of failure judgments, which are dependent on category qualities resulting from the interplay of several parts of a new product and growth stage, are not very well suited for the conventional perspective. Particularly for complex goods, such characteristics as intrinsic safety, durability, or longevity, cannot simply be limited to a single functionality or small combination of features defined as inputs or outputs within the functional organization of a product. However, type-level qualities are essential for the

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creation of successful goods and are significant in defining the needs of the product (Grossmann, 2004). There is indeed a lot of discussion in the world of engineering processes about the difference between practical and non-demands. Since a need is defined as “a declaration that defines a product or a service operating, functioning, or design feature or restriction,” the IEEE 1220 (2005) standards deliberately avoid mentioning the concept of non-functional requirements. The term “designed feature” itself, however, refers to a broad range of “efficiency, usefulness, protection, ease of maintenance, and a plethora of other traits” (Hull et al., 2010). It is important to note that the case made in this work for a larger definition of failing doesn’t rely on a consensus on how to distinguish between specific and non-needs. The conventional idea of failure has to be acknowledged since it, among many other things, relies on the idea that the functionalities of industrial applications must be defined via input-output relationships of energy, matter, and information streams that may be arranged in functional decomposition diagrams. Numerous design methodologists, such as (Pahl et al., 2007; Stone & Wood, 2000), in assistance of the advanced engineering work, including in evaluating, disseminating, and preserving innovative solutions, have persuasively addressed the multiple advantages afforded by this approach. However, it’s never been emphasized that even to realize these advantages, this approach must include all pertinent aspects of consumer durables. On the other hand, it seems to sense that a too-broad definition of functionality would work against the advantages already indicated (Saavalainen et al., 2015). The major aim of this section, therefore, has become to demonstrate that objects might have properties that engineers consider important for failure assessment and that are difficult to describe in terms of their functionality and operational hierarchy (Del Frate, 2013). A more effective strategy to resolve this concept problem is to develop a wider understanding of failures since scientists often make these types of judgments and the old approach seems to have trouble managing them. The New Concept of Failing, which also explores how well the new thought ties towards the life cycle view of product innovation, will present the new idea of failure. Before doing that, the next part will look more closely at the theoretical underpinnings of the conventional approach to failures and how it relates to the process model for product design (Landucci & Paltrinieri, 2016).

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4.5. ONE CUSTOMER TO MANY STAKEHOLDERS The existence of difficult examples does not mean that the conventional method is unjustified since it does reflect many other types of failure occurrences that engineers are aware pose a danger to the viability of respective products. Additionally, it aligns nicely with finished’ common intuitions. The reason why and how the troublesome circumstances weren’t included in the major failure occurrences is the next obvious issue (Khalil et al., 2012). However, it must be emphasized that the identifying of both the finished and the consumer might not even hold for product features that are somehow not useful. The accompanying specific example will help to demonstrate this notion. Earlier in the 1920s, when residential refrigerators were still being developed, General Dynamics technicians had to determine how to handle the issue of chilling the compressors, or the part that circulates the refrigerant (Cowan, 1985; Tam & Gordon, 2009). They proposed two options: air conditioning or cooling systems. The needed functionality of a fridge, which is to keep food products fresh, was deemed to be accomplished by both technologies equally well. Energy usage was the primary difference between the two strategies. The estimates indicate that “the electrical energy bill of the cooling system machine would’ve been around $ 1.30 higher than that of the liquid-cooled unit in six months.” (Syan & Menon, 2012). The forced air technique was chosen since electrical utility companies were Consolidated Electric’s most significant clients. Of course, there are still no norms or laws requiring energy efficiency at the time to stop General Electric from making its choice. However, it is plausible to suppose that, given all other factors being equal, a corporation would emphasize certain non-functional qualities that are more advantageous to its clients even in more strictly controlled marketplaces (Rangaiah et al., 2020). Even though engineers and designers still largely agree on this explanation of the stated mission, both these explanations have been put forth that assess the needs (both system and non) of numerous stakeholders implicated with in life cycle of products, leading to more liberal views on failings (Andreasen & Hein, 2000).

4.5.1. The Sequential Model of Product Development It is important to note that the focus on the finished is not peculiar or isolated from reality; rather, it may be seen as a component of the intellectual

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underpinnings of the so-called “linked list” of product design (Kahn, 2005; Yang, 2007) (Figure 4.7).

Figure 4.7. A methodical approach to product development. Source: https://www.researchgate.net/figure/A-sequential-product-development-process_fig1_228813862.

This paradigm divides the overall look effort into smaller tasks that are completed by several divisions in a certain order. The specifics may differ, although overall, the process begins with the identifying of several necessities which the commodity is assumed to discuss (Suess, 1992). The necessities then are transformed into necessities, although from the specifications, a concept design arises which is successively refined until everything is accepted for manufacturing. Route choices made upstream get the impact of freezing the key elements of both the final design while also establishing the parameters or framework that downstream divisions must operate inside to complete their jobs and move the process along (Del Frate, 2013). The methodology also serves as a benchmark for determining what types of practices can help constitute successes or failures. The approach thus encourages the acceptance of a concept of failures that takes the fulfillment of needed activities as the primary criteria since it depends just on a list of functionality arising from the study of a user’s demands (Barella et al., 2011).

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Up until the 1970s, the sequence served as the industrial standard for product development. However, this paradigm began to change even as the industry began to adopt new models predicated just on the advancement of cross-disciplinary assimilation, which was crucial to the business potential of Japanese manufacturers, especially in the automotive sector (Sudhakar & Paredes, 2005). Again for the creation of matured goods today, when businesses already have thoroughly investigated a variety of various models over a range of product generations, the systematic approach may still be useful (Liker et al., 1996). As a result, subsequent departments have access to a wide range of options that can be integrated into practically any structure. The expenses of significant adjustments may grow dramatically as the product design moves onto the later stages, which is one of the sequencing model’s key drawbacks. Major adjustments need a foundation redesign (Stone & Wood, 1999). All subsequent choices must be verified once again to ensure that they are consistent with the amended structure when a sub-task could be finished near the final step, for example, closer to a manufacturing step. Delays are likely to occur though in the ideal scenario when the majority of downstream choices may be preserved without risk. Levin and Kalal (2003) projected that “the cost to resolve a dependability issue grows by magnitude within every succeeding step,” which serves as an example of the scope of such risks (Zakocs et al., 2015). Alternative approaches may enable a variety of advantages, including more durable designs, quicker development timelines, and investigation of a wider variety of design possibilities, in addition to reducing the cost of critical stage design adjustments, according to studies on design approaches (Beatriz Machado et al., 2020). Due to these factors, an increasing number of engineering firms are adopting integrated methods for product innovation, following the lead of Japanese automakers including Toyota (Womack et al., 2007). Numerous research has shown support for this shift (McDonough, 2000). Griffin (1997) provides the findings of a five-year study project that demonstrates how interdisciplinary or multidisciplinary groups are becoming more and more important in the creation of new products. Whereas agreements used to be short-term, arm’s-length interactions, Helper, and Sako (1995) observe that “contract terms have progressively become long-term partnerships” (p. 77), allowing for improved information exchange and collaboration (Sims, 1999).

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4.5.2. Integrated Models Alternate models were proposed, such as system engineering, derived from the work and continuous product innovation (Andreasen & Hein, 1987), as well as the literature is thriving. These approaches share the idea of seeing the object and the activities occurring throughout the production cycle as an interconnected solution and emphasizing the interdependencies between elements and systems (Stamatis, 2003). The numerous organization to ensure availability may be considered as adaptations of a common life cycle viewpoint to product creation since the idea of product cycle provides a vital part in some way or another (Sherr, 2011). The objective of a life cycle approach, according to the most latest ISO/IEC benchmark System and Application Engineering (ISO/IEC 15288, 2008), is to provide a “popular framework for improving coordination and interaction among the stakeholders that generate, utilize, as well as maintain contemporary structure in order how they can collaborate in an incorporated, cohesive manner” (Mirošević, 2015) (Figure 4.8).

Figure 4.8. A comprehensive analysis of alliance failure. Source: https://www.researchgate.net/figure/An-Integrated-model-of-AllianceFailure_fig1_290593361.

The insight that now every system does have a life cycle and may be described as a life cycle made up of a series of phases, from idea development to retiring, is the cornerstone of both the life cycle viewpoint (Schlager, 1994). Based on the type of final product manufactured and the organizational structure, a different number and types of stages may be used. Although it

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is simple to deduce first from the model, a project life cycle is more than just a historical description of how a product’s components typically grow (Hanson, 2011). In contrast, it is a “judgment concept subdivision” that is used to describe and control technical and commercial choices and actions throughout a company unit. A product lifecycle model’s phases serve two major purposes: they organize similar technical and management tasks into a single group as well as provide a systematic perspective of the standards that products must meet to advance to the next stage (Mehta et al., 2017). From such a life cycle approach, the goal of engineering product design is wider in scope than the one envisaged either by the flowchart but there are various parties with varying—and occasionally competing—interests inside one or even more stages within the lifetime of a product, rather than the finished, who really should receive a product that performs optimally and consistently (Birolini, 2013). The methodology has an impact on both the idea of the popular product and also the idea of an unsuccessful product. In reality, a product must fulfill end-user demands by carrying out its necessary activities while in use to be entirely successful (Mode, 2002). Conversely, a technology that excels in the real world but falls way short of satisfying the needs of all stakeholders, such as the manufacturing unit, may have limited profitability and ultimately collapse. The realization that a life span is just an integrated whole where the requirements of several stakeholders must be addressed to prevent failure is exactly what distinguishes the life cycle approach to failures (Behrendt et al., 2012).

4.6. A SCIENTIFIC DEFINITION OF FAILURE The original definition has been attempted to be expanded on several occasions, although these efforts were only partially effective. The US Army Standards. Definition of the key terms concerning Reliability and Performance, for example, links the idea of failures to the product standards by completing the appropriate definition (Blache & Shrivastava, 1994): Failure When a product or a component of a product does not work as expected, it is said to be in an inoperable condition (Bradsher, 1997) (Figure 4.9).

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Figure 4.9. User experience criteria. Source: https://www.interaction-design.org/literature/topics/usability.

The issue with these concepts would be that product standards may end up being insufficient, resulting in goods that fail even if they adhere to the requirements. According to Groot Boerle (2002), an electric wheelchair operator was seriously hurt after losing control of a vehicle and driving off a train platform. The analysis determined that such limited electromagnetic radiation operating at such a wavelength of 1.89 GHz, one of the frequencies utilized by digital telecommunication systems, actuated the wheelchair (Bradsher D. & Bradsher K., 2002). The producer rejected culpability during the subsequent trial, arguing that perhaps the chair complied with the product requirements, which required the electrical grid to be shielded against electromagnetic energy of approximately 1 GHz. The court disagreed with just this line of thinking, stating that a wheelchair’s requirements must take into consideration the likelihood of interfering with telecommunication systems if it is to be used in public areas (Bradsher, 2000). The updated definition’s goal is to accurately reflect how engineers now utilize the concept of failure. The earlier chapters have seen how this usage goes beyond what the conventional method can handle. Additionally, the growing scope of the idea of failure seems to be tied to the broader variety of problems addressed by life cycle methods to product planning (Rausand

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& Øien, 1996). To better understand the types of failure judgments and complications that may arise within genderfluid teams working on product design, a new term has been developed. The redefinition must, however, maintain backward compatibility and be able to confront failure judgments, which are the purview of the conventional method. The new idea has been given the name “product failure” to emphasize how it differs from the conventional concept, but it is stated as follows (Chillarege, 1996): Failure of a product refers to its inability to achieve the objectives set out by the design team under which it was produced (Iansiti & Clark, 1994). Although the concept of a product’s life cycle served as a major source of motivation for such dictionary amendment, the term “life cycle” is not used directly inside the definition’s language. The reason for this is that despite the life cycle perspective to product innovation, which has led to an increase in the use of multidisciplinary teams, it is doubtful that this method will take over as the only one used for product design (Collings, 2008; Prasad et al., 1996). The sequential approach is still important in the design of matured goods, as mentioned above (see “The Sequencing Model of Research And development”). Additionally, not every design team is always crossdisciplinary. Consequently, this has been chosen to refer to “design teams” to make the concept of the defective product as inclusive as feasible (Qi et al., 2008). The new description corresponds with the conventional strategy when used in the sequence model’s environment, where design engineers aim to create products that meet end-user demands by carrying out necessary functions. Design engineers become genderfluid when used from a life cycle approach, and their objectives are derived by integrating the demands and interests of many stakeholders (Kuwashima & Fujimoto, 2013). It is important to keep in mind that genderfluid teams may include distributors, consultants, and employees of the business that owns the products in addition to group members within the same firm. As a result, the set of objectives that the engineering team will choose to pursue may also be considered a synthesis of the objectives of the many firms or groups involved in the project. Therefore, “the aims of the engineering team” is a much more inclusive phrase than “the goals of the organization” (Collins, 1993). It is important to emphasize that every design team’s objectives must include meeting the demands of the end customer while carrying out the necessary duties. Therefore, the new definition agrees with the old definition except that a failed event occurs when a necessary function can no longer

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be performed. Additionally, it inherited the belief that failures must occur when acceptability constraints are violated for variables to diverge from goal values (MacKenzie & Wajcman, 1985). However, the redefinition covers more ground. The idea of a defective product may address the abovediscussed instances of incorrect criteria and specifications since it is focused on the objectives of the engineering team. When the electrical wheelchair is given as such an instance, it is clear that now the product failed since the creation of such an electric cart was primarily motivated by the need for a technology that could be utilized safely for transit on public highways (Carter & Norton, 2013). The product is inappropriate again for intended use because it is subject to contamination from communication systems. Similar to this, products may well be created that adhere to the specifications, but the specifications may not adequately describe the design objectives (such as Ford’s spark modules). The idea of defective products also applies in these circumstances (Dasgupta & Pecht, 1991). The new definition’s approaches to the conventional method and how it views its underlying assumptions—missing functioning, identification mechanism, usage context, and negative assumptions—are its most crucial features. It has been shown that both two techniques adopt various postures, apart from their consensus on the negative premise (Del Frate et al., 2011). It could be worthwhile to summarize the key phases in the argument produced so far before the study goes on to further the life cycle approach components. A conventional strategy may be found that can deal with a significant percentage of failure occurrences that concern engineers, even though the idea of failure is defined differently in engineering. The assumptions of the conventional method, however, often conflict with both engineering suppositions and linguistic conventions, as shown by several troubling situations that have been demonstrated (Gagg & Lewis, 2007; Pulselli et al., 2009). The conventional approach’s relationship to the linked list of product innovations, through which it has acquired a bias in favor of the end-user viewpoint, is the reason why these breaches cannot be accounted for. Appeared differently about complex systems of product design, which focus on the participation of several stakeholders in addition to the enduser and are founded on such a life cycle approach, is the one shown here. Using this life cycle approach, a definition of “device failure” has already been put forward. The novel idea can handle troublesome circumstances in addition to the failures handled by the conventional technique, it has been demonstrated (Erden et al., 2008).

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It would be worthwhile to pause for a moment and question if this study has proposed an unnecessary expansion of loss as a result of its search for the benefits of a bigger definition. More precisely, it may be argued that any justification for the process of design, or any explanation for a defective product, is perilously near to being equated with a design defect (Griffin, 1997; Petroski, 2018). Despite the similarity between the two ideas, it has been shown that these are still easily distinguished from one another. First, product problems may be handled without constantly changing the design. Apple said that the new iPhone 4 would’ve been offered in color combinations, black, and white, whenever the device was first revealed later in June 2010. The white model, unfortunately, was postponed without any more explanations, and it wasn’t until the end of July that Apple finally acknowledged that now the white handsets had proven to be “extra hard to build” than planned (Apple Inc., 2010). The corporation took great care not to divulge the specifics of the production difficulties. Technology experts speculated that the delay was caused by issues the manufacturer of both the white glass walls that make up for the front and rear panels of a device was having (Lai, 2010). Even though the provider was able to produce the prototype following the strict transparency and thicknesses standards, the production yield substantially decreased when scaling back to maximum scale projects, with just three panels satisfactorily constructed each hour. The issue wasn’t resolved until January 2011, when Apple discovered a new supplier that could match the requirements while producing a satisfactory yield (Boerle, 2002; Petroski, 1985). Just at the end of April 2011, the much anticipated white versions finally made it to the stores. Because the concept and indeed the technical specification was maintained as well as the new supplier demonstrated that they’ll be met, but no need for design iteration. However, the experts think that the product was a flop since many potential buyers put off their purchasing and instead acquired the next iPhone model, which was released in October 2011. (Sherr, 2011). This is not unexpected that Stamatis chooses to limit the critical framework to a minimum by only presenting his definitions without going into the reasons for his decision because he is creating a handbook for professionals. Furthermore, two significant hints are provided that draw attention to the similarities between his concept of failing and the one

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covered in this essay (Hoopes & Postrel, 1999). The definition’s short note that states that now the design purpose “typically originates from a study and an assessment of the requirements, wishes, or desires of the customers” provides the first hint (p. 74). The second hint is a line first from the book’s introduction that emphasizes the need of seeing the client as both a service process and the following or downstream activity rather than simply the end-user (Stamatis, 1995). Together, these two sentences enable Stamatis’ definition of failure to include the same assortment of failure verdicts that gave rise to the idea of defective products examined in this essay (Helper & Sako, 1995).

4.7. THE SCIENTIFIC APPROACH TO ACTION It should come as a shock, in light of the explanation above, that now the phrase “service life” does not exist among some of the vocabulary words established either by IEC (IEC 50(191), 1990). Instead, a few definitions of the reasons for failure are discovered to make an implicit connection toward the life cycle idea (Pecht, 2006). The conditions during design, production, or usage that resulted in such a failure are referred to as the failed cause. The IEC language analysis ends here; the product lifecycle phases and their relationships are not further discussed. The life cycle method then takes control at this point (Houkes & Vermaas, 2010) (Figure 4.10).

Figure 4.10. According to the conventional method, these failure paths are acceptable: Faults in design, manufacture, and use are shown by the arrows (A, B, and C, respectively). Source: https://www.proquest.com/docview/1418096587.

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Figure 4.11. Product failure trajectories that occur throughout the production process. Source: https://www.proquest.com/docview/1418096587.

Making visible the framework used by the IEC study will be the first stage, as shown in Figure 4.11. Next, failed paths that link the stage in which the causative factor sits with both the phase where the failing event is placed are used to illustrate the three conditions “that have resulted in a loss” (Iansiti, 1995). Because only a lack of consistent pathways can be depicted in Figure 4.11 due to the use of conceptual frameworks from the old method, all of which lead to the study of the project, the life cycle ends at this level. The life cycle method, however, is exempt from these restrictions (Pahl & Beitz, 1988). More complex life lifecycle modeling may be presented that goes beyond the usage phase since the demands of many other stakeholders than the finished are taken into consideration (for example, those such as individuals having interests in phases like installations, shipping, repair, recycling, etc) (see below) (Kletz & Amyotte, 2010). Failures before production may have just as much of an impact as failures while being used. In engineering, this is particularly true. The collapse of the Quebec Bridge in 1907, which was being built in Quebec City, Canada, is a noteworthy historical instance. Several design flaws, including the “use of weight fraction beams with just an ineffective layout of substance, analyzes utilizing output from most smaller chunks, insufficient fastening, [and] above that incomplete or inaccurate lateral load,” were found to be the cause of a collapse, according to the conducting an investigation commission (Masanet & Horvath, 2007). (2008) Collings as a consequence, the structure’s structural performance to support its very own load was compromised. Though not eliminated, the incidence of comparable failures has been significantly decreased by the advent of more potent modeling software and computational techniques (McDonough III, 2000). The Commission of

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Inquiry found that even a pier support’s insufficient design was a major contributing cause in the June 1970 collapse of a part of the Cleddau Bridge (Neyland, UK) under completion (Collings, 2008; Merrison, 1973). The drawback is that when the interior tensions between both the glass screen and also the central point are not evenly distributed, they may accumulate during the heating cycle and cause the glass to fracture (Marks, 1989). As a result, the necessity to regulate internal tensions places several geometrical restrictions just on the designer. If holes are required in the end product (for example, to place metal hinges on such a door frame), their minimum diameter must not result in a lower width of the glass, as well as the space between both the holes must be at least three times the width of something like the glass (Le Bourhis, 2008). A device that doesn’t adhere to these restrictions is certain to fail during manufacture due to poor design, an arrow (D). On the flip side, if indeed the internal tensions between both the glass screen and central point are not properly regulated, they may start building up during the heat treatment process and cause the glassware to shatter. Because of the necessity to balance internal tensions, the designer should be cognizant of several geometrical restrictions (Liker et al., 1996). If perforations are required in the finished product (for example, to place steel latches on such a glass window), their minimum diameter must not result in a lower width of both the glass and also the space between both the holes must be at least 40 per cent the width of the glass (Le Bourhis, 2008). Failure to adhere to these restrictions may produce good results that are destined to malfunction during manufacture due to poor design, an arrow (D) (Levin & Kalal, 2003) (Figure 4.12).

Figure 4.12. An electronic appliance’s retirement stage-related product failure trajectories. Source: https://link.springer.com/article/10.1007/s11948–012–9360–0.

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Failures of the product as a result of factors connected to the usage stage are shown by the arrow (H). A common instance is when consumers just dispose of items rather than bringing them to the proper service facilities, which results in the inability to meet established recycling targets. This is particularly likely for small equipment that is often thrown over with domestic waste, as shown by (Behrendt et al., 1997). Just direct failure trajectories—those that start in a certain stage, like design, but then lead straight to the failure—have been studied so far (e.g., utilization). Nevertheless, more complex situations that might entail several elements and intermediary phases often present themselves to engineers (Le Bourhis, 2008). Let’s use a failure situation that Barella et al., examined as just an example (2011). Six to eight months after manufacturing, when consumers began complaining that the products seemed to be tainted, a significant quantity of tuna fish in olive oil (1 million cans) broke at the usage stage. Reactions that were forming close to the can’s welding region were the contaminants. Usually, sheets of metal steel are joined together along one side to create tuna cans. After joining a polymer, take extra safety precautions (Latin & Kasolas, 2002). To sum up, there were two issues: poor welds and oil with such a jug of water inside it. Each circumstance by itself would not be able to result in the loss, but their combination dictated the unfavorable result. A life cycle prototype similar to that in Figure 4.13 divides the production of both the Tupperware containers into two steps, wherein the steel plates are converted into varnished cans, and arrangement, during which sardines and oil are beginning pouring into the cans—can be used to symbolize this failure scenario (Kortge & Okonkwo, 1989). Insufficient welding throughout manufacturing is where the inability to the direction of travel begins, an arrow (K). The faulty cans moved over to the such that, an arrow (L), when oil with just an exceptionally high moisture content was being used to replace them because, for reasons that are not clear, the quality testing was ineffective (M). The ultimate stage of a failing pathway resulting in the defective product even during the usage stage represents the combined influence of (L) and (M) (Karun et al., 2021).

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Figure 4.13. The life cycle model of tuna can failure is outlined in Barella et al., (2011). Source: https://link.springer.com/article/10.1007/s11948–012–9360–0.

To emphasize that the product failure occurs during this period, Figure 4.13 shows the usage phase as a darkened rectangle. Indeed, the tuna cans’ intention to provide the end customers with perfectly good food was not fulfilled. The technical and commercial group in charge of the operation, nevertheless, can conclude that the other objectives were also not met, making the production and design errors and flaws failures in itself themselves. Particularly the issue with the improperly soldered cans was likely to be seen as a production failure that can be fixed with corrective actions like greater training, better processes, etc. (Kletz, 1999).

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REFERENCES 1.

Abidin, M. Z., Rusli, R., Khan, F., & Shariff, A. M., (2018). Development of inherent safety benefits index to analyze the impact of inherent safety implementation. Process Safety and Environmental Protection, 117, 454–472. 2. Affonso, L. O. A., (2013). Machinery Failure Analysis Handbook: Sustain Your Operations and Maximize Uptime (Vol. 1, pp. 2–9). Elsevier. 3. Álvarez-Chávez, C. R., Edwards, S., Moure-Eraso, R., & Geiser, K., (2012). Sustainability of bio-based plastics: General comparative analysis and recommendations for improvement. Journal of Cleaner Production, 23(1), 47–56. 4. Anastas, P., & Eghbali, N., (2010). Green chemistry: Principles and practice. Chemical Society Reviews, 39(1), 301–312. 5. Andreasen, M. M., & Hein, L., (2000). Integrated Product Development, 1, 2–6. 6. Barella, S., Cincera, S., Boniardi, M., Bellogini, M., Gelati, S., & Montanari, A., (2011). Failure analysis of tuna cans. Journal of Failure Analysis and Prevention, 11(4), 446–451. 7. Baybutt, P., (2016). Insights into process safety incidents from an analysis of CSB investigations. Journal of Loss Prevention in the Process Industries, 43, 537–548. 8. Beatriz, M. A., Silva, F. J., Carlos Sá, J., Barreiras, A., Pinto, F. L., Pereira, M. T., & Santos, G., (2020). Improvement in external logistics of an automotive component manufacturing company towards costs reduction. International Journal for Quality Research, 14(4), 4. 9. Behrendt, S., Jasch, C., Peneda, M. C., & Van, W. H., (2012). Life Cycle Design: A Manual for Small and Medium-Sized Enterprises (Vol. 1, pp. 2–5). Springer Science & Business Media. 10. Birolini, A., (2013). Reliability Engineering: Theory and Practice (Vol. 1, pp. 3–9). Springer Science & Business Media. 11. Biswas, S. K., Mathur, U., & Hazra, S. K., (2021). Fundamentals of Process Safety Engineering (Vol. 1, pp. 2–9). CRC Press. 12. Blache, K. M., & Shrivastava, A. B., (1994). Defining failure of manufacturing machinery and equipment. In: Proceedings of Annual Reliability and Maintainability Symposium (RAMS) (Vol. 1, pp. 69– 75). IEEE.

