Process Control and Yarn Quality in Spinning 9380308183, 9789380308180

Table of contents :
Content: Quality management. Application of statistics in textiles. Cotton fibre selection and bale management system. Control of wastes in spinning. Control of neps and fibre rupture. Control of count, strength and its variation. Yarn evenness and imperfection. Short-term irregularity. Interpretation and analysis of diagram, spectrogram 253and V-L curve. Control of yarn hairiness in spun yarns. Yarn faults. Productivity of a spinning mill. Yarn quality requirements for high-speed machines.

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Process control and yarn quality in spinning

Process control and yarn quality in spinning

G. Thilagavathi and T. Karthik

WOODHEAD PUBLISHING INDIA PVT LTD New Delhi

Published by Woodhead Publishing India Pvt. Ltd. Woodhead Publishing India Pvt. Ltd., 303, Vardaan House, 7/28, Ansari Road, Daryaganj, New Delhi - 110002, India www.woodheadpublishingindia.com

First published 2015, Woodhead Publishing India Pvt. Ltd. © Woodhead Publishing India Pvt. Ltd., 2015 This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing India Pvt. Ltd. The consent of Woodhead Publishing India Pvt. Ltd. does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing India Pvt. Ltd. for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Woodhead Publishing India Pvt. Ltd. ISBN: 978-93-80308-35-7 Woodhead Publishing India Pvt. Ltd. e-ISBN: 978-93-80308-18-0

Contents

Preface

ix

Acknowledgement xi 1. Quality management

1



1.1

What is quality?

1



1.2

Quality as input–output system

1



1.3

Quality feedback cycle

2



1.4

Seven tools of quality

3



1.5

Quality management in spinning industry

10



1.6

Organization of quality control

12

1.7 References

18

2. Application of statistics in textiles

20

2.1 Introduction

20



2.2

Measures of central tendency

22



2.3

Measures of variation

25

2.4 Distributions

27



2.5

Comparison of two results

32



2.6

Quality control within the spinning mill

36

2.7 References

40

3. Cotton fibre selection and bale management system

42

3.1 Introduction

42

3.2 Cotton

44

3.3 HVI

46



50

3.4

Spinning Consistency Index (SCI)

vi

Process control and yarn quality in spinning

3.5

Cotton fibre engineering

52

3.6 References

67

4. Control of wastes in spinning

69



4.1

Yarn realization

69



4.2

Control of blow room waste

81



4.3

Control of card waste

96



4.4

Control of comber waste

106



4.5

Contamination removal techniques

120

4.6 References

138

5. Control of neps and fibre rupture

141

5.1 Introduction

141



5.2

Guideline values for neps in bale as per Uster

143



5.3

Evaluation of machine efficiency

144



5.4

Control of nep generation and fibre rupture in blow room 146



5.5

Control of neps and fibre rupture in card

152



5.6

Control of neps and short fibre content in comber

161



5.7

Influence of modern developments on nep removal

165

5.8 References

174

6. Control of count, strength and its variation

175

6.1 Introduction

175



6.2

Control of count

175



6.3

Control of count variation

178



6.4

Between-bobbin count variation

188



6.5

Control of variability of lea strength

190



6.6

Control of yarn elongation

192

6.7 References

195

7. Yarn evenness and imperfection

196



7.1

Introduction

196



7.2

Categories of yarn faults

197

Contents vii



7.3

Unevenness (Um%)

199



7.4

Mass CV (Coefficient of Variation Cvm%) 200



7.5

Yarn imperfections

216

7.6 References

224

8. Short-term irregularity

226

8.1 Autolevelling

226



8.2

Autolevellers in carding

230



8.3

Autolevellers in draw frame

231



8.4

Advantages of high performance leveling

241



8.5

Control of yarn evenness (U%)

241

8.6 References

251

9. Interpretation and analysis of diagram, spectrogram and V-L curve

253

9.1 Introduction

253



9.2

Measuring principle of mass evenness

253



9.3

Normal diagram

254

9.4 Spectrogram

257



9.5

Variance-length curve

295



9.6

Deviation rate

301



9.7

Histogram of mass variations

304

9.8 References

305

10. Control of yarn hairiness in spun yarns

307



10.1 Introduction

307



10.2 Parameters influencing the generation of yarn hairiness

309



10.3 Influence of ring frame parameters on yarn hairiness

311



10.4 Influence of preparatory process on yarn hairiness

318



10.5 Effect of Post Spinning Operations on hairiness

319



10.6 Control of hairiness of ring spun yarns

320



10.7 Influence of hairiness on subsequent processing

322



10.8 References

322

viii

Process control and yarn quality in spinning

11. Yarn faults

325



11.1 Introduction

325



11.2 Distinction between frequent and seldom-occurring yarn faults

328



11.3 Causes for seldom-occurring yarn faults

329



11.4 Standard settings in classimat

330



11.5 Analysis of classimat faults

331



11.6 Common yarn faults in ring yarn

334



11.7 References

341

12. Productivity of a spinning mill

343



12.1 Introduction

343



12.2 Productivity indices

344



12.3 Control of end-breakage rate in ring spinning

346



12.4 Control of end breaks in ring spinning

350



12.5 Effect of climatic conditions on spinning process

354



12.6 References

355

13. Yarn quality requirements for high-speed machines

356



13.1 Yarn quality requirements for hosiery yarns

356



13.2 Yarn quality requirements for export

361



13.3 Yarn quality characteristics of sewing threads

361



13.4 Yarn quality requirements for shuttleless weaving

362



13.5 Measures to produce better yarns

365



13.6 References

366

Annexure: Basic conversion charts

367

Preface

Changes are taking place very fast all over the world in all fields, such as technological developments, the living styles, social environment, and the perception of people. In this changing scenario, rising expectations of the customer and open market economics are forcing businesses to compete with each other. Therefore, basic quality of the product at competitive market price is a key factor. The same holds good for textile industry also which is one of the oldest and has a number of players all over the world. Today textile industry is facing higher competition in the globalized market than ever before. When it comes to textile, spinning is the key process, which has been given vital importance because many of the fabric properties, working of weaving machines and weaving preparatory machines are dependent on yarn quality. The overall level of quality is increasing constantly. Due to steadily growing production capacities, the quality consistency must be improved. Keeping this in mind, process control and yarn quality in spinning outlines the concepts of raw material selection, control of various process parameters to optimise the process conditions, and analysis and interpretation of various types of test reports to find out the source of fault. The book is divided into thirteen chapters, each discusses some specific area in process and quality control. This book takes a close look at the advancing technology in manufacturing and process and product quality control. It provides a basic overview of the subject and also presents applications of this technology for practicing engineers. It also includes real-time case studies involving typical problems that arise in spinning processes and strategies used to contain them. This book finds worthy to broad range of readers, including students, researchers, industrialists and academicians, as well as professionals in the spinning industry. Chapter 1 presents the various definitions and dimensions of quality and their significance on process and quality control. Chapter 2 discusses the significance of statistical quality control in textile industry. Chapter 3 converses about the significance of raw material selection and bale management in a

x

Process control and yarn quality in spinning

spinning industry for the production of consistent yarn quality. Chapter 4 presents the various control points and remedial measures in each process for the control of waste to improve the yarn realization in spinning. The effect of contamination on final yarn quality and various techniques of contamination removal during spinning processes have also been discussed in detail. Chapter 5 provides insight into the types of neps and their measurement and control in blow room, carding and comber processes. Chapter 6 deals with the control of yarn count and strength and its variation to produce the uniform and consistent yarn quality. The influence of material and process parameters in each stage of process on count variation and sampling of materials for testing the count variation have also been discussed. Chapter 7 discusses the basic category of yarn faults with their basic characteristics and their usefulness on evaluation of yarn quality. Chapter 8 provides the concept of autolevelling and the influence of various process and machine parameters in each processing stages on yarn evenness. Chapter 9 provides an insight about the various quality control graphical representations from the evenness testers such as normal diagram, spectrogram and V-L curves. Chapter 10 presents the influence of material and process parameters on yarn hairiness and its influence on fabric appearance. Chapter 11 provides causes and remedial measures of various types of yarn faults created by the raw material, preparatory process and ring frame. Chapter 12 deals with the various productivity indices and factors influencing the productivity of the ring spinning. The yarn quality requirements for hosiery, shuttless weaving and for export are discussed in Chapter 13.

Acknowledgement

We would like to thank the Management and the Principal of PSG College of Technology for providing us the excellent facilities and environment for writing the book. We would like to express our sincere gratitude to spinning machinery manufacturers Lakshmi Machine Works, Rieter India Pvt Ltd and Trutzschler for giving us permission to utilize their machinery photographs in the book. Finally, we are thankful to those who have inspired and helped me directly or indirectly in writing this book. Dr. G. Thilagavathi T. Karthik

1 Quality management

Abstract: This chapter discusses about the various definitions and dimensions of quality and their significance on process and quality control. The seven tools of quality control and their application have been discussed. The problems faced, need for quality management systems and organisational structure of spinning industries are also discussed in this chapter. Key words: quality, quality control, quality management, process management

1.1

What is quality?

The concept of quality seems to have emerged since around World War II, and the concept of quality has been with us since the dawn of civilization and the quest for quality is inherent in human nature. The simplest way to answer “what is quality?” is to look it up in a dictionary. According to Webster’s II New Revised University Dictionary, “Quality is essential character: nature, an ingredient or distinguishing attribute: property, character train, superiority of kind, degree of grade or excellence”. Quality is the ratio between performance (P) and Expectation (E) i.e. Q = P/E. Quality can also mean to meet the customer expectations all the time. It is satisfying the explicit and implicit needs of customer (Kothari 1999). Garvin proposed that a definition of quality can be product based, user based, manufacturing based or value based. A product-based definition of quality views quality as a precise and measurable variable. Differences in quality reflect differences in the quantity of some ingredient or attribute possessed by a product. A manufacturing-based definition of quality means meeting specifications, conformance to requirements, etc. Any deviation from meeting requirements means poor quality. A value-based definition of quality takes into consideration cost or price of a product or service.

1.2

Quality as input–output system

The quality can be seen as input–output model as shown in Fig. 1.1. The distinction between standards and standardization is given below. Standards – It denotes a uniform set of measures, agreements, conditions or specifications between parties, i.e. between buyer and seller or manufacturer–

2

Process control and yarn quality in spinning

user or government and industry. It can be guidelines or characteristics for the activities. Standardization – It is the process of formulating, issuing and implementing standards. System parameters

Input parameters (Raw material specification & quality)

Process parameters

Textile production system

Output parameters (Product specification)

Feed back (QC department, marketing & consumer)

Figure 1.1  Quality as input-output model

1.3

Quality feedback cycle

The feedback system that ensures the quality of the product is as shown in Fig. 1.2. End - use

Design

Production

Material specification

Technology, machine, process parameters

Product Performance, aesthetic, functional, cost Usage Figure 1.2  Quality feedback cycle



Quality management

1.4

3

Seven tools of quality

Quality pros have many names for these seven basic tools of quality, first emphasized by Kaoru Ishikawa, a professor of engineering at Tokyo University and the father of “quality circles.” The seven tools of quality are: 1. Cause-and-effect diagram (also called Ishikawa or fishbone chart): This identifies many possible causes for an effect or problem and sorts ideas into useful categories. 2. Check sheet: A structured, prepared form for collecting and analyzing data; a generic tool that can be adapted for a wide variety of purposes. 3. Control charts: Graphs used to study how a process changes over time. 4. Histogram: The most commonly used graph for showing frequency distributions, or how often each different value in a set of data occurs. 5. Pareto chart: Shows on a bar graph which factors are more significant. 6. Scatter diagram: Graphs pairs of numerical data, one variable on each axis, to look for a relationship. 7. Stratification: A technique that separates data gathered from a variety of sources so that patterns can be seen (some lists replace “stratification” with “flowchart” or “run chart”).

1.4.1

Fishbone Diagram / Cause-and-Effect Diagram / Ishikawa Diagram

The Fishbone Diagram identifies many possible causes for an effect or problem. It can be used to structure a brainstorming session. It immediately sorts ideas into useful categories. Figure 1.3 shows the simple Cause-andEffect Diagram. Eccentric gear

Missing of teeth in gear

Improper meshing Periodic variation

Eccentric rollers

Causes

Effect

Figure 1.3  Cause-and-Effect Diagram for periodic variation in yarn

4

Process control and yarn quality in spinning

When to use a Fishbone Diagram

• •

When identifying possible causes for a problem. Especially when a team’s thinking tends to fall into ruts.

1.4.2

Check sheet

A check sheet is a structured, prepared form for collecting and analyzing data. This is a generic tool that can be adapted for a wide variety of purposes. Figure 1.4 shows an example of a check sheet. Type of defects

50

45

40

35

30

25

20

15

10

5

Number of defects Total

Warp breaking

23

Weft breaking

26

Shuttle trap

14

Shuttle change

40

Slack weft

20

Faulty transfer

13

No pim transfer

27

Miscellaneous

38 Figure 1.4  Check sheet of fabric faults

Each mark in the check sheet indicates a defect. The type of defects, number of defects and their distribution can be seen at a glance, which makes of defects, and their distribution can be seen at a glance, which makes analysis of data very quick and easy. When to use a check sheet

• When data can be observed and collected repeatedly by the same person or at the same location. • When collecting data on the frequency or patterns of events, problems, defects, defect location, defect causes, etc. • When collecting data from a production process.

1.4.3

Control chart

The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the



Quality management

5

average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, we can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation). For example, in spinning industry, just before shipping, pull a number of sample packages, inspect them, and note the number of defective cones and calculate percent defective. The results may look as shown in Table 1.1 and Fig. 1.5. Table 1.1  Inspection of cone packages No. of samples inspected

No. of samples defective

% defective

392

14

3.6

346

10

2.7

132

2

1.5

141

6

4.2

344

2

0.6

170

7

4.1

164

0

0

8 7

% Defective

6

UCL

5 4 3

X

2 1 0

LCL

1 2 Figure 1.5  Control chart for defective cone packages

When to use a control chart

• When controlling ongoing processes by finding and correcting problems as they occur. • When predicting the expected range of outcomes from a process.