Scientific Approach on Engineering Failures

121

13. Boerle, D. G., (2002). EMC and functional safety, impact of IEC 610001-2. In: 2002 IEEE International Symposium on Electromagnetic Compatibility (Vol. 1, pp. 353–358). IEEE. 14. Bradsher, D., & Bradsher, K., (2002). High and Mighty: SUVs-the World’s Most Dangerous Vehicles and How They Got That Way (Vol. 1, pp. 6–9). Public Affairs. 15. Bradsher, K., (1997). Light Trucks, Heavy Risk: A Special Report (Vol. 1, p. 5). A deadly highway mismatch ignored. The New York Times A. 16. Bradsher, K., (2000). Study of Ford Explorer’s Design Reveals a Series of Compromises (Vol. 1, pp. 2–9). The New York Times. 17. Brown, K. R., Addo, A., Khan, F. I., & Amyotte, P. R., (2020). Inherently safer design principles in risk management. In: Methods in Chemical Process Safety (Vol. 4, pp. 379–440). Elsevier. 18. Brown, K. R., VanBerkel, P., Khan, F. I., & Amyotte, P. R., (2021). Application of bow tie analysis and inherently safer design to the novel coronavirus hazard. Process Safety and Environmental Protection, 152, 701–718. 19. Carter, C. B., & Norton, M. G., (2013). Glass and glass-ceramics. In: Ceramic Materials (Vol. 1, pp. 389–409). Springer, New York, NY. 20. Centi, G., & Perathoner, S., (2009). From green to sustainable industrial chemistry. Sustainable Industrial Chemistry, 1, 1–72. 21. Chillarege, R., (1996). What is software failure?. IEEE Transactions on Reliability, 45(3), 354. 22. Collings, D., (2008). Lessons from historical bridge failures. In: Proceedings of the Institution of Civil Engineers-Civil Engineering (Vol. 161, No. 6, pp. 20–27). Thomas Telford Ltd. 23. Collins, J. A., (1993). Failure of Materials in Mechanical Design: Analysis, Prediction, Prevention (Vol. 1, pp. 3–9). John Wiley & Sons. 24. Dasgupta, A., & Pecht, M., (1991). Material failure mechanisms and damage models. IEEE Transactions on Reliability, 40(5), 531–536. 25. De Rademaeker, E., Suter, G., Pasman, H. J., & Fabiano, B., (2014). A review of the past, present and future of the European loss prevention and safety promotion in the process industries. Process Safety and Environmental Protection, 92(4), 280–291. 26. Del Frate, L., (2013). Failure of engineering artifacts: A life cycle approach. Science and engineering Ethics, 19(3), 913–944.

122

Scientific Principles of Engineering

27. Del Frate, L., Franssen, M., & Vermaas, P. E., (2011). Towards a transdisciplinary concept of failure for integrated product development. International Journal of Product Development, 14(1–4), 72–95. 28. Dimian, A. C., Bildea, C. S., & Kiss, A. A., (2014). Integrated Design and Simulation of Chemical Processes (Vol. 1, pp. 2–9). Elsevier. 29. Edwards, V. H., (2011). Designing safer process plants: Several oftenoverlooked strategies to increase inherent safety are discussed here. Chemical Engineering, 118(4), 44–49. 30. Eini, S., Abdolhamidzadeh, B., Reniers, G., & Rashtchian, D., (2015). Optimization procedure to select an inherently safer design scheme. Process Safety and Environmental Protection, 93, 89–98. 31. Erden, M. S., Komoto, H., Van, B. T. J., D’Amelio, V., Echavarria, E., & Tomiyama, T., (2008). A review of function modeling: Approaches and applications. AI Edam, 22(2), 147–169. 32. Erythropel, H. C., Zimmerman, J. B., De Winter, T. M., Petitjean, L., Melnikov, F., Lam, C. H., & Anastas, P. T., (2018). The green chemisTREE: 20 years after taking root with the 12 principles. Green Chemistry, 20(9), 1929–1961. 33. Gagg, C. R., & Lewis, P. R., (2007). Wear as a product failure mechanism–overview and case studies. Engineering Failure Analysis, 14(8), 1618–1640. 34. Gangadharan, P., Zanwar, A., Zheng, K., Gossage, J., & Lou, H. H., (2012). Sustainability assessment of polygeneration processes based on syngas derived from coal and natural gas. Computers & Chemical Engineering, 39, 105–117. 35. Griffin, A., (1997). PDMA research on new product development practices: Updating trends and benchmarking best practices. Journal of Product Innovation Management: An International Publication of The Product Development & Management Association, 14(6), 429–458. 36. Grossmann, I. E., (2004). Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment. Computers & Chemical Engineering, 29(1), 29–39. 37. Hanson, R. G., (2011). Using Six Sigma Methodologies to Improve Design and Detailing (Vol. 1, pp. 4–7). 38. Hassim, M. H., Hurme, M., Amyotte, P. R., & Khan, F. I., (2012). Fugitive emissions in chemical processes: The assessment and

Scientific Approach on Engineering Failures

39.

40. 41. 42.

43.

44.

45. 46.

47.

48.

49.

123

prevention based on inherent and add-on approaches. Journal of Loss Prevention in the Process Industries, 25(5), 820–829. Helper, S. R., & Sako, M., (1995). Supplier relations in Japan and the United States: Are they converging?. MIT Sloan Management Review, 36(3), 77. Hendershot, D. C., (2010). A summary of inherently safer technology. Process Safety Progress, 29(4), 389–392. Hendershot, D. C., (2012). Inherently safer design: The fundamentals. Chemical Engineering Progress, 108(1), 40–42. Hoopes, D. G., & Postrel, S., (1999). Shared knowledge, “glitches,” and product development performance. Strategic Management Journal, 20(9), 837–865. Houkes, W., & Vermaas, P. E., (2010). Technical Functions: On the Use and Design of Artefacts (Vol. 1, pp. 4–8). Springer Science & Business Media. Iansiti, M., & Clark, K. B., (1994). Integration and dynamic capability: Evidence from product development in automobiles and mainframe computers. Industrial and Corporate Change, 3(3), 557–605. Iansiti, M., (1995). Technology integration: Managing technological evolution in a complex environment. Research Policy, 24(4), 521–542. Jafari, M. J., Mohammadi, H., Reniers, G., Pouyakian, M., Nourai, F., Torabi, S. A., & Miandashti, M. R., (2018). Exploring inherent process safety indicators and approaches for their estimation: A systematic review. Journal of Loss Prevention in the Process Industries, 52, 66– 80. Jiménez-González, C., Poechlauer, P., Broxterman, Q. B., Yang, B. S., Am Ende, D., Baird, J., & Manley, J., (2011). Key green engineering research areas for sustainable manufacturing: A perspective from pharmaceutical and fine chemicals manufacturers. Organic Process Research & Development, 15(4), 900–911. Jung, S., Ng, D., Lee, J. H., Vazquez-Roman, R., & Mannan, M. S., (2010). An approach for risk reduction (methodology) based on optimizing the facility layout and siting in toxic gas release scenarios. Journal of Loss Prevention in the Process Industries, 23(1), 139–148. Karun, B., Renjith, V. R., & Elayidom, S., (2021). Inherently safer design approaches and improvement strategies in process industries.

124

50.

51.

52. 53. 54. 55.

56.

57.

58.

59.

60.

Scientific Principles of Engineering

In: 2021 8th International Conference on Smart Computing and Communications (ICSCC) (Vol. 1, pp. 94–99). IEEE. Khalil, M., Abdou, M. A., Mansour, M. S., Farag, H. A., & Ossman, M. E., (2012). A cascaded fuzzy-LOPA risk assessment model applied in natural gas industry. Journal of Loss Prevention in the Process Industries, 25(6), 877–882. Khan, F., Rathnayaka, S., & Ahmed, S., (2015). Methods and models in process safety and risk management: Past, present and future. Process Safety and environmental protection, 98, 116–147. Klein, J. A., & Vaughen, B. K., (2017). Process Safety: Key Concepts and Practical Approaches (Vol. 1, pp. 2–9). CRC Press. Kletz, T. A., & Amyotte, P., (2010). Process Plants: A Handbook for Inherently Safer Design (Vol. 1, pp. 6–9). CRC Press. Kletz, T. A., (1999). The constraints on inherently safer design and other innovations. Process Safety Progress, 18(1), 64–69. Knegtering, B., & Pasman, H. J., (2009). Safety of the process industries in the 21st century: A changing need of process safety management for a changing industry. Journal of Loss Prevention in the Process Industries, 22(2), 162–168. Koc, R., Kazantzis, N. K., Nuttall, W. J., & Ma, Y. H., (2012). Economic assessment of inherently safe membrane reactor technology options integrated into IGCC power plants. Process Safety and Environmental Protection, 90(5), 436–450. Koc, R., Kazantzis, N. K., Nuttall, W. J., & Ma, Y. H., (2013). An economic evaluation framework for membrane reactor modules in the presence of uncertainty: The case for process safety investment and risk reduction. Journal of Loss Prevention in the Process Industries, 26(3), 468–477. Kortge, G. D., & Okonkwo, P. A., (1989). Simultaneous new product development: Reducing the new product failure rate. Industrial Marketing Management, 18(4), 301–306. Kuwashima, K., & Fujimoto, T., (2013). Performance measurement in product development research a literature review. Annals of Business Administrative Science, 12(4), 213–223. Lakhe, P., Prehn, E. M., Habib, T., Lutkenhaus, J. L., Radovic, M., Mannan, M. S., & Green, M. J., (2019). Process safety analysis for

Scientific Approach on Engineering Failures

61.

62.

63.

64.

65.

66. 67. 68.

69.

70.

71.

125

Ti3C2Tx MXene synthesis and processing. Industrial & Engineering Chemistry Research, 58(4), 1570–1579. Landucci, G., & Paltrinieri, N., (2016). A methodology for frequency tailorization dedicated to the oil & gas sector. Process Safety and Environmental Protection, 104, 123–141. Landucci, G., Argenti, F., Cozzani, V., & Reniers, G., (2017). Assessment of attack likelihood to support security risk assessment studies for chemical facilities. Process Safety and Environmental Protection, 110, 102–114. Latin, H., & Kasolas, B., (2002). Bad designs, lethal profits: The duty to protect other motorists against SUV collision risks. BUL Rev., 1(82), 1161. Le Bourhis, E., (2008). Glass rheology. Glass: Mechanics and Technology (Vol. 1, pp. 83–134). Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. Leong, C. T., & Shariff, A. M., (2009). Process route index (PRI) to assess level of explosiveness for inherent safety quantification. Journal of Loss Prevention in the Process Industries, 22(2), 216–221. Levin, M. A., & Kalal, T. T., (2003). Improving Product Reliability: Strategies and Implementation (Vol. 1, pp. 5–9). John Wiley & Sons. Lewis, P. R., Reynolds, K., & Gagg, C., (2003). Forensic Materials Engineering: Case Studies (Vol. 1, pp. 2–9). CRC Press. Li, X., Zanwar, A., Jayswal, A., Lou, H. H., & Huang, Y., (2011). Incorporating exergy analysis and inherent safety analysis for sustainability assessment of biofuels. Industrial & Engineering Chemistry Research, 50(5), 2981–2993. Liew, W. H., Hassim, M. H., & Ng, D. K., (2014). Sustainability assessment for biodiesel production via fuzzy optimization during research and development (R&D) stage. Clean Technologies and Environmental Policy, 16(7), 1431–1444. Liker, J. K., Sobek, D. K., Ward, A. C., & Cristiano, J. J., (1996). Involving suppliers in product development in the United States and Japan: Evidence for set-based concurrent engineering. IEEE Transactions on Engineering Management, 43(2), 165–178. MacKenzie, D., & Wajcman, J., (1985). The Social Shaping of Technology, Milton Keynes (Vol. 192, pp. 47–52). Open University Press.

126

Scientific Principles of Engineering

72. Marks, A. P., (1989). The Sinclair C5–an investigation into its development, launch, and subsequent failure. European Journal of Marketing, 23(1), 61–71. 73. Masanet, E., & Horvath, A., (2007). Assessing the benefits of design for recycling for plastics in electronics: A case study of computer enclosures. Materials & Design, 28(6), 1801–1811. 74. McDonough, III. E. F., (2000). Investigation of factors contributing to the success of cross‐functional teams. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 17(3), 221–235. 75. Mehta, A., Eilers, L. F., Campbell, A. M., Ramirez, D. W., & Godambe, S. A., (2017). Using the PRISM model to drive quality improvement in the emergency department. Clinical Pediatric Emergency Medicine, 18(2), 103–114. 76. Mirošević, J. K., (2015). Indicators of inclusion implementation in the process of school self-evaluation. Indicators of Implementation of Inclusive Practice in the School Self-Evaluation Process, 17, 207–218. 77. Misra, K. B., (2008). Handbook of Performability Engineering (Vol. 1256, pp. 5–9). London: Springer. 78. Mkpat, E., Reniers, G., & Cozzani, V., (2018). Process safety education: A literature review. Journal of Loss Prevention in the Process Industries, 54, 18–27. 79. Mode, P. F., (2002). Effects Analysis in Design (Design FMEA) and Potential Failure Mode and Effects Analysis in Manufacturing and Assembly Processes (Process FMEA) Reference Manual (Vol. 1, pp. 2–9). Society of Automotive Engineers, Surface Vehicle Recommended Practice J, 1739. 80. Pahl, G., & Beitz, W., (1988). Engineering design: A systematic approach. NASA STI/Recon Technical Report A, 89, 47350. 81. Paltrinieri, N., Khan, F., Amyotte, P., & Cozzani, V., (2014). Dynamic approach to risk management: Application to the Hoeganaes metal dust accidents. Process Safety and Environmental Protection, 92(6), 669–679. 82. Paltrinieri, N., Tugnoli, A., Buston, J., Wardman, M., & Cozzani, V., (2013). Dynamic procedure for atypical scenarios identification (DyPASI): A new systematic HAZID tool. Journal of Loss Prevention in the Process Industries, 26(4), 683–695.

Scientific Approach on Engineering Failures

127

83. Pasman, H. J., Knegtering, B., & Rogers, W. J., (2013). A holistic approach to control process safety risks: Possible ways forward. Reliability Engineering & System Safety, 117, 21–29. 84. Pecht, M. G., (2006). Establishing a relationship between warranty and reliability. IEEE Transactions on Electronics Packaging Manufacturing, 29(3), 184–190. 85. Petroski, H., (1985). To Engineer is Human: The Role of Failure in Successful Design (Vol. 1, pp. 2–6). St. Martin’s Press. 86. Petroski, H., (2018). Success Through Failure: The Paradox of Design (Vol. 92, pp. 3–9). Princeton University Press. 87. Planas, E., Arnaldos, J., Darbra, R. M., Muñoz, M., Pastor, E., & Vílchez, J. A., (2014). Historical evolution of process safety and majoraccident hazards prevention in Spain. Contribution of the pioneer Joaquim Casal. Journal of Loss Prevention in the Process Industries, 28, 109–117. 88. Prasad, D., McDermid, J., & Wand, I., (1996). Dependability terminology: Similarities and differences. IEEE Aerospace and Electronic Systems Magazine, 11(1), 14–21. 89. Pulselli, R. M., Ridolfi, R., Rugani, B., & Tiezzi, E., (2009). Application of life cycle assessment to the production of man-made crystal glass. The International Journal of Life Cycle Assessment, 14(5), 490–501. 90. Qi, H., Ganesan, S., & Pecht, M., (2008). No-fault-found and intermittent failures in electronic products. Microelectronics Reliability, 48(5), 663–674. 91. Qi, R., Prem, K. P., Ng, D., Rana, M. A., Yun, G., & Mannan, M. S., (2012). Challenges and needs for process safety in the new millennium. Process Safety and Environmental Protection, 90(2), 91–100. 92. Rangaiah, G. P., Feng, Z., & Hoadley, A. F., (2020). Multi-objective optimization applications in chemical process engineering: Tutorial and review. Processes, 8(5), 508. 93. Rausand, M., & Øien, K., (1996). The basic concepts of failure analysis. Reliability Engineering & System Safety, 53(1), 73–83. 94. Rocha-Valadez, T., Mentzer, R. A., Hasan, A. R., & Mannan, M. S., (2014). Inherently safer sustained casing pressure testing for well integrity evaluation. Journal of Loss Prevention in the Process Industries, 29, 209–215.

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95. Rogers, L., & Jensen, K. F., (2019). Continuous manufacturing–the green chemistry promise?. Green Chemistry, 21(13), 3481–3498. 96. Saavalainen, P., Kabra, S., Turpeinen, E., Oravisjärvi, K., Yadav, G. D., Keiski, R. L., & Pongrácz, E., (2015). Sustainability assessment of chemical processes: Evaluation of three synthesis routes of DMC. Journal of Chemistry 1, 2–9. 97. Schlager, N., (1994). When Technology Fails: Significant Technological Disasters, Accidents, and Failures of the Twentieth Century, 1, 2–4. 98. Shariff, A. M., & Zaini, D., (2013). Inherent risk assessment methodology in preliminary design stage: A case study for toxic release. Journal of Loss Prevention in the Process Industries, 26(4), 605–613. 99. Shariff, A. M., Wahab, N. A., & Rusli, R., (2016). Assessing the hazards from a BLEVE and minimizing its impacts using the inherent safety concept. Journal of Loss Prevention in the Process Industries, 41, 303–314. 100. Sherr, I., (2011). Apple says white iPhone 4 is coming this spring [online]. In: WSJ. Com. (Vol. 1, pp. 2–6). 101. Sims, B., (1999). Concrete practices: Testing in an earthquakeengineering laboratory. Social Studies of Science, 29(4), 483–518. 102. Srinivasan, R., & Natarajan, S., (2012). Developments in inherent safety: A review of the progress during 2001–2011 and opportunities ahead. Process Safety and Environmental Protection, 90(5), 389–403. 103. Stamatis, D. H., (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (Vol. 1, pp. 2–7). Quality Press. 104. Stephanopoulos, G., & Reklaitis, G. V., (2011). Process systems engineering: From Solvay to modern bio-and nanotechnology.: A history of development, successes and prospects for the future. Chemical Engineering Science, 66(19), 4272–4306. 105. Stone, R. B., & Wood, K. L., (1999). Development of a functional basis for design. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19739, pp. 261–275). American Society of Mechanical Engineers. 106. Sudhakar, K. V., & Paredes, J. C., (2005). Failure mechanisms in motor bearings. Engineering Failure Analysis, 12(1), 35–42. 107. Suess, M. E., (1992). Bacteria-induced corrosion of a stainless steel chemical trailer barrel. ASM International, Handbook of Case Histories in Failure Analysis, 1, 70–73.

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108. Syan, C. S., & Menon, U., (2012). Concurrent Engineering: Concepts, Implementation and Practice (Vol. 1, pp. 2–7). Springer Science & Business Media. 109. Tam, A. S., & Gordon, I., (2009). Clarification of failure terminology by examining a generic failure development process. International Journal of Engineering Business Management, 1(1), 33–36. 110. Tugnoli, A., Cozzani, V., Di Padova, A., Barbaresi, T., & Tallone, F., (2012). Mitigation of fire damage and escalation by fireproofing: A risk-based strategy. Reliability Engineering & System Safety, 105, 25–35. 111. Tugnoli, A., Landucci, G., Salzano, E., & Cozzani, V., (2012). Supporting the selection of process and plant design options by inherent safety KPIs. Journal of Loss Prevention in the Process Industries, 25(5), 830–842. 112. Turton, R., Bailie, R. C., Whiting, W. B., & Shaeiwitz, J. A., (2008). Analysis, Synthesis and Design of Chemical Processes (Vol. 1, pp. 2–9). Pearson Education. 113. Villa, V., Paltrinieri, N., Khan, F., & Cozzani, V., (2016). Towards dynamic risk analysis: A review of the risk assessment approach and its limitations in the chemical process industry. Safety Science, 89, 77–93. 114. Wanhill, R. J. H., (2002). Milestone Case Histories in Aircraft Structural Integrity, 1, 2–9. 115. Wilkie, C. A., & Morgan, A. B., (2009). Fire Retardancy of Polymeric Materials (Vol. 1, pp. 3–9). CRC Press. 116. Wilson, M. P., & Schwarzman, M. R., (2009). Toward a new US chemicals policy: Rebuilding the foundation to advance new science, green chemistry, and environmental health. Environmental Health Perspectives, 117(8), 1202–1209. 117. Womack, J. P., Jones, D. T., & Roos, D., (2007). The Machine that Changed the World: The Story of Lean Production--Toyota’s Secret Weapon in the Global Car Wars that is now Revolutionizing World Industry (Vol. 1, pp. 2–9). Simon and Schuster. 118. Wood, R. H., & Sweginnis, R. W., (1995). Aircraft Accident Investigation (Vol. 1, pp. 2–7). Endeavor Books LLC. 119. Xing, L., & Amari, S. V., (2008). Fault tree analysis. Handbook of Performability Engineering, 1, 595–620.

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120. Xue, J., Reniers, G., Li, J., Yang, M., Wu, C., & Van, G. P. H. A. J. M., (2021). A bibliometric and visualized overview for the evolution of process safety and environmental protection. International Journal of Environmental Research and Public Health, 18(11), 5985. 121. Yellman, T. W., (1999). Failures and related topics. IEEE Transactions on Reliability, 48(1), 6–8. 122. Zakocs, R., Hill, J. A., Brown, P., Wheaton, J., & Freire, K. E., (2015). The Data-to-Action framework: A rapid program improvement process. Health Education & Behavior, 42(4), 471–479.

5

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SCIENTIFIC OPTIMIZATION OF ENGINEERING SYSTEMS

CONTENTS 5.1. Introduction..................................................................................... 132 5.2. Design Variables and Parameters...................................................... 132 5.3. Objectives of Scientific Optimization.............................................. 133 5.4. Constraints and Bounds................................................................... 134 5.5. Optimization Problems And Methods.............................................. 135 5.6. Design and Structural Optimization Methods.................................. 139 5.7. Optimization Examples in Science and Engineering......................... 143 5.8. Graphical Optimization................................................................... 151 5.9. Mathematical Optimization............................................................. 151 5.10. Discrete Optimization................................................................... 152 5.11. Numerical Optimization Methods................................................. 153 5.12. Optimization Case Studies............................................................. 153 References.............................................................................................. 159

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5.1. INTRODUCTION The term “optimization” comes from the Oxford Dictionary’s description of a procedure or approach that may “make anything flawless and efficient.” Something about this context might be a structure, a technology, or a judgment that, through the use of a process known as optimization, develops progressively better (Sinha et al., 208). The term “optimization” comes from the Cambridge Dictionary and refers to a procedure that aims to make anything as nice as feasible or even as efficient as feasible. We may look to several different conventional descriptions and discover one thing that is consistent across all of them, and that is the modification of a process, technique, design, or choice in addition to making something a little more efficient (Mandalay, 2018). The term “optimization” may refer to either a procedure or a technique; but, in practice, optimization is comprised of several components, including decision factors, restrictions, and goals. Restrictions are the specific requirements that must be met for optimization to reach its intended aim, whereas factors are the most essential and leading aspects that decide the output that will be created either by optimizing (it’s objective) (Patel & Sinha, 2015).

5.2. DESIGN VARIABLES AND PARAMETERS Variables comprise design elements utilized in just about any system that is truly in charge of producing the intended result or item. The design parameters often consist of a collection of one maybe multi-dimensional vector, including such X = (x1, x2, x3, …, xn), (1.1)

where x1, x2, x3.…, are n different variables that make up the whole vector X. The vector X is one dimension in this instance, but it may also be two, three, or more dimensions. During the optimization procedure, various variables are managed, changed, and regulated (Moftah et al., 2014). While sometimes just one variable has to be changed or regulated to increase performance and make

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optimization work, other times a bigger number of factors may also need to be altered to accomplish the system’s intended goal. Many parameters are employed in situations in which the concept of stability or inequality has been used to govern and attain equilibrium (Sun et al., 2009). A physical system’s behavior may be optimized in a variety of ways if the improvement is desired. Although the phrases parameters and factors are commonly used synonymously, there is little distinction between the two. While parameters could be some standardized operating variables, constant, or even the features that link the system parameters, factors were vectors or numbers that fluctuate in the system. Therefore, they are both properties, and the only variation in them is how variables are used to establish a relationship among the variables (Santosh & Hegadi, 2019).

5.3. OBJECTIVES OF SCIENTIFIC OPTIMIZATION An aim is a target that must be attained. Every increase in STEM ability will also have a target to hit and maybe a degree of error to cut down. In each of these scenarios, optimization serves the crucial function of helping to accomplish the objective established by the system reaction or technique (Pinho et al., 2021). As a result, the optimizing process is given a minimal objective that must be satisfied. Figure 5.1 shows a few photos that are the outcomes of breast cancer separation and identification as an illustration. An improved technique that integrated gray levels grouping improvement and automatic threshold separation was used to get the findings (GLCEATS) (Fei, 2018). It is highly challenging to identify the instances in which there are extra cancerous components (cancerous components) existing if we examine four separate classification findings that emphasize breast cancer tumors. The goal of an optimization approach is to detect as many malignant tumors as possible during breast mammography (Garg et al., 2021). The goal of optimizing is to reduce the error or quantity of an appropriate function, such as e(x) or f(x), using the formula y = minimize f(x) or minimize e(x).