6

Process control and yarn quality in spinning

• When determining whether a process is stable (in statistical control). • When analyzing patterns of process variation from special causes (nonroutine events) or common causes (built into the process). • When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

1.4.4 Histogram

Short thick

Foreign cuts

Neps

Long thin

50 45 40 35 30 25 20 15 10 5 0 Long thick

No. of faults

A frequency distribution shows how often each different value in a set of data occurs. A histogram is the most commonly used graph to show frequency distributions. It looks very much like a bar chart, but there are important differences between them. Figure 1.6 shows the histogram of category of yarn faults in a classimat.

Figure 1.6  Histogram of yarn faults in classimat

When to use a histogram

• When the data are numerical. • When you want to see the shape of the data’s distribution, especially when determining whether the output of a process is distributed approximately normally. • When analyzing whether a process can meet the customer’s requirements. • When analyzing what the output from a supplier’s process looks like. • When seeing whether a process change has occurred from one time period to another.



Quality management

7

• When determining whether the outputs of two or more processes are different. • When you wish to communicate the distribution of data quickly and easily to others.

1.4.5

Pareto chart

Foreign cuts

Long Thin

Long thick

Short thick

50 45 40 35 30 25 20 15 10 5 0 Neps

No. of faults

A Pareto chart is a bar graph. The lengths of the bars represent frequency or cost (time or money), and are arranged with longest bars on the left and the shortest to the right. In this way the chart visually depicts which situations are more significant. The Pareto diagram of yarn faults in classimat is shown in Fig. 1.7.

Figure 1.7  Pareto Chart of yarn faults in classimat

When to use a pareto chart

• When analyzing data about the frequency of problems or causes in a process. • When there are many problems or causes and you want to focus on the most significant. • When analyzing broad causes by looking at their specific components. • When communicating with others about your data.

1.4.6

Scatter diagram

The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter

8

Process control and yarn quality in spinning

the points will hug the line. For example, yarn strength may depend on twist per inch; moisture absorbency in a fabric may depend on fabric thickness and so on. By plotting one variable against another, it may or may not become obvious how they are related; in other words, a pattern may or may not emerge. Various possible patterns of a scatter diagram are shown in Figure 1.8. (a) Very good positice correlation (b) Positice correlation but not as strong as above (c) No correlation (d) Negative correlation (e) Strong negative correlation

Figure 1.8  Scatter Diagram

When to use a scatter Diagram

• When you have paired numerical data. • When your dependent variable may have multiple values for each value of your independent variable. • When trying to determine whether the two variables are related, such as… – When trying to identify potential root causes of problems. – After brainstorming causes and effects using a fishbone diagram, to determine objectively whether a particular cause and effect are related.





Quality management

9

– When determining whether two effects that appears to be related both occur with the same cause. – When testing for autocorrelation before constructing a control chart.

1.4.7

Stratification

Stratification is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be impossible to see. This technique separates the data so that patterns can be seen. Figure 1.9 shows an example of a flow chart of manufacturing of shirt in garment unit. Marker lay Spreading

Machine cutting

Die cutting small parts

Sorting & bundling Sewing department

Cuff Collar Under Sleeves department department front

Back

Front

Yolke

Assembly of parts Join shoulder seam Join collar to shirt Set sleeve Cuff attachment Button attachment Finishing Packing

Figure 1.9  Flow chart of manufacturing process for shirt

10

Process control and yarn quality in spinning

When to use stratification

• Before collecting data. • When data come from several sources or conditions, such as shifts, days of the week, suppliers or population groups. • When data analysis requires separating different sources or conditions.

1.5

Quality management in spinning industry

With the globalization the market competition has been increased manifold. Thus today’s competition in the field of textile is no more restricted at domestic level but spreads to international level where a manufacturer has to compete with his international counter-part in respect of cost, delivery schedule, flexibility in terms of payment and of course quality. It is needless to say that, in this highly competitive market, customers all over the world have become so demanding and expecting higher quality level increasingly, that meeting the quality requirement is no longer a competitive advantage but a sheer necessity to survive in the market. Spinning industry is no exception to this. It is a common misbelief that prevails in the textile industry in general and spinning industry in particular is that achieving and maintenance of quality is the job of a Quality Control Manager. But in reality to achieve quality, top management commitment and involvement of all is a must. Achievement of quality is only possible through coordinated approach of all the functions/ departments of an organization. In view of this, International Organization for Standardization (ISO) at Geneva introduced Quality Management System Standard – ISO 9000 in 1987 and last revised the standard in 2008. This standard provides necessary guidelines to an organization in meeting customer and applicable regulatory requirements and continual improvement in the quality system. The standard requires that the organization shall establish, document, implement and maintain quality management system in line with the clauses laid down in this standard. The organization needs to prepare and follow a comprehensive plan pertaining to inspection and testing, maintenance and internal quality audit to ensure compliance with customer and applicable regulatory requirement (if any). Establishment and adherence with the system makes the organization more proactive, system oriented. It enables the organizations to continuously challenge the status quo with foresight, insight and action. Implementation of quality management system forms a solid platform for total quality management.



1.5.1

Quality management

11

Problems faced by the textile industry in India

Spinning mills today face various challenges. The most important challenges are represented in Figure 1.10. On top of all these challenges, spinning mills need to remain competitive in quality. Now more than ever, yarn quality is the parameter most influencing the market value of the product – as well as the reputation of the spinning mill. It’s also recognized that most quality problems, that knitters, weavers and finishers are facing, are traced back to the yarn. Shortage of operating personnel Shortage of skilled textile technologists

Increased energy costs Problems Faced by the Textile Industry in India

Globalized ?? competition

Volatile raw material prices Higher demand for consistent quality

Figure 1.10  Challenges of Indian textile industry

1.5.2

Need for quality management in spinning

• The need to spin a quality yarn from an essentially non-standard raw material • Numerous processes and numerous process variables. • Ever increasing quality demands because of the high speed post spinning processes. • To reduce the cost of manufacturing and more importantly, the probability of rejections • An ever increasing competition – both domestic and global • Low profit margins in the spinning industry – around 5% in many mills. So low quality is a risk • To develop a brand image.

12

Process control and yarn quality in spinning

1.5.3 • • • • • • • • • • • • • •

Reasons for poor quality in spinning industry

Lack of top management commitment Lack of long term vision Lack of team sprit Poor quality of man power Lack of systems and procedures Poor work methods Lack of clarity about customer quality requirements Incorrect raw material and too frequent changes of raw material Inadequate process control Lack of transparency regarding raw material/process Lack of modernization / poor upkeep of machines and the departments Incorrect choice of machinery and accessories Frequent Run-ins and Run-outs Poor infrastructure.

1.6

Organization of quality control

The basic problem in the cotton textile mill is the manufacture of a standard product from an essentially non-standard and highly variable raw material. The quality of yarn should conform to certain accepted norms depending on the end use. It is equally important that this is achieved at the minimum cost possible. It is the function of quality control to ensure that these twin objectives of control of quality and minimizing cost are realized. Quality control should be exercised at all key stages of processing so that variation in the final product can, if necessary, is traced back to the variation in raw material or from the process from which it originated. It is also essential to keep the products under continuous observation to obtain immediate warning of any new source of variation, which might have been caused by the development of a defect in a machine. The emphasis should be to prevent defects before they occur by exercising appropriate technical controls at different stages, good machinery maintenance, and application of statistical techniques for the analysis and consideration and interpretation of data. Norms or standards for quality should be fixed by the mill not only for the raw material and the yarn but also for the product at various stages of processing. The quality of yarn produced should conform to the quality norms specified by the customer. It is equally important that this should be achieved without making any compromise in productivity, which otherwise affects the yarn costing. Quality Control is concerned with sampling, specifications and



Quality management

13

testing as well as the organisation, documentation and release procedures which ensure that the necessary and relevant tests are carried out, and that materials are not released for use, nor products released for sale or supply, until their quality has been judged satisfactory. Quality Control is not confined to laboratory operations, but must be involved in all decisions, which may concern the quality of the product. The independence of Quality Control from Production is considered fundamental to the satisfactory operation of Quality Control. Generally, Quality Control or Quality Assurance department is isolated from production and maintenance; it is assumed that quality is responsibility of Quality Control department. The Quality Control Department as a whole will also have other duties, such as to establish, validate and implement all quality control procedures, keep the reference samples of materials and products, ensure the correct labelling of containers of materials and products, ensure the monitoring of the stability of the products, participate in the investigation of complaints related to the quality of the product, etc. All these operations should be carried out in accordance with written procedures and, where necessary, recorded. All these operations should be carried out in accordance with written procedures and, where necessary, recorded.

1.6.1

Organizational structure

The lines of communication and authority within the company need to be defined, in particular any co-ordination between different activities and the specific quality responsibilities. The standard has to be put in place from the top down and it is considered necessary to have the person who is in overall charge of the quality programmed at a suitable level in the company management. The general organizational structure of spinning industry is shown in Fig. 1.11. ( 1) General Manager (R&D) General Manager (R&D) should be a highly qualified and knowledgeable person. He co-ordinates all QA activities along with product development and market complaint department. Apart from these regular assignments, he keeps a close eye on cotton purchase, production planning and maintenance activities. Along with top management, he prepares quality norms and strives for the same along with his team to achieve the same. (2) Manager (QA) Manager (QA) is working directly under general manager (R&D). His responsibilities are:

14

Process control and yarn quality in spinning Chairman/managing director

General manager Factory manager Godown keeper

Shift Quality control supervisors manager Workers

Filters

General manager finance

Human resources manager Store keeper

Time keeper

Accounts manager

Assisant Cashier

Canteen

Purchase manager

Sale manager

Costing manager

Assisant

Assisant

Assisant

Figure 1.11  Organizational structure of spinning industry

(i) Cotton and raw material testing (Bale management) Cotton samples received will be tested against mill norms and a decision regarding purchase of the lot or rejection will be taken by QA manager. Lots which fulfil the quality norms will be purchased, and 100% testing of the bales from the lot will be carried out Bale Management should be strictly followed. (ii) In-process testing and process optimization In-process material at every process stage must be checked and wherever deviations are observed, the process must be optimized by conducting trials. (iii) Finished product testing Before the final product is being dispatched to the customer, the same should be checked against the norms specified by customer. Non-conforming product must be packed separately and given separate lot/batch number. (iv) Calibration of testing equipment To arrive at reliable results, the testing instruments must be calibrated (internally or by service engineer as the case may be) as per the prescribed method and schedule. (3) Deputy manager (Quality Assurance) Deputy manager (QA) is working as a trouble-shooter. But, he should not wait for the trouble to arise in the department. Therefore, he has to plan the activities in such a way that there should not arise any problem in the department. His main areas of interest are: (i) Process control studies Process control studies such as hank checking, waste study, breakage study, A%, stretch%, etc., come under process control studies. A plan should be prepared for these studies so that at a given interval of time all the machines are covered for all studies.



Quality management

15

(ii) Machinery auditing Generally, maintenance gang will be doing auditing of the machine at the time of cleaning or during maintenance of particular machine. But while the machine is working, some of the things can be checked, which have influence on quality, for example stop motion, lap licking, web cut, abnormal noise from machine, etc. A list of such points, machine wise is to be prepared and an auditing schedule is followed. Second part of auditing is while the machine is stopped for cleaning. At that time along with maintenance person, machine should be audited for all settings, condition of gears, etc., by quality control person. Sometimes, it may happen that two machines working with same count/mixing may be working with different settings, drafts, etc. (iii) Follow-up of cleaning and preventive maintenance Some times because of production planning, some of the machines get delayed for cleaning or preventive maintenance. External person must keep an eye on this and see that the machine is not skipped from cleaning or maintenance allowing a delay of one or two days. (iv) Follow-up of replacement schedules A detailed schedule of all replaceable items for all machines should be prepared by maintenance manager. The same should be circulated to production and QA manager. Production manager will make necessary arrangements so that during that period, the machine is made available for replacing the items. QA will organise the studies to access the performance of machine before and after replacement in terms of quality improvement. A watch from QA is also required to see that the replacement schedule is followed strictly as per the given plan. (4) Manager (product development) Product development can be classified into two categories: (i) Development in existing product (ii) New product development Till now the concept of product development was not given sufficient importance. But in today’s competitive market, unless you are different from others, you cannot survive. Therefore, product development department must work hard to give recognition to the product in market. (i) Development in existing product Suppose a mill is spinning slub yarn. Number of trials can be conducted by varying slub length, slub frequency, slub diameter, etc., and further improvement can be achieved.

16

Process control and yarn quality in spinning

(ii) New product development By studying the market requirements, new product development must be carried out for e.g. today there is a demand for stretch denim, a product with Lycra spun slub yarn is developed which has a great demand in market. A close eye in market changes is required for new product developments. (5) Good R&D Laboratory Practice (i) Documentation Following details should be readily available to the quality control department: • Specifications • Sampling procedures • Testing procedures and records (including analytical worksheets and/ or laboratory notebooks) • Analytical reports and/or certificates • Data from environmental monitoring, where required • Validation records of test methods, where applicable • Procedures for and records of the calibration of instruments and maintenance of equipment (ii) Sampling The sample taking should be done in accordance with approved written procedures that describe: • Method of sampling • Equipment to be used • Amount of the sample to be taken • Identification of containers sampled • Storage conditions (iii) Testing Analytical methods should be validated. All testing operations should be carried out according to the approved methods. The tests performed should be recorded and the records should include: • Name of the material or product • Batch number • References to the relevant specifications and testing procedures • Dates of testing • Initials of the persons who performed the testing • Initials of the persons who verified the testing and the calculations • Status decision and the dated signature of the designated responsible person



1.6.2

Quality management

17

Ways to achieve optimum quality and cost conditions

Quality management system should serve to optimize quality conditions, and also ensure optimum cost conditions. For the spinning mill, at least the following three areas of application of a quality management system are to be taken into consideration: • Bale management • Yarn engineering • Process management Bale management

Due to the absence of suitable and quickly-operating fibre testing methods, one knew too little in the past about the raw material characteristics, their variations and its influence on the yarn quality. As a result, and for safety reasons, a higher quality raw material than necessary was often used in order to prevent any quality complaints. Although, these preventive measures seemed to be the best compromise, they cost money. The new generation of fibre testing instruments makes possible, a much more comprehensive and quicker means of testing the raw material than previously. Bale management is based on the categorizing of cotton bales according to their fibre quality characteristics. Bale management covers: • measurement of more important fibre properties per bale or per series of bales • separation of these bales into classes. • Arranging of those bales in a lay down which have similar fibre properties and a defined variation of the more important fibre characteristics. This results in a process-oriented bale mix, and accordingly constant running conditions. It also results in yarn quality with minimum between and within bobbin variation. Yarn engineering

It is obvious that the fibre characteristics of every single bale have an influence on the yarn quality. Thus, there is a possibility of predicting the yarn strength based on the raw material data, or of selecting the raw material to achieve the required yarn strength. The “yarn engineering” is the engineered production of yarn with required characteristics based on the fibre characteristics. The yarn engineering refers to the following: • Obtaining optimum conditions in terms of product quality with respect to the yarn and the end product

18

Process control and yarn quality in spinning

• Optimum selection of the raw material for the required quality • Increase of the added value by means of a better use of the raw material • Pre-determination of the yarn properties based on raw material and process data • Ensuring the quality level throughout the complete process • Keeping constant quality in order to ensure long-term marketing conditions • Reduction of manufacturing costs by increasing efficiencies

Process management

With process management, each individual machine in the spinning mill is set to run under optimum conditions, and also separate processing stages are exactly tuned to the other processing stages, in order to see that a reasonable and process-oriented compromise, with respect to quality and costs, can be managed. For achieving this, the following are necessary: • Testing of the fibre properties before and after each important processing stage. • Correct settings, in order to achieve optimum conditions at all machines, taking into consideration the yarn as the end product • Determination of the most suitable machine or equipment • Arranging optimum conditions for machine maintenance in order that there is no reduction in quality as a result of long-term running of the machine • Introduction of early warning systems

1.7 References 1. Bhaduri, S.N. (1962), Quality control: Productivity tool in textiles, Productivity: National Productivity Council Journal, 3, 481–488. 2. Bogdan, J.F. (1956), Characterization of spinning quality, Textile Res. J, 26, 20–26. 3. Bona, M. (1994), Textile Quality, Textila, Italy. 4. Crosby, Philip B. (1979), Quality is free, McGraw Hill. 5. Cross, A. (1958), Quality: Measurement and interpretation, Text. Mercury, 139, 53–57. 6. Current practices in measuring quality (1989), Research Bulletin No. 234, The Conference Board, New York, USA. 7. David A. Garvin (1988) Managing Quality: The Strategic & Competitive Edge, The Free Press, New York. 8. David M. Gardener (1970), An experimental investigation of the price/quality relationship, Journal of Retailing, 46, 25–41.



Quality management

19

9. Duties and Responsibilities of Quality Control Staff in a Spinning Mill (1996), SITRA Focus, 4(3). 10. Frey, M. and Klien W. (1995), Quality consciousness and new management structures, Zellweger Uster Publication. 11. Genichi Taguchi and Don Clausing (1990), Robust Quality, Harvard Business Review, 68, 65–72. 12. Hisham A. Azzam, and Sayed T. Mohamed (2005), Adapting and tuning quality management in spinning industry, Autex Research Journal, 5, 246–258. 13. Juran, J.M., and Frank M. Gryna (1988), Quality Control Handbook, McGraw-Hill Book Co. 14. Pradip V, Mehta and Satish K. Bhardwaj (1990), Managing quality in the apparel industry, New Age International Limited. 15. Shanmuganandam D. (2000), Spinning Mills: Challenges, Threats and Opportunities, Asian Textile Journal, 9, 58–63. 16. Sidney Schoeffler, Robert D. Buzzell and Donald F. Henry (1974), Impact of strategic planning on profit performance, Harvard Business Review, 1–12. 17. Thakare, A.M. (2005), Retaining Customers Through Quality Assurance in Textile Mills, Asian Textile Journal, 14, 85–87. 18. Uster News Bulletin NO. 39, (1993) “Quality management in spinning mill”. 19. Walker T.W. (1960), Spinning mill quality control, Textile Weekly, 60, 79–83. 20. Walker, T.W. (1960), Spinning mill quality control, Text. Weekly, 60, 79–85.

2 Application of statistics in textiles

Abstract: This chapter discusses the significance of statistical quality control in textile industry. The basics of statistics such as central tendency, distributions and comparison results which are necessary for scientific analysis of textile products are discussed with suitable examples. The application and interpretation of various quality control charts are also discussed in this chapter. Key words: statistics, central tendency, distributions, control charts

2.1 Introduction The inherent variability in the textile raw material introduces a certain minimum amount of variation in the output material. Consequently, yarns spun from same fibre, processing conditions from the same ring frame vary in count and strength, and fabrics woven from the same loom vary in appearance and faults. If such variation is not present and every individual member of the output (say, sliver cans, ring bobbins, etc.) is exactly identical, then it is sufficient if only one sample of each individual is tested. Due to the presence of variation, it becomes necessary to test more than one sample to determine the various quality characteristics. The manufacture of textile materials is largely a system of mass production. A spinning mill produces thousands of ring bobbins every day and a weaving mill weaves hundreds of meters of fabric. It is impossible to test each and every item of the output material and it is time consuming and tests are destructive in nature. Hence ‘samples’ are tested for the various quality parameters. The whole bulk of the material theoretically available for testing is called as the ‘population’ and the ‘sample’ is a relatively small number of individual members which is selected to represent that population. This process of testing a representative sample and attributing these sample characteristics to the entire population introduces a certain error in the methodology of quality control. Besides, the instruments used for assessing the various quality parameters also have a certain tolerance range representing the accuracy/precision of the instrument which is another source of error. Adequate consideration of the sampling error and the instrument tolerances



Application of statistics in textiles

21

for interpreting the test results necessitates the use of appropriate statistical measures. Statistics is a branch of mathematics in which groups of measurements or observations are studied. The subject is divided into two general categories descriptive statistics and inferential statistics. In descriptive statistics one deals with methods used to collect, organize and analyze numerical facts. Its primary concern is to describe information gathered through observation in an understandable and usable manner. Similarities and patterns among people, things and events in the world around us are emphasized. Inferential statistics takes data collected from relatively small groups of a population and uses inductive reasoning to make generalizations, inferences and predictions about a wider population. Throughout the study of statistics certain basic terms occur frequently. Some of the more commonly used terms are defined below: A population is a complete set of items that is being studied. It includes all members of the set. The set may refer to people, objects or measurements that have a common characteristic. Examples of a population are bales of cotton purchased for spinning a yarn. A relatively small group of items selected from a population is a sample. If every member of the population has an equal chance of being selected for the sample, it is called a random sample (Fig. 2.1). Population Entire bulk theoretically available for testing

Sample Restricted no. of individuals selected to represent the population

Figure 2.1  Population Vs Sample

Variables  are characteristics or attribute that enables to distinguish one individual from another. They take on different values when different individuals are observed. Some variables are height, weight, age and price. Variables are the opposite of constants whose values never change.

22

2.2

Process control and yarn quality in spinning

Measures of central tendency

A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. These values can be used to take many decisions regarding the entire set of individual values. The measure of central tendency allows comparing two or more sets of data. The following are some of the important measures of central tendency which find common applications in the textile industry. 1. Arithmetic Mean 2. Weighted Arithmetic Mean 3. Median 4. Geometric Mean 5. Mode 6. Harmonic Mean

2.2.1

Arithmetic mean

The arithmetic mean (or mean or average) is the most commonly used and readily understood measure of central tendency. The arithmetic mean is defined as being equal to the sum of the numerical values of each of every observation divided by the total number of observations. Symbolically, it can be represented as ΣX N Where ‘ SX ‘ indicates the sum of the values of all observations and N is the total number of observations. For example, let us consider the situation wherein the neps recorded in an evenness tester when testing a sample of 10 bobbins are as follows. 151, 126, 147, 117, 133, 156, 141, 130, 139, 130 The arithmetic mean is computed as follows.

X =

X = 156 + 121 + 150 + 114 + 130 + 156 + 144 + 130 + 139 + 130 10 1370 = = 137 10

Therefore, the mean no. of neps is 137. The arithmetic mean has the great advantages of being easily computed and readily understood. It has, however, a major disadvantage in that its value can be easily distorted by the presence of extreme values in a given set of data.



2.2.2

Application of statistics in textiles

23

Weighted arithmetic mean

The arithmetic mean, as discussed earlier, gives equal importance to each observation. In some cases, all observations do not have the same importance. When this is so, we compute weighted arithmetic mean. The weighted arithmetic mean can be defined as ΣWX ΣW Where ‘ X W ’ represents the weighted arithmetic mean and ‘w’ are the weights assigned to the variable x. A typical application where the weighted mean is encountered is in the entry of the micronaire value during evenness testing. Let us consider that the yarn tested is spun from a mixing prepared by a combination of three cottons A, B and C in the proportion 60%, 30% and 10%, respectively. Let us say the average micronaire for the individual cottons are 3.5, 4.0 and 2.8, respectively. In such a situation, the entry in the evenness tester is to be made after calculating the weighted arithmetic mean as follows.

X W =

X W = (60 × 3.5) + ( 30 × 4.0 ) + (10 × 2.8 ) = 500 = 5 100 100 In the above example, the proportions of the cottons in the mixing were used as the weights for calculation. Weighted mean is specifically useful in problems dealing with ratios, proportions and indices.

2.2.3 Median Median is the value which divides the distribution into two equal parts. Fifty percent of the observations in the distribution are above the value of median and the other fifty percent of the observations are below the value of median. The median is the value of the middle observation when the series is arranged in order of size or magnitude. If the number of observations is odd, then the median is equal to one of the original observations. If the number of observations is even, then the median is the arithmetic mean of the two middle observations. For instance, consider that the U% values of a test series of 5 tests are as follows: 9.54, 10.12, 9.83, 9.98, and 10.25. The median of this set of values would be 9.98 since this is the middle value when the values are arranged in numerical ascendance or descendence. Although the median is not as popular as that of the mean, it does have the advantage of being both easy to determine and easy to explain. The median is affected by the number of observations rather than the values of observations;

24

Process control and yarn quality in spinning

hence it will not be easily distorted by abnormal values. A major disadvantage of the median, apart from being a less familiar measure than the mean, is that it is not capable of algebraic treatment.

2.2.4 Mode The mode is the typical or most commonly observed value in a set of data. It is defined as the value which occurs most often or with the greatest frequency. For example, in the series of numbers 3, 4, 5, 5, 6, 7, 8, 8, 8, 9, the mode is 8 because it occurs the maximum number of times. The difference between mean, median and mode at different situations are shown in Fig. 2.2. Symmetric

Mean median mode

Right skewed

Mean Median Mode

Left skewed

Mode Median Mean

Figure 2.2  Relationship between mean, median and mode

2.2.5

Geometric mean

The geometric mean, like the arithmetic mean, is a calculated average. The geometric mean, GM, of a series of numbers, X1, X2, X3, XN is defined as GM = N X1X 2 X3 ... ... ... X n or the Nth root of the product of ‘N’ observations. When the number of observations is three or more, the task of computation becomes quite tedious. Therefore a transformation into logarithms is useful to simplify calculations. Taking logarithms on both sides, the formula becomes 1 (log X1 + log X 2 + ... + log X n ) log GM = N X N The geometric mean is very useful in averaging ratios and percentages. It also helps in determining the rates of increase and decrease. The geometric mean has the disadvantage that it cannot be computed if any observation has either a value zero or negative.

Therefore, GM = Antilog



Application of statistics in textiles

2.2.6

25

Harmonic mean

The harmonic mean is a measure of central tendency for data expressed as rates such as meters per sec, tones per day, kilometers per liter, etc. The harmonic mean is defined as the reciprocal of the arithmetic mean of the reciprocal of the individual observations. If X1, X2, X3,... XN are N observations, and then harmonic mean can be represented by the following formula.



N 1 = 1 1 1 1 + + ... . Σ  X1 X 2 XN X

For instance, the harmonic mean of 20 and 40 is calculated as follows:





HM =

HM =

=

N 1 Σ  X 2 2 80 = = = 26.67 1 1 3 3 + 20 40 40

i.e. harmonic mean of 20 and 40 is 26.67.

2.3

Measures of variation

A measure of variation describes the spread or scattering of the individual values around the central value. Common measures of variation used in the textile industry are the ‘Standard deviation’ and ‘Coefficient of variation’.

2.3.1

Standard deviation

The standard deviation is the most widely used and important measure of deviation. The standard deviation, also known as root mean square deviation, is generally denoted by the lower case Greek letter σ. The standard deviation is calculated using the following formula.

s =

s(X − X) 2 N −1

The square of the standard deviation is called variance. Therefore variance = σ2. The standard deviation and variance becomes larger as the variability or spread within the data becomes greater. The calculations for the estimation of standard deviation for a set of 10 nep readings are shown in Table 2.1.

26

Process control and yarn quality in spinning

Table 2.1  Estimation of standard deviation N

X

X

X1 − X

(Xi − X) 2

1

151

136.5

+14.5

210.25

2

126

136.5

−10.5

110.25

3

147

136.5

+10.5

110.25

4

117

136.5

−19.5

380.25

5

133

136.5

−3.5

12.25

6

156

136.5

+19.5

380.25

7

141

136.5

+4.5

20.25

8

130

136.5

−6.5

42.25

9

139

136.5

+2.5

6.25

10

125

136.5

−11.5

132.25



S (X – X)2 = 1404.50

2

Σ(X − X) = 1404.50

Standard Deviation s =

Σ(X − X) 2 1404.5 = = 12.49 N −1 9

In the formula, the sum of the squared deviations are usually divided by ‘N − 1’ for all tests of samples and by N for the test of a population. However, for larger sample sizes, ‘N − 1’ can be replaced by N since the standard deviation values are not significantly affected by such a change.

2.3.2

Coefficient of variation

A frequently used relative measure of variation is the coefficient of variation, denoted by CV. This measure is simply the ratio of the standard deviation to mean expressed as a percentage.

Coefficient of Variation, C.V. =

S.D. × 100 X

When the coefficient of variation is less in the data it is said to be less variable or more consistent. The CV% expression is particularly useful in evaluating the precision of tests having different mean and standard deviation values. Consider the following data in Table 2.2 which relate to the mean number of thick places and standard deviation of three samples.