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Figure 5.1. Outcomes of the GLCE-ATS approach for detecting breast cancer, with varying results depending on the optimizing settings utilized. Source: https://iopscience.iop.org/book/978–0-7503–2404–5.

5.4. CONSTRAINTS AND BOUNDS There are several restrictions and circumstances under which an optimization approach or method may be used. If a specific set of conditions are fulfilled, the approach effectively enhances the system’s efficiency. The restrictions also contain certain constraints, which might be higher, lower, or intermediary limits (Erfanian et al., 2022).

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The improvement process uses a collection of data or sampling that are subject to restrictions which may be determined by specific traits or components of the application getting optimized or by outside variables that have an impact on the functionality or design of the proposed system (Ruiz-López et al., 2021). Although parameters could also be a factor, the boundaries are often determined by design factors. For instance, Y = minimize {e(x)} under x2 < x0 and x1 > xp.

The objective in this case is to reduce the warning transmitter intensity. The optimum must be completed under the circumstances specified in the formula, with the least or ideal value being attained as Y (Plhák, 2014). These requirements are constraints that, in the cases of x2 and x1, seem to be bottom and top limits. Between x1 and x2, the signals or values must be kept to a minimum. Various forms of limits and restrictions are employed in various optimization algorithms and purposes (Sinha, 2020). While the limitations are particular to the application or issues being dealt with, the optimization algorithms may be extended. As a result, the optimization techniques are factual again for application but the restrictions are emotional. The application of optimizing in calls for several restrictions to achieve stability (Kumar, 2014).

5.5. OPTIMIZATION PROBLEMS AND METHODS There are several issues which need to be resolved in systems associated with STEM and similar topics in respect of bettering systems quality and construction, minimizing mistakes, a decrease of labor and price, etc. Appropriate optimization techniques are used to fix these problems (Patel & Sinha, 2010). Many common factors are used to determine optimization challenges, including: • The ideal quantity of parameters for input. • Procedures’ highest and lowest values. • The system’s features. • Boundaries and restrictions. The selection of an optimization process is significantly influenced by the quantity of arguments or qualities (Chong & Zak, 2004). When the set of input parameters is raised, the optimization sometimes works such that the computation cost reduces, but other times it causes the complexity to rise. Lowering computation costs should have been a priority in addition to bettering the performance of a system or design (Cruz et al., 2011). The

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number of inputs must be carefully chosen to ensure that optimizing yields the greatest feasible outcome with the fewest number of stages as well as the lowest amount of time. When constructing and putting into practice an optimization technique, it’s crucial to keep in mind the boundaries, that are upper limits to several criteria (Labanda, 2015). As a result, the restrictions and bounds’ superior and inferior limits, as well as the measurement items that may be assigned to the variables that are being improved, regulate the issues. The goals for optimizations are sometimes chosen based on certain features of dynamic systems, design, or reaction (Ahmad, 2012).

5.5.1. Workflow of Optimization Methods Figure 5.2 illustrates one common process diagram for the performance of every overall optimization technique, which may be used to solve every optimization issue. The key steps are highlighted in this image (Luo & Yu, 2006).).

Figure 5.2. Major phases in a typical optimization product’s process. Source: https://www.amazon.com/Optimization-Methods-Engineering-Technology-Expanding/dp/0750324023.

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An overall optimization method is put into action. The initial phase in each program is an improvement, and this is where thing begins. Assessment of potential optimization issues is aided by the necessity for optimization (Bezovski, 2015). A single technique or cross-based improvement could be used to solve many issues in a program. Following the final issue selection, the layout variables are chosen, which further establishes the primary goal of using an appropriate optimization technique. Before really selecting a suitable approach, we define several design restrictions and limits since the optimization must be performed inside a set of conditions and considered (Chatterjee et al., 2021). Then, depending on the design factors, needs, optimum issues, restrictions, and boundaries, the optimizing technique is chosen. It is determined whether or not the outcomes of the appropriate optimization procedure are the finest outcomes that might have been anticipated. Or else, multiple reversal procedures may be included in the implementation and adoption (the opposite way missiles in the diagram are not exposed as they are contingent on what is needed) (Gruber et al., 2003). To get the optimum response or result from the optimization process, these factors are adjusted if the design factors need to have some tweaking or manipulation or if the restrictions need little adjustments along with built-in limit values. So, it is clear from the flow chart that the optimizing issue is decided upon at the outset following the requirements, as well as the optimization technique is decided after several processes including the definition of model parameters, restrictions, and boundaries (Luenberger, 1969).

5.5.2. Classification of Optimization Methods As we have stated, several variables determine the optimization technique, and restrictions can be both regular and non-linear. Thus, the methodologies may be roughly divided into the following categories (Bamigbola & Osinuga, 2009): • phrases and limits for linear constraints. • parameters and limits for nonlinear constraints. The criteria for picking an appropriate strategy, namely whether the restrictions and limits are regular or non-linear, may vary depending on the types of optimization techniques used. The various optimization techniques are even further divided into (Absil et al., 2009): • •

Methods with a single value or several variables. Methods that are limited or unconstrained.

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• Non-linear or linear approaches to optimizing. • Techniques for single- or multi-objective optimizing. • Specific or all-purpose techniques. • Conventional or unconventional techniques. The attributes or qualities of an optimization’s optimal solution are also used to categorize optimization techniques (Abrudan et al., 2009). For instance, the optimization technique is known as optimization algorithms if the parameters are continuously and have real values. Similar to this, it is recognized as typically specify for integer, and this is recognized as mixed-integer linear optimizing for continuously actual figures and integer arithmetic. The optimization approach is classified as exponential, regular, or non-linear depending on the many kinds of model parameters, including sequential, quaternion, and non-linear (Ehrgott & Gandibleux, 2003). An optimizing approach is referred to as a restricted optimization technique if indeed the optimum issue is subject to rules or limits, and an uncontrolled optimization technique if there are no restrictions or constraints. The most common traditional optimization algorithms include a simplex method, non-linear coding, modular coding, and arithmetic programming skills. The term “human evolution techniques of optimization” refers to techniques for enhancement that are premised on evolutionary principles (Hartley & Kahl, 2009). These techniques include several popular ones, including metaheuristics, genetic algorithms, evolution strategies, particle swarm optimization (PSO), evolutionary algorithms, microbial hunting and gathering, and methodologies differential equations. Gradients, Newton’s, quasi-Newton, conjugated directives, and other approaches are instances of unrestricted optimization techniques (Gallier, 2013). Optimization algorithm approaches, which include certain deterministic and probabilistic techniques, are also another developing field of management. These techniques are based on the principles of statistical mechanics and other mathematics. Numerous applications of signal analysis including control employ functionality and worldwide optimization techniques for quantitative analysis (Nocedal & Wright, 1999). Optimizing is used when the methodologies, concepts, and issues are dynamically in character, such as in the rapidly changing dynamic settings of many new applications of signal analysis, controlling, transportation, etc. The issues and optimizing models that are used evolve quickly in a variety of industries, including telecommunication, artificially intelligent developments, corporate finance, and many others (Wright & Nocedal,

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1999). The approaches that are now in use in these applications likewise need adjustment and periodic upgrades. Ant colony optimization, genetic improvement, genetic programming, artificial neural, and other swarm intelligence-based techniques are only a few of the optimization techniques that are suggested for this kind of application (Simon, 2013). Quantitative metrics of performance to evaluate how well the optimization strategy is functioning are important in the context of global optimization. All operational plans demand such measurements, however, in a situation where the surroundings frequently occur, the appraisal or evaluation is crucial (Dubourg, 2011). Regardless of the kind of optimization issue and implementation, a few of the significant metrics that are often utilized include: • • • • • • • • • •

Mean fitness and cumulative fitness. Off-line mistake. Robustness. Diversity. The standard deviation Time. The complexity of computation. Median error. The best function value on average. The most recent development.

5.6. DESIGN AND STRUCTURAL OPTIMIZATION METHODS The economic and technological breakthroughs of today are constantly evolving. Optimizing is necessary to obtain improved design and structural properties in ways that suit the constantly evolving demands of innovation industries (Mohanty et al., 2020). For instance, several studies have suggested that aerofoils architecture plays a major role within the aircraft industry, as well as a lot of important research contributions, could be presented in the literature where airfoils, blades, as well as other flying elements are designed in a way that now the effectiveness of airplanes is significantly improved (Engelbrecht, 2007). Any system’s architecture is largely determined by its volume, structure, as well as other characteristics, and optimizing aims to improve one or both of these characteristics (Mitchell, 2021).

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5.6.1. Structural Optimization Structure-related factors, including size, form, region, boundary, density, etc., are directly addressed by structure determination. If the

Figure 5.3. The exterior covering of the aircraft’s standard structural layout. Source: https://store.ioppublishing.org/page/detail/Modern-OptimizationMethods-for-Science-Engineering-and-Technology/?K=9780750324021.

To optimize the systems or equipment in which ergonomics need to be improved, the comfort level must first be raised in the area of ergonomics. Figure 5.3 depicts a straightforward and common architectural schematic of an aircraft’s outermost layer, in which the construction of the wing or blades and its optimizations crucial to a flight’s efficiency. Since an aircraft contains hundreds of tiny, moderate, and top-rate structures, this figure is only metaphorical (Fogel, 2006). Focuses on the structural features, there are many forms of structure determination. The following are a few significant structural optimizing approaches or techniques (Sterne, 2017): • •

•

Shape optimization: Based on a variety of forms of organizations or goods, this. Area and Volume Optimization: In transportation as well as authority systems, size, and density determine the performance of a large number of goods. As a result, efficiency may be increased by maximizing either the space or capacity, combination, or even other related factors. Size Optimization: The optimization of the architectural efficiency of the network may take into account length, breadth, and other comparable dimensional parameters.

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•

Topological Optimization: The purpose of enhancement is to enhance the topography of the system design because the topography depicts the whole connectivity of different parts of the system as well as how they are organized together helps determine the effectiveness of the device, particularly in the automotive industry and other sectors related to it. Examples of physical optimization techniques include the topology optimization of constructions for seismic forces, structural analysis for steel factories, and dependability optimization (Adams, 2003). Topographic optimization is carried out following a predetermined process; Figure 5.4 shows however one flow chart. When improvement is required, structural characteristics are researched. The optimizing issue is located, and a workable technique is put into practice that makes use of the structural features during the optimization problem (Wahalathantri et al., 2011). Even with an optimization technique that is responsive to the architectural aspects of the system, the improvement results certain comments.

Figure 5.4. Design flow for topographical optimization. Source: https://store.ioppublishing.org/page/detail/Modern-OptimizationMethods-for-Science-Engineering-and-Technology/?K=9780750324021.

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5.6.2. Design Optimization Since a program’s structure as well as its structural characteristics are linked, the design process and design analysis are not wholly dissimilar (Al Aboud et al., 2020). Utilizing structurally optimization techniques, it is possible to increase system efficiency, reduce costs, and reap other advantages of optimizing. Computer-aided design (CAD) for a variety of applications is mostly connected with design optimization approaches (Burtan et al., 2017). The Systems include software applications, making it simple to modify or manipulate designs to achieve the necessary system features. Designing an infrastructure facility involves choosing one or even more factors to achieve several goals. If a suitable cost function can be decreased, a superior design is achieved. Whenever the expense is the lowest of all possible designs, the layout is said to be optimal (Hao et al., 2013). Restrictions on resources, such as labor and equipment shortages, along with physiological and other limitations, more often than not restrict overall design options. The restriction boundaries define a workable area in the design process. More significantly, it is possible to represent the objective functions and restrictions as mathematical operations including design factors (Wahalathantri et al., 2012). The techniques covered in this book may then be used to solve the resultant numerical optimization issue. Designing engineering systems is a multidisciplinary process that needs collaboration between designers from different technical specialties (Larsen & Ormarsson, 2014). The process of structural engineering may be difficult. To create models which can be submitted to examination and confirmation by tests, the assumption must be established (Zahedmanesh & Lally, 2012). Analyzing multiple choices is the first step in systems engineering. For the majority of applications, the whole design project has to be divided into several smaller challenges that are then dealt with separately. It is possible to formulate each of the smaller problems as an optimal design issue that may be resolved by mathematical programming (Park et al., 2014). A traditional optimized engineering design difficulty might involve the following: research questions that describe the issue, preliminary research and data gathering before problem definition, identifiers of model parameters, optimal solution criteria, and restrictions, numerical specification of the optimization process, and resolving issues (Duan et al., 2018). The final two product steps—mathematical derivation and techniques for resolving the optimum design issue—are covered in this article.

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Optimizing engineering designs is a complex issue. The right mathematical description of the issue may be the most crucial stage in the solution. Analytic and computational methods are possible to discover a solution after the issue has already been formally defined (Yuan et al., 2012). The term “mathematics programming” refers to a collection of numerical methodologies for solving mathematical optimization issues. A generic and adaptable approach for resolving engineering design issues is provided by the frameworks (Kluess et al., 2010). There may be no answer to certain mathematics optimization issues. This often occurs as a result of the wrong definition of the optimization issue and competing needs. Examples include restricted restrictions that make it impossible to find a viable area or missing requirements that cause the yield point to be unlimited (Pintér, 2006). We’ll assume throughout this article that the issue has been appropriately stated, leaving a closed, constrained viable area.

5.7. OPTIMIZATION EXAMPLES IN SCIENCE AND ENGINEERING With the use of real-world examples, we want to explain the subject of optimizations. These illustrations were chosen from a variety of STEM disciplines (scientists, technologies, architecture, and mathematics) (Thielman & Ge, 2006; Alexandrov & Hussaini, 1997). To improve (achieve maximum or reduce) a generic cost that might reflect the production cost, revenue, power, energy, length, mean square, etc., for every instance, the best values for a collection of design factors must be determined. With more parameters included, each design challenge becomes more complicated (Sobieszczanski-Sobieski, 1995). There are just a few input parameters used for each of the first-presented smaller challenges. The issues that come next are trickier and could include hundreds of design variations. Following that problem identification is a quantitative treatment of each issue. Whereas simpler issues can usually be solved manually, complicated ones need to be solved using specialist optimization tools (Holl et al., 2014; Sharpe & Hansman, 2022).

Problem 1: Shortest distance problem To find the best path between a fixed location (x0, y0) and a specified curve, use the formula y = f(x).

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Formulation: The goal of the optimizing challenge is to reduce the Euclidian distance between the supplied point as well as the curve mathematical model (Biegler et al., 2007):

Problem 2: Open box problem How much paper (8.5” x 11”) can be made into the maximum volume of the open container by taking off rectangles just at the sides and bending the edges? Formulation: The unrestricted optimization model is thus written as: Let denote the edge of both the rectangles to be sliced (Chen & Lee, 2011).

Problem 3: Logging problem What are all the highest lengths (or volumes) of such a rectangular section that could be cut from such a log with the above specifications? Formulation: Let indicate the circumference of a log, and let [2x, 2y] indicate the breadth and heights of beams to also be cut (only with the source in the middle). The optimizing issue is therefore stated as follows:

Problem 4: Ladder placement problem What is the biggest box which can be put beneath a long ladder while it is leaning against a vertical surface, in terms of its width and height? Formulation: Take (a, 0) as well as (0, b) to indicate the ladder’s point of lateral and vertical contact with the ground and also the wall, accordingly. Let [x, y] indicate the box’s measurements. The optimizing issue would then be mathematically represented as:

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Problem 5: Soda can design problem Create a soda can with the desired diameter and length that really can store 200 ml of liquid while adhering to the h 2d limitation and minimizing manufacturing costs (a function of the surface). Formulation: Let the can’s length and width be represented as xT = [d, h]. The optimizing issue is therefore written as follows to reduce the Coutinho’s contact area:

Problem 6: Simplified manufacturing problem Chairs and tables are two items that a company makes. Every table costs 10 kg of materials, and 5 laborers, and produces a return of $7.50. Every chair costs 5 kg of material, employs 12 workers, and generates a $5 profit. There are 60 kg of materials and 80 labor units accessible in total. To maximize your profit, choose the ideal manufacturing mix. Formulation: Let 𝑥𝑇 = The number [x1,x2] represent the number of chairs and tables that will be produced. The optimizing issue is then expressed in mathematical form:

Problem 7: Student diet problem To meet his or her nutritional demands (1000 calories, 100 g protein), each student seems to have a restricted ($10) expenditure and a selection of breakfast items (egg whites, cereals, pastries). Cereals have 500 calories and 40 grams of protein per serving, eggs have 500 calories and 50 g of

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protein per serving, while tarts have 600 calories and 20 grams of protein per serving. How well does he/she choose their morning cereal? Formulation: Let 𝑥𝑇 = The amounts of eggs, porridge, and pastries selected for the morning are [x1,x2,x3]. The optimization issue is then expressed mathematically as follows:

Problem 8: Data-fitting problem The amounts of eggs, porridge, and pastries selected for the morning are [x1,x2,x3]. The optimization issue is then expressed mathematically as follows: Formulation: Let the polynomial be given as 𝑦 = (𝑥) = 𝑎0 + 𝑎1𝑥 + ⋯ + 𝑎𝑚𝑥𝑚; then, the unconstrained optimization problem is formulated as:

Problem 9: Neural network training problem Select the weights to reduce the uncertainty in identifying specific data to flow based on a single-layered convolutional neural network with input neurons, output vector, and a collection of weight vectors wij, I = 1.…, p, j = 1.…, n. Formulation: The optimal solution problem is stated to minimize the standard deviation provided as j = yj dj, where j represents the estimation error and intended output just at the node.

Problem 10: Classification problem Obtain the equations of a hyper-plane that divides data into categories with the shortest inter-class separation given a collection of data points: xi Rn, I = 1.…, n, and binary class labels: yi 1, 1.

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Formulation: To make the issue simpler, we suppose that now the measured values are differentiable and also that they reside in a plane, or that xi R2. We take into consideration a hyperplane using the formula: wTx b = 0, where is a weight vector concerning the hyperplane’s standard. We presume that to separate the provided data values that 𝒘𝑇𝒙𝑖 − 𝑏 ≥ 1 for opinions characterized as 1, and 𝒘𝑇𝒙𝑖 − 𝑏 ≤ −1 for opinions labeled as −1. The binary hyperplanes (lines) are detached by problem is distinct as:

. Thus, the optimization

Problem 11: Transportation problem Through supply locations with the following capacities: s1, s2…., sm, goods must be transported to distribution centers with the following demands: 1, d2.…, dn. Find the ideal amounts, xij, to be transported via those paths to reduce the overall cost of the cargo, considering the transport costs, cij, for enetwork link. Formulation: The optimizing issue is thus stated as: Let xij signify the amount that has to be transported from node to node j.

Problem 12: Knapsack problem Fill a knapsack with a certain volume (weight) to maximize the worth of the contained things given a variety of objects, each of which has a value ci > 0 as well as a weight wi > 0. Formulation: We suppose that W = 1 without losing clarity. The knapsack issue is expressed as: Let xi 0,1 signify the occurrence whereby the item is chosen for placement in the bag.

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Problem 13: Investment problem Select a combination of assets to invest $1 million in to improve the return on the investment, taking into account stock prices and the projected required rate of return linked with a grouping of investments. Formulation: The investments issue is treated also as a knapsack problem if xi 0,1 expresses the addition of security to the mixture (Problem 12).

Problem 14: Set covering problem Assumed a set 𝑆 = {𝑒𝑖: 𝑖 = 1,…, 𝑚} and a gathering 𝒮 = {𝑆𝑗: 𝑗 = 1, …, 𝑛} of subsections of , with related costs 𝑐𝑗, find the minimum sub-collection of that shelters 𝑆, i.e., ⋃𝑆𝑗∈Σ 𝑆𝑗 = 𝑆.

Formulation: Let aij 0 represent the requirement that ei Sj and xj 0 represent the requirement that Sj; The set coverage issue is therefore stated as follows:

Problem 15: Airline scheduling problem Create the best-scheduled flight possible given the same fixed costs and operating costs per section, taking interconnection, suitability, and recourses (Aeroplan, personnel, and equipment) accessibility constrictions into consideration. This will minimize the overall operational cost again for provided passenger numbers on each section more than a network of highways to be repaired. Formulation: Let 𝑆 = {𝑒𝑖: 𝑖 = 1, …, 𝑚} indicate the collection of flight lengths that must be covered, and let each subset Sj S signify a set of linked flight sections that may be coated by an Aeroplan or a crew, it is possible to define the smallest problem to covers the accessible routes as just a setcovering issue (Problem 10).

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Problem 16: Shortest path problem In a linked graph (V, E), where represents the vertex and represents the connections, find the most efficient route between the nodes. Formulation Let f be the edges adjacent both to the node then and let eij represent that edge.: 𝐸 → ℝ characterize a real-valued mass drive; further, let 𝑃 = (𝑣1,𝑣2,…, 𝑣𝑛) mean a track, anywhere 𝑣1 = 𝑝, 𝑣𝑛 = 𝑞; Finally, the unrestricted solitary shortest route issue is stated as follows: (1.14)



Alternately, if xij stands for modifiability to eij, the integer linear programming formulation of something like the shortest route issue is provided as follows in Chapter 6:

Note: In graphs, this shortest route issue is a very well issue that can be solved using algorithms like Dijkstra’s algorithm or the Bellman-Ford method.

Problem 17: Traveling salesman problem A salesperson is required by a corporation to attend its geographically dispersed (let’s say 50 stores total) stores. Determine the visiting order that will need the lowest amount of movement overall. Formulation: Inside an unguided weighted network with stores acting as its vertex, the traveling salesman problem is described as just the shortest route issue. The issue then resembles Issue 10.

Problem 18: Power grid estimation problem To get the best approximation of the condition of an electricity network given observations of real and reactive power flows (pij, qij) among nodes I j and observations vi of node voltages, calculate for complicated node voltages using the formula vi = vii, where I denote the phase difference. Formulation: Let vi, pij, and qij portray the measurement items, and then let and denote, including both, the trust in measurement techniques of voltage magnitude and reactive power flow. Additionally, let zij = zij ij

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portray the complicated input resistance among nodes I j. Next, the electric grid condition monitoring problem is defined as follows (Pedregal, p. 11):

Problem 19: Steady-state finite element analysis problem Determine nodal displacements ui which, while adhering to structurally and load limitations, reduce the overall energy stored associated with such a collection of point weights mi linked by continuous springs kij. Formulation: For the sake of simplicity, we’ll look at a one-dimensional variant of the issue in which the nodal deflections are denoted by the numbers u1, u2.…, and uN. The possible energy reduction issue is thus written as: Let fi indicate an applied load at node i.

Problem 20: Optimal control problem Find an acceptable command sequence u(t) that moves the flexible model x = Ax + Bu between predetermined endpoints while minimizing a quadratic polynomial function (x, u, t). Minimal power and minimum level issues are two examples of problems in the category of optimization techniques. Formulation: We discuss the best controlling of an elastic system with such a unit weight that is described using location (x) and velocities as a reduced issue (). The input is represented by u(t), t [0, T], and the simulation model are defined by x = v, v = u. We take into account a quadratic cost that has input parameters and time integral of the square of location. The optimum control issue that results is described as:

Subject to: 𝑥̇ = 𝑣, 𝑣̇ = 𝑢

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5.8. GRAPHICAL OPTIMIZATION For lower-dimensional issues, often those with one or two aspects, a graphic method is advised. In addition to being straightforward, the graphical technique offers an important insight into the issue that may not be available when using mathematical and numerical optimization techniques, especially when dealing with two-dimensional situations (Hu & Youn, 2011; Ho, 1994). Whenever the optimization issue is defined including one or two aspects, the graphic technique may be used (Lisnianski & Levitin, 2003). Graphic minimization aids in improving our comprehension of an underlying issue and creating a case for the anticipated solution. The process entails drawing the expense function’s outlines across a practical area bounded by the bounds of the constraints. The intended optimum may often be found via examination (Rangaiah, 2010). The outlines of the optimization problem and the borders of the constraints are shown using software that implements the graphical technique using a grid of matched numbers for the optimal values (Dhingra, 1992). The cost stored procedure lowest could then be seen on the plot. Therefore, the different stages are included in the graphic minimizing process (Alkhanak et al., 2016; Kamil et al., 2009): •

Determining the practical area. The restriction boundaries are plotted to do this. • Actively planning the expense function’s levels curve (or outlines) and determining the lowest. A mathematical software program like Matlab or Mathematics often implements the graphical approach (Martins & Lambe, 2013). Both programs have tools for charting and visualizing the outlines and bounds of cost functions. The Appendix contains the code needed to implement the graphic optimizing examples discussed in this chapter in Matlab (Varma & Palsson, 1994).

5.9. MATHEMATICAL OPTIMIZATION By methodically selecting the choices of a collection of variables that are subjected to inequalities and/or fighting for equal rights, the numerical optimization issue entails minimization (or maximum) of such real-valued objective functions (Zhang et al., 2014). Expense and constrain variables both are considered to be analytically such that their first and second

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derivatives could be calculated but they can be effectively approximated using the Taylor series (Yang & Gandomi, 2012). A collection of potential candidate points is revealed by the calculation of first and phase b required requirements, which would then be utilized to assess the act as the point utilizing sufficient criteria for an optimal (Saeedi et al., 2020). The viable area, or even the data point that fulfills the requirements, is just a convex collection in convex optimization algorithms because both the objective function and the restriction function were convex. The presence of a single world minimal in such situations is guaranteed (Ding & Tian, 2011; Koziel & Yang, 2011).