Application of statistics in textiles

27

Table 2.2  Mean and standard deviation of sample Sample

Mean

Standard Deviation

1 2 3

136.5 134.3 134.3

12.5 10.5 12.5

With the above data, it is easier to determine which sample is more consistent among the samples ‘2’ and ‘3’. Since the mean for these two samples is the same, it can be easily concluded that the sample which shows the lower standard deviation i.e., sample 2 is more consistent or less variable. Similarly, between samples 1 and 3, it is easier to conclude that sample 1 is more consistent than sample 3 since it has recorded the same standard deviation for a higher mean value. However, if both the means and standard deviations are different for the two samples, say samples 1 and 2, then a simple firsthand look at the data does not provide sufficient information on the consistency of one sample relative to the other. The coefficient of variation helps us out in this aspect. 12.5 × 100 = 9.16 CV1 = 136.5 12.5 × 100 = 7.82 CV2 = 136.5 12.5 × 100 CV3 = 134.3 A quick glance at the three CV values clearly shows that sample 2 is more consistent of the lot followed by sample 1 and then sample 3.

2.4 Distributions Distributions are graphical representations showing the frequency of occurrence of a particular value at different statistical levels. Distributions can also be called as ‘frequency curves’ which are essentially histograms or frequency polygons taking the appearance of a smooth curve as the number of values become infinitely higher and the class intervals become infinitely smaller. In the textile industry, two types of distributions are of greater practical relevance. These are the Normal Distribution and the Poisson Distribution.

2.4.1

The normal distribution

The normal distribution is of fundamental importance in the textile industry since most of the quality parameters of textile materials produce this type of

28

Process control and yarn quality in spinning

0.2

0.3

0.4

curve which is also the reason why such a distribution is called as ‘normal’ (Fig. 2.3).

0.0

0.1

34.1% 34.1%

0.1% –3s

2.1%

–2s

2.1%

13.6%

13.6% µ

–1s

1s

0.1%

2s

Figure 2.3  Normal distribution curve

This type of distribution curve is symmetrical about a central value. For this type of distribution, the three fundamental statistical measures – mean (the average of all individual values), median (the central value(s) when the individual values are arranged in an ascending or descending order) and the mode (the value with the highest frequency) – coincide. The curve for the normal distribution is defined by the equation

y =

1 s 2π

e

− x(x − µ) 2s

Where, y = Vertical height of a point on the normal distribution x = Distance along the horizontal axis σ = Standard deviation of the data distribution μ = Mean of the data distribution e = Exponential constant (2.71828) Where ‘y’ is the frequency (i.e., height of the curve at the point x), X is the mean and s is the standard deviation. The equation for the curve clearly indicates that the normal distribution curve is completely defined by its mean and standard deviation. If we use σ as a unit on the horizontal scale, the area under the curve between any given limits can be calculated in terms of the proportion of the total area. Since the area under the curve represents frequencies or numbers of observations, we can calculate the proportion of the observations which lie between chosen limits. For instance, 68% of the total area lies between the limits of mean ±1 standard deviation, and therefore 68% of the observations



Application of statistics in textiles

29

lie between these limits and consequently 32% outside these limits. Similarly, it will be noted that about 95% of the values lie between −2σ and +2σ and 99.7% of the values between the limits of −3σ and +3σ.

2.4.2

The poisson distribution

The Poisson distribution is used when randomly occurring events are being studied. Examples of such randomly occurring events in the textile industry include end breakages in spinning and seldom occurring faults. Trials have shown that even when the imperfection results follow a Poisson distribution (Fig. 2.4) if the values are less than 30 per 1000 m of yarn. The Poisson distribution curve is asymmetrical in shape and is defined by the equation. e –µ .µr f(r) = r! Where ‘ µ ‘ is the mean value of the Poisson distribution (with respect to the population) and ‘r’ is the number of events. Normal distribution

Poisson distribution

Figure 2.4  Normal and Poisson Distribution Curves



The standard deviation of the Poisson distribution is



s = µ

i.e., the mean and variance are equal for a Poisson distribution with the increasing mean value and increasing no. of events, the Poisson distribution tends to approach a Normal distribution.

30

Process control and yarn quality in spinning

2.4.3

The confidence range

The means, standard deviations and other statistical parameters are mere estimates of the population values and a certain degree of error will always be associated with such values. These estimates can therefore be best expressed by a range within which the population value is expected to lie. This range is called as the ‘Confidence Range’ and the 95% limits of this range are provided as the ‘Q95±’ in the statistical block of the evenness tester. This range would obviously depend on the size of the sample since the estimate based on a large sample size would always be more precise when compared to an estimate based on a smaller size. When the ‘Q95±’ values are available, we are saying that 95 times out of 100, we would be right in assuming that the population mean would fall within the Q95 limits.

2.4.4

Confidence range for a normal distribution

The confidence range for a sample test on a property following a normal distribution is given by the following expression. Q95% = ±



t Xs n

where X is the mean value s is the standard deviation n is the number of tests and t is a statistical factor The factor t is dependent on the chosen statistical significance ‘s’ and the ‘degree of freedom’ f = n − 1. The values for t and f can be obtained from any book containing statistical tables. For some common sample sizes, the t values for 95% level of confidence are given in Table 2.3. Table 2.3  t-value of 95% confidence limit Sample size

5

10

20

30

40

50

100

t

2.78

2.26

2.09

2.04

2.02

2.00

1.98

In the case of imperfection results, trials have shown that results with mean values above 30 generally follow a normal distribution. Let us consider an evenness test where 5 test values of thin places (−40%) are 175, 190, 120, 135 and 185. For these values, Mean = 161 Standard deviation = 31.5 n = 5



31

Application of statistics in textiles

From statistical tables, t (for s = 95%, f = 4) = 2.78 Q95% = ±



2.78 × 3.15

= ±39 5 i.e., the Confidence range for the mean value of 161 would be 122 to 200.

2.4.5

Confidence range for a poisson distribution

In practical situations, if the sample size is sufficiently large, the confidence range formula used in the earlier section can be directly applied since the distribution would be expected to be normal. A sample size larger than 30 is generally considered to be large enough for this purpose. Many practical samples are of size higher than 30. With low sample sizes (i.e., less than 30), the distribution is generally asymmetric and approximate to the Poisson Distribution. When the distribution is either unknown or known to be not normal, then we need to use the central limit theorem to arrive at the confidence limits for the population mean. The central limit theorem is defined as follows. If X1, X2, X3 ...Xn are n random variables which are independent and having same distribution with mean µ and standard deviation s, then if n → a, the limiting distribution of the standardized mean

Z=

X −µ s/ n

is the standard normal distribution.

Table 2.4  Yarn Evenness results of samples Series Bobbin

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10

7 9 6 9 8 7 8 9 8 9

4 5 6 6 5 4 7 5 8 4

7 9 7 6 8 7 5 4 4 7

6 7 5 9 7 5 6 8 7 8

7 9 8 6 5 6 4 7 8 10

Mean

8.0

5.4

6.4

6.8

7

32

Process control and yarn quality in spinning

In other words, mean values of distributions which are not normal can be combined to form a new mean value which would follow a normal distribution. Therefore, in such cases, we need to carry out many measurement series, consider the resulting mean values as normally distributed single values and calculate the confidence range. The application of central limit theorem is explained with an example. 5 series of evenness tests on a 40s CH yarn in a spinning mill recorded the values as per the following Table 2.4 for the thin places. The confidence range for the data is calculated as follows. Overall Mean Value X =

X1 + X 2 + X3 + X 4 + X5 5

8 + 5.4 + 6.4 + 6.8 + 7 = 6.72 5 Standard Deviation (of the means) = 0.94 t.s Confidence Range X ± X95% = X + n (2.78 × 0.94) 6.72 ± = 6.72 ± 1.17 5

2.5

Comparison of two results

It is often required in a spinning mill to determine whether the values obtained from two separate tests are significantly different. We look for this ‘significant difference’ because two apparently different values obtained by testing of samples could sometimes represent the same estimate for the population due to the ‘sample error’ or the ‘standard error in estimate’ or the presence of a ‘confidence range’. For instance, if the mean U% obtained by testing 10 cops of a ring frame doff is 11.25 and the standard deviation is 1.09 then the 95% confidence interval (Q95) is given by

11.25 ±

2.26 × 1.09

= 11.25 ± 0.78 10 Now if another test shows a mean U% of 12.0, it looks to be apparently different from the earlier mean of 11.25. However, the confidence range indicates that 95 times out of 100, the population mean or any other sample mean would lie between 10.47 (i.e., 11.25 − 0.78) and 12.03 (i.e. 11.25 + 0.78) which means the second test mean is not significantly different from the first test mean.



2.5.1

Application of statistics in textiles

33

Comparison of two means for large samples

To determine whether there is significant difference between two mean values, at value is first calculated using the following formula.

tcal =

| X1 × X 2 | S12 S22 + n1 n 2

Where X1 = Mean of first test series X 2 = Mean of second test series S1 = Standard deviation of first series S2 = Standard deviation of second series n1 = No. of readings of first series n2 = No. of readings of second series In the formula, the denominator i.e., s12 s 22 represents the standard error of the difference of the two + n1 n 2 means. If the number of readings in both the test series is the same, the equation simplifies to



tcal = | X1 × X 2 | X

n S12

× S22

This calculated t value is now to be compared with the 5% values for the t from statistical tables. The degrees of freedom to be used is (n1 − 1) + (n2 − 1) i.e., n1 + n2 − 2 [if, n1 = n2 = n, then degrees of freedom would be 2(n − 1)]. If tcal > tsf from tables, there is significant difference between the two means tcal < tsf from tables, there is no significant difference between the two means. The procedure is illustrated with an example below. Let us consider that the evenness test results from two test series are as follows 1st Test 2nd Test n1 = 30 n2 = 30 X1 = 16.5% (CV%) X2 = 17.2% (CV%) S1 = 0.78 S2 = 0.85 tcal = |16.5 + 17.2 | X

30 2

(0.78 ) + (0.85) 2

= 3.32

tsf = (s = 95%f = (30 – 1) + (30 – 1) = 58) = 2.00

34

Process control and yarn quality in spinning

Since tcal is greater than tsf, there is significant difference between the two means and the C V% of the 2nd test is significantly higher than the CV% of the first test.

2.5.2

Comparison of two means for small samples

When the sample size is small (less than 30), the methodology adopted is the same except that the following formula is used for calculating the t value.

t =

| X1 − X 2 | s



1 1 + n1 n 2

Where ‘s’ is the pooled standard deviation given by



2.5.3

S =

(n1 − 1)S12 + (n 2 − 1)S22 n1 + n 2 − 2

Comparison of variation of two samples

The previous sections described how the means of two samples may be compared. We are also often interested to know whether one material is more variable than the other in which case we conduct significance tests on the measures of variation, say the standard deviation. Figure 2.5 indicates three distributions with the same mean value but differing variation levels, distribution A being the most variable and distribution C the least variable.

C

B

A

Figure 2.5  Distributions with different variability



2.5.4

Application of statistics in textiles

35

Difference between the standard deviation of two large samples (n > 30)

When the standard deviations are compared for large samples, the t-test as discussed before can be used. However, in this case, the t value is calculated using the following formula | S1 − S2 | t = S12 S2 + 2 2n1 2n 2 with the terms S1, n1, S2, n2 having their usual meanings

2.5.5

Difference between the standard deviation of two small samples (n < 30)

The standard deviation of small samples is compared using an ‘F-test’. The value F represents the Variance Ratio and is given by the following equation.

F =

Higher variance value Smaller variance value

S12 , where S12 > S22 S22 This calculated F-value is now to be compared with the statistical F-tables corresponding to the degrees of freedom f1 and f2 where f1 = n1 − 1 and f2 = n2 − 1 and the required level of confidence. If Fcal < Ftable, there is no statistical difference between the standard deviations. If Fcal > Ftable, there is statistical difference between the standard deviations. Let us consider an example with two tests of neps 1st test 2nd test n1 = 10 n2 = 10 Mean = 385(X1) Mean = 374(X2) Standard Deviation = 42(S1) Standard Deviation = 46(S2) S22 In this case, Fcal = 2 since 462 > 422 S1 =

= 462/ 422 = 1.2 From the statistical Ftables, Ftable (for f1 = 10 − 1, f2 = 10 − 1) = 3.18 for 95% level of confidence Since Fcal < Ftable, the variability in neps from the two tests are not statistically significant.

36

2.6

Process control and yarn quality in spinning

Quality control within the spinning mill

The quality of yarn obtained in a spinning mill is influenced by a variety of factors starting from the raw material, the process parameters, machinery condition and a multitude of other such factors. Any abnormal deviation or inadequacy in any of these factors will significantly affect the final yarn quality. It is therefore important to trace the quality values for a product over a longer period and control them. The measured results can be entered periodically onto a chart prepared based on certain statistical considerations. If the chart contains warning and action limits, then the chart could serve as a useful tool for initiating correction action whenever the values exceed these preset limits.

2.6.1

Quality control through control charts

The basis of all control charts is the observation that, in any production process, some variation is unavoidable. The sources of variation can be divided into two groups, namely, random variation and variation due to assignable causes. (a) Random variation is variation in quality produced by a multitude of causes, each one of them slight and intermittent in action. There is very little one can do about this kind of variation, except (drastically and expensively) to modify the process. (b) Assignable variation, on the other hand, consists of the relatively large variations over which we have some control. Examples are differences among machines and/or operatives, variations in quality of raw materials, and so on. The effect of such causes tends to be permanent or at least long-term and it is these kinds of variation that control charts are designed to detect. Once some knowledge about the process behavior in stable conditions is available, the extent of the variation expected from random causes alone can be calculated and allowed for. If the process is then inspected regularly, the variation it exhibits at these inspections can be compared with the allowable random variation. If the observed variation conforms to the expected random variation, the process is said to be out of control; it would then be concluded that at least one assignable cause was operating, and efforts would be made to discover what it was and hence to remove it.

2.6.2

The General Principle of Control Charts

At any instant of time, a process is either ‘in control’ or it is ‘out of control’. In a well-organized factory, it should normally be in control, and what is required



Application of statistics in textiles

37

is a means for detecting when there has been a significant departure from the usual state of affairs. It is convenient for this purpose, to have a means for recording the results of the inspections, and this can be made possible by having an XY graph wherein the x-axis represents the time period or the sample no. and the y-axis represents the quality value with the control limits drawn in. Such a representation is called as the ‘Control chart’ of which a typical example is shown in Fig. 2.5. The distribution on the left of the chart is provided merely for purposes of understanding and is not generally included as a part of the control chart. The results of regular inspection are plotted on this chart. So long as the plotted points lie within the control limits, the process is assumed to be in control. A point falling outside either control limit is an indication that the process has gone out of control and that an investigation to find the assignable cause responsible is indicated. 0

UL

LL

1

2

3

4

5

6

7

8

9

10 11

Time (sample no)

Figure 2.6  Typical Control Chart

Since most of the parameters in textiles tend to follow a normal distribution, the 2σ and 3σ limits are usually used as the warning and action limits, i.e.

38

Process control and yarn quality in spinning

Upper Warning Limit = X + 2s Upper Action limit = X + 3s Lower Warning Limit = X – 2s Lower Action Limit = X – 3s The limits are shown in Fig. 2.7.

µf +3.09s f µf +1.9s f

Upper action limit (UAL) Upper warning limit (UAL)

µf

µf –1.96s f µf –3.09s f

Lower warning limit (LWL) Lower action limit (LAL)

Time Figure 2.7  Control limits in control chart

The interpretation of control charts

The basic indication that a process has gone out of control is given when a sample point plots outside the action limits. Experience in using control charts, however, leads to the evolution of other indications of lack of control, and some of these are illustrated in Figs. 2.8(a–f). (a) Figure 2.8(a) illustrates the basic rule, that a single point outside an action limit is strong evidence that the process is out of control. (b) A similar lack of control is demonstrated if two consecutive points fall between the same action and warning limits, as in Fig. 2.8(b). The reason for this is that, if the process is control, the probability that a point will plot between a warning and an action limit is about 0.0214. Thus the probability that two successive points will fall between the same limit lines is (0.0214)2 or about 0.0005, if the samples are independent. This is a very small probability, and we should therefore



Application of statistics in textiles

39

conclude that the process is out of control. To take account of this line of reasoning, the following rule is often adopted.

(a)

(c)

(e)

Time

Time

Time

(b)

(d)

(d)

Time

Time

Time

Figure 2.8  Interpretation of control charts

• If a point falls between action and warning limits, inspect another sample immediately. • If the second sample falls outside the warning limit, take action. • If the second sample falls inside the warning limit, assume the process is in control.

40



Process control and yarn quality in spinning

(c) A sequence of points sometimes occurs in which all the points lie between the central line and one of the warning limits, as in Fig. 2.8(c). Such a sequence is called a run. It can be shown that, in probability terms, a run containing nine points is equivalent to a single point outside the action limits and thus indicates a lack of control. (d) Another indication of possible trouble is a trend upwards (or downwards) of the kind illustrated in Fig. 2.8(d). When this occurs, it is prudent to check the process for assignable causes before a point eventually falls outside any of the limit lines Fig. 2.8(e) and Fig. 2.8(f). Any non-random pattern such as those shown in Fig. 2.8(e) and Fig. 2.8(f) may indicate that the process is not subject only to random sources of variation, and it should be investigated.

2.7 References 1. Barilla, A., and Viertel, L. (1957), Quality control in cotton spinning and weaving – Some practical results, J. Text. Inst. 48, 520. 2. Bcrtrcnd, L.H. (1963), Quality Control Theory and Application. Prentice Hall Inc., New Jersey, USA. 3. Bradbury, E., and Hacking, H. (1949), Experimental technique for mill investigation of sizing and weaving, J. Text. Inst. 40, 532. 4. Brearley, A., and Cox, D.R. (1961), An outline of statistical methods for use in the textile industry, Wool Industries Research Association. 5. Duding, B.P., and Jennett, W.J. (1942), Quality control charts, B.S. 600R, British Standards Institution, London. 6. G.A.V. Leaf (1984), Practical Statistics for the Textile Industry, Part I and II, The Textile Institute, Manchester. 7. Grant, E.L. (1952), Statistical Quality Control, McGraw Hill Book Co. Inc., NY, USA. 8. Gregory, G. (1957), Statistical quality control, A review of continuous sampling plans, J. Text. Inst. 48, 467. 9. Handa, T. (1970), Quality Control in Textile Industries. Asian Productivity Organisation. 10. Murphy, T., Norris, K.P., and Tippett, L.H.C. (1960), Statistical methods for textile technologists, Textile Institute. 11. Newbery, R.G. (1958), The implementation of quality control charts in spinning mills, J. Text. Inst., 49, 229. 12. Schwartz, W.A. (1939), Statistical Methods from the Viewpoint of Quality Control, The Graduate School, Dept. Agri., Washington, DC, USA. 13. Stout, H.P. (1954), Conformity limits in specifications, J. Text. Inst. 45, 6.



Application of statistics in textiles

41

14. Tippet, L.H.C. (1930), Statistical methods in textile research – Part 1, J. Text. Inst. 21, 105. 15. Tippet, L.H.C. (1935), Statistical methods in textile research – Part 2, J. Text. Inst. 26, 13. 16. Tippet, L.H.C. (1952), The methods of statistics, Williams and Norgate, London. 17. Yule, G.U., and Kendall, M.G. (1949), An introduction to the theory of statistics, Griffin, London. 18. Zulfiqar, H. (1988), Statistical Application on the Spinning Process. Research Report, Dept. Math. & Statistics, Univ. of Agri., Faisalabad.

3 Cotton fibre selection and bale management system

Abstract: This chapter discusses about the significance of raw material selection in a spinning industry for the production of consistent yarn quality. The significance and application of HVI and spinning consistency index on cotton fibre selection are also discussed. The various bale management techniques such as bale inventory analysis system, engineered fibre selection and linear programming techniques have been discussed in detail. Key words: cotton, HVI, SCI, bale management, inventory, linear programming

3.1 Introduction Raw material is the most important factor influencing yarn quality. To a great extent, it can determine whether a product is good and is also responsible for the cost factor. Mistakes made at selecting raw material and later at preparing blends cannot be made up for in further processing, even if all available means are used. Each stage of processing in a spinning mill will proceed properly only if the raw material is uniform and is contained in the acceptable range of tolerance. Subjective and reasonable savings made at purchasing a raw material are still the most effective method of cost reduction available to spinning mills. Proper choice and use of a raw material are the factors that determine whether a spinning mill can operate efficiently, successfully and competently. It must be understood and taken into account that raw materials constitute 50–60% of costs of produced yarns. The significance of raw material on yarn quality and cost are shown in Fig. 3.1. The main technological challenge in any textile process is to convert the high variability in the characteristics of input fibres to a uniform end product. This critical task is mainly achieved in the blending process, provided three basic requirements are met: accurate information about fibre properties, capable blending machinery, and consistent input fibre profiles. Over the years, developments in fibre selection and blending techniques have been largely hindered by insufficient fibre information resulting from a lack of capable and efficient testing methods. Accordingly, art and experience have been the primary tools. One of the common approaches was massive blending, in which vast quantities of bales were mixed by grade or growth area to reduce



Cotton fibre selection and bale management system

43

variability. These mixed cottons were then rebaled and fed to the opening line in random order to further enhance the mixing effect.

Figure 3.1  Significance of raw material on yarn quality

Traditionally, three fibre parameters have been used to determine the quality value of cotton fibre. These are grade, fibre length and fibre fineness. The development of fibre testing instruments such as the High Volume Instrument (HVI) and the Advanced Fibre Information System (AFIS) has revolutionized the concept of fibre testing. With the HVI, it is pragmatically possible to determine most of the quality characteristics of a cotton bale within 2 minutes. Using these instruments, thousands of cotton bales can be tested for several fibre properties at rates exceeding 150 bales/hour. Data generated by these instruments can easily be manipulated with microcomputers and powerful software programs. These revolutionary developments have led to substantial rethinking of cotton fibre selection, driven by the rising costs of both labour and raw material and the more demanding quality requirements of end products. Based on the HVI results, composite indexes such as the fibre quality index (FQI) and spinning consistency index (SCI) can be used to determine the technological value of cotton; this can play a pivotal role in an engineered fibre selection program. These systems, in conjunction with microcomputers, have made it possible to develop scientific techniques in this critical area. Bale Management System Software has earned the reputation of providing mills and merchants with the ability to rapidly process massive quantities of HVI data. This feature enables cotton to be selected so that all-important HVI measurements can be taken into account through the active control of averages, and statistical distributions of selected inventories of cotton bales.

44

Process control and yarn quality in spinning

Such control is economically important because cotton cost and related mill qualities, as well as processing efficiencies and associated costs, can be positively affected when cotton is acquired and used with the benefit of HVI data.

3.2 Cotton Cotton, being a product of nature, is a highly variable raw material, which nevertheless is used to meet a very significant portion of the world’s demand for textile products. Certainly, cotton is unsurpassed in meeting the demands of the apparel and home furnishing industries for comfortable, colourful, useful, interesting, and desirable fabrics. The conversion of bales of cotton into high-quality yarns and fabrics has always traditionally been as much an art as a science. Management of cotton’s many attributes has always been a challenge and there are many traditional approaches that can be utilized to source cotton successfully. These include: by description, type, or government class as described in Cotton Council International’s (CCI) Cotton Buyers Guide. The move by the textile industry to the use of modern high-speed opening, spinning, weaving, knitting, dyeing and finishing machinery, which to earn a profit must run at high efficiencies with very little labour oversight and few seconds, has resulted in a paradigm change. This new paradigm requires, among other things, that cotton sourced for a given mill’s machinery setup and end-product quality must be introduced into the mill in a very uniform manner over long periods of time. When managing the purchasing and consumption of cotton many factors such as variety, weather, insect problems, irrigation and harvesting practices, and ginning procedures should be considered as they often have a significant effect on the market and technical value of cotton. These inherent and often unpredictable variances complicate the buying of cotton that must, by necessity, combine the art of buying at the lowest price while ensuring the production of high quality end-products.

3.2.1

Importance of cotton quality

For the spinner the following cotton fibre properties are considered important: • Length, length uniformity, short fibre content • Micronaire (linear density/fibre maturity) • Strength • Trash (including the type of trash) • Moisture • Fibre entanglements known as neps (fibre and seed coat fragments)



Cotton fibre selection and bale management system

45

• Stickiness • Colour and grade • Contamination These fibre properties, however, vary in importance according to the spinning system used and the product to be made. Table 3.1 lists the most important fibre properties required by each system to process high quality yarns. Table 3.1  Important considerations of fibre properties for different spinning processes Order of importance

Ring spinning

Rotor spinning

Air-jet spinning

Friction spinning

1

Length and length uniformity

Strength

Fineness

Friction

2

Strength

Fineness

Cleanliness

Strength

3

Fineness

Length and length uniformity

Strength

Fineness

Cleanliness

Length and length uniformity

Length and length uniformity

Friction

Cleanliness

4

–

5

–

–

The effect of cotton fibre properties on the ring and rotor yarn strengths are given in Figs. 3.2 and 3.3, respectively.

Figure 3.2  Effect of cotton fibre properties on ring-spun yarn strength

46

Process control and yarn quality in spinning

Figure 3.3  Effect of cotton fibre properties on rotor-spun yarn strength

For the fabric manufacturer, the quality of the fibre is largely characterised by the quality of yarn they buy or are provided with, where good quality fibre translates to good quality yarn. However, the following fibre properties also have significance when appraising the finished fabric quality. These include: • Micronaire (maturity) • Trash • Contamination • Short Fibre Content (SFC) • Neps • Colour and grade However there are fibre properties not yet routinely measured, which could contribute to a more accurate prediction of the spinning and dyeing properties of cotton fibres. These properties might include such things as fibre elongation, fibre cross-sectional shape, surface and inter-fibre friction, the makeup of a cotton fibre’s surface wax, the crystalline structure of cotton’s cellulose, and the level of microbial activity or infection. Consequences of poor fibre quality are presented in Table 3.2.

3.3 HVI The value of HVI data and bale management software program is that, if the program is properly used, users are able to minimize the risk of purchasing unsuitable cotton as well as minimizing the risk of selecting mixes which are not statistically the same which otherwise would lead to unexpected costly deficiencies in the production processes. The application of HVI in cotton fibre selection and bale management is shown in Fig. 3.4.



Cotton fibre selection and bale management system

47

Table 3.2  Consequences of poor fibre quality Fibre property

Description

Ideal range

Consequences of poor fibre quality – cotton price

Length

Fibre length varies with variety; Length and length distribution are also affected by stress during fibre development, and mechanical processes at and after harvest Short fibre content (SFC) is the proportion by weight of fibre shorter than 0.5 inch or 12.7 mm

UHML in excess of 1.125 inch or 36/32nds; For premium fibre 1.250 or 40/32nds

Significant price discounts below 33/32nds

Length uniformity or uniformity index (UI) is the ratio between the mean length and the UHML expressed as a percentage Micronaire is a combination of fibre linear density and fibre maturity. The test measures the resistance offered by a weighed plug of fibres in a chamber of fixed volume to a metered airflow.

> 80%

Micronaire values between 3.8 and 4.5 are desirable; Maturity ratio >0.85 and linear density < 220 mtex; Premium range is considered to be 3.8 to 4.2 with a linear density < 180 mtex

Significant price discounts below 3.5 and above 5.0.

The strength of cotton fibres is usually defined as the breaking force required for a bundle of fibres of a given weight and fineness

> 29 grams/ tex, small premiums for values above 29 /tex. For premium fibre > 34 grams/tex.

Discounts appear for values below 27 g/tex

Short fibre content

Uniformity

Micronaire

Strength

< 8%

Consequences of poor fibre quality – spinning

Fibre length determines the settings of spinning machines; Longer fibres can be spun at higher processing speeds and allow for lower twist levels and increased yarn strength No premiums or The presence of discounts apply short fibre in cotton causes increases in processing waste, fly generation and uneven and weaker yarns Small price Variations in discounts at length can lead values less than to an increase in 78; No premiums waste, deterioration apply in processing performance and yarn quality Linear density determines the number of fibres needed in a yarn cross-section, and hence the yarn count that can be spun; Cotton with a low Micronaire may have immature fibre; High Micronaire is considered coarse (high linear density) and provides fewer fibres in cross section The ability of cotton to withstand tensile force is fundamentally important in spinning. Yarn and fabric strength correlates with fibre strength Contd...

48

Process control and yarn quality in spinning

Contd...