5.10. DISCRETE OPTIMIZATION This group includes a lot of actual design issues. For instance, discrete elements that can only have integer values are represented by parameters in optimization issues occurring in the manufacture and/or delivery of items (Hwang et al., 2014; Popov, 2014). Furthermore, parameters that can only have decimal values are frequently used to describe programming and connectivity difficulties (such as allocating cars to transport systems or spectrum assignments in mobile telephone systems, etc.). Special instances of optimization issues with discrete set solutions include the integer linear programming issue and binary integer linear programming challenge (Coutinho et al., 2015). Combinatorial optimization, which seeks to identify the optimal discrete item from a list of candidates, is closely connected to discrete optimization (Moorhouse & Camberos, 2011). The trying-to-travel salesperson issue, minimal level binary tree problem, vertices coloring issue, and other connection and graph-based problems are examples of classic optimization problems. They also include scheduling issues (facility layout problem, fleet task problem), macroeconomic difficulties (knapsack difficulty, budgetary control issue), and missed appointments (facility layout problem) (Utomo & Darma, 2020; Rangaiah, 2016). Discovering a solution in a limited amount of time also isn’t assured since combinatorial optimization issues are NPcomplete, which means that are non-deterministic quadratic problems (Meng et al., 2019). Therefore, heuristic methods are often used to address combinatorial objective functions. Finding computing techniques that make use of the polyhedral architecture of the integers programs has indeed received a lot of academic attention.

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5.11. NUMERICAL OPTIMIZATION METHODS These techniques have been applied to the creation of computing methods and are found in commercialized optimization tools. The optimization algorithm, which covers both linear and nonlinear software, is the technique of numerically addressing the optimization issue (Omran et al., 2005). The iteration solution approach, which starts with an initial estimate and repeatedly improves that in an attempt to obtain the lowest (or optimum) of such a multi-variable optimization problem, is indeed the fundamental statistical method for solving the nonlinear issue. In essence, the iteration technique is a two-part procedure that looks for a search path that doesn’t violate the restrictions but along whereby the optimal value declines, as well as a step length that reduces the fitness values along the specific search path (Chekhlov et al., 2005). Whenever a minimum has already been reached, as shown by function derivatives being near 0, the algorithm stops, or when a predetermined maximum number of generations has been reached, suggesting that there is no practical answer to the crisis.

5.12. OPTIMIZATION CASE STUDIES Due to mounting market demand, engineers are compelled to create products with more performance and greater complexity. As a consequence, more intricate numerical simulations that include nonlinear processes are required. Patterns are frequently inaccessible theoretically in such models; hence optimization must be carried out using techniques that do not depend on gradients (Duckstein, 1995). The technology was improved for several issues in this work by utilizing a proposed hybrid optimization method which doesn’t need a gradient. The development of a chemical reaction, the design of an automobile front rear suspension, and the design of the rubber car engine mount were the three issues to which this approach was used (Venter & Sobieszczanski-Sobieski, 2003). A novel hybrid, adaptable optimization technique named SHERPA was applied to the three challenges under study. In their commercialized optimizing and smart manufacturing code, HEEDS, Red Cedar Technologies distributes this unique method. SHERPA is just a hybrid, adaptive approach that employs many optimization methods simultaneously and adjusts to the issue at hand.

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5.12.1. Design of a Chemical Process The development of such a chemical reaction with a combined flowing stream with Dichloromethane (DCM), a typical chemical byproduct, as inputs were the first issue researched (Duckstein, 1995). DCM and water are separated because DCM has a lesser boiling point than water. This is accomplished by heating the combination with such a stream of superheated steam (Birge, 2003). This uses the chemical procedure seen in Figure 5.5. To keep the quantity of DCM within the output stream (EFFLUENT) at less than 150 ppm, as little steam as possible should be introduced to a process. The system was examined using Aspen Plus 2006, and also the improvement was carried out using HEEDS. The following is the formulation of the optimization process:

Figure 5.5. The process flowchart includes data from the streams and processes. Source: https://www.lsu.edu/mpri/textbook/index.php.

The adjustment lowered the overall steam consumption by 14% while keeping the output channel’s DCM content at 150 ppm.

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5.12.2. Design of an Automotive Front Suspension To accommodate the toe as well as camber vs wheel traveling curves to predetermined goal curves, the front shock absorber was investigated. Moving multiple command posts in the network was required to do this, as shown in Figure 5.6. The positions of the interconnections between the upper control arms as well as the framework, as well as the link between the outer rotating shaft as well as the hub assemblies, served as that of the command posts (De Weck, 2004).

Figure 5.6. System of control points for a front coil spring suspension (De Weck, 2004). Source: https://www.researchgate.net/publication/301551131_Optimization_ of_Engineering_Systems.

By determining the roots means squared (RMS) error between both the toe as well as camber design contours as well as the targeted curves, the contours successfully matched. The goal was to reduce the total RMS error between these two RMS values. The study was done using MSC/Adams. Figure 5.7 displays the optimization outcomes. The toe, as well as camber curves, became significantly more similar to the desired curves following optimization (Knowles & Nakayama, 2008).

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Figure 5.7. Curves for the goal, the basic design, and the improved design’s toe and camber. Source: https://www.researchgate.net/publication/301551131_Optimization_ of_Engineering_Systems.

5.12.3. Design of a Rubber Engine Mount An engine mounting (bushing) constructed of rubber was indeed the third and last issue that was investigated. Figure 5.8 depicts the basic design. This bushing is attached to that same car’s frame and enclosed in a metallic sleeve (Yang & Koziel, 2011). The engine is connected to the middle cylinder. Whenever the center shifts towards the left, one nonlinear rigidity curve is sought, and when it adjusts, a nonlinear dynamic rigidity curve is required. Figure 5.9 displays these curves. The model parameters were represented by the spline lines in Figure 5.8. The goal was to reduce the RMS error between both the planned and goal rigidity curves. Abaqus, a multipurpose architectural FEA code, was used to conduct the study (Yang & Koziel, 2011).

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Figure 5.8. Rubber mounting baseline design with highlighted design variations. Source: https://www.lsu.edu/mpri/textbook/index.php.

Figure 5.9. For rubber bushings, desirable piecewise rigidity curves. Source: https://www.lsu.edu/mpri/textbook/index.php.

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Figure 5.10 depicts the structural performance, while Figure 5.11 depicts the associated rigidity curve.

Figure 5.10. Improved bushing construction. Source: https://user.eng.umd.edu/~austin/ence202.d/optimization.html.

The reduction also resulted in a significant and novel modification in shape. Highly nonlinear of the rubber and also the contact, as well as the intricate stiffness constraints, this issue proved challenging to solve mechanically.

Figure 5.11. Target and optimum design stiffness curves. Source: https://user.eng.umd.edu/~austin/ence202.d/optimization.html.

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REFERENCES 1.

Abrudan, T., Eriksson, J., & Koivunen, V., (2009). Conjugate gradient algorithm for optimization under unitary matrix constraint. Signal Processing, 89(9), 1704–1714. 2. Absil, P. A., Mahony, R., & Sepulchre, R., (2009). Optimization algorithms on matrix manifolds. In: Optimization Algorithms on Matrix Manifolds. Princeton University Press. 3. Adams, M. S. C., (2003). MSC Software Corporation (Vol. 1, pp. 1–16). Ann. Arbor, Michigan. 4. Ahmad, H. A., (2012). The best candidates method for solving optimization problems. Journal of Computer Science, 8(5), 711. 5. Al Aboud, A., Dˆrsam, E., & Spiehl, D., (2020). Investigation of printing pad geometry by using FEM simulation. Journal of Print and Media Technology Research, 9(2), 81–93. 6. Alexandrov, N. M., & Hussaini, M. Y., (1997). Multidisciplinary Design Optimization: State of the Art, 1, 1–20. 7. Alkhanak, E. N., Lee, S. P., Rezaei, R., & Parizi, R. M., (2016). Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. Journal of Systems and Software, 113, 1–26. 8. Bamigbola, O. M., & Osinuga, I. A., (2009). Resolution of extremization problem using duality principle. Journal of Natural Sciences Engineering and Technology, 8(1). 9. Bezovski, Z., (2015). The historical development of search engine optimization. In: Information and Knowledge Management (Vol. 5, No. 15, pp. 91–96). IISTE. 10. Biegler, L. T., Ghattas, O., Heinkenschloss, M., Keyes, D., & Van, B. W. B., (2007). Real-Time PDE-Constrained Optimization (Vol. 1, pp. 1–13). Society for Industrial and Applied Mathematics. 11. Birge, B., (2003). PSOT-a particle swarm optimization toolbox for use with MATLAB. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No. 03EX706) (pp. 182–186). IEEE. 12. Burtan, Z., Chlebowski, D., Cieślik, J., & Zorychta, A., (2017). An analysis of fault behavior in the proximity of mining operation in the context of seismic hazard. Scientific Journals of the Mineral Resource Management Institute of the Polish Academy of Sciences, 1, 1–17.

160

Scientific Principles of Engineering

13. Chatterjee, D., Hokayem, P., & Lygeros, J., (2011). Stochastic receding horizon control with bounded control inputs: A vector space approach. IEEE Transactions on Automatic Control, 56(11), 2704–2710. 14. Chekhlov, A., Uryasev, S., & Zabarankin, M., (2005). Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 8(01), 13–58. 15. Chen, C. H., & Lee, L. H., (2011). Stochastic Simulation Optimization: An Optimal Computing Budget Allocation (Vol. 1). World scientific. 16. Chong, E. K., & Zak, S. H., (2004). An Introduction to Optimization. John Wiley & Sons. 17. Coutinho, R. D. C., Drummond, L. M., Frota, Y., & De Oliveira, D., (2015). Optimizing virtual machine allocation for parallel scientific workflows in federated clouds. Future Generation Computer Systems, 46, 51–68. 18. Cruz, C., González, J. R., & Pelta, D. A., (2011). Optimization in dynamic environments: A survey on problems, methods and measures. Soft Computing, 15(7), 1427–1448. 19. De Weck, O. L., (2004). Multiobjective optimization: History and promise. In: Invited Keynote Paper, GL2-2, The Third China-JapanKorea Joint Symposium on Optimization of Structural and Mechanical Systems (Vol. 2, p. 34). Kanazawa, Japan. 20. Dhingra, A. K., (1992). Optimal apportionment of reliability and redundancy in series systems under multiple objectives. IEEE Transactions on Reliability, 41(4), 576–582. 21. Ding, F., & Tian, Z., (2011). Opportunistic maintenance optimization for wind turbine systems considering imperfect maintenance actions. International Journal of Reliability, Quality and Safety Engineering, 18(05), 463–481. 22. Duan, Y., Gonzalez, J. A., Kulkarni, P. A., Nagy, W. W., & Griggs, J. A., (2018). Fatigue lifetime prediction of a reduced-diameter dental implant system: Numerical and experimental study. Dental Materials, 34(9), 1299–1309. 23. Dubourg, V., (2011). Adaptive Surrogate Models for Reliability Analysis and Reliability-Based Design Optimization. Doctoral dissertation, Université Blaise Pascal-Clermont-Ferrand II.

Scientific Optimization of Engineering Systems

161

24. Duckstein, L., (1995). Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems (Vol. 8). CRC Press. 25. Ehrgott, M., & Gandibleux, X., (2003). Multiobjective combinatorial optimization—theory, methodology, and applications. In: Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys (pp. 369–444). Springer, Boston, MA. 26. Engelbrecht, A. P., (2007). Computational Intelligence: An Introduction. John Wiley & Sons. 27. Erfanian, N., Derakhshani, A., Nasseri, S., Fereidouni, M., Baradaran, B., Tabrizi, N. J., & Safarpour, H., (2022). Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective. Biomedicine & Pharmacotherapy, 146, 112558. 28. Fei, B., (2018). Precision imaging of prostate cancer: Computer-aided detection and their clinical applications. In: Prostate Cancer Imaging (pp. 259–293). CRC Press. 29. Fogel, D. B., (2006). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (Vol. 1, pp. 1–22). John Wiley & Sons. 30. Gallier, J., (2013). Fundamentals of Linear Algebra and Optimization CIS515, Some Notes.(pp. 20-24) 31. Garg, D., Wong, K., Sarangapani, J., & Gupta, S. K., (2021). Advanced Computing: 10th International Conference, IACC 2020, Panaji, Goa, India, December 5–6, 2020, Revised Selected Papers, Part I (Vol. 1367). Springer Nature. 32. Gruber, W. H., Marquis, D. G., Hackman, R. C., Le Breton, P. P., & Luenberger, D. G., (2003). Marketing Series (p. 392). New York, John Wiley & Sons. 33. Hao, Z., Wan, C., Gao, X., Ji, T., & Wang, H., (2013). The effect of screw fixation type on a modular hemi-pelvic prosthesis: A 3-D finite element model. Disability and Rehabilitation: Assistive Technology, 8(2), 125–128. 34. Hartley, R. I., & Kahl, F., (2009). Global optimization through rotation space search. International Journal of Computer Vision, 82(1), 64–79. 35. Ho, Y. C., (1994). Overview of ordinal optimization. In: Proceedings of 1994 33rd IEEE Conference on Decision and Control (Vol. 2, pp. 1975–1977). IEEE.

162

Scientific Principles of Engineering

36. Holl, S., Zimmermann, O., Palmblad, M., Mohammed, Y., & HofmannApitius, M., (2014). A new optimization phase for scientific workflow management systems. Future Generation Computer Systems, 36, 352– 362. 37. Hu, C., & Youn, B. D., (2011). Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems. Structural and Multidisciplinary Optimization, 43(3), 419– 442. 38. Hwang, J. T., Lee, D. Y., Cutler, J. W., & Martins, J. R., (2014). Largescale multidisciplinary optimization of a small satellite’s design and operation. Journal of Spacecraft and Rockets, 51(5), 1648–1663. 39. Kamil, S., Oliker, L., Pinar, A., & Shalf, J., (2009). Communication requirements and interconnect optimization for high-end scientific applications. IEEE Transactions on Parallel and Distributed Systems, 21(2), 188–202. 40. Kluess, D., Mittelmeier, W., & Bader, R., (2010). Intraoperative impaction of total knee replacements: An explicit finite-elementanalysis of principal stresses in ceramic vs. cobalt–chromium femoral components. Clinical Biomechanics, 25(10), 1018–1024. 41. Knowles, J., & Nakayama, H., (2008). Meta-modeling in multiobjective optimization. In: Multiobjective Optimization (Vol. 1, pp. 1–23, 245– 284). Springer, Berlin, Heidelberg. 42. Koziel, S., & Yang, X. S., (2011). Computational Optimization, Methods and Algorithms (Vol. 356). Springer. 43. Kumar, A. V., (2014). Fuzzy Expert Systems for Disease Diagnosis (Vol. 1, pp. 1–16). IGI Global. 44. Labanda, S. R., (2015). Mathematical Programming Methods for Large-Scale Topology Optimization Problems (Vol. 1, pp. 1–21). DTU Vindenergi. 45. Larsen, F., & Ormarsson, S., (2014). Experimental and finite element study of the effect of temperature and moisture on the tangential tensile strength and fracture behavior in timber logs. Holzforschung, 68(1), 133–140. 46. Lisnianski, A., & Levitin, G., (2003). Multi-state System Reliability: Assessment, Optimization and Applications (Vol. 1, pp. 1–22). World scientific.

Scientific Optimization of Engineering Systems

163

47. Luenberger, D. G., (1969). Hyperbolic pairs in the method of conjugate gradients. SIAM Journal on Applied Mathematics, 17(6), 1263–1267. 48. Luo, Z. Q., & Yu, W., (2006). An introduction to convex optimization for communications and signal processing. IEEE Journal on Selected Areas in Communications, 24(8), 1426–1438. 49. Mandalay, M. I. I. T., (2018). BER performance analysis of MIMOOFDM over wireless channel. International Journal of Pure and Applied Mathematics, 118(5), 195–206. 50. Martins, J. R., & Lambe, A. B., (2013). Multidisciplinary design optimization: A survey of architectures. AIAA Journal, 51(9), 2049– 2075. 51. Meng, D., Li, Y., Zhu, S. P., Lv, G., Correia, J., & De Jesus, A., (2019). An enhanced reliability index method and its application in reliabilitybased collaborative design and optimization. Mathematical Problems in Engineering, 1, 1–18. 52. Mitchell, M., (2021). Abstraction and analogy‐making in artificial intelligence. Annals of the New York Academy of Sciences, 1505(1), 79–101. 53. Moftah, H. M., Azar, A. T., Al-Shammari, E. T., Ghali, N. I., Hassanien, A. E., & Shoman, M., (2014). Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Computing and Applications, 24(7), 1917–1928. 54. Mohanty, S. N., Chatterjee, J. M., Jain, S., Elngar, A. A., & Gupta, P., (2020). Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries (Vol. 1, pp. 1–25). John Wiley & Sons. 55. Moorhouse, D. J., & Camberos, J. A., (2011). Exergy Analysis and Design Optimization for Aerospace Vehicles and Systems. American Institute of Aeronautics and Astronautics. 56. Nocedal, J., & Wright, S. J., (1999). Numerical Optimization (Vol. 1, pp. 1–30). New York, NY: Springer New York. 57. Omran, M., Engelbrecht, A. P., & Salman, A., (2005). Particle swarm optimization method for image clustering. International Journal of Pattern Recognition and Artificial Intelligence, 19(03), 297–321. 58. Park, J. T., Han, S. W., & Choi, N. S., (2014). Robust design of the back-plate shape of the disc brake pad for reduction of uneven wear.

164

59.

60.

61.

62. 63. 64.

65.

66. 67.

68.

69.

Scientific Principles of Engineering

Transactions of the Korean Society of Automotive Engineers, 22(1), 8–19. Patel, B. C., & Sinha, G. R., (2010). Early detection of breast cancer using self-similar fractal method. International Journal of Computer Applications, 10(4), 39–43. Patel, B. C., & Sinha, G. R., (2015). Gray level clustering and contrast enhancement (GLC–CE) of mammographic breast cancer images. CSI Transactions on ICT, 2(4), 279–286. Pinho, M. C., Bera, K., Beig, N., & Tiwari, P., (2021). MRI Morphometry in brain tumors: Challenges and opportunities in expert, radiomic, and deep-learning-based analyses. Brain Tumors, 323–368. Pintér, J. D., (2006). Global Optimization: Scientific and Engineering Case Studies (Vol. 85). Springer Science & Business Media. Plhák, J., (2014). Dialogue-Based Exploration of Graphics for Users with a Visual Disability.(pp. 1-15) Popov, A. V., (2014). Ecological optimization of the architectural environment of higher education institutions in Moscow-the use of Phyto-metal structures. In: Advanced Materials Research (Vol. 869, pp. 162–166). Trans Tech Publications Ltd. Rangaiah, G. P., (2010). Stochastic Global Optimization: Techniques and Applications in Chemical Engineering (With Cd-rom) (Vol. 2). World Scientific. Rangaiah, G. P., (2016). Multi-Objective Optimization: Techniques and Applications in Chemical Engineering (Vol. 5). world scientific. Ruiz-López, E., Calatayud-Pérez, J., Castells-Yus, I., GimenoPeribáñez, M. J., Mendoza-Calvo, N., Morcillo, M. Á., & Schuhmacher, A. J., (2021). Diagnosis of glioblastoma by immuno-positron emission tomography. Cancers, 14(1), 74. Saeedi, S., Khorsand, R., Bidgoli, S. G., & Ramezanpour, M., (2020). Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Computers & Industrial Engineering, 147, 106649. Santosh, K. C., & Hegadi, R. S., (2019). Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part I (Vol. 1035). Springer.

Scientific Optimization of Engineering Systems

165

70. Sharpe, P., & Hansman, R. J., (2022). Core components of an optimization framework for engineering systems based on automatic differentiation. In: AIAA AVIATION 2022 Forum (p. 3937). 71. Simon, D., (2013). Evolutionary Optimization Algorithms. John Wiley & Sons. 72. Sinha, G. R., (2020). Modern Optimization Methods for Science, Engineering and Technology (Vol. 1, pp. 1–27). IOP Publishing. 73. Sinha, G. R., Raju, K. S., Patra, R. K., Aye, D. W., & Khin, D. T., (2018). Research studies on human cognitive ability. Int. J. Intell. Def. Support Syst., 5(4), 298–304. 74. Sobieszczanski-Sobieski, J., (1995). Multidisciplinary design optimization: An emerging new engineering discipline. In: Advances in Structural Optimization (pp. 483–496). Springer, Dordrecht. 75. Sterne, J., (2017). Artificial Intelligence for Marketing: Practical Applications (Vol. 1, pp. 1–22). John Wiley & Sons. 76. Sun, Y., Wong, A. K., & Kamel, M. S., (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(04), 687–719. 77. Thielman, J., & Ge, P., (2006). Applying axiomatic design theory to the evaluation and optimization of large-scale engineering systems. Journal of Engineering Design, 17(1), 1–16. 78. Utomo, I. G. W., & Darma, G. S., (2020). Measuring optimization of digital military programs: An innovation of information and communication systems in industrial digitalization 4.0. International Research Journal of Engineering, IT and Scientific Research, 6(2), 39–46. 79. Varma, A., & Palsson, B. O., (1994). Metabolic flux balancing: Basic concepts, scientific and practical use. Bio/technology, 12(10), 994–998. 80. Venter, G., & Sobieszczanski-Sobieski, J., (2003). Particle swarm optimization. AIAA Journal, 41(8), 1583–1589. 81. Wahalathantri, B., Thambiratnam, D., Chan, T., & Fawzia, S., (2011). A material model for flexural crack simulation in reinforced concrete elements using ABAQUS. In: Proceedings of the First International Conference on Engineering, Designing and Developing the Built Environment for Sustainable Wellbeing (pp. 260–264). Queensland University of Technology.

166

Scientific Principles of Engineering

82. Wahalathantri, B., Thambiratnam, D., Chan, T., & Fawzia, S., (2012). Evaluation of strain energy-based damage indices for flexural crack detection of RC beams. In: ACOME 2012: Proceedings of the International Conference on Advances in Computational Mechanics (pp. 421–433). Tri Thuc Publisher. 83. Wright, S., & Nocedal, J., (1999). Numerical Optimization (Vol. 35, No. 67, 68, p. 7). Springer Science. 84. Yang, X. S., & Gandomi, A. H., (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations (Vol. 1, pp. 1–19). 85. Yang, X. S., & Koziel, S., (2011). Computational Optimization and Applications in Engineering and Industry (Vol. 359). Springer Science & Business Media. 86. Yuan, F., Jiang, P., Xie, J., & Wu, X., (2012). Analysis of spherical indentation of materials with plastically graded surface layer. International Journal of Solids and Structures, 49(3, 4), 527–536. 87. Zahedmanesh, H., & Lally, C., (2012). A multiscale mechanobiological modelling framework using agent-based models and finite element analysis: Application to vascular tissue engineering. Biomechanics and Modeling in Mechanobiology, 11(3), 363–377. 88. Zhang, G., Rong, H., Neri, F., & Pérez-Jiménez, M. J., (2014). An optimization spiking neural P system for approximately solving combinatorial optimization problems. International Journal of Neural Systems, 24(05), 1440006.

6

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ENGINEERING COMMUNICATIONS

CONTENTS 6.1. Introduction..................................................................................... 168 6.2. Communications and Information Resources................................... 168 6.3. The Engineer as a Writer.................................................................. 171 6.4. Graphical Communications............................................................. 177 6.5. The Engineer as a Speaker................................................................ 188 References.............................................................................................. 192

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6.1. INTRODUCTION It is impossible to overestimate the significance of excellent communication in engineering achievement. No matter how inventive and lovely a concept is, it is worthless unless it could be conveyed to people who would approve, pay for, sustain, and transform it into the actual world. Effective engineers have to be capable of communicating effectively with their superiors, colleagues, as well as the broader population. Because these people are often separated by distance, good communication through technology as well as conventional means is critical.

6.2. COMMUNICATIONS AND INFORMATION RESOURCES Information is becoming omnipresent in our precious lives of advances in computational technologies and the fast rise of the Internet. A lot of information is accessible in a range of shapes via a plethora of portals. Engineers seek knowledge from a variety of resources, including the World Wide Web, academic publications, textbooks, reference materials, online worlds, coworkers, and full-text libraries. With so much data accessible, the difficulty for contemporary engineers is not only finding it, but also knowing how to filter, assess, integrate, and apply it (McBride & Dickstein, 1998; Rothenberg, 1998). Engineers are relied upon to undertake research, apply that knowledge to solve issues and explain their ideas to others, whether they are undergrad students finishing a semester project or professionals who work on a design challenge.

6.2.1. The Information-Seeking Process Engineers’ information-seeking attitudes, as well as methods, have changed dramatically as a result of technological advancements. Engineers have incorporated new World Wide Web-driven materials into the knowledge procedures while continuing to utilize old sources like colleagues, manuals, print indexes, as well as study findings (Freedenberg & Duro, 2012). A scholar can use the Internet almost as readily as they can access a reference guide or stroll to the local library. Engineers attend library functions less commonly as an outcome. Engineers grow increasingly reliant on remote access to a computer as relevant academic shifts to internet sites, specialized Web portals, and desktop capability, but they are also more overloaded by data noise (Wright, 2003). The outcome is twofold. Engineers require help

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to develop their study as well as communication skills as they do even more of their study as well as interactions distantly through the computer (Rich et al., 2018). Understanding the stages involved in discovering and utilizing good info is the best approach to assure that you are doing so. The classic research method consists of five essential steps: •

Determine as well as describe the subject, study question, or issue. • Choose the words or concepts that best represent your issue. • Choose an acceptable source of information and do research. • Review the search queries and choose the books, papers, technical reports, sections, pages, etc that would provide you with the most useful details. • Identify the sources and combine the knowledge in able to fix an issue. Even though the Web has a significant effect on the development of finding content, the first four stages are still necessary for any form of search. It is possible to locate data resources by just clicking a hyperlink (Scott & Silbey, 2000). Long before the Internet, engineers either had to speak with anyone immediately (in person or on the phone with just a partner), check their library, or attend a research catalog to obtain relevant resources. If an engineer employs conventional research tools or the Internet, the capacity to evaluate the issue at hand, select the proper resource, and conduct a successful search remains critical.