Fibre property

Description

Ideal range

Grade

Grade describes the colour and ‘preparation’ of cotton. Under this system colour has traditionally been related to physical cotton standards although it is now measured with a colorimeter Trash refers to plant parts incorporated during harvests, which are then broken down into smaller pieces during ginning

> MID 31, small Significant premiums for discounts for good grades poor grades

Low trash levels of < 5%

High levels of trash and the occurrence of grass and bark incur large price discounts.

Stickiness

Contamination of cotton from the exudates of the silverleaf whitefly and the cotton aphid.

Low / none

High levels of contamination incur significant price discounts and can lead to rejection by the buyer.

Seed-coat fragments

In dry crop conditions seedcoat fragments may contribute to the formation of a (seed-coat) nep. Neps are fibre entanglements that have a hard central knot. Harvesting and ginning affect the amount of nep.

Low / none

Moderate price discounts

< 250 neps/ gram. For premium fibre < 200

Moderate price discounts.

Trash / dust

Neps

Consequences of poor fibre quality – cotton price

Consequences of poor fibre quality – spinning Aside from cases of severe staining the colour of cotton and the level of ‘preparation’ have no direct bearing on processing ability. Significant differences in colour can lead to dyeing problems. Whilst large trash particles are easily removed in the spinning mill too much trash results in increased waste. High dust levels affect open end spinning efficiency and product quality. Bark and grass are difficult to separate from cotton fibre in the mill because of their fibrous nature. Sugar contamination leads to the build-up of sticky residues on textile machinery, which affects yarn evenness and results in process stoppages. Seed-coat fragments do not absorb dye and appear as ‘flecks’ on finished fabrics. Neps typically absorb less dye and reflect light differently and appear as light coloured ‘flecks’ on finished fabrics.

Contd...



Cotton fibre selection and bale management system

49

Contd...

Fibre property

Description

Ideal range

Consequences of poor fibre quality – cotton price

Consequences of poor fibre quality – spinning

Contamination

Contamination of cotton by foreign materials such as woven plastic, plastic film, jute / hessian, leaves, feathers, paper leather, sand, dust, rust, metal, grease and oil, rubber and tar.

Low / none

A reputation for contamination has a negative impact on sales and future exports.

Contamination can lead to the downgrading of yarn, fabric or garments to second quality or even the total rejection of an entire batch.

Fibre quality measurements applies

Gins

On-line process control

On-line classing colour and leaf

Classing laboratories

Ware housing

Marketing

Textile mills

Procurement

Ware housing

Mix selection Off-line classing micronaire, strength and length

Off-line process control

Figure 3.4  Application of HVI

Obviously, for a mill to attempt to fully control the variance in their cotton inventories, HVI data for every bale is a prerequisite. Achieving this level of HVI testing is not difficult. As a consequence of fully controlling the variance of their cotton inventories, mills have completely abandoned statistical sampling techniques because such techniques cannot adequately predict the bale-to-bale variation that directly affects product quality, mill efficiency,

50

Process control and yarn quality in spinning

and cost. When every bale testing is not used unexpected mill production problems are likely. Cotton is usually the single largest cost component in the spinning of yarn. HVI data makes it possible to better control of the natural variability found in cotton and improve profits.

3.4

Spinning Consistency Index (SCI)

The spinning consistency index (SCI) is a calculation for predicting the overall quality and spinnability of the cotton fibre. The regression equation uses most of the individual HVI measurements, and is based on the data taken from US Department of Agriculture’s (USDA) annual crop reports. The main use of SCI in selecting bales is to gain the advantage that all major cotton properties have been selected in a controlled way, and a consistency in fibre properties exist between fibres obtained from the selected bales throughout a season. Without the SCI, the spinner faces an insurmountable task. However, the SCI could be used to solve the complexity of cotton bale selection. Within the SCI there are various fibre properties which allow us to take the advantage of inherent correlation prevailing among the fibre properties. Thus, the use of the SCI will drastically reduce the real number of cotton varieties available for selection. Practically, the SCI could be used as the first priority for the selection of bales, followed by micronaire as the second priority, in order to exert additional control in the fibre selection. As the SCI contains six interrelated properties, good distribution control of all the cotton properties could be achieved by controlling the SCI and micronaire. The regression equation used to calculate the SCI is as follows: For HVI calibrated cotton, SCI = SCI = −414.67 + 2.9 × Strength − 9.32 × Micronaire + 49.17 × UHML + 4.74 × Uniformity Index + 0.65 × Rd + 0.36 × (+b) For ICC calibrated cotton, SCI = −414.67 + 2.9 × Strength − 9.32 × Micronaire + 49.17 × UHML + 8.61 × Uniformity Ratio + 0.65 × Rd + 0.36 × (+b) Where: UHML is upper half mean length in inches, UI is the uniformity index, Rd is the reflectance degree, and (+b) is the yellowness of cotton fibre.

3.4.1

Advantages of SCI in fibre selection

1. SCI and Yarn strength and quality parameter co-relates well as shown in Fig. 3.5



Cotton fibre selection and bale management system

51

Figure 3.5  Relationship between SCI and yarn tenacity



2. Reduce and simplify the number of warehouse categories 3. Control all measured HVI™ properties 4. Maintain day-to-day consistency of fiber properties 5. SCI based bale management ensures mixing consistency, thereby consistent yarn strength and spinning end breaks as shown in Fig. 3.6

Figure 3.6  Relationship between SCI and end breakage

52

Process control and yarn quality in spinning

6. Control within and between lay-down (mix) variations (Fig. 3.7)

Figure 3.7  Relationship between SCI and CV% of yarn tenacity

3.5

Cotton fibre engineering

In view of today’s technology, the process of cotton fiber selection should undergo an inevitable transition from the traditional pure art to a sound scientific technique. In order to achieve this transition, fiber selection should be integrated into a cotton fiber engineering program that attempts to optimize cotton fiber use with respect to cost and quality of end product. A cotton fiber engineering program should be based on fiber information that meets quality requirements imposed by the rapidly developing technology and continuous change in customer demand. A proposed scheme for such a program is illustrated in Fig. 3.8. In this scheme, four main interactive elements are demonstrated: cotton purchasing strategy, cotton testing, bale management, and cotton fiber selection. The cotton purchasing strategy should be based on an evaluation of the technological value of cotton. In other words, cotton fibres should primarily meet technological requirements. Depending on the marketing system, cotton that may have a premium market value may not be the best for the particular process or end product. Cotton suitable for ring spinning may not necessarily be right for rotor or air-jet spinning. For a given spinning method, other factors such as yam count, twist, and end-product specifications also determine what kind of cotton to use. Purchasing strategies should also optimize cotton blend components under inventory and quality constraints. Scientific procedures that use parametric linear programming can provide powerful tools for achieving this task.



Cotton fibre selection and bale management system The cotton department

53

Technological value Vs market value

Cotton purchasing Cotton testing

Optimization of cotton blend components under inventory and quality characteristics

Warehouse bale management Cotton fibre selection

Bale picking schemes

Fibre/yarn modeling

Bale lay-downs Figure 3.8  Cotton fibre engineering program

Cotton bales have traditionally been purchased based on nominal specifications. In a fiber engineering program, cotton testing may serve as a verification of nominal values of fiber characteristics. More importantly, testing fiber characteristics will produce an accurate evaluation of the variability of the bale population. Generated data can then be used for bale management and for implementing suitable fiber selection techniques. The third element in a cotton fiber engineering program is bale management, in other words, the storage and retrieval of cotton bales. Two main methods of bale acquisition may be used: storage and retrieval by group and category or by bale identification number. Selecting one of these methods will depend on the warehouse structure and the bale population size. The fiber selection technique for a cotton fiber engineering program should involve two main procedures: implementation of suitable bale picking schemes and fiber/ yam modelling. A bale picking scheme provides the mill with uniform fiber characteristics on a mix-to-mix basis without violating inventory constraints. Fiber/yarn modelling controls the desired output characteristics. Bale management is a process to mix fiber homogeneously to get consistent production and quality of yarn and inventory control and selection of fibres according to its properties. According to the fiber characteristics, bale management refers to a choice of cotton bales in order to achieve acceptable

54

Process control and yarn quality in spinning

and a constant yarn quality and economical processing conditions. The objectives of bale management systems are (i) to get uniform yarn quality (ii) to minimize shade variation of the finished fabric and (iii) to reduce or control fabric barre. The following programs available for Bale selection are 1. BIAS (Bale Inventory Analysis System) 2. EFS (Engineered Fiber Selection) 3. Linear Programming Technique

3.5.1

Bale Inventory Analysis System (BIAS)

Bale Inventory Analysis System (BIAS) is developed by M/S Zellweger Uster. According to this system, mix is formed in a way that bales taken from stock are having minimum variation in quality parameters from bale to bale and from day to day.

Steps:



1. 100% testing of bales: All the bales received are tested in HVI. Tested results are exported to BIAS either directly or by floppy disk.



2. Categorization of bales: All bales tested are numbered and divided into categories. If categorized according to SCI (Spinning Consistency Index) and Mic (Micronaire).



3. Mix formation: For example, mix for 20 bales

For SCI – Make 6 categories < 80, 80–90, 90–100, 100–110, 110–120, >120. For MIC – Make 6 categories < 3.0, 3.0–3.3, 3.4–3.6, 3.7–3.9, 4.0–4.2, >4.2 These categories are decided by analyzing at least 1000 bales for particular season. Since total categories are 6 × 6 = 36, so all the bales tested are stocked into 36 categories. In other words, total of SCI category in any direction will be the same. Now bales issued are subtracted from stock.

4. Print out of mix plan: Enter bale number of each bale issued. Average of all quality parameters will be printed. Thus, BIAS helps to reduce variation in quality parameters on a day-to-day basis for particular mixing. Fibre information from HVI and yarn information from Tenso Rapid have been used to improve performance and profitability of the yarn manufacturing process.



3.5.2

Cotton fibre selection and bale management system

55

Engineered Fiber Selection (EFS)

The transition from a process that has been primarily based on intuitive and subjective judgment to a total engineering system of fibre selection could not have been possible without the introduction of powerful bale management and selection software programs. The Engineered Fibre Selection (EFS) system, developed by Cotton Incorporated, is the leading program in this regard. Scientific fiber to yarn/fabric engineering can be undertaken using Bale Management System Software in a series of steps as follows: 1. Determination of cotton specifications 2. Opening line configuration and availability 3. In-house inventory management 4. Mix profile(s) 5. Bale selection 6. Mix evaluation and performance verification 3.5.2.1

Determination of cotton specifications

Cotton specifications are a function of end-product performance expectations and the machinery complement, including process flow design and related maintenance, settings, production rates, and the management philosophy of a given mill. It has been demonstrated over the years that mills that buy cotton solely based on price and without regard for mill and product needs are not likely to be able to compete effectively in their markets long term. Specifications of the end product should be determined and allocated into needed cotton attributes and intermediate product and machinery performance requirements. Various quality control reports and charts can be assembled to support this aspect of cotton management. The control charts provided by the Bale Management System Software can be used to assist the manager in determining which process/product variables are correlated to cotton HVImeasured properties. 3.5.2.2

Opening line configuration and availability

In most cases, it is not cost-effective to spin multiple yarn counts on multiple spinning systems and preparations from the same cotton mix. If a large quantity of cotton is being spun under such circumstances, it can usually be demonstrated that the quality of the laydown is geared to the most critical of the various yarns being spun and not to the highest volume yarn. Blending machinery performance is a critical factor in achieving a uniform end product. In today’s technology, fiber preparation lines have been substantially condensed. In a preparation process, fiber tufts are automatically

56

Process control and yarn quality in spinning

detached from the bales, blended using two to three fiber mixers, opened and cleaned using two to three cleaning units, and finally fed to the carding process for final blending, opening, and cleaning. Throughout these sequential processes, the bulk of blended fibres is gradually and rapidly reduced to the size of the carded sliver. Blending and opening unit types and various arrangements within a given system are among factors affecting the performance of blending machinery. In a typical fiber mixing system, the main challenge is to produce a blend from successively fed bale proportions, which have to exist simultaneously in the end to achieve uniformity. The inherent blending efficiency of opening lines has a direct influence on the consistent quality of yarn. Blending efficiency is different from cleaning efficiency. Cleaning efficiency has been demonstrated to be best when the cotton is cleaned with the least amount of work possible in order to reduce the chance that the fiber will be nepped up and shortened. Higher opening line blending efficiencies enable mills to successfully process a wider range (%CV) of critical fiber properties such as micronaire. The better the opening line blending efficiency, the broader the range of cotton properties that can be purchased to make a given product. Thus, a high blending efficiency is a competitive advantage. The Bale Management System Software program assists mill management in determining the best %CV for various HVI-measured properties through the use of control charts, which plot averages and %CV’s to facilitate correlating these trends to mill quality and efficiency. For example, correlation of spinning ends down, yarn imperfections, warper stops, etc. 3.5.2.3

In-house inventory management

Mill inventory management is divided into three basic approaches: 1. Mill-owned central warehouses 2. Mill warehouses at each plant 3. Just-in-time shipments from merchant warehouses Mill-owned warehouses may be central or plant located. The bale management schemes used by many mills may be divided into one of three basic concepts. In the first, cotton is received at individual plants from multiple merchants and processed at the receiving plant regardless of its quality. In the second, cotton bales are received at a central location, also from multiple merchants, and distributed in uniform lots or mixes to individual plants. In the third, a merchant in a just-in-time relationship with the mill ships cotton laydowns (mixes) to each plant site directly.