6.2.2. Turning Information into Knowledge Therefore, the Internet is not a panacea for engineers’ data to provide and improve. It has, if something, created problems to find relevant sources and assess google results to locate high-quality content. Quantity, as well as connectivity, have not improved the quality of knowledge, and academics and companies are starting to recognize this (Mauch & Park, 2003). Employers are emphasizing the importance of incorporating “soft skills” like composition, oral communication, collaboration, and continuous learning into the skills and experience of engineers. Knowledge and the ability to better and quickly access, analyze, and analyze information to help in study, problem-solving, and learning are essential to successful communication and lifelong learning (Brody, 2012).

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For engineers seeking knowledge, the World Wide Web is rife with both potential and danger. Before the emergence of the Internet and online fulltext papers, it was simpler for professors, learners, managers, and workers to evaluate the credibility of the material they discovered. Books, as well as journal papers in the company as well as university bookstores, as well as journal papers in the company as well as university bookstores, are evaluated and vetted by other experts. In a chemical soup of knowledge on the Internet, peer-reviewed material coexists alongside inaccurate and unconfirmed material, biased knowledge, and marketing. On occasion, it appears as if everybody has published something on the Internet, since anybody may do so. Add to this the fact that almost all books have created at least a portion of their collections accessible through the Internet, and it may be challenging to distinguish between excellent and misleading facts (Kirkman & Darrell, 1980).

Whenever they publish, Kari Boyd McBride and Ruth Dickstein (1) highlight the Internet’s potential in terms of knowledge. Many detractors dispute the efficacy of the World Wide Web as a scientific resource, particularly for students who could depend only on it for study. However, the Internet will heavily influence education and serve as a vital research resource that students love utilizing. There are compelling reasons on both sides of the issue. In actuality, the Internet is a fantastic study group for professionals. The Internet has great potential for sharing, finding, and disseminating technical data. Furthermore, the website’s existing structure, content, as well as search algorithms recommend caution. Consider the following example (Eitzen, 1988). Mr. Wilson’s Cabinet of Wonder (3) by Lawrence Weschler analyzes the development of Western museums. The book describes their roots around 300 decades previously, when private collections arose in the houses of the wealthy. Such private collections paved the way for the contemporary museum (Rowlands, 2016). Weschler cites the Museum of Jurassic Technology in Los Angeles as a contemporary illustration of such themed personal museums. The museum features and shows outlandish inventions and discoveries that defy imagination and have been painstakingly investigated and recorded. After understanding the necessity to properly analyze and filter Internet materials, it is necessary to acquire the necessary skills. CARDS is a frequent evaluation approach for data sources. The acronym CARDS represents credibility, accuracy, relevance, date, as well as source (Stone, 1984).

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Credibility: Whose information is the available source? Is this website a reputable information source? Is the author a teacher, graduate student, or a reputable professional? Could you identify the author as well as provide a summary of their qualifications? Any reliable source would provide the author’s name, contact details, organization, and rank.

6.3. THE ENGINEER AS A WRITER Engineers will most certainly be forced to write memos and letters, as well as scientific documents, journal articles, as well as standards, throughout their careers. Before actually delving into various styles of writing in-depth, let’s go over several basic writing standards (Gomery, 1986).

6.3.1. Guidelines for Effective Writing That section contains various recommendations for improving your writing skills. However, this list was created with engineering students in mind, these standards apply to many forms of text (Straus & Donnelly, 2017; Anthony, 1999). •

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Before writing, Organize your Ideas: “Planning had to be an intentional antecedent to writing,” writes Strunk and White (4). As a result, the fundamental principle of composing is to anticipate or decide the form about what is to follow, and then follow this shape.” Outline: Some authors, particularly those who are a novice, believe that preparing an overview of the intended work is extremely beneficial. This kind of activity assists the writer in thinking thru the work and improving its coherence and organizational structure. It also serves as a logical structure and foundation for better literary efficiency and productivity (Davis, 1977). Prevent a Monotonous Framework: Making the paragraph the compositional unit. It must have a central concept that is presented by a topic phrase. It is frequently advantageous in scientific and other sorts of literature to split a document into sections, parts, as well as subheadings with suitable headers or names. Present complicated material or data as lists or charts instead of narrative to promote readability as well as range.

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Strive for Conciseness and Simplicity: According to Zinsser (5), “the key of successful writing is to remove every phrase to its purest elements.” Sentences are often favored over short ones. Simple sentences are frequently preferable to lengthy ones (6). For instance, close is chosen to intimate closeness to rare is chosen to rare now is chosen to at this moment (Houp et al., 1998). Tailor Your Style of Writing to the Reader: Writers should evaluate the readers’ academic credentials, economic status, age, as well as hobbies before selecting a style of writing acceptable to the target market. Technical magazine publications, for instance, could include chemical formulae, theoretical results, thorough explanations of study techniques, and carefully constructed conclusions and findings. Articles or studies designed for a public readership demand a different style that emphasizes practical, personal interpretations and uses of such data being delivered, as well as clear language as well as simple graphics (Duderstadt et al., 1982). Prevent Using Slang and Trendy Terminology: Words like “OK,” “terrific,” and “tremendous” are inappropriate for scientific communication, which requires a professional tone. Prevent using fad phrases or idioms like “take priority,” “approve the final,” as well as “bottom line.”

6.3.2. Types of Engineering Writing In 1977, Richard M. Davis (7) did a study of work to develop and discovered that two-thirds of their time was spent composing on the median. Engineers compose a range of documents, such as entries in study journals and logs, office memos, internal communications, scientific documents and studies, and design standards. Logs and Notebooks Numerous engineers typically document their work in a journal, logbook, or notebook. This kind of writing often comprises a daily account of the engineer’s work, such as the findings of laboratory tests, meeting minutes, and other significant information. Carefully created as well as preserved records of this kind give fast dissemination of knowledge for notes, correspondence, and scientific documents (Larice & Macdonald, 2013; Moran, 2018).

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Notes and Professional Letters In the majority of big firms and companies, internal communication is conducted through memos. Memoranda are often concise and limited to a particular topic. The above medium of expression generally refers to the date, the addressee (TO), the author (FROM), the subject (SUBJECT), as well as the text. Even though the layout might very well vary between groups, this medium of expression generally refers to the date, the addressee (TO), the author (FROM), and the subject (SUBJECT) (Winter et al., 2011; Albrecht & Naeemi, 1984). Figure 6.1 shows an illustration of a memorandum.

Figure 6.1. Sample memorandum. Source: https://www.amazon.com/Introduction-Engineering-Paul-H-Wright/ dp/047105920X.

Business letters are often used for external contact, i.e., with that outside of the writer’s company. As seen in Figure 6.2, engineers follow a common structure when writing business letters. The language and tone of business letters must be clear, succinct, exhaustive, and kind (Jones, 1984). E-mail The majority of engineers nowadays frequently converse through email. Usually, this mode of communicating is concise. In speaking, it is best to limit the length of an email to one display. The title tag of the email ought to be relevant, and long topic content should be linked instead of contained in the email. Technical Documents Technical documentation is by far the most prevalent form of technical communication associated with engineering activities (O’Kelly & Miller, 1984). This information allows the engineer to communicate the outcomes of one’s work to coworkers, customers, supervisors, as well as other managerial staff, as well as the public at large (Meyer & Miller, 1984).

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Based on the kind of report, working paper formats may differ. There are organizational parallels between status reports, bids, and empirical study findings, but every sort of scientific communication has its framework. The style for a specific kind of report is pretty uniform. Such sections are not often presented in the order mentioned (Sproule, 2020). The cover page specifies the report’s author or authors, their firm or group, as well as the date of issue. It may include a reference number, a supporting organization’s address and phone number, a mailing list, and limits on the study’s replication and usage. The abstract provides a succinct description of the report’s content and purpose. Its objective is to give sufficient information needed to determine whether to either acquire as well as read the full report. An informative summary explains the contents of the comprehensive document but does not state its conclusions. A piece of information that can be found provides a concise summary of the study’s method and significant findings and suggestions (Ciment & Russell, 2007). Numerous customers, especially numerous government agencies, demand an executive summary that succinctly summarizes the report’s conclusions and suggestions.

Figure 6.2. An illustration of a business letter. Source: https://www.amazon.com/Introduction-Engineering-Paul-H-Wright/ dp/047105920X.

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The types of experiences the study’s topic, objective, and range, as well as its creative strategy. It might include conceptual or historic context information gleaned from the specialized literature. The technique or process section describes in detail how the work mentioned in the research or inquiry was carried out. In descriptions of experimental experiments, this part often describes the equipment employed (Sriraman & DeLeon, 2001). In the findings section, the outcomes of the study or research are detailed. Typically, this portion of the report includes tables and graphs, as well as a summary and explanation of the findings or conclusions. The findings are “the inferences taken from the report’s factual material.” In technical research concerned with complicated or contentious topics, many authors preface their findings with an overview of the data and label this part overview and findings. The suggestions section provides a suggested plan of action derived from the findings. The suggestions are given, often in the select list, and do not require argumentation. In the part under “Acknowledgments,” contributors to the work who have brought significant efforts are recognized (Wang & Roush, 2000). The bibliography enumerates the textbooks, scholarly articles, and other sources consulted for the report. A collection of referenced books in scientific documents is often titled References. Technical documentation utilizes numerous bibliographic types. Figure 6.3 depicts two of these layouts. In the main report, citations might well be mentioned obliquely by name, number, or parenthesis (Brewer & Hsiang, 2002): • Studies were conducted at the University of Texas (Carter, 2001). • Research performed at the University of Texas (1) • Research performed at the University of Texas (1) Many organizations choose to add bibliographic references as footnotes instead of in a different section after the study’s main structure. Typically, good detailed information like computer software, datasets, and the like are presented in one or even more appendices after the report. Typically, the author of a technical paper (8) utilizes four types of discussion (Voas & Bojanova, 2014; Pratt, 2001): •

The process of relating a set of events in an orderly and often historical manner is narration.

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A verbal depiction of something, often articulated in words like size, form, color, as well as texture is the description. Exposition, that states the author’s intention or goal for the goal of elucidating or explaining a subject. The objective of argumentation is to convince the consumer of the likelihood that a certain assertion is true.

Figure 6.3. Example of bibliographies and reference lists. Source: https://www.academia.edu/30141729/_Paul_H_Wright_Introduction_ to_engineering_BookZZ_org_.

A technical report author can utilize instances, descriptions, categories, analogies, and explanations of causality to teach or teach the reader (Lubell et al., 2004). Journal Articles Engineers frequently report their findings in technical journals. Journal articles are often shorter than scientific documents, but they are organized and include comparable information. Journals allow for the broader adoption of technical information (Bajaj et al., 2005). Details The engineer uses technical specs to transmit detailed information to constructors, fabricators, and producers about resources, measurements, and craftsmanship for anything that is to be constructed installed, or made. To guarantee accuracy and completeness, requirements must be developed with considerable care. These papers are often included in engineering agreements and can be used to record design specifics (Feeney & Price, 2000). As a result, the terminology used in such papers is exact, and the style of writing is judicial. Figure 6.4 depicts an illustration of certain technical standards.

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6.4. GRAPHICAL COMMUNICATIONS The majority of engineers interact with one another and with other professionals using a world language of technical visualization tools. Hand drawings to extremely complex computer-produced representations and models are all examples of such expressions. The drawings and computergenerated images transcend culture and traditions (Flater & Morris, 2001; Patil et al., 2002).

Figure 6.4. Illustration of engineering specs. Source: https://www.academia.edu/30141729/_Paul_H_Wright_Introduction_ to_engineering_BookZZ_org_.

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They are specially designed to eliminate misunderstanding by precisely describing construction and production specifics. Every one of these depictions is constructed utilizing industry-adopted standards and nomenclature, which become communication standards utilized in both hand-made messages and computer processor forms (Thornton & Huang, 2013; Bailey, 1974). Several of the presentations are specified by groups like ANSI as well as ASME. As an artist, you should be able to develop these forms to explain your work as well as process and comprehend graphical presentations created by others. You might well be requested to generate a cost estimate for building or producing the things indicated in the graphic renderings, or you’d be explaining the planning of a project to a field supervisor who might make the artifact specified in a design (Blackman & Tukey, 1958). The level of information in such depictions often differs depending on the target audience. When the goal is to demonstrate to a customer the overall design, specifics might well be kept out of the depiction to concentrate on the overall project challenges. On occasions, the schematic diagram is employed for a machining or building worker who requires specific measurements and tolerances as well as manufacturing data to manufacture the component or item (Kotz et al., 2001).

6.4.1. Any Line Conventions and Lettering The American National Standards Institute (ANSI) has found that having line as well as lettering techniques to be used in the creation of technical drawings. The document states the length, structure, and suitable usage of several kinds of lines in engineering drawings. Figure 6.5 depicts many sorts of lines as well as how they are created and utilized (Yablonovitch & Kane, 1988). Visible lines are broad, solid lines that are used to illustrate the apparent borders or outlines of things. Hidden lines are small, uniformly spaced thin lines that show an object’s underlying characteristics. In a plan view, segment lines are simple in nature lines that depict the sliced surfaces of an item (Versteeg et al., 1996). Occasionally special line symbols are being used to denote the sort of material portrayed. As seen in Figure 6.5, center lines are made up of alternating long and short thin dashes. Axes of symmetrical sections and features, bolt circles, and pathways of motion are represented by center lines.

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Sizing uses solid sharp lines to represent the range as well as the orientation of measurements, extension lines to show the spot or line on the design whereby the size applies, and rulers to guide notes, dimensions, signs, members value, or component numbers to elements on the design (Ippolito, 2017). Cutting-plane, as well as viewing-plane lines, show where the cutting planes are for segment perspectives and where the viewing location is for deleted partial views. Figure 6.5 depicts a typical design as well as the application of these lines. Break lines show that just a section of an item has been drawn. As seen in Figure 6.5, these lines are widely drawn. Long regulated thin dots connected by zigzags can also be utilized as break lines. Phantom outlines are being used to represent alternative locations of rotating parts, neighboring locations of interconnected parts, as well as repeating detailing. Phantom lines are represented by long skinny dots divided by short thin dots (Allen & Gerstberger, 1973; Aggarwal et al., 2010).

Figure 6.5. ANSI standard line practice. (Reprinted from ASME). Source: https://books.google.com.pk/books/about/Introduction_to_Engineering_Library.html?id=I_5gLih6Y7MC&redir_esc=y.

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For technical drawing, the ANSI advises using single-stroke gothic writing. As seen in Figure 6.6, the lettering might be slanted or straight. If lowercase letters are necessary to comply with other set norms or terminology, uppercase characters are given for all writing on drawings. The ANSI standard sets the basic letter sizes for different size drawings (Zhou et al., 2014).

Figure 6.6. (a) ANSI lettering standard procedure. (b) Letters that are angled: Lateral letters (Reproduced with permission from the American Society of Mechanical Engineers from ASME Y14.2M-1979.) Every right is reserved.). Source: https://books.google.com.pk/books/about/Introduction_to_Engineering_Library.html?id=I_5gLih6Y7MC&redir_esc=y.

6.4.2. Types of Graphical Communications To help engineers communicate with the general public and with one another, many graphical communication formats are specified. Similar to

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how a transition plan has a size that is explained in its legend, those kinds have related scales, a specified measuring system that enables a visual picture to depict either shorter or bigger than the real artwork. Such models often include geometry and measurements along with industrial assembly methods (Ishak et al., 2010). The methods provide details on welding, casting, machining, and allowable tolerances. Sometimes the designs include schematics or representations of building and assembling procedures, like a user instruction booklet for a model kit or prefabricated piece of wood. Last but not least, the engineer could visualize data using line, bar, or bar graphs (Grace, 1982; Mahdavi et al., 2103). Dates for building, spending plans and facility capability are a few examples of the data that should be included. A two-dimensional or three-dimensional depiction of an item might be used by the engineer. The target audience for the data determines which form is most often used. Common three-dimensional drawings, known as pictorials, such as viewpoints and isometric renderings are produced for communication with non-engineering experts. The drawings provide a depiction that is closer to what one would see, but they might be misleading when it comes to precise proportions. There is not much information regarding manufacturing or building methods in these drawings (Al-Sahhaf et al., 2005; Zydney & Colton, 1986). To illustrate real unit measurements, tolerances, and materials, engineering experts often develop a three-dimensional solid model with two-dimensional views providing orthographic projections, section views, and auxiliary views. To describe, model, and interact with products before they are manufactured or built, engineers also employ simulations and virtual reality representations. This helps them prevent expensive fabrication errors. Below is a short description of all of such representations (Joback & Reid, 1987).

6.4.2.1. Sketching The skill of sketching involves expressing graphical concepts without the use of conventional mechanical instruments. Although talent is required in the first presentation of ideas, sketching is often thought of as a fast or imprecise drawing. A structural feature of a cored pilaster block is shown in a drawing in Figure 6.7. These kinds of sketches need to be unambiguous, succinct, and to some degree exact (Makinde & Ogulu, 2008). Sketching skills are connected to visualizing. The mental process of visualization enables the designer to create a mental image of the object

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under consideration. The design process is stimulated by quickly sketching out concepts (Weber et al., 1996). It is possible to assess and improve the design by capturing ideas in a visual form.

Figure 6.7. A drawing demonstrating a structural feature of a cored pilaster block is shown (Courtesy of Dr. W. Rodriguez-Ramos.). Source: https://www.goodreads.com/book/show/2607961-introduction-to-engineering.

An engineering sketch may be distinguished from an esthetic sketch by the standards that were used to create it. The engineering drawing preserves the proportionality of the proportions and complies with the orthographic or visual rules previously mentioned (Vajda et al., 1989). This help discusses construction or manufacturing processes in the field or swiftly communicate design ideas among coworkers.

6.4.2.2. Pictorial Representations There are several methods for drawing three-dimensional models of things. With perceived distances from the viewer’s sight, perspective visualizations allow for a directional decrease in size. Putting parallel lines on the item as

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arcs that merge to an edge of the frame gives the appearance of distance. When things are further away from the spectator in one-point views, the physical often “vanishes,” or is rendered smaller (Soltanieh & GILL,’ 1981). While using more vanishing points in more directions, second – and third viewpoint approaches provide the same impression of separation from the viewer’s eye. When presenting data to those who are not engineers, viewpoint representations may be the most convincing to the naked eye of all graphs and charts. Figure 6.8 depicts an item from one point of view, whereas Figure 6.9 depicts the same thing from two points of view (Khan & Richardson, 1989). Isometric representations are yet another kind of visual display. In that the observer is often gazing at a corner of an item, these illustrations are comparable to two-point viewpoint presentations. However, in isometric drawings, the parallel lines on the object stay parallel to one another in the drawing instead of converging to an edge of the frame (Javed et al., 2013). All such sides are often drawn at a 30° angle to the horizontal.

Figure 6.8. A one-point perspective drawing of an item.

Source: https://www.goodreads.com/book/show/2607961-introduction-toengineering.

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Figure 6.9. A two-point perspective depiction of an item. Source: https://www.goodreads.com/book/show/2607961-introduction-to-engineering.

6.4.2.3. Orthographic Representations Orthographic representations have traditionally been used to depict engineering objects. This approach entails seeing an item from three orthogonal orientations and sketching the image from both these vantage points. Part (a) of Figure 6.10 displays a “glass” observation box surrounding an item. Usually, three conventional perspectives are derived: the front perspective, the top perspective, and the side (Tunç et al., 2022). To convert such three perspectives of a similar item into a two-dimensional sheet of paper, the vertices are “unfolded” as seen in Figure (b). Every view is now positioned as seen in Figure 6.10, part (c). This way of expressing objects ensures that line measurements are not twisted or changed in dimensions according to the viewpoint, as is the case with perspective visualizations. Throughout this approach, the object’s ambiguity is reduced (Silaban et al., 1996). In certain fields, the various perspectives have distinct names. For instance, the top perspective is often referred to as a plane surface, while the front, as well as side perspectives, are occasionally referred to as elevations. Figure 6.11 is an engineering sketch depicting the positioning of a railroad crossover gate across a two-lane highway (Chamkha & Aly, 2010). The model’s upper part depicts two altitudes, the front, and side views. In-plane surface, the bottom section of the picture depicts the location of the crossover gate concerning the railroad track as well as a two-lane road.

6.4.2.4. Section Views Occasionally, the three conventional perspectives of an object’s orthographic portrayal do not provide adequate information to describe (build or produce) the component. In some instances, portions or observation planes positioned

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inside the component are presented. The resultant view from an airplane is known as a segment (Johansen, 1984). There are numerous sorts of segments, but a whole section, like the one seen in Figure 6.12, is generally utilized. By placing a viewing axis in the center of the component and thereafter drawing a segment, the inside of the component is revealed more clearly (Sargeant, 2005). By displaying crosshatching indications (several diagonal dashes) across the substance where the viewing surface was positioned, the portion is distinguished from other viewpoints (see Figure 6.12, a portion (c)).

Figure 6.10. The primary orthographic drawing lines. (Reference: James H. Earle, Engineering Design Graphics, 5th Edition, Addison-Wesley Publishing Co., Boston, MA, 1987.) Reprinted with Pearson Education’s permission. Source: https://www.wiley.com/en-us/Introduction+to+Engineering+Library %2C+3rd+Edition-p-9780471059202.

Figure 6.11. A contrast between an orthographic perspective and a cross-sectional picture of a similar item. (Source: James H. Earle, Engineering Design Graphics, Fifth Edition, Addison-Wesley Publishing Company, Boston, Massachusetts, 1987. Source: https://www.wiley.com/en-us/Introduction+to+Engineering+Library %2C+3rd+Edition-p-9780471059202.

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6.4.2.5. Auxiliary Views Line lengths may be warped from their real dimensions when an item has sloping faces or portions that are not parallel to the viewing planes. Engineering drawings are created to accurately reflect the lines and faces of objects in a given view (Murdock & Golding, 1989; Hsu et al., 1991). In the situation of inclined faces, auxiliary views are created. Such images are acquired by gazing from a normal to the sloped face position. Figure 6.13 depicts a wedge, often known as a doorstop. In the orthographic depiction, the sloping face of this component is not depicted in its real proportions in some of the three typical perspectives (Nicholas et al., 2014). As a result, an auxiliary image is created to depict the genuine face. Only the sloped face is appropriately presented in the auxiliary view; the level top of the square is now deformed in the auxiliary view. Engineers are required to precisely assess distorted perspectives to employ the right components, measurements, and resources in construction (Von Solms & Van Niekerk, 2013). To understand more about engineering graphical communications, read Engineering Design Graphics by researchers.

Figure 6.12. An example of a supplementary viewpoint. (Reference: James H. Earle, Engineering Design Graphics, 5th Edition, Addison-Wesley Publishing Co., Boston, MA, 1987.) With the permission of Pearson Education, Inc., Upper Saddle River. Source: https://www.thriftbooks.com/w/introduction-to-engineering-library_ paul--h-wright/321203/#edition=3163613&idiq=23853888.

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6.4.3. Computer Tools for Creating Representations Engineering practice often uses computer technologies to create engineering visual representations. Both two-dimensional and three-dimensional representations of engineering data are available via a variety of engineering software applications (Viswanath, 2005; Dede, 2007). When developing new designs, many engineering firms reuse or change standard component files. Over manually generated graphical representations, computer-based software has many benefits. First off, updating, and changing the information is simple (Adam, 1996). Through electronic communications, such as e-mail, the information may be swiftly shared with those who need it. Second, a lot of the computer-based software tools in use today represent engineering products in databases so that the data may be referenced throughout the whole engineering design, production, and support cycle (Ball-Rokeach & DeFleur, 1976). In addition to allowing engineers who designed the part to release the information, this also enables marketing people to achieve information on the portion to be used in research on the application, engineers, and maintenance workers to obtain component specifics for service calls, and inventory management system for using components and subcomponents required for stocking (Lee, 2007). Engineers may examine the component for the strength of the material, fatigue, as well as other qualities using computer-based tools that connect to analysis and design software programs (Forsythe, 1997; Pigg & Crank, 2004). Software tools may communicate with the other software applications by supporting several exchange protocols and exchanging data about the component, its layout, and its manufacture. The International Organization for Standardization (ISO) standard 10303, the Initial Graphics Exchange Specification (IGES), the Standard for the Exchange of Product (STEP) model data, and the Product Data Exchange using STEP (PDEs) are all examples of standards and specifications that take the opportunity to exchange intelligence among software programs (Ayanso et al., 2014). As a consequence of the specification and standards work for the interchange of development, machinery, use, repair, and disposal details related to the actions of an engineering bit’s life cycle, several international guidelines have been set (Van Cuilenburg, 1999; Timbus et al., 2009). Through the National Institute of Standards and Technology in the United States, the majority of such standards are coordinated by and linked up with the ISO.

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6.4.4. Simulation and Virtual Reality The user may define a range of output settings with the majority of software applications. Traditional orthographic projections may be made, as well as more intricate representations that use light and material modeling to give the part photorealistic features like shadows and gloss (Marchionini & Maurer, 1995). With this sort of output, engineers may search for engineering interference, manufacturability, or constructability of the component before spending a lot of money manufacturing the part, in addition to providing customers with very realistic representations of the finished product (Marchionini, 2006). When these models are utilized in virtual reality settings, engineers, and the consumers of the things being built may communicate and practice using the design before it is produced. To facilitate speedy user feedback on design choices and perhaps save significant amounts of money, these models and computing environments enable the engineer and the client to experience the constructed artifact before it is ever completed (Nichols Hess et al., 2016).