Cotton fibre selection and bale management system

57

The Bale Management System Software program may include an EDI translation program to pass the documents between cotton merchants and mills. The use of the extensive information (weights, bale numbers, HVI properties, price, etc.) contained in these documents can be used to fill in most of the needed fields in Bale Management System Software program used to manage a cotton department thus eliminating tedious and error prone hand keying of data. A powerful advantage of receiving EDI documents is the verification of the HVI properties of shipments before they are sent from the vendor. If there are any great differences between the current inventory HVI property averages and the coming shipments, the mill managers can prepare in advance for any changes that may be needed in mix selection. Inventory size has been found to be related mainly to two factors, the first being the number of bales required to cover any delayed delivery of cotton from the mill’s suppliers. The second has been determined to be that the minimum size of an inventory should be large enough to ensure that no single replenishment can change significantly the averages and distribution (%CV’s) of the important HVI-controlled cotton properties of the on-site inventory. This degree of control can only be achieved when HVI data are available for every bale. Just-in-time shipments generally refer to an agreement between a mill and its cotton suppliers to ship mixes, selected by the Bale Management System Software program, to its various plants rather than normal truckloads of cotton randomly drawn from the inventory purchased from the supplier. The main advantages of just-in-time shipments are twofold. First, inventory held on site by the mill is minimized, and second, mixes drawn from large inventories typically held by suppliers that use the Bale Management System Software tend to be quite uniform from mix-to-mix over time. Both of these factors have the potential to reduce costs. Disadvantages may include such things as higher shipping costs and distance to the supplier’s warehouse, which may result in the necessity to hold excessively large numbers of bales on site. It is important to understand that special services such as Just-InTime services may generate mill savings while adding to supplier costs. Therefore, they may command a premium while still providing overall cost advantages. 3.5.2.4

Mix profile(s)

Cotton fibre mixing model (Fig. 3.9) gives the impact of software program solution on the cotton fibre mixing quality and cost. For the programming visual basic language is used and for storing the database SQL Server is used.

Process control and yarn quality in spinning

Software programme for optimum mixing of cotton fibres

Cotton fibre mixing

Formulate mixing

w Ne tion lu so

V re erif su y lts

58

Impact on cotton fibre mixing quality and cost

Figure 3.9  Cotton fibre mixing model

The program is written on the basis of principles of linear programming. The constraints of the mixing used in the program are cotton fibre minimum length in mm, strength in grams per tex, micronaire value in a range, maximum trash percentage, and price per kilogram of the cotton. Also some of the practical constraints are considered while formulating the mixing like maximum and minimum bales to be taken for mixing from a lot. This solution is verified by the spinning experts and then laid down. The software program is most useful when it is used at the time of cotton fibre procurement. As software program give the mixing of satisfying mixing quality parameters and at lowest cost, you can save maximum amount of mixing cost if you are buying the cotton fibre required for the mixing. The following are the typical strategy of fiber selection: • Establishing the mix profile • Population profile analysis • Bale picking system • Mix evaluation and verification Establishing the cotton mix profile The starting point of implementing a fiber selection strategy is to establish cotton mix profile, or to determine the desired fiber characteristics of the cotton mix. Specifically, a mill implementing a fiber selection strategy should first establish the average values of different fiber attributes of bale laydown and maximum allowable variability within a bale laydown. Thus, “a cotton mix of optimum profile is a bale laydown, which exhibits average values and



Cotton fibre selection and bale management system

59

variability levels of fiber attributes that upon processing will result in best yarn characteristics and best processing performance at the lowest cost possible.” However, particular cottons of low market prices may not necessarily result in significant reduction in manufacturing coast (i.e. clean ability). In a fiber to yarn engineering program, cotton mix profile should be selected using following basic steps: • Gathering information about fiber-to-yarn conversion system • Gathering reliable data base of fiber properties, yarn properties, and processing parameters • Brainstorming of the effects of fiber attributes on yarn quality and processing performance • Developing reliable fiber to yarn relationships • Developing systematic methods for determining the optimum cost of the cotton mix The second step of establishing the cotton mix profile is gathering reliable data of fiber attributes, yarn characteristics, and processing parameters. The third step is brainstorming of the effects of fiber attributes on yarn quality and processing parameters. Experience and daily practice provide a great deal of insight into the desired cotton mix profile. There are no specific tools to perform brainstorming, but a layout of the different yarn parameters and that are expected to influence these parameters proves to be useful in this regard. Population profile analysis Once a cotton mix profile is established, the next step of implementing a fiber selection strategy is population profile analysis. The objective of this analysis is to ensure that cotton bales available in the warehouse exhibit fiber attributes that satisfy the cotton mix profile. Cotton bales should be purchased with values of fiber attributes falling within the range dictated by desired cotton mix profile. In a fiber selection process, the bale population profile is typically identified by three main parameters: 1. The size of the population 2. The mean values of fiber attributes 3. The variability of the fiber attributes These three parameters are described by the frequency distribution of the population. As the population size approaches infinity, its distribution approaches the normal distribution in an ideal fiber selection strategy, the cotton mix profile should statistically match the population profile. Therefore, we should select cotton bales from the warehouse that are truly representative of the bale population.

60

Process control and yarn quality in spinning



In practical terms, these criteria indicate that: (i) the average of fiber attribute in the cotton mix should be equal to its corresponding average value in the bale population (ii) the variability within single cotton mix should be equal to the corresponding population variability (iii) the between mix variability will depend on the mix size or the number of bales in the lay-down in comparison with the population size and the population variability Each yarn quality should be considered as a candidate for its own mix. Practically, this depends on the quantity of yarn being spun and the number of available opening lines. In any event, the mix profile for a given mill’s opening line should be selected to maintain appropriate control of those HVI-measured properties which are deemed by the mill to be important to machinery performance, efficiency, and product quality. The Bale Management System Software System provides users with histograms, color charts, and also, control charts, which can be used to establish the best mix profile. The control of the distribution (%CV) of selected HVImeasured fiber properties is the key to maintaining uniform, even-running mixes over long periods. The distribution %CV’s should be no higher than 10 and in many instances no higher than 5. The actual value is dependent on mill and end-product parameters and available inventory. 3.5.2.5

Bale selection

The fiber selection approach should meet two main objectives: it should achieve a uniform profile of the characteristics of input fibres and corresponding end products, and it should maintain the average values of output characteristics at their desired levels. From an economical viewpoint, a proper fiber selection strategy should result in better bale management, improved cotton bale acquisition, improved mill efficiency, and optimum cotton use. A fiber selection program should involve these basic steps: (1) Examine the population distributions of fiber properties of cotton bales. (2) Implement reliable bale picking schemes based on the distributions of fiber properties of the bale population and bale management methodology. (3) Control average output characteristics by developing reliable fiber-yam relationships. Bale selection considerations include the determination of the required run time of the mix. More importantly, the bale selection process should include the creation of mini-mixes within the mix to ensure that the cotton fed into the opening and cleaning line is always representative of the mix’s overall properties and distributions. Waste cotton and/or other non-HVI measured bales should be distributed uniformly within the mix and not grouped all at one location.



Cotton fibre selection and bale management system

61

By-group and category fiber selection Selecting fibres by group and categories requires knowledge of the fiber characteristics of each individual bale in the warehouse population. In a typical bale warehouse, the cotton bale population may be represented by one group or may be divided into several groups according to growth area, grade, cotton type, certain fiber characteristics, spinning systems, end products, etc. A single-group system is rarely found today because of the increasing trend of buying different cotton types for economical reasons and for satisfying different product specifications. In a by-group selection system, bales belonging to each group are mainly identified by their corresponding group, as shown in Fig. 3.10. Cotton bales are then picked from each group to form a mix representing average fiber characteristics of various groups. The way in which bales are selected from each group may be by random picking or by category picking. In random picking, each bale in a group population has virtually an equal chance of being selected in the fiber mix or bale lay-down. In category picking, each group population of a fiber characteristic is divided into a number of categories, and bales are picked by both group and category values. Figure 3.11 illustrates the principle of the by group and category fiber selection technique. A

B

C

Ware house (Bales in each group) A

A

A

A

B

B

B B C

C C C

A

A

A

A

B

B

B B C

C C C

A

A

A

A

B

B

B B C

C C C

A

A

A

A

B

B

B B C

C C C

A1

B1

C1

A2

B2

C2

A3

B3

C3

A4

B4

C4

Figure 3.10  The general principle of fibre selection by-group

62

Process control and yarn quality in spinning Group B Group C B2

Group A A2 A1

A3

B1

C2 B3

C3

C1

A1B2C3

Figure 3.11  Fibre selection by-group and category

For best utilization of existing inventory and to avoid step changes in HVI averages, or distribution %CV’s, bales are best selected each time a mix is required. In other words, it is better for process control and product quality to let each mix correctly reflect the averages and distribution %CV’s of the existing inventory than it is to try to always run exactly the same mix. This is true because inventory variances make it virtually impossible to select a mix that can be run over long periods without substantial changes in property averages and/or distributions. The three types of bale selection methods are given below. Stock

Issue

100

10

6

60

5

50 3

30 10 3.6

3.7

3.8

3.9

4

1 3.6

3.7

3.8

3.9

4

Figure 3.12  Standard distribution of bale selection



(a) Standard Distribution (Fig. 3.12) – Also referred to as Proportional Distribution. The Proportional Weight Category approach (PWC) should satisfy two main conditions:



63

Cotton fibre selection and bale management system

•  Consistent mix or bale lay-down •  Stable bale inventory The underlying concept of the PWC bale picking is that cotton bales belonging to a certain category are represented in the mix in numbers proportional to the relative frequency of their category in the population. Within a given bale category, bales are picked randomly. Issued bales will have the same distribution as that of stock. The average quality of all parameters is same from the first issue to the final issue. Enables uniform consumption of bales in stock. (b) Normal Distribution (Fig. 3.13) – The issue should contain the required average for the selected quality parameters, and it should follow the theory of normal distribution. Adequate bale stock is essential. Ensures uniform bale quality issue irrespective of bales in stock. Stock 100

Issue 95 10

40

6

6

30 2

10 3.6

3.7

3.8

3.9

4

3.4

3.6

2 3.8

4

4.2

Figure 3.13  Normal distribution of bale selection



(c) Moving point distribution (Table 3.3) – If the user prefers to have a very narrow working range for the selected quality parameter, the system should generate the issue and according to the availability the shift in the parameter should happen gradually till all the bales are exhausted in the lot. For example, if the user would like to have the Micronaire working range of 4.2–4.6. After exhausting the bales in 4.6 Micronaire, the system should automatically shift to 4.1 to 4.5. After exhausting 4.5, the system should go for 4.0 to 4.4.

64

Process control and yarn quality in spinning

Table 3.3  Moving point average method of bale selection Micronaire/ Mix no.

3.7

3.8

3.9

4.0

1

22

16

12

8

58

2 3 4 5 6 7

24 26

16 14 18 20 20

12 10 20 16 16 15

10 10 15 14 14 10

7 10 10 15

20

62 60 60 60 60 60

8

14

11

14

21

60

9

16

10

20

14

60

3.5.2.6

3.6

4.1

4.2

4.3

4.4

Total

Mix and process performance evaluation

A great deal of useful information may be obtained using the Bale Management System Software program to provide mill management the average and distribution %CV of each mix plotted as control charts. The variations reflected on these charts can quickly be correlated with mill production, efficiency and quality considerations. In some mills, mixes are selected far enough in advance to allow management the opportunity to request that the mix be reselected when certain averages and/or distributions appear to be out of line. By closely monitoring mill process and product performance using various observations and reports, cotton buyers typically can refine their purchasing practices to lower costs while improving mill operations and lowering inventory levels. In many instances, labour cost can be reduced as well throughout the company.

3.5.3

Linear Programming Technique (LPT)

LP is a mathematical model in which we represent existing situation in terms of linear equations and try to find solutions which will satisfy these equations and at the same time maximize or minimize one of such equations y = Ax1 + Bx2 + Cx3 x1, x2, x3 are variables Interpretation x1, x2, x3 are quantity sold for three different products A, B, C are profit per unit Y is the total profit of the company on the sale of three products Usage in textile industry • Determining the optimum blend for mixing • Determining the optimum product mix in production planning



Cotton fibre selection and bale management system

•

Reduction of inventory of dyes and chemicals

•

Determining the optimum raw material purchases

•

Identifying the bottleneck areas in production

65

Usage of LP/need for LP •

Minimize total cost and maximize quality

•

Formulation of LP model in cotton mixing

Let C1, C2, C3 and Cn be the costs of n cottons P1, P2, P3 and Pn be the percentage of each cotton to be mixed L1, L2 , L3 and Ln be the length of the cotton fibre S1, S2, S3 and Sn be the strength of the fibre M1, M2, M3 and Mn be the maturity co-efficient F1, F2, F3 and Fn be the fineness of fibre Objective function

Min Z = (C1 × P1) + ( C2 × P2) + (C3 × P3) + ………… + (Cn × Pn)

Subject to constraints

L1P1 + L2P2 + L3P3 + ………………. + LnPn ≥ Lr



S1P1 + S2P2 + S3P3 + ………………. + SnPn ≥ Sr



M1P1 + M2P2 + M3P3 + ……………… + MnPn ≥ Mr



F1P1 + F2P2 + F3P3 + … + FnPn ≤ Fr



P1 + P2 + P3 + ……………………… + Pn = 1



Where P1, P2, P3 ……….. Pn ≥ 0

Example To manufacture 10 tex cotton yarn, the required properties of the raw materials are the following:

Length: 31.5–34 mm



Strength: 20–23 gptex



Maturity coefficient: 80–83%



Micronaire: 3.6–3.9



Properties of cotton available and their costs are given in Table 3.4.