6.5. THE ENGINEER AS A SPEAKER Engineers must sometimes talk to groups of their peers, give talks to customers, preside over business meetings, as well as give technical papers at conferences. The successful engineer recognizes such a need to hone public speaking abilities and seizes chances to improve and expand such abilities (Lucas, 2008; Nyeng & Ostergaard, 2011).

6.5.1. Guidelines for Effective Speaking Here are a few broad pointers that should make the reader a more persuasive public speaker. Such recommendations, which were taken from a Toastmasters International brochure, apply to numerous forms of public speeches (Hughes, 2002; Pea, 1994). •

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Be organized. By preparing in advance, a speaker may talk with confidence and focus on what has to be conveyed rather than on themselves. Speak clearly and loudly. If a speaker speaks incoherently or with slovenly speech, there is little chance of effective communication. Make eye contact with your audience. Making eye contact with the audience may boost their attention and engagement in the presentation. Experienced speakers get the ability to evaluate

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their audience as well as their responses to alter their presentation accordingly (Clement & Shade, 2000). Speak with sincerity, confidence, and thoughtful consideration. By altering their speaking tempo, pitch, and loudness, a speaker may keep or pique an audience’s attention. Keep in mind that your audience is intelligent. Don’t think about them all the time. Make utilization of your voice’s deeper tones (Barraket et al., 2000). Write a much while preparing a speech. Remember just short passages of text and quotes. Don’t ever be intimidated by your crowd and your judgment. Avoid attempting to cover excessive land. Be conscious of time restrictions and modify the presentation’s duration as necessary (Aboyade, 1984).

6.5.2. Use of Visual Aids Visual aids, when designed and used correctly, maybe the key to successful vocal communication. Engineers may benefit from them particularly when it comes to teaching complicated issues and presenting the outcomes of huge projects (Bers, 2005). The speaker has to be ready to put up the work to achieve appropriate graphs and charts and organize their usage carefully. Visual aids should be seen as an inherent component of the spoken presentation instead of something “tacked on” to pass the time provided (Torrent-Moreno, 2007). The usage of graphics must thus be deliberately scripted while keeping the overall goals of the presentation in mind. There is a large range of visual equipment available, including 35-mm photography projectors, “overhead projectors” for 8-inch 10-inch slides, and 16-mm movie projectors. It is, nevertheless, preferable to employ visual content that could be displayed using generally available projection technology (Schmid et al., 2008). 35-mm slides and, to a lesser degree, 8-inch 10-inch projectors are often used in engineering conferences.

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6.5.3. Use of Computer Technology Engineers often plan and give public speaking using computer technologies. It is feasible to use PowerPoint slides not only to make desktop slides but also to select a backdrop and visuals that are suited for the content (Tsiknakis et al., 1996). The speaker may utilize changes to go from one screen to the next using software like PowerPoint. Slides may be disclosed in several ways, including disintegrating from one presentation to the next, phasing out or in, or employing “flying text” from any orientation. These impacts may increase a presentation’s visual impact (Cohen et al., 2002). Furthermore, any use of digital technology does not ensure the effectiveness of a presentation. If the presentation has merely an esthetic effect and no information, all viewers will notice. As a result, the main attention must be on the content of the lecture instead of the technology (Bowles-Terry et al., 2010).

6.5.4. Technical Presentations Engineers often communicate the outcomes of their labor via technical presentations at industry events. Professional meetings are often planned in three-hour “sessions” that allow for the presentation of four to six technical presentations (Comfort et al., 2006; Mumtaz, 2000). Each session is presided over by a moderator, who introduces the speakers, enforces time restrictions, and directs periods of questions and answers. A technical presentation typically lasts around 20 minutes, with 5 to 10 minutes set out for questions and responses. Technical group papers are typically authored and published in the conference proceedings. Technical reports containing sophisticated information or a large amount of statistical data may be read aloud to the assembly (Wang et al., 2021). However, it is generally preferable to summarize the content of the paper and present it in the form of an extemporaneous speech. A well-organized technical display must contain the same information as a scientific report or paper but in a concise form. The report typically includes an introduction, methodology, results, and conclusions. The introduction’s objective is to establish a welcoming environment, pique the audience’s interest in this subject, and declare the presentation’s academic content, purpose, range, as well as organizational structure (Bowles-Terry et al., 2010).

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The major part of the presentation consists of the processes followed to complete the task being reported on, as well as the project’s outcomes or conclusion. Since a listener’s mental capacity is restricted, public speaking professionals advise that speeches be structured around clever and engaging repetition (8). It is best to cover no more than two or three primary issues in a 20 to 30-minute presentation. These themes should be emphasized by relevant examples, explanations, and analogies, and they’ll be shown with suitable visual aids. The presentation’s conclusion often includes a quick review of the important points and several suggestions, containing ideas for future work.

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REFERENCES 1.

Aboyade, B. O., (1984). Communications potentials of the library for non-literates–an experiment in providing information services in a rural setting. Libri, 34(Jahresband), 243–262. 2. Adam, L., (1996). Electronic communications technology and development of Internet in Africa. Information Technology for Development, 7(3), 133–144. 3. Aggarwal, A., Kunta, S., & Verma, P. K., (2010). A proposed communications infrastructure for the smart grid. In: 2010 Innovative Smart Grid Technologies (ISGT) (pp. 1–5). IEEE. 4. Albrecht, P., & Naeemi, A. H., (1984). Performance of weathering steel in bridges. NCHRP Report, (272). 5. Allen, T. J., & Gerstberger, P. G., (1973). A field experiment to improve communications in a product engineering department: The nonterritorial office. Human Factors, 15(5), 487–498. 6. Al-Sahhaf, T., Elkamel, A., Suttar, A. A., & Khan, A. R., (2005). The influence of temperature, pressure, salinity, and surfactant concentration on the interfacial tension of the n-octane-water system. Chemical Engineering Communications, 192(5), 667–684. 7. Anthony, R. G., (1999). North Carolina bibliography, 1997–1998. The North Carolina Historical Review, 76(2), 203–227. 8. Ayanso, A., Cho, D. I., & Lertwachara, K., (2014). Information and communications technology development and the digital divide: A global and regional assessment. Information Technology for Development, 20(1), 60–77. 9. Bailey, J. E., (1974). Periodic operation of chemical reactors: A review. Chemical Engineering Communications, 1(3), 111–124. 10. Bajaj, M., Kim, I., Mocko, G., Peak, R., Udoyen, N., Wilson, M., & Srinivasan, V., (2005). Diagnosing engineering information interoperability: Recognizing the value of standards-based PLM—Part 1. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 47403, pp. 789–797). 11. Ball-Rokeach, S. J., & DeFleur, M. L., (1976). A dependency model of mass-media effects. Communication Research, 3(1), 3–21.

Engineering Communications

193

12. Barraket, J., Payne, A. M., Scott, G., & Cameron, L., (2000). Equity and the Use of Communications and Information Technology in Higher Education (Vol. 1, pp. 1–10). Canberra: Commonwealth of Australia. 13. Bers, T., (2005). Parents of traditionally aged community college students: Communications and choice. Research in Higher Education, 46(4), 413–436. 14. Blackman, R. B., & Tukey, J. W., (1958). The measurement of power spectra from the point of view of communications engineering—Part II. Bell System Technical Journal, 37(2), 485–569. 15. Bowles-Terry, M., Hensley, M. K., & Hinchliffe, L. J., (2010). Best Practices for Online Video Tutorials in Academic Libraries: A Study of Student Preferences and Understanding, 1, 1–15. 16. Brewer, J. D., & Hsiang, S. M., (2002). The ‘ergonomics paradigm’: Foundations, challenges and future directions. Theoretical Issues in Ergonomics Science, 3(3), 285–305. 17. Brody, E. W., (2012). Ing Well: An Informal Guide to Writing Nonfiction (p. 187). New York: Harper & Row, Publishers, Inc., 1980. (paper). 18. Chamkha, A. J., & Aly, A. M., (2010). MHD free convection flow of a nanofluid past a vertical plate in the presence of heat generation or absorption effects. Chemical Engineering Communications, 198(3), 425–441. 19. Ciment, J., & Russell, T., (2007). The Home Front Encyclopedia: United States, Britain, and Canada in World Wars I and II (Vol. 1). ABC-CLIO. 20. Clement, A., & Shade, L. R., (2000). The access rainbow: Conceptualizing universal access to the information/communications infrastructure. In: Community Informatics: Enabling Communities with Information and Communications Technologies (pp. 32–51). IGI Global. 21. Cohen, G., Salomon, I., & Nijkamp, P., (2002). Information– communications technologies (ICT) and transport: Does knowledge underpin policy?. Telecommunications Policy, 26(1, 2), 31–52. 22. Comfort, L. K., & Haase, T. W., (2006). Communication, coherence, and collective action: The impact of Hurricane Katrina on communications infrastructure. Public Works Management & Policy, 10(4), 328–343. 23. Davis, R. M., (1977). Technical writing in industry and government. Journal of Technical Writing and Communication, 7(3), 235–242.

194

Scientific Principles of Engineering

24. Dede, C., (2007). Reinventing the role of information and communications technologies in education. Teachers College Record, 109(14), 11–38. 25. Duderstadt, J. J., Knoll, G. F., & Springer, G. S., (1982). Principles of Engineering (Vol. 1, p. 1–20). New York: John Wiley and Sons. 26. Eitzen, D. S., (1988). The introduction of graduate students to the profession of sociology. Teaching Sociology, 16(3), 279–283. 27. Feeney, A. B., & Price, D. M., (2000). A modular architecture for STEP. In: Proceedings of World Automation Congress (Vol. 1, pp. 1–10). 28. Flater, D., & Morris, K. C., (2001). Harmonized conformance testing for product data managers. In: 25th Annual International Computer Software and Applications Conference. COMPSAC 2001 (pp. 46–54). IEEE. 29. Forsythe, C., (1997). Human factors in agile manufacturing: A brief overview with emphasis on communications and information infrastructure. Human Factors and Ergonomics in Manufacturing & Service Industries, 7(1), 3–10. 30. Freedenberg, E. B., & Duro, P., (2012). “No One May Ever Have the Same Knowledge Again”: Reading the Museum of Jurassic Technology, 1, 1–10. 31. Gomery, D., (1986). Annual round-up of motion picture books. Communication Booknotes Quarterly, 17(11, 12), 115–125. 32. Grace, H. P., (1982). Dispersion phenomena in high viscosity immiscible fluid systems and application of static mixers as dispersion devices in such systems. Chemical Engineering Communications, 14(3–6), 225–277. 33. Houp, K. W., Pearsall, T. E., Tebeaux, E., & Dragga, S., (1998). Reporting Technical Information (Vol.1, pp. 1–10). Boston: Allyn and Bacon. 34. Hsu, C., Bouziane, M. H., Rattner, L., & Yee, L., (1991). Information resources management in heterogeneous, distributed environments: A metadatabase approach. IEEE Transactions on Software Engineering, 17(6), 604–625. 35. Hughes, D. M., (2002). The use of new communications and information technologies for sexual exploitation of women and children. Hastings Women’s LJ, 13, 127.

Engineering Communications

195

36. Ippolito, Jr. L. J., (2017). Satellite Communications Systems Engineering: Atmospheric Effects, Satellite Link Design and System Performance (Vol.1, pp. 1–10). John Wiley & Sons. 37. Ishak, A., Lok, Y. Y., & Pop, I., (2010). Stagnation-point flow over a shrinking sheet in a micropolar fluid. Chemical Engineering Communications, 197(11), 1417–1427. 38. Javed, T., Ali, N., Abbas, Z., & Sajid, M., (2013). Flow of an EyringPowell non-Newtonian fluid over a stretching sheet. Chemical Engineering Communications, 200(3), 327–336. 39. Joback, K. G., & Reid, R. C., (1987). Estimation of pure-component properties from group-contributions. Chemical Engineering Communications, 57(1–6), 233–243. 40. Johansen, R., (1984). Teleconferencing and Beyond: Communications in the Office of the Future (Vol. 1, pp. 1–10). McGraw-Hill, Inc. 41. Jones, J. S., (1984). Comparison of prediction equations for bridge pier and abutment scour. Transportation Research Record, (950). 42. Khan, A. R., & Richardson, J. F., (1989). Fluid-particle interactions and flow characteristics of fluidized beds and settling suspensions of spherical particles. Chemical Engineering Communications, 78(1), 111–130. 43. Kirkman, R. E., & Darrell, B., (1980). Writing well: Ten commandments for educators. Peabody Journal of Education, 58(1), 61–68. 44. Kotz, S., Kozubowski, T., & Podgórski, K., (2001). The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance (No. 183). Springer Science & Business Media. 45. Larice, M., & Macdonald, E., (2013). The Urban Design Reader (p. 78). London: Routledge. 46. Lee, G. K., (2007). The significance of network resources in the race to enter emerging product markets: The convergence of telephony communications and computer networking, 1989–2001. Strategic Management Journal, 28(1), 17–37. 47. Lubell, J., Peak, R. S., Srinivasan, V., & Waterbury, S. C., (2004). STEP, XML, and UML: Complementary technologies. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 46970, pp. 915–923).

196

Scientific Principles of Engineering

48. Lucas, H., (2008). Information and communications technology for future health systems in developing countries. Social Science & Medicine, 66(10), 2122–2132. 49. Mahdavi, S., Jalali, M., & Afkhami, A., (2013). Heavy metals removal from aqueous solutions using TiO2, MgO, and Al2O3 nanoparticles. Chemical Engineering Communications, 200(3), 448–470. 50. Makinde, O. D., & Ogulu, A., (2008). The effect of thermal radiation on the heat and mass transfer flow of a variable viscosity fluid past a vertical porous plate permeated by a transverse magnetic field. Chemical Engineering Communications, 195(12), 1575–1584. 51. Marchionini, G., & Maurer, H., (1995). The roles of digital libraries in teaching and learning. Communications of the ACM, 38(4), 67–75. 52. Marchionini, G., (2006). Exploratory search: From finding to understanding. Communications of the ACM, 49(4), 41–46. 53. Mauch, J., & Park, N., (2003). Guide to the Successful Thesis and Dissertation: A Handbook for Students and Faculty. CRC Press. 54. McBride, K. B., & Dickstein, R., (1998). The Web Demands Critical Thinking by Students. Chronicle of Higher Education, B6. 55. Meyer, M. D., & Miller, E. J., (1984). Urban Transportation Planning: A Decision-Oriented Approach.(Vol.1, pp.1-5) 56. Moran, E. F., (2018). Human Adaptability an Introduction to Ecological Anthropology: An Introduction to Ecological Anthropology (Vol. 1, pp. 1–10). Routledge. 57. Mumtaz, S., (2000). Factors affecting teachers’ use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319–342. 58. Murdock, G., & Golding, P., (1989). Information poverty and political inequality: Citizenship in the age of privatized communications. Journal of Communication, 39(3), 180–195. 59. Nicholas, D., Watkinson, A., Volentine, R., Allard, S., Levine, K., Tenopir, C., & Herman, E., (2014). Trust and authority in scholarly communications in the light of the digital transition: Setting the scene for a major study. Learned Publishing, 27(2), 121–134. 60. Nichols, H. A., & Greer, K., (2016). Designing for engagement: Using the ADDIE model to integrate high-impact practices into an online information literacy course. Communications in Information Literacy, 10(2), 6.

Engineering Communications

197

61. Nyeng, P., & Ostergaard, J., (2011). Information and communications systems for control-by-price of distributed energy resources and flexible demand. IEEE Transactions on Smart Grid, 2(2), 334–341. 62. O’Kelly, M. E., & Miller, E. J., (1984). Characteristics of multistop multipurpose travel: An empirical study of trip length. Transportation Research Record, (976). 63. Patil, L., Dutta, D., Bhatt, A. D., Jurrens, K., Lyons, K., Pratt, M. J., & Sriram, R. D., (2002). A proposed standards‐based approach for representing heterogeneous objects for layered manufacturing. Rapid Prototyping Journal, 1, 1–18. 64. Pea, R. D., (1994). Seeing what we build together: Distributed multimedia learning environments for transformative communications. the Journal of the Learning Sciences, 3(3), 285–299. 65. Pigg, K. E., & Crank, L. D., (2004). Building community social capital: The potential and promise of information and communications technologies. The Journal of Community Informatics, 1(1). 66. Pratt, M. J., (2001). Introduction to ISO 10303—The STEP standard for product data exchange. Journal of Computing and Information Science in Engineering, 1(1), 102, 103. 67. Rich, R. C., Brians, C. L., Manheim, J. B., & Willnat, L., (2018). Empirical Political Analysis: Quantitative and Qualitative Research Methods (Vol. 1, pp. 1–19). Routledge. 68. Rothenberg, D., (1998). How the web destroys student research papers. The Education Digest, 63(6), 59. 69. Rowlands, K. D., (2016). Slay the monster! Replacing form-first pedagogy with effective writing instruction. English Journal, 52–58. 70. Sargeant, J. M., (2005). Medical education for rural areas: Opportunities and challenges for information and communications technologies. Journal of Postgraduate Medicine, 51(4), 301. 71. Schmid, K. L., Rivers, S. E., Latimer, A. E., & Salovey, P., (2008). Targeting or tailoring? Maximizing resources to create effective health communications. Marketing Health Services, 28(1), 32. 72. Scott, J. E., & Silbey, S. S., (2000). Turning an internship into a research opportunity. Center for Work and Service (Vol. 1, pp. 1–26). 73. Silaban, A., Narcida, M., & Harrison, D. P., (1996). Characteristics of the reversible reaction between CO2 (g) and calcined dolomite. Chemical Engineering Communications, 146(1), 149–162.

198

Scientific Principles of Engineering

74. Soltanieh, M., & Gill, W. N., (1981). Review of reverse osmosis membranes and transport models. Chemical Engineering Communications, 12(4–6), 279–363. 75. Sproule, J. M., (2020). Democratic Vernaculars: Rhetorics of Reading, Writing, Speaking, and Criticism Since the Enlightenment (Vol. 1, pp. 1–30). Routledge. 76. Sriraman, V., & DeLeon, J., (2001). Assembly modeling for product design and analysis. Journal of Engineering Technology, 18(2), 8. 77. Stone, G., (1984). Book Review: Death in the Afternoon: America’s Newspaper Giants Struggle for Survival, 1, 1–25. 78. Straus, M. A., & Donnelly, D. A., (2017). Beating the Devil Out of Them: Corporal Punishment in American Families and its Effects on Children (Vol. 1, pp. 1–17). Routledge. 79. Thornton, J., & Huang, K. C., (2013). Modern Lens Antennas for Communications Engineering (Vol. 39). John Wiley & Sons. 80. Timbus, A., Larsson, M., & Yuen, C., (2009). Active management of distributed energy resources using standardized communications and modern information technologies. IEEE Transactions on Industrial Electronics, 56(10), 4029–4037. 81. Torrent-Moreno, M., (2007). Inter-vehicle communications: Assessing information dissemination under safety constraints. In: 2007 Fourth Annual Conference on Wireless on Demand Network Systems and Services (pp. 59–64). IEEE. 82. Tsiknakis, M., Katehakis, D. G., & Orphanoudakis, S. C., (1996). Intelligent image management in a distributed PACS and telemedicine environment. IEEE Communications Magazine, 34(7), 36–45. 83. Tunç, M. S., Yıldız, B., & Taşar, Ş., (2022). Removal of paracetamol from aqueous solution by wood sawdust-derived activated carbon: Process optimization using response surface methodology. Chemical Engineering Communications, 209(8), 1130–1150. 84. Vajda, S., Rabitz, H., Walter, E., & Lecourtier, Y., (1989). Qualitative and quantitative identifiability analysis of nonlinear chemical kinetic models. Chemical Engineering Communications, 83(1), 191–219. 85. Van, C. J., (1999). On competition, access and diversity in media, old and new: Some remarks for communications policy in the information age. New Media & Society, 1(2), 183–207.

Engineering Communications

199

86. Versteeg, G. F., Van, D. L. A. J., & Van, S. W. P. M., (1996). On the kinetics between CO2 and alkanolamines both in aqueous and non-aqueous solutions. An overview. Chemical Engineering Communications, 144(1), 113–158. 87. Viswanath, K., (2005). The communications revolution and cancer control. Nature Reviews Cancer, 5(10), 828–835. 88. Voas, J., & Bojanova, I., (2014). NIST: Building a solid foundation. IT Professional, 16(2), 13–16. 89. Von, S. R., & Van, N. J., (2013). From information security to cyber security. Computers & Security, 38, 97–102. 90. Wang, J. X., & Roush, M. L., (2000). What Every Engineer Should Know About Risk Engineering and Management (Vol. 1, pp. 1–117). CRC Press. 91. Wang, Y., Hao, H., & Platt, L. S., (2021). Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter. Computers in Human Behavior, 114, 106568. 92. Weber, R. J., Marti, J. J., McMurry, P. H., Eisele, F. L., Tanner, D. J., & Jefferson, A., (1996). Measured atmospheric new particle formation rates: Implications for nucleation mechanisms. Chemical Engineering Communications, 151(1), 53–64. 93. Winter, D., Koger, S., Koger, S. M., & Winter, D. D., (2011). The Psychology of Environmental Problems: Psychology for Sustainability (Vol.1, pp. 1–13). Psychology Press. 94. Wright, D. B., (2003). Making friends with your data: Improving how statistics are conducted and reported 1. British Journal of Educational Psychology, 73(1), 123–136. 95. Yablonovitch, E., & Kane, E. O., (1988). Band structure engineering of semiconductor lasers for optical communications. Journal of Lightwave Technology, 6(8), 1292–1299. 96. Zhou, H., Liu, B., Luan, T. H., Hou, F., Gui, L., Li, Y., & Shen, X., (2014). ChainCluster: Engineering a cooperative content distribution framework for highway vehicular communications. IEEE Transactions on Intelligent Transportation Systems, 15(6), 2644–2657. 97. Zydney, A. L., & Colton, C. K., (1986). A concentration polarization model for the filtrate flux in cross-flow microfiltration of particulate suspensions. Chemical Engineering Communications, 47(1–3), 1–21.

7

CHAPTER

SCIENTIFIC PRINCIPLES OF MANAGEMENT

CONTENTS 7.1. Introduction..................................................................................... 202 7.2. Fundamentals of Scientific Management.......................................... 203 7.3. Replacement of Old Rule of Thumb Method.................................... 205 7.4. Scientific Selection and Training of Workers..................................... 206 7.5. Co-Operation Between Labor and Management.............................. 207 7.6. Maximum Output............................................................................ 208 7.7. Equal Division of Responsibility....................................................... 208 7.8. Mental Revolution........................................................................... 209 7.9. Examples of Scientific Management................................................. 210 7.10. Criticism of Scientific Management................................................ 212 References.............................................................................................. 214

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7.1. INTRODUCTION Science is a mindset and an ideology that rejects the classical “strikes” and “guideline” way of managing tasks as well as workers. It recognizes the existence and implementation of the technique of scientific study to resolve issues in business engineering. In its most basic form, knowledge management is just a mindset and an ideology (Taneja et al., 2011) (Figure 7.1).

Figure 7.1. Scientific management principles. Source: https://theinvestorsbook.com/scientific-management.html.

The investigation, as well as experiments, collecting data, data processing, and the formation of specific principles just on foundation of these analyzes, are the approaches that are used in every scientific endeavor. The application of these procedures again for plant operations is one of the tenets of scientific management, which aims to achieve the highest possible level of productivity (Rodrigues, 2001). The following is a list of many the scientific management (Bobbitt, 1913): • • • • •

Alternative to the traditional method based on the rule of thumb. Scientific methods will be used to evaluate or train employees. Co-operation among workers and capital. Highest output A fair and equal sharing of the responsibilities.

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• • • •

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Logical revolution. Not disharmony, but rather harmony. The growth of each individual to their highest efficiency and success. Personal interests are given priority over specific interests.