66

Process control and yarn quality in spinning

Table 3.4  Properties of cotton fibres Cotton varieties Properties

1

2

3

Norms

Length (mm)

33

31

30

32

Strength (gptex)

24

20.5

19

21.5

Maturity Coefficient

83

80.2

79.8

82

Micronaire

3.5

3.85

3.9

3.7

Cost/lb

2.05

1.70

1.66

–

Objective function Min Z = 2.05P1 + 1.70P2 + 1.66P3 Constraints 33P1 + 31P2 + 30P3 ≥ 32 24P1 + 20.5P2 + 19P3 ≥ 21.5 0.83P1 + 0.802P2 + 0.798P3 ≥ 0.82 3.5P1 + 3.85P2 + 3.9P3 ≤ 3.7 P1+ P2 + P3 = 1 Minimum Constraints P1, P2, P3 ≥ 0 P1, P2, P3 values are obtained by solving this LP model using simplex method. Results Objective function value: Min Z = 1.925

3.5.4

Cotton

Percentage to be mixed

1

64.3

2

35.7

3

0

Benefits of bale management system software

The following are the benefits most often reported by Bale Management System Software users: 1. Use of HVI data eliminates cotton bale sample cutting and classing at the mill 2. Reduction of inventory carried by the mill 3. Just-In-Time delivery of cotton improves quality





Cotton fibre selection and bale management system

67

4 Yarn quality is improved including yarn count variation, strength and Uster statistics 5. Fewer fabric defects 6. Elimination of cotton mix selection as a cause of barre 7. Reduction of comber noils without loss of quality 8. Improved efficiencies lead to lower labor costs 9. Improved warehouse management 10. Short fiber control improved 11. Adjustment of mix averages and distribution %CV’s based on values of incoming but not yet received cotton 12. Better contract management, reporting, and improved communications/ understanding between textile mills and their cotton suppliers

3.6 References 1. Balasubramanian N. (1995). Fibre properties by HVI and conventional testing at different stages of spinning, Indian Journal of Fibre and Textile Research, 20, pp. 63–72. 2. Bona M. (1994). Testing quality, physical methods of product and process control, textilia-Eurotex, pp. 182–399. 3. Davidonis G.H. et al. (1999). The cotton fiber property variability continuum from motes through seeds. Textile Research Journal, 69(10), pp. 754–759. 4. El Mogahzy Y. (2004). An integrated approach to analyzing the nature of multicomponent fiber blending – Part I: Analytical aspects. Textile Research Journal, 74(8), pp. 701–712. 5. El Mogahzy Y.E. and Gowayed Y. (1995). Theory and practice of cotton fiber selection – Part 2: Sources of cotton mix variability and critical factors affecting it. Textile Research Journal, 65(2), pp. 75–84. 6. El Mogahzy Y.E., Broughton R. and Lynch W.K. (1990). Statistical approach to determining the technological value of cotton using High Volume Instrument fiber properties. Textile Research Journal, 60(9), pp. 495–500. 7. El Mogahzy, Y. E. (1992). Optimizing Cotton Blend Cost with Respect to Quality Using HVI Fiber Properties and Linear Programming, Part I: Fundamentals and Advanced Techniques of Linear Programming, Textile Research Journal, 62(1), pp. 1–8. 8. El Mogahzy, Y. E. (1992). Optimizing Cotton Blend Costs with Respect to Quality Using HVI Fiber Properties and Linear Programming, Part II: Combined Effects of Fiber Properties and Variability Constraints, Textile Research Journal, 62(2), pp. 108– 114. 9. El Mogahzy, Y. E., Broughton, R., and Lynch, W. (1990). A Statistical Approach for Determining the Technological Value of Cotton Using HVI Fiber Properties, Textile Research Journal, 60(9), pp. 495–500.

68

Process control and yarn quality in spinning

10. El Mogazhy (1995). Manufacture of Staple Yarns, Handbook of Industrial Textiles, pp. 69–83. 11. El Mogazhy, Y. E., and Gowayed, Y. (1995). Theory and practice of cotton fibre selection, Part I: Fibre selection techniques and bale picking algorithms, Textile Research Journal, 65(1), pp. 32–40. 12. El Mogazhy, Y. E., and Gowayed, Y. (1995). Theory and practice of cotton fibre selection, Part II: Sources of cotton mix variability and critical factors affecting it, Textile Research Journal, 65(2), pp. 75–84. 13. Fryer, L.F (1996). Effects of cotton fiber blending and processing on HVI Measurements –Part- 1, Textile Research Journal, 66(6), pp. 349–357. 14. Garde, A.R., and Deshpande, S.P. (1969). Linear programming for reducing mixing costs, Proceedings of ABS Joint Technological Conference, pp. 17–22. 15. Garde, A.R. and Deshpande, S.P. (1970). How linear programming is used to decide optimum mixings, Proceedings of ATIRA Technological Conference, pp. 1–8. 16. Kang B. et al. (2000). A simplified optimization in cotton bale selection and lay-down. Fibers and Polymers, 1(1), pp. 55–58. 17. Leifeld, F. (1993). The Secret of Perfect Blending from Bale to Sliver, presented to the Beltwide Cotton Conference, New Orleans, LA, pp. 10–14. 18. Bhattacharya, S. et al (1995). Testing and bale management, the Indian Textile Journal, pp. 114–117. 19. Y. E. El Mogahzy and Yasser Gowayed (1995). Theory and Practice of Cotton Fiber Selection: Part I: Fiber Selection Techniques and Bale Picking Algorithms Textile Research Journal, 65(1), pp. 32–40.

4 Control of wastes in spinning

Abstract: This chapter deals with the various control points and remedial measures in each process for the control of waste to improve the yarn realization in spinning. The various factors influencing the yarn realization and control of hard waste and their norms are also discussed. The influence of process and machine parameters on control of waste in blow room, carding and comber and the influence of modern developments on waste control have been discussed. The effect of contamination on final yarn quality and various techniques of contamination removal during spinning processes have also been discussed in detail. Key words: yarn realization, hard waste, invisible loss, cleanability, noil

4.1

Yarn realization

The growing global competition forces the cotton spinning mills to produce yarns in constant quality at internationally competitive prices. When comparing the cost structures in different locations it can be clearly seen – under consideration of all the regional dissimilarities – that the raw material price now as before represents the dominating factor in yarn manufacturing costs. This means that the key to survival in the international market is to best possibly utilize the raw material, despite all influences of labour costs and capital costs. A high yarn realization is a factor of great importance in the production economics of a spinning mill. One per cent reduction in yarn realization would cause almost the same economic impact on the mill’s profit as 1% increase in the raw material cost would make. This is because resale value of waste is much less than the actual price of cotton till it reaches yarn stage. To illustrate, in the prevailing cotton cost and yarn selling price, even a 1% improvement in yarn realization would lead to a saving of Rs 20 lakhs per year for a 30000 spindle mill manufacturing 40s yarn. Hence control of yarn realization is important to a mill as the control of cotton and mixing costs. Yarn Realization is the percentage of yarn output produced from the given cotton input. Yarn Realization largely depends on the level of trash in cotton.

70

Process control and yarn quality in spinning

4.1.1

Method of yarn realization calculation

The estimation of yarn realization has to be done accurately by maintaining proper recording of bale weights, wastes and yarn produced. Because the estimation of quantities such as moisture content in cotton and yarn, tare weights, allowances for twist contraction and idle spindles and invisible loss are subjected to a number of assumptions as well as possible sources of error. Generally most of the mills use the following formula:

Yarn realisation (%) =

Yarn production Cotton consumption

× 100

Where, Cotton consumption = Cotton issued (kg) + Opening process stock − Closing process stock The percentage of yarn realization from any mixing depends upon the magnitude of two types of wastes: 1. Process waste – Wastes taken out in blow room, cards and combers, which together form about 80% of the total waste. These wastes are extracted for the purpose of cleaning, so there should be a direct measurement and control of process wastes. 2. Product waste – Wastes which are incurred at each stage of processing. The product wastes can be controlled through proper supervision. The types of waste incurred in the spinning mills can be categorized as: 1. Usable wastes: •  Lap bits and card web •  Sliver waste in drawing and fly frames •  Waste at comber preparatory and combers •  Roving ends •  Pneumafil and roller waste (ring frames) 2. Non-usable wastes (process and product waste): •  Blow room droppings •  Card waste (licker-in, flat strips and other wastes) •  Gutter / filter waste •  Micro dust •  Comber noils •  Yarn waste (hard waste) • Sweep waste (includes fan wastes of draw frame and speed frame and OHTC waste) •  Invisible loss For the calculation of yarn realization only non-usable wastes are taken into consideration. Formula for estimating the yarn realization given by SITRA is shown on the next page:



71

Control of wastes in spinning



Yarn Realization YR (%) = (100 – (Wbr + Wk + Wh + Ws + Wg) – I)…. for carded counts = 100 – (Wbr + Wk + Wc + Wh + Ws + Wg) – I)…. for combed counts Where, Wbr = Blow room waste% Wk = Card waste% Wc = Comber noil% Wh = Yarn waste% Ws = Sweep waste% Wg = Gutter / Filter waste% I = Invisible loss% If a mill does not reuse the usable wastes in the same mixing, the corresponding usable wastes (%) must be deducted in the above estimation.

4.1.2

Norms for yarn realization and waste in different departments

The percentage yarn realization depends primarily on the process waste taken out at the blow room, cards and combers. Of these, the waste taken out in the blow room depends on the trash content of the mixing, the waste in cards, on the type of cards and also on the trash in the lap and the waste in combers on the nature of fibre length distribution of typical mixing used for different combed counts. The norms for the various waste losses and yarn realization for types of yarns are given in Tables 4.1 and 4.2, respectively. Table 4.1  Norms for types of waste in spinning Content Count Trash%

Carded

Combed

MMF

4–9

10–13

14–25

26–34

28–34

35–44

45–70

71–99

–

11

10

7

5

5

4

3

2

–

B.R. dropping

12

11

7.7

5.4

5.4

4.4

3.2

2.2

0.1

Card waste

4.2

4.2

4.4

4.5

4.5

4.3

4.3

6.4

0.1

Comber waste

–

–

–

–

9

10.9

12

13

–

Sweeping

2

1.8

1.6

1.4

1.4

1.2

1

1

0.5

Clearer waste

0.6

0.5

0.4

0.4

0.4

0.3

0.2

0.1

0.1

Hard waste

0.6

0.5

0.3

0.3

0.3

0.3

0.3

0.3

0.4

Invisible loss

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

Y.R.%

78.1

79.7

83.6

86.5

77.5

77.4

77.9

77.9

97.6

72

Process control and yarn quality in spinning

Table 4.2  Standards for yarn realization and waste% Category

Blow room waste

Card waste

Comber waste

Ring frame

Yarn realization

Carded cotton count

5–6%

5–6%

–

3%

85–88%

Combed cotton count

5–6%

5–6%

3–5%

3%

70–75%

1%

1%

–

2%

95–97%

Manmade fibers

The actual waste collected should be compared with the norms and causes for deviation should be thoroughly investigated. Weekly waste indices showing the ratios of actual hard waste and sweep wastes to the respective norms should be calculated for each section. Often, the causes of high hard waste, soft waste and sweepings are due to negligence of workers, rough handling of materials and poor working conditions. A high sweep waste arises due to operatives throwing away the waste like bonda waste etc. on the floor. Periodically the sweep waste should be checked for the presence of good fibres. The spinning tenters should be provided with bags and it should be ensured that the bonda waste is kept in the bags during piecing. Proper supervision, maintenance and strict control would help to reduce the incidence of these wastes. The process waste need to be weighed only once a month and percentage for all categories of waste estimated taking the total cotton consumed as the basis. Estimates of the invisible loss or gain in ring spinning, reeling and winding stages should be made at periodic intervals. The norm for usable waste in cotton processing is given in Table 4.3. By exercising good control over end breaks in various machines, material handling and storage and work practices of operatives a mill could maintain the usable waste below 5%. In Synthetic processing all wastes are reusable except Blow room droppings and carding flat strips. Maximum waste achieved in blow room dropping is 0.5%, card flat strip is 1.5%, and Invisible loss is 0.5%. So, all the synthetic mills can able to achieve 97–98% yarn realization. Table 4.3  Norms for usable waste Type of waste

Norms (%)

Lap bits

0.5

Card web

0.2

Sliver waste in drawing and fly frames

0.5

Comber preparatory and comber waste

1.0

Roving ends

0.3

Pneumafil and roller waste (ring frames)

2.0



4.1.3

Control of wastes in spinning

73

Effect of fibre parameters on yarn realization

The fibre parameters which can affect the yarn realization are: 1. Trash% in mixing If the trash percentage is higher in mixing, more waste in blow room and card be removed to get the required quality compared to the cotton having less trash% in mixing, which in turn affects the yarn realization. 2. Short fibre content (SFC) in mixing Higher SFC in mixing resulted in more waste in blow room. Improper control of short fibres leads to fluff liberation in the departments which in turn higher invisible loss and affects the yarn realization. 3. Moisture content in mixing If there is more moisture content in cotton, the amount of invisible loss will be higher. 4. Micronaire In less Micronaire cotton, the amount of immaturity is higher, so that due to fibre rupture in blow room and card the waste will be higher. MCU5 – 3.8 to 4.2, DCH32 – 3.0 to 3.2. 5. Maturity Ratio In more immaturity cotton, due to fibre rupture in blow room and card the waste will be higher. 6. Stickiness of cotton / Honey dew content Higher honey dew content in cotton leads to more white waste and higher micro dust/fluff accumulation on the machine components and leads to higher invisible loss or micro dust which in turn affects the yarn realization.

4.1.4

Records for estimating yarn realization and waste

The various quantities for which systematic records have to be kept for the purpose of obtaining the yarn realization and waste losses, and their interrelationships are shown in Fig. 4.1. It contains only one quantity which is not directly measured, namely, the invisible loss. The quantity of the invisible loss is obtained by subtraction. By implication the invisible loss refers to the loss caused by the evaporation of part of the moisture content in the cotton, and by the escape into the atmosphere of some fibres and dust at various stages of processing. In practice, however, the invisible loss is the total unaccounted loss and consequently reflects immediately any mistake, or systematic error in record keeping. A relatively small inaccuracy in the waste or production records would cause a large proportionate change in the invisible loss.

Bale cotton

Closing

Processed through blow room B.R. + cards B.R. + Cards + comber

Opening

Stock in process

Gutter waste droppings

Blow room

Flat strips, strippings, droppings

Cards

Process waste

Noil

Combers

Clearer waste sweepings hard waste

Product waste

Consists of

Is obtained from

Cotton issued

Waste generated

Cotton consumed

Yarn realization

Figure 4.1  Records recommended to account for yarn realization

Doff weights

Or

Hank meter readings

Unaccounted Tare weights, Wrapping count, losses: calibration of twist contraction, Moisture and bonda waste, balances fly errors hank meter in record correction, idle keeping spindle

Invisible loss

Is recorded as

Yarn produced

74 Process control and yarn quality in spinning



Control of wastes in spinning

75

Almost all data are recorded daily; the only exceptions are the stock in process and the gutter loss in blow room. The overall and mixing-wise values of yarn realization should be calculated once every month and the overall invisible loss also determined at that time. If the values of overall invisible loss remain steady over the months, but the overall yarn realization fluctuates, then it is clear that the changes in the yarn realization are real and are due to some changes in the waste levels. If however, the invisible loss also fluctuates substantially then it indicates some mistake in calculation or in recording data, besides a possible change in the waste levels themselves.

4.1.5

Control of invisible loss

While calculating the yarn realization, the quantity of wastes which are not weighable / quantifiable