7.2. FUNDAMENTALS OF SCIENTIFIC MANAGEMENT Securing the greatest success for such an employer and the greatest success for every worker should become the main objective of the company. The phrase “ultimate success” is often used in its broadest meaning to refer to a condition of economic success that is long-lasting and includes both substantial profits for the firm or owner and the growth of every division of the enterprise to the greatest level of quality. In a similar vein, achieving ensures that the potential for every employee entails not only paying them increased salaries than men of their class typically do, and also developing each person to their highest level of efficiency, enabling them to perform, on average, at the top mark of task that their innate abilities allow them to be doing, as well as assigning them this type of work whenever it is practical to do just that (Robbins & Coulter, 2007). This would appear to be obvious that the two main goals of administration must be the greatest happiness for such employer and greatest success for such worker that it shouldn’t even be important to convey this. However, it is undeniable that during the industrialized world, a sizable portion of the institution of leaders and workers is more in favor of war than harmony, as well as that maybe the large percentage along either side does not genuinely think that about their consensual relations can be set up in such a way that about there preferences could become similar (Sadler, 1983). A large percentage of such men think that the basic interests of both workers and employers must be at odds. Contrarily, knowledge management is based on the firm belief that now the two parties’ true preferences are intertwined; that economic success for the company cannot last over a long period unless it has been preceded by economic success for the worker, and conversely; and therefore it is possible to offer both parties what they desire for their respective manufacturing operations, namely, high salaries and cheap labor (Bendavid et al., 2011). It is wished that at least a few of the of someone who does not share every one of these objectives will be persuaded

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to change their minds; that some companies who have historically tried to get the most work out of their workers for the lowest possible salaries will be persuaded to see how an extra liberal policy forward into their men would then pay people effectively; although some of these workers who feel that their own companies really shouldn’t make a fair or perhaps a sizable profit should indeed be persuaded to change their views (Reesal & Lam, 2001). Nobody can be discovered who will dispute the fact that, within the instance of every one person, the highest wealth could only occur whenever that person has achieved his or her peak efficiency, i.e., when he or she is producing the majority of his or her daily production. This fact’s veracity is also undeniably evident in the scenario of two guys cooperating (Fayol, 1916). To give an example, when you and your worker are producing two pairs of sneakers per day whereas your contender and his worker are only producing one pair, it is obvious that you really can compensate your worker much-increased salaries after having to sell someone two pairs of sneakers than your competing product, who only generates one pair of sneakers, is capable of paying his person, and that there’ll still be sufficient money left around for all of you to make a bigger profit than ones competing product (Kobersy et al., 2015). It must also be obvious that, within a particular instance of a more complex manufacturing institution, the finest permanent economic success for the workforce and the highest wealth for the company could only be achieved so when the leadership’s work is completed with the least amount of merged human work, merged use of natural resources, merged cost for using wealth in the economy of types of machinery, houses, etc. As well as, to look at it another way, the highest economic success could only result from the leadership’s men as well as machines being as productive as they possibly can be, producing the most output for every man and per device. This is because if someone men as well as your machines aren’t consistently producing more tasks than those before you, it’s obvious that competitive rivalry will help stop you from paying your workers more money than those made available to the competitors. But what is accurate for the prospect of high salaries being paid when two businesses are in direct rivalry with each other is equally true for whole areas of the country and competing countries. In other words, maximum output is the only way that optimum wealth is possible. In a later section of this essay, examples of various enterprises that are profitable and offer their workers salaries that are between 30% and 100% greater than those of similarly situated males in their near vicinity but whose companies they compete with should be provided. From the simplest

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to the most complex sorts of labor, these examples will explain them all (Liu et al., 2017). If the aforementioned logic is sound, it implies that each employee in the institution should be trained and developed to do the offer’s maximum of job that his or her natural skills allow him or her to accomplish, at the quickest possible rate and also with the most amount of efficiency. Many folks may find it almost juvenile to articulate these concepts since they seem to be so self-evident (Yoo et al., 2006). Therefore, let’s focus on the truths as they truly stand in this nation and also in England. The world’s top athletes come from the English and American populations. It is reasonable to assume that every neuron in an American or an English laborer’s body is put to the test to win for his team whenever they play sports or cricket. He makes every effort to complete as many runs as he can. Any guy who does not give all he has during a sporting event is referred to be a “quitter” and is held in low regard by people about him due to his pervasive mentality (Edwards, 2018). The same worker typically decided to work as few as reasonably possible the next day, producing so much fewer tasks than he is well allowed to be doing and frequently finishing no and over one-third around one of a full day’s worth of work, rather than making each effort to produce the greatest volume of work conceivable (Li, 1998). In contrast, if he tried to do the most amount of work possible in one day, his coworkers would berate him for that too, but this would be worse if he had shown himself being a “complete loser” in sports. Underworking, also known as “military service,” “sticking that out,” and “ca canae,” seems to be almost ubiquitous in industrial enterprises which are also prevalent to a great extent in the construction industry. The author contends without fear of backlash that all this creates the greatest crime in which the having to work including both Scotland and England must contend (Vasudevan et al., 2016).

7.3. REPLACEMENT OF OLD RULE OF THUMB METHOD Instead of basing judgments on judgment, instinct, or general rule, managers should employ scientific study. The foundational difference between classical management theory and conventional administration with the use of research as a rule of thumb (Sakalauskienė & Jauniškienė, 2010). Decisions taken under modern management are based on actions produced by applying the scientific approach to the relevant situation. That’s

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in opposition to the strategy used in conventional management when choices are made based on assumptions, preconceptions, or rules of thumb. Taylor’s major contribution to the realm of administration is the replacement of general rule or opinion with a scientific method (Massano & Bhatia, 2012).

7.4. SCIENTIFIC SELECTION AND TRAINING OF WORKERS The method for choosing employees should be developed properly. The mistakes made throughout the choice process might end up costing a lot of money. The organization will operate less efficiently if the appropriate people are not assigned to the appropriate tasks. Therefore, a systematic selection process should be used by all organizations (Reuler & Cooney, 1981) (Figure 7.2).

Figure 7.2. Process of scientific hiring and selection. Source: https://hrmpractice.com/recruitment-and-selection-process/.

The chosen employees must get training to prevent using improper working techniques. The management is involved in scientific skills and

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retraining. The administration must provide employees with greater qualities and the chance to grow (Taylor, 2015).

7.5. CO-OPERATION BETWEEN LABOR AND MANAGEMENT The management and the staff need to work together. Changes in the manager’s and employees’ psychological attitudes toward one another are necessary for this. The psychological revolution was Taylor’s term for it. Management and staff begin to focus on boosting earnings whenever this psychological revolution occurs. Regarding the division of earnings, they don’t argue (Perig, 2018) (Figure 7.3).

Figure 7.3. Management and labor working together. Source: https://www.semanticscholar.org/paper/Transforming-Your-Workplace-%3A-A-Model-for-Change-Alexander/31bbe38180b64747b209bbcc515 2aa60669d6499.

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7.6. MAXIMUM OUTPUT Instead of limiting production, management, and employees should endeavor to produce as much as possible. Both sides will benefit from this. The community will also benefit from maximizing productivity (Angelstam, 1997).

7.7. EQUAL DIVISION OF RESPONSIBILITY The management and the employees must each have an equal share of responsibility. The job that the administration is best qualified to handle should fall within its purview. For example, rather than putting these decisions up to the employees’ judgment, the administration should determine the technique of labor, conditions of employment, the duration for finishing the project, etc. (Bilovodska et al., 2017) (Figure 7.4).

Figure 7.4. The process of sustainable management. Source: https://www.researchgate.net/figure/The-Responsible-ManagementProcess_fig1_327100670.

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The administration must be in charge of organizing and organizing the activity, and the employees should be in charge of carrying out the senior management orders (Myers, 2011).

7.8. MENTAL REVOLUTION The fundamental tenet of modern management would be to alter the management’s and employees’ psychological attitudes toward others. ‘Brain Revolution,’ Taylor dubbed it (Nonaka, 1988). Essentially, knowledge management, in the words of Taylor, “gives a comprehensive psychological revolution just on typical working men attached in just about any particular institution or sector and it includes an equitably total mental revolution just on side of someone on the senior management side—the construction workers, the supervisor, the owners of the company, the executive board. Science-based administration doesn’t exist without even a total conceptual transformation along both sides (Shogar, 2018). Through modern management, there is a significant shift in the two parties’ mental attitudes where they stop seeing how the surplus is shared as the most important issue and instead work together to grow the surpluses to the point where a disagreement over how to divide it is essential (O’Connor, 1997). They realize that the extent of the excess produced by their combined efforts is remarkable when they cease pushing against each other and rotate to push together in the very same direction (Majumder & Biswas, 2021). They both understand because when they replace rivalry and conflict with amicable collaboration and social aid, they can work together to significantly increase the above surplus relative to its previous level, creating enough space for both a significant increase in wages for the workers as well as an equally significant increase within the product’s profits (Geraskin et al., 2014). Taylor aimed to inspire a psychological revolution both among management and staff. He couldn’t imagine scientific methods even without a radical shift in views (Halidu, 2015). Three aspects make up a mental rebellion (Halidu, 2015): • •

Making every attempt to improve productivity; Development of a culture of trust and security amongst people; and

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• Establishing a problem-solving philosophy based on science. Taylor recommended that management look for the best ways to complete diverse tasks and offer standardized supplies, tools, and equipment to cut down on wastage. To improve the level of production, managers should choose the correct sorts of employees and provide them with the necessary training (Locke, 1982). For the staff to work as effectively as possible, it must offer comfortable working circumstances. To guarantee all work is completed following scientific methodologies, it should fulfill the judgment role and constantly provide the employees full collaboration. The employees’ attitudes towards the management must also shift (Ghosh, 1955). They shouldn’t be slackers at work. Workers should carry out the duties given to them with discipline, loyalty, and sincerity. They shouldn’t engage in resource waste. To achieve optimal output, managers, and employees should have mutual trust as well as work together (Latham, 1988).

7.9. EXAMPLES OF SCIENTIFIC MANAGEMENT Taylor’s theories served as the foundation for establishing the rules of modern management, which intended to maximize the output and effectiveness of each worker and create a system which allows for the optimization of interactions between workers and employers (Good, 1936).

7.9.1. Principle # 1. Science, Not Rule of Thumb Rule-of-thumb techniques are conventional strategies that worked well for organizations in the past. Under mass manufacturing, the amount of work has increased, necessitating scientific ways for simple management and control. To prevent errors, Taylor advised that a given company activity’s operating procedures be thoroughly researched before being used. He advised using standardized, guaranteed procedures that increased operational efficiency (Washington et al., 2020). For instance, there are five distinct units and 150 employees working in the textile industry (rotating, interlacing, sewing, and adding buttons and samplers). A specialized supervisor who is knowledgeable and experienced in that specific task was appointed to all of these divisions. There’ll be five expert foremen appointed, and each will have 30 staff spread over five business divisions (Yang, 2018).

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7.9.2. Principle # 2. Harmony, Not Discord According to this notion, management, and employees need to collaborate and coordinate their efforts in a way that promotes harmony. In addition to not feeling overworked, employees shouldn’t be unhappy with both the effort and cash they’ve invested in a company. According to Taylor, a company may grow to the fullest extent if its employees and management get along well with one another, foster a sense of teamwork, and make it easier to accomplish organizational objectives (Dieleman et al., 2003). For instance, one supervisor may allocate tasks to employees (like a useful foreman). Based on the action’s intricacy, these supervisors have been given independent bureaucratic authority and may break it up into various tasks. They enforce rules, mediate conflicts, and assess employees’ performance. Such connection is essential for fostering peace and preventing conflict as well as a misunderstanding among employees and bosses (Nebeker & López-Arenas, 2016).

7.9.3. Principle # 3. Cooperation, Not Individualism In keeping with the prior premise, collaboration rather than individuality will help the organization achieve its objectives. Collaboration seems to be the action of functioning collectively, and Taylor asserts that workers and management must collaborate to foster a strong sense of teamwork and enthusiasm. Workers are required to collaborate, assume responsibility, and be accountable for completing tasks in the prior instance of cooperation between subordinates and superiors. Taylor noted that industries suffered from unnecessary expenses and resource waste because soldering resulted in underwork and individuality (Pinto et al., 2018).

7.9.4. Principle # 4. Development of a Person Taylor argued that almost all human resources should be used to their fullest, resulting in productivity and profitability for both the workforce and the company. This implies that employees with the necessary competencies and skills should be properly taken into account for certain tasks inside an organization. This implies that employees should only be engaged in specialized tasks that may only need a limited amount of effort and even prevent mistakes and resource waste (Kidwell & Scherer, 2001) (Figure 7.5).

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Figure 7.5. Several useful techniques for coaching team members by leaders. Source: https://blog.stewartleadership.com/7-ways-leaders-develop-themselves-and-others.

For instance, a strong employee who can lift a specific amount of weight or even a load must be assigned the duty of bringing gunny bags of goods inside the company. A position like that of accounting, which requires little manual labor, is provided to a person with poor physical structure, no expertise or knowledge of lifting heavy objects, but appropriate experience within this subject (Scott et al., 2018).

7.10. CRITICISM OF SCIENTIFIC MANAGEMENT Even though it is acknowledged that science assists the company to allocate resources in the most effective possible way, it’s not been exempt from strong criticism. •

Unemployment: People believe that management deprives individuals of employment possibilities by replacing men with machinery and boosting employees’ productivity, which results in the firing of employees from current positions (Hill et al., 1996).

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•

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Exploitation: Employees believe they are being abused as Earnings do not increase according to output growth. Anxiety and unease are brought on by salary payments (elsewhere a typical output, there is no increase in income amount). Expensive: The development of such a planning department, standardization, work studies, and employee training are expensive aspects of the scientifical management platform. Small businesses might be unable to afford it (Paffenbarger et al., 2001). Time Consuming: A fundamental reorganization of the organization is necessary for administrative management. Working, education, standardization, and specialization all consume a lot of effort (Shipunov, 1973).

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REFERENCES 1. 2.

3.

4.

5.

6.

7. 8.

9.

10. 11. 12.

13.

Angelstam, P., (1997). Landscape analysis as a tool for the scientific management of biodiversity. Ecological Bulletins, 1, 140–170. Bendavid, R., Abrahamson, J., Arregui, M. E., Flament, J. B., & Phillips, E. H., (2001). Abdominal wall Hernias: Principles and Management (Vol. 1, pp. 133–137). New York: Springer. Bilovodska, O., Golysheva, I., Gryshchenko, O., & Strunz, H., (2017). Theoretical and practical fundamentals of scientific and educational projects: A case of Ukraine. Journal of International Studies, 10(2), 2–8. Bobbitt, F., (1913). Some general principles of management applied to the problems of city-school systems. Teachers College Record, 14(6), 7–96. Dieleman, M., Cuong, P. V., Anh, L. V., & Martineau, T., (2003). Identifying factors for job motivation of rural health workers in North Viet Nam. Human Resources for Health, 1(1), 1–10. Edwards, R., (2018). An elaboration of the administrative theory of the 14 principles of management by Henri Fayol. International Journal for Empirical Education and Research, 1(1), 41–51. Fayol, H., (1916). General principles of management. Classics of Organization Theory, 2(15), 57–69. Geraskin, N. I., Kulikov, E. G., Milman, I. E., & Pilyugin, V. V., (2014). CLP4NET platform to support the course “fundamentals of scientific visualization”. Scientific Visualization, 6(5), 35–44. Ghosh, S. P., (1955). Selection tests for skilled and semi-skilled workers. Sankhyā: The Indian Journal of Statistics (1933–1960), 14(4), 375–384. Good, C. V., (1936). Training field workers and graduates in methods of research. The Journal of Educational Research, 130–132. Halidu, S. G., (2015). The impact of training and development on workers’ productivity. Review Pub Administration Manag., 3(160), 2. Halidu, S. G., (2015). The impact of training and development on workers’ productivity in some selected Nigerian universities. International Journal of Public Administration and Management Research, 3(1), 10–16. Hill, M. N., Bone, L. R., & Butz, A. M., (1996). Enhancing the role of

Scientific Principles of Management

14.

15.

16. 17. 18.

19. 20.

21.

22.

23.

24.

25.

215

community-health workers in research. Image: The Journal of Nursing Scholarship, 28(3), 221–226. Kidwell, R. E., & Scherer, P. M., (2001). Layoffs and their ethical implications under scientific management, quality management and open-book management. Journal of Business Ethics, 29(1), 113–124. Kobersy, I. S., Barmuta, K. A., Muradova, S. S., Dubrova, L. I., & Shkurkin, D., (2015). The system of the methodological principles of management of enterprise development. Mediterranean Journal of Social Sciences, 6(3 S4), 25. Latham, G. P., (1988). Human resource training and development. Annual Review of Psychology, 39(1), 545–582. Li, T. C., (1998). Guides for practitioners: Recurrent miscarriage: Principles of management. Human Reproduction, 13(2), 478–482. Liu, Y., Han, L., Li, J., Gong, M., Zhang, H., & Guan, X., (2017). Consumption coagulopathy in acute aortic dissection: Principles of management. Journal of Cardiothoracic Surgery, 12(1), 1–8. Locke, E. A., (1982). The ideas of Frederick W. Taylor: An evaluation. Academy of Management Review, 7(1), 14–24. Majumder, S., & Biswas, D., (2021). Fundamentals, present and future perspectives of quality of work life. In: Proceeding of First Doctoral Symposium on Natural Computing Research (Vol. 1, pp. 271–279). Springer, Singapore. Massano, J., & Bhatia, K. P., (2012). Clinical approach to Parkinson’s disease: Features, diagnosis, and principles of management. Cold Spring Harbor Perspectives in Medicine, 2(6), a008870. Myers, L. A., (2011). One hundred years later: What would Frederick W. Taylor say?. International Journal of Business and Social Science, 2(20), 4–9. Nebeker, C., & López-Arenas, A., (2016). Building research integrity and capacity (BRIC): An educational initiative to increase research literacy among community health workers and promotores. Journal of Microbiology & Biology Education, 17(1), 41–45. Nonaka, I., (1988). Creating organizational order out of chaos: Selfrenewal in Japanese firms. California Management Review, 30(3), 57–73. O’Connor, P. D. T., (1997). In: Dennis Jr, F. G.., (ed.), Book Review:

216

26.

27.

28.

29.

30.

31.

32.

33. 34.

35.

Scientific Principles of Engineering

The Fundamentals of Quality Management (Vol. 1, p. 289). Chapman and Hall. Paffenbarger, Jr. R. S., Blair, S. N., & Lee, I. M., (2001). A history of physical activity, cardiovascular health and longevity: The scientific contributions of Jeremy N Morris, DSc, DPH, FRCP. International Journal of Epidemiology, 30(5), 1184–1192. Perig, A. V., (2018). Didactic student-friendly approaches to more effective teaching of the fundamentals of scientific research in a digital era of scientometrics. Eurasia Journal of Mathematics, Science and Technology Education, 14(12), em1632. Pinto, A. C. C. S., Silva, D. A. S., Ensslin, L., Reis, P. F., Vilagra, J. M., Vergara, L. G. L., & Moro, A. R. P., (2018). Injuries of repetitive efforts in workers from the poultry meat industry: A bibliometric analysis of literature. Sustainability, 10(1), 250. Reesal, R. T., & Lam, R. W., (2001). Clinical guidelines for the treatment of depressive disorders. II. Principles of management. Canadian Journal of Psychiatry/ Revue Canadienne de Psychiatrie, 46, 21S–28S. Reuler, J. B., & Cooney, T. G., (1981). The pressure sore: Pathophysiology and principles of management. Annals of Internal Medicine, 94(5), 661–666. Robbins, S. P., & Coulter, M., (2007). In: Seyyed, M. A., Mohammed, A. H. R., & Behrouz, A. E., (eds.), Principles of Management (4th edn., Vol. 1, pp. 3–8). Tehran: Office of Cultural Studies. Rodrigues, C. A., (2001). Fayol’s 14 principles of management then and now: A framework for managing today’s organizations effectively. Management Decision, 39(10), 880–889. Sadler, P., (1983). Principles of management. In: Lock, D., & Farrow, N., (eds.), The Gower Handbook of Management (Vol. 1, pp. 1–5). Sakalauskienė, G., & Jauniškienė, D., (2010). Osteoarthritis: Etiology, epidemiology, impact on the individual and society and the main principles of management. Medicina, 46(11), 790. Scott, K., Beckham, S. W., Gross, M., Pariyo, G., Rao, K. D., Cometto, G., & Perry, H. B., (2018). What do we know about community-based health worker programs? A systematic review of existing reviews on community health workers. Human Resources for Health, 16(1), 1–17.

Scientific Principles of Management

217

36. Shipunov, V., (1973). Effectiveness of vocational-technical training of workers. Problems in Economics, 16(4), 82–94. 37. Shogar, I. A., (2018). 05| Fundamentals of scientific thinking in Islamic tradition. Revelation and Science, 8(1), 3–9. 38. Taneja, S., Pryor, M. G., & Toombs, L. A., (2011). Frederick W. Taylor’s scientific management principles: Relevance and validity. Journal of Applied Management and Entrepreneurship, 16(3), 60. 39. Taylor, F. W., (2015). Fundamentals of scientific management. In: Working in America (Vol. 1, pp. 35–42). Routledge. 40. Vasudevan, B., Neema, S., & Verma, R., (2016). Livedoid vasculopathy: A review of pathogenesis and principles of management. Indian Journal of Dermatology, Venereology and Leprology, 82(5), 2–9. 41. Washington, P., Leblanc, E., Dunlap, K., Penev, Y., Varma, M., Jung, J. Y., & Wall, D. P., (2020). Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder. In: Biocomputing 2021: Proceedings of the Pacific Symposium (Vol. 1, pp. 14–25). 42. Yang, D., (2018). On construction of competency evaluation model of knowledge workers in high-tech enterprises. In: 2018 4th International Conference on Humanities and Social Science Research (ICHSSR 2018) (Vol. 1, pp. 674–684). Atlantis Press. 43. Yoo, J. W., Lemak, D. J., & Choi, Y., (2006). Principles of management and competitive strategies: Using Fayol to implement porter. Journal of Management History, 1, 5–7.

8

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ETHICS IN SCIENCE AND ENGINEERING

CONTENTS 8.1. Introduction..................................................................................... 220 8.2. The Part of Morals in Engineering and Science................................. 221 8.3. Ethical Principles in Science............................................................ 222 8.4. Ethics of Techniques and Process..................................................... 223 8.5. Morals of Topics and Findings.......................................................... 225 8.6. Faults Versus Misconduct................................................................. 226 8.7. Everyday Moral Decisions................................................................ 228 8.8. Enforcing Moral Standards............................................................... 229 References.............................................................................................. 231

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8.1. INTRODUCTION Like in every profession, there are those who attempt to exploit the system in science. One of them was Charles Dawson, a British enthusiast paleontologist and archaeologist born in 1864. Dawson had produced a series of ostensibly significant fossil finds by the late 19th century. He wasn’t given to modesty and gave himself the names of many of the novel species he unearthed. For instance, Dawson discovered the fossilized teeth of the mammal species that he later named Plagiaulax dawsoni (Bolton, 2002). He gave the names Iguanodon dawsoni to one of the three dinosaur species he discovered and Salaginella dawsoni to a new variety of fossil plant. He received recognition for his efforts by being inducted into the Society of Antiquaries of London and chosen a member of the British Geological Society. He received the prestigious collector status from the British Museum, and the English publication The Sussex Daily News called him the Wizard of Sussex (Satyapal et al., 2007) (Figure 8.1).

Figure 8.1. Smith Woodward in center and Charles Dawson on right digging the Piltdown gravels. Source: https://profjooecain.net/piltdown-man-excavation-location-east-sussex-england/.

His greatest renowned discovery, meanwhile, came in 1912, when Dawson displayed to the public portions of a jawbone and skull looking

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similar to humans and persuaded experts that the fossils were of the novel species that provided the missing piece among ape and man (Smith et al., 2012; Crabtree & Dresselhaus, 2008). Dawson’s Piltdown Man, as the discovery became known, had quite an influence, perplexing scientists for decades following Dawson’s death in 1915. Though some researchers questioned the discovery from the start, it was widely accepted and respected (Richter et al., 2008). Lecturer of anthropology at Oxford University, Kenneth Oakley, aged the skull in 1949 with the help of a currently added fluorine absorption technique and determined that it was 500 years old, not 500,000. However, even Oakley remained to consider that this skull was authentic, just mistakenly dated (O’brien et al., 2010). Physical anthropology graduate at Oxford University, Joseph Weiner, joined a paleontology symposium in 1953 and started noticing that Piltdown Man didn’t fit with other human predecessor fossils. He relayed his suspicions to his Oxford teacher, Wilfred Edward Le Gros Clark, who then contacted Oakley. Soon later, the three scientists recognized that the skull didn’t reflect the missing element, but instead an ingenious hoax in which the jawbone of an ape and the teeth of a preserved chimpanzee were fused with the skull of an ancient human (Peterson et al., 2018). The bones were altered chemically to make them appear older, and the teeth were manually filed to match the cranium. In the aftermath of this disclosure, it has been determined that at least 38 of Dawson’s discoveries were fakes, made in his quest of fame and glory (Green et al., 2012). Because scientific progress depends on the validity of the study record, it is a good thing that Dawson and other con artists are the exception instead of the rule. But instances like Dawson’s help us better comprehend the framework of scientific morals that has developed to guarantee accuracy and proper conduct in science (Cai et al., 2014).

8.2. THE PART OF MORALS IN ENGINEERING AND SCIENCE Ethics refers to a set of moral requirements that define what is correct and incorrect in our actions and judgments. Various professions have a structured system of moral standards that assist professionals in their sector. They, for instance, typically take the Hippocratic Oath, which mandates, among many other things, that doctors don’t harm their patients. Engineers adhere to a moral framework that specifies that they prioritize the public’s safety, welfare, and health. The ideas become so established in these occupations,

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and in science, that professionals hardly have to consider about conforming to the ethic – it’s essential of how they practice. And a violation of morals is deemed highly serious, punished within the community (for instance, by the cancellation of a license) often by the law too (Wee et al., 2012). In all phases of scientific investigation, including the analysis of responses and the attribution of colleagues, scientific honesty and transparency are essential. This code of ethics governs the scientific method, from data gathering through publication and even beyond (Kreith & West, 2004). Like in other occupations, the scientific morality is profoundly ingrained in the manner scientists operate, and they are conscious that conforming to this ethic is crucial to the trustworthiness of their job and scientific understanding in general. Several of the ethical standards in science pertain to the generation of objective scientific information, which is essential when others plan to create upon or expand upon study findings. The scientific morality requires the open disclosure of data, peer assessment, duplication, and cooperation, all of which contribute to the advancement of science by confirming study findings and validating or challenging conclusions (Istok et al., 2004).

8.3. ETHICAL PRINCIPLES IN SCIENCE However, official ethical standards didn’t emerge until the middle of the 20th century, following a number of well reported ethical transgressions and war crimes. Researchers have long preserved an unofficial system of morality and principles for doing research. Today, the term “scientific ethics” describes the standards of ethics for researchers that is typically divided into two main areas (Bolton, 2002). The design, processes, analysis of data, assessment, and presentation of research endeavors are all covered by standards of techniques and processes (Dillon, 2010).). The utilization of animal and human subjects in research as well as the ethical ramifications of specific study findings are addressed by guidelines of topics and conclusions. Collectively, these ethical guidelines support the direction of scientific inquiry and guarantee that research activities adhere to a number of fundamental standards, such as: • • •

Righteousness in recording of scientific information Independent evaluation of the data and analysis of the findings without reference to outside sources Carefully transcribing and analyzing scientific findings to prevent error

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Open dissemination of procedures, information, and conclusions via publications and presentations Giving appropriate credit to the information, data, and concept sources Enough peer collaboration and replication to sufficiently validate results The nee to balance the rights of animal and human subjects in some disciplines, as well as general moral responsibility to society

8.4. ETHICS OF TECHNIQUES AND PROCESS

Figure 8.2. Jan Hendrick Schön asserted to have found a molecular-scale replacement for a conventional transistor. Source: https://www.visionlearning.com/en/library/Process-of-Science/419/ Scientific-Ethics/161.

Scientists are people, and people do not often follow the rules. Considering some cases of scientific fraud will assist us appreciate the significance and ramifications of scientific integrity. Jan Hendrik Schön, a German physicist, suddenly rose to attention in 2001 for what seemed to be a set of major findings in nanotechnology and electronics. Schön and two co-authors claimed to have created a molecular-scale equivalent to the transistor (Figure 8.2) utilized in consumer gadgets in the paper presented in the magazine Nature (Abe et al., 2019). The consequences were groundbreaking: a

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molecule transistor might enable the fabrication of computer microchips considerably smaller as compared to any currently available. As an outcome, Schön received numerous exceptional research prizes, and the study was named one among Science magazine’s “breakthroughs of the year” in 2001 (Figure 8.2). However, issues started to surface very rapidly. Scientists who tried to replicate Schön’s work were unable to do so. Lydia Sohn was a nanotechnology scientist at Princeton University, she noted that background noise levels in two tests by Schön that were conducted at quite various temperatures and reported in different papers seemed to be identical (Voormeij & Simandl, 2004). Schön first argued that he had accidentally presented the identical graph with two distinct publications when challenged with the issue. But soon later, Cornell University’s Paul McEuen discovered the identical graph in a 3rd publication. Bell Laboratories, the research facility where Schön worked, opened an inquiry into his findings in May 2002 as an outcome of these concerns. When the investigation’s oversight committee tried to examine Schön’s notes and scientific data, they discovered that he didn’t keep laboratory notepads, wiped away all raw data sets from his computer (stating he wanted the extra storage space for novel researches), and discarded or dented beyond identification all of his test sample. Schön was found to have manipulated or altogether forged data in at least sixteen times between 1998 and 2001, according to the committee. Schön was sacked from Bell Laboratories on 25 September, 2002, the very day they got the investigation committee’s report (Succar & Williams, 2008). Schön’s papers were recalled by the journal Science on October 31; the journal Physical Review rejected six of his papers on December 20; and Nature retracted 7 that they had released on March 5, 2003. Retractions and terminations are the means through which the science world addresses grave scientific wrongdoing. Furthermore, he was prohibited from engaging in science for 8 years. In 2004, the University of Konstanz, where Schon earned his doctorate, took the matter a step even more by requesting the return of his thesis papers in an attempt to invalidate his doctorate. After numerous challenges, the supreme German court affirmed the university’s jurisdiction to cancel Schon’s degree in 2014. Schon was employed in industry at the moment of the last plea, not even as a researcher, and it is uncertain that he will manage to find job as the research scientist again (Kozima, 1998). Evidently, scientific malfeasance can have serious consequences, including expulsion from the research world.

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8.5. MORALS OF TOPICS AND FINDINGS No charges were ever brought against Schön in spite of his flagrant violation of scientific integrity. In some situations, behavior that is against the scientific morality is also against more essential ethical and practical principles. One such incident, the discriminating and harsh cruelty of Nazi scientists during World War II, prompted the creation of an international code guiding research morality (Kozima, 2015). During WW 2, Nazi scientists conducted a series of experiments, most of which tested the boundaries of human unmasking to the elements in an effort to better prepare German soldiers for combat. Human hypothermia trials were particularly notable among these endeavors (Kozima, 2016). During these tests, prison camp inmates were subjected to hours of sitting in cold water or being exposed to freezing temperatures while nude. Many individuals were left to gradually freeze to death, whereas others were ultimately re-warmed with warm water, blankets, or other means that caused them irreparable harm (Fisher, 1936).

Figure 8.3. The chamber of judges from the Nuremberg trials. Source: trumanlibrary.gov/photograph-records/72–889.

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In Nuremberg, Germany, at the conclusion of the battle, 23 people were prosecuted for war crimes, and fifteen of them were convicted (Figure 8.3). The Nuremberg Code, a collection of regulations that restricts research using human beings, was developed as a result of the court hearings. The Nuremberg Code stipulates, among many other things, that people must be informed about and provide their consent before taking part in the research; the first requirement states, “The voluntary participation of the human subject is certainly important.” Additionally, the code mandates that scientists refrain from purposefully causing bodily or mental pain for research reasons and that the dangers of study must be balanced against any positive advantages (Fleischmann et al., 1990). Significantly, the code holds “any person who starts, directs or participates in the experiment” accountable for upholding the code. This is a crucial part of the code that holds accountable all scientists indulged in a test, not simply the senior researcher or the paper’s first author. The Nuremberg Code, which was published in the year 1949, is still a key text governing moral conduct in research involving human beings, and it has been complemented by other rules and regulations in the majority of nations (Grant, 2002). Other ethical standards govern human subjects investigation as well. Because of the ethical concerns presented by the procedure, a majority of government sources of funding prohibit or exclude financing for human reproduction (Henschke, 2008). One other set of ethical rules addresses research with medicinal medications and equipment. If a medication is discovered to have substantial unfavorable side effects, research into its therapeutic capabilities is halted earlier than scheduled. Likewise, largescale therapy investigations in which the drug or substance is discovered to be particularly effective might be completed early, allowing the control patients to continue with their treatment (Henschke, 2008).

8.6. FAULTS VERSUS MISCONDUCT Mistakes made by scientists don’t constitute misbehavior because they are human and imperfect. The distinction among an error and wrongdoing, however, isn’t always obvious. For instance, a variety of research teams were looking into the possibility of forcing deuterium atoms to merge together at ambient temperature and generating enormous amounts of energy in the procedure in the late 1980s (Raghava & Siddque, 2017). In 1980, nuclear fusion wasn’t a novel concept, but because scientists could only start fusion reactions at extremely high temperatures, low temperature fusion offered promising potential as a source of energy.

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Two University of Utah researchers, Stanley Pons and Martin Fleischmann, created a setup employing the palladium (Pd) electrode and deuterated water to study the possibility of fusion reactions at low temperature. As they experimented on their system, they saw excessive heat production (Louis et al., 2002). Even while not many of the obtained data was convincing, they argued that the warmth was proof that fusion was happening in their system. Instead of repeating and publishing their study so that others might check the correctness, Pons, and Fleischmann feared that another scientist would soon declare identical results and wished to copyright their innovation, so they hurriedly announced their discovery to the public (Dustira, 1996). Pons and Fleischmann staged a press conference on 23 March, 1989, with the backing of their institution, to declare their finding of an infinite energy source.

Figure 8.4. A nuclear research center cell for cold fusion. The field’s legitimate research efforts were harmed by Pons and Fleischmann’s hasty declaration. Source: https://en.wikipedia.org/wiki/Cold_fusion.

The revelation of cold fusion reactor (Figure 8.4) by Pons and Fleischmann’s generated instant interest in the press and was reported by

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national and worldwide news outlets. Their revelation was equally praised and ridiculed by scientists (Mwaka, 2017). On April 12, over 7,000 chemists gave Pons an applause at the semi-annual gathering of American Chemical Society. However, some academics criticized the scientists for publicizing their discoveries in the mainstream media as opposed to peer-reviewed journals. Ultimately, Pons, and Fleischmann did report their outcomes in a scientific publication (Fleischmann et al., 1990), but issues had already started to emerge (Gopalakrishna et al., 2022). The researchers had difficulty demonstrating that their system produced neutrons, a trait that would have verified the existence of fusion processes. On 1 May, 1989, less than 5 weeks after the press briefing in Utah, Nathan Lewis, Charles Barnes, and Steven Koonin from Caltech officially stated at a significant gathering of the American Physical Society that they had reproduced Pons and Fleischmann’s reaction conditions, discovered multiple mistakes in the scientists’ findings, and presented no proof of fusion happening in the system (Roush, 2019; Schön et al., 2001). Almost immediately thereafter, the United States Department of Energy produced a paper stating that “the experimental findings. recorded till date don’t provide persuasive proof that viable energy sources would come from the phenomenon associated to cold fusion (Schön et al., 2001).

8.7. EVERYDAY MORAL DECISIONS In more routine situations, scientists must also make ethical considerations. Authorship of research publications, for instance, may be in issue. Authors on publications are assumed to have made a significant contribution in some form, and they have a duty to be aware of the work and give supervision of it (Kumar et al., 2013). The coauthors of Jan Hendrik Schön blatantly failed in this duty. To boost the apparent value of their work, beginners to a subject may try to add the names of seasoned researchers to articles or funding submissions. Although this can result in fruitful scientific cooperation, it raises morality concerns over accountability in research publications if the senior authors just accept “honorary” credit without contributing to the study (Yekta, 2021; Kuzin, 2007). The funding source of a researcher may also influence their research. Though scientists routinely mention their sources of funding in their articles, a few of incidents have sparked worries about a lack of sufficient disclosure (Service, 2002). Dr. Claudia Henschke, a radiologist at Weill Cornell Medical College, for instance, released a report in 2006 suggesting that monitoring

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smokers and ex-smokers using CT chest scans might significantly reduce the lung cancer fatalities (Waldrop, 1989). Henschke, on the other hand, failed to mention that the organization that financed her study was virtually entirely supported by Liggett Tobacco. Due to the obvious perceived bias toward oversimplifying the threat of lung cancer, the case sparked outrage in the scientific community (Griffiths et al., 1995). Almost 2 years later, Dr. Henschke released a revision in the journal that disclosed the study’s financing sources. As a consequence of this and other examples, numerous journals imposed stronger standards for revealing the sources of funding for research papers.

8.8. ENFORCING MORAL STANDARDS Several occurrences have motivated the formation of explicit and legally enforced morality standards in the scientific community. For instance, in 1932, the United States Public Health Service stationed in Tuskegee, Alabama, conducted research of the consequences of syphilis in men. Available medicinal treatments for syphilis were extremely toxic and of doubtful efficacy when the research started. Therefore, the purpose of the study was to establish whether or not individuals with syphilis would benefit from taking these risky medications (Olson & Griffiths, 1995). The researcher selected around 400 black men who had syphilis, and around 201 men without syphilis. Individuals registered in the Tuskegee Syphilis Research were neither asked for permission nor notified of their treatment; rather, they were informed they had bad blood and might receive free medical care (which frequently comprised of nothing), trips to the clinic, food, and mass grave insurance in exchange for their participation (Loui, 2005). Penicillin seemed to be a successful syphilis therapy by 1947. To learn more about how syphilis develops and affects its victims, the Tuskegee investigators delayed penicillin and data about the drug instead of treating the afflicted individuals with penicillin and ending the research. Up until 1972, when the press disclosure caused a national uproar and the study’s end, the despicable research wasn’t stopped. But by then, 28 of the initial participants had passed away from syphilis, and also another 100 had passed away from syphilis-related medical problems. Additionally, nineteen children were born with syphilis, and 40 wives of respondents had the condition (Han, 2015). The National Research Act was created by the US Congress in 1974 as a reaction of the Tuskegee Syphilis Research and the Nuremberg

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Doctors’ prosecution. The Act established the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research to monitor and govern the utilization of human experimentation, as well as the standards for Institutional Review Boards (IRBs) (Herkert, 2000; Graber & Pionke, 2006). As an outcome, all universities receiving federal research money are required to form and manage an IRB, which is an independent body of trained personnel that reviews work plans involving human beings to ensure that morality standards are met. Any research involving human participants should be approved by an institution’s IRB before it can begin (Jeske, 2020). The United States Department of Health and Human Services issues regulations controlling the functioning of the IRB. Comparable to morality in our larger society, the morals of science promote acceptable behavior and improving coordination among persons. While violations of scientific morals sometimes happen, like they do in community as a whole, they are typically dealt with rapidly when found and help us realize the significance of ethical conduct in our professional operations (Keefer et al., 2014). Adherence to the scientific morality ensures that data obtained during study are accurate and that conclusions are logical and meritorious, permitting a researcher’s work to be incorporated into the expanding corpus of scientific understanding.

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REFERENCES 1.

Abe, J. O., Popoola, A. P. I., Ajenifuja, E., & Popoola, O. M., (2019). Hydrogen energy, economy and storage: Review and recommendation. International Journal of Hydrogen Energy, 44(29), 15072–15086. 2. Bolton, P. A., (2002). Scientific ethics. In: Washington Research Evaluation Network’s (WREN) Management Benchmark Study (vol. 1, p. 1–15). Washington, DC. 3. Cai, S. L., Zhang, Y. B., Pun, A. B., He, B., Yang, J., Toma, F. M., & Liu, Y., (2014). Tunable electrical conductivity in oriented thin films of tetrathiafulvalene-based covalent organic framework. Chemical Science, 5(12), 4693–4700. 4. Crabtree, G. W., & Dresselhaus, M. S., (2008). The hydrogen fuel alternative. MRS Bulletin, 33(4), 421–428. 5. Dillon, A. C., (2010). Carbon nanotubes for photoconversion and electrical energy storage. Chemical Reviews, 110(11), 6856–6872. 6. Dustira, A. K., (1996). The federal role in influencing research ethics education and standards in science. Professional Ethics, A Multidisciplinary Journal, 5(1, 2), 139–156. 7. Fisher, R. A., (1936). Has Mendel’s work been rediscovered?. Annals of Science, 1(2), 115–137. 8. Fleischmann, M., Pons, S., Anderson, M. W., Li, L. J., & Hawkins, M., (1990). Calorimetry of the palladium-deuterium-heavy water system. Journal of Electroanalytical Chemistry and Interfacial Electrochemistry, 287(2), 293–348. 9. Gopalakrishna, G., Ter Riet, G., Vink, G., Stoop, I., Wicherts, J. M., & Bouter, L. M., (2022). Prevalence of questionable research practices, research misconduct and their potential explanatory factors: A survey among academic researchers in The Netherlands. PloS One, 17(2), e0263023. 10. Graber, G. C., & Pionke, C. D., (2006). A team-taught interdisciplinary approach to engineering ethics. Science and Engineering Ethics, 12(2), 313–320. 11. Grant, P. M., (2002). Scientific credit and credibility. Nature Materials, 1(3), 139–141. 12. Green, S. J., Prakash, O., Jasrotia, P., Overholt, W. A., Cardenas, E., Hubbard, D., & Kostka, J. E., (2012). Denitrifying bacteria from the

232

13.

14.

15.

16. 17.

18.

19.

20.

21. 22.

23.

Scientific Principles of Engineering

genus Rhodanobacter dominate bacterial communities in the highly contaminated subsurface of a nuclear legacy waste site. Applied and Environmental Microbiology, 78(4), 1039–1047. Griffiths, P. A., McCormick, A. R., Albertis, B. M., Blout, E. R., Browder, F. E., Challoner, M. D., & Stine, D. D., (1995). On Being a Scientist: Responsible Conduct in Research (2nd edn., Vol. 1, pp. 1–25). Han, H., (2015). Virtue ethics, positive psychology, and a new model of science and engineering ethics education. Science and Engineering ethics, 21(2), 441–460. Henschke, C. I., (2008). Clarifying enrollment procedures in the trial of CT screening for lung cancer. New England Journal of Medicine, 359(8), 871–873. Henschke, C., (2008). Clarification of funding of early lung cancer study. New England Journal of Medicine, 358(17), 1862. Herkert, J. R., (2000). Engineering ethics education in the USA: Content, pedagogy and curriculum. European Journal of engineering education, 25(4), 303–313. Istok, J. D., Senko, J. M., Krumholz, L. R., Watson, D., Bogle, M. A., Peacock, A., & White, D. C., (2004). In: Situ bioreduction of technetium and uranium in a nitrate-contaminated aquifer. Environmental Science & Technology, 38(2), 468–475. Jeske, M., (2020). Lessons from theranos: Changing narratives of individual ethics in science and engineering. Engaging Science, Technology, and Society, 6, 306–311. Keefer, M. W., Wilson, S. E., Dankowicz, H., & Loui, M. C., (2014). The importance of formative assessment in science and engineering ethics education: Some evidence and practical advice. Science and Engineering Ethics, 20(1), 249–260. Kozima, H., (1998). Discovery of the Cold Fusion Phenomenon (pp. 1–370). Ohotake Shuppan Inc., Tokyo. Kozima, H., (2015). Trapped Neutron Catalyzed Fusion Model (TNCF Model) for the Cold Fusion Phenomenon and the Neutron Behavior in Solids (Vol. 10, pp. 1–10). From the History of Cold Fusion Research. Kozima, H., (2016). From the history of CF research–A review of the typical papers on the cold fusion phenomenon. Proc. JCF16, 16(13), 116–157.

Ethics in Science and Engineering

233

24. Kreith, F., & West, R., (2004). Fallacies of a hydrogen economy: A critical analysis of hydrogen production and utilization. J. Energy Resour. Technol., 126(4), 249–257. 25. Kumar, A., Mansour, H. M., Friedman, A., Blough, E. R., & Press, C. R. C., (2013). Nanomedicine in Drug Delivery (pp. 8–12). Boca Raton, FL, USA: CRC Press. 26. Kuzin, D. V., (2007). Competitiveness of Russia: Problems and perspectives. Pecunia: Journal of the Faculty of Economic and Business Sciences,, (4), 109–142. 27. Loui, M. C., (2005). Educational technologies and the teaching of ethics in science and engineering. Science and Engineering Ethics, 11(3), 435–446. 28. Louis, K. S., Jones, L. M., & Campbell, E. G., (2002). Macroscope: Sharing in science. American Scientist, 90(4), 304–307. 29. Mwaka, E. S., (2017). Responsible conduct of research: Enhancing local opportunities. African Health Sciences, 17(2), 584–590. 30. O’brien, J. E., McKellar, M. G., Harvego, E. A., & Stoots, C. M., (2010). High-temperature electrolysis for large-scale hydrogen and syngas production from nuclear energy–summary of system simulation and economic analyses. International Journal of Hydrogen Energy, 35(10), 4808–4819. 31. Olson, S., & Griffiths, P. A., (1995). On Being a Scientist: Responsible Conduct in Research (Vol. 1, pp. 1–17). National Academy Press. 32. Peterson, R. A., Buck, E. C., Chun, J., Daniel, R. C., Herting, D. L., Ilton, E. S., & Clark, S. B., (2018). Review of the scientific understanding of radioactive waste at the US DOE Hanford site. Environmental Science & Technology, 52(2), 381–396. 33. Raghava, S., & Siddque, S., (2017). PUB075 survival of patients with stage I lung cancer detected on CT screening in South Indian population. Journal of Thoracic Oncology, 12(1), S1491, S1492. 34. Richter, B., Goldston, D., Crabtree, G., Glicksman, L., Goldstein, D., Greene, D., & Dawson, J., (2008). How America can look within to achieve energy security and reduce global warming. Reviews of Modern Physics, 80(4), S1. 35. Roush, D. W., (2019). Recalibrating Juvenile Detention: Lessons Learned from the Court-ordered Reform of the Cook County Juvenile Temporary Detention Center (Vol. 1, pp. 1–20). Routledge.

234

Scientific Principles of Engineering

36. Satyapal, S., Petrovic, J., Read, C., Thomas, G., & Ordaz, G., (2007). The US department of energy’s national hydrogen storage project: Progress towards meeting hydrogen-powered vehicle requirements. Catalysis Today, 120(3, 4), 246–256. 37. Schön, J. H., Meng, H., & Bao, Z., (2001). correction: Self-assembled monolayer organic field-effect transistors. Nature, 414(6862), 470. 38. Schön, J. H., Meng, H., & Bao, Z., (2001). Self-assembled monolayer organic field-effect transistors. Nature, 413(6857), 713–716. 39. Service, R. F., (2002). Pioneering Physics Papers Under Suspicion for Data Manipulation.(pp. 1-25) 40. Smith, N. A., Bowers, D. L., & Ehst, D. A., (2012). The production, separation, and use of 67Cu for radioimmunotherapy: A review. Applied Radiation and Isotopes, 70(10), 2377–2383. 41. Succar, S., & Williams, R. H., (2008). Compressed Air Energy Storage: Theory, Resources, and Applications for Wind Power (Vol. 8, p. 81). Princeton Environmental Institute Report. 42. Voormeij, D., & Simandl, G., (2004). Geological, ocean, and mineral CO2 sequestration options: A technical review. Geoscience Canada, 31(1), 11–22. 43. Waldrop, M. M., (1989). Cold water from Caltech. Science, 244(4904), 523. 44. Wee, H. M., Yang, W. H., Chou, C. W., & Padilan, M. V., (2012). Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16(8), 5451–5465. 45. Yekta, M. M. J., (2021). Scientific zero to one: Some common properties of highly-influential papers. Malaysian Journal of Library & Information Science, 26(3), 1–32.

INDEX

A Administration 203, 205, 206, 207, 208, 209 aerospace engineering 90 Altshuller’s theory 36 American National Standards Institute (ANSI) 178 anthropology 221 Astronomy 2 automobile 4, 18

B behavioral sciences 4 biased knowledge 170 Bohr atomic model 50 Brainstorming 37 breast cancer 133, 134, 164 breast mammography 133

C carnivores interaction 50 Case-based reasoning 34, 35 catastrophes 3 Chaos theory 4, 5 Check sheet 37

climatic models 50 collaboration 169 collaboration modeling 49 collecting data 202 combination constant phenomena 48 commodity 107 communication systems 113 Computer models 90 computer simulation 38 conflict resolution 36 continuous learning 169 Continuous System Simulation Language (CSSL) 48, 61 Control Chart (CC) 37

D Data analysis 39 data collection 12 data processing 202 decision analysis 39 Define, Measure, Analyze, Optimize, Control (DMAIC) 37 derivative calculus 39 differentially algebra equations (DAEs) 48

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discrete-time subsystems specification (DEVS) 48 droughts 3

E earth 5 earthquakes 3 econometric modeling 39 Electric motor 59 electromagnetic radiation 111 electronics 223 energy 226, 227, 228, 231, 233, 234 engineering achievement 168 engineering firms 108 engineering innovation 34 Engineering research 93 environment 33 epistemology 52, 58 equilibrium 133 error 133, 139, 146, 155, 156 Ethics 219, 221, 223, 231, 232, 233 evolutionary algorithms 138 evolution strategies 138 excellent communication 168

F Failure 90, 92, 93, 94, 95, 109, 110, 112, 117, 120, 121, 122, 126, 127, 128 Failure Modes 90 Fishbone Diagram 37 flow chart 137, 141 Forecasting 9 full-text libraries 168

G game theory 39 genetic algorithms 138 graphical approaches 39

I ideology 202 imagination 30, 34 impact diagram 39 inequality 133 Initial Graphics Exchange Specification (IGES) 187 Institutional Review Boards (IRBs) 230 integrated management 48 International Electrotechnical Commission (IEC) 92 International Organization for Standardization (ISO) 187

K knowledge management 202, 203, 209

L law 4, 9, 11, 27 learning 3, 17, 18, 19, 21, 25 legality 3, 4 linearity 8 literature 97, 109, 111, 124, 126

M material failure 90 mathematical analysis 50 mathematical programming 39 mechanical systems 36 mechanics 48, 60 memory 3 metaheuristics 138 methodologies differential equations 138 microbial hunting and gathering 138 Minimalism 11

Index

modern management 205, 209, 210 morals 221, 222, 230 Multi-attribute utility (MAU) theory 39 multi-dimensional vector 132 multi-disciplinary networks 48, 49 multi-disciplinary organizations 48 MultiDisciplinary Teamwork (MDT) 37

N nanotechnology 223, 224 networks 39, 44 nominal group methodology 38 Numerical iteration 39

O obligation 90 optimization 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 146, 147, 151, 152, 153, 154, 155, 159, 160, 161, 162, 163, 165, 166 optimization algorithms 135, 152 oral communication 169

135, 141, 150, 158, 164, 138,

P parametric sensitivity analysis 39 Pareto analysis 37 parsimony 11 particle swarm optimization (PSO) 138 PDCA (Plan, Do, Check, Act) 37 physiology 5, 15, 19 potential optimization issues 137 print indexes 168 probability theory 39

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problem-solving 30, 32, 33, 37, 39 Product Data Exchange 187 programming language 48, 81 psychological revolution 207, 209 Psychology 2, 21, 24 psychophysiological problems 3

Q Quality Function (QFD) 38

Deployment

R reductionism 10 reference materials 168 regression 39 reliability analysis 39 Restrictions 132, 142 risk analysis 39

S Schizophrenia 13 science 2, 4, 5, 6, 7, 8, 10, 12, 14, 18, 19, 20, 21, 22, 23, 24, 25 scientific integrity 223, 225 scientific management 202, 214, 215, 217 signal analysis 138 social context 33 soft skills 169 stability 133, 135 Standard for the Exchange of Product (STEP) 187 statistical approaches 39 Statistics 9 straightforward 49 system dynamics 4

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T telecommunication systems 111 textbooks 168, 175

U user-friendly interaction 49

W World Wide Web 168, 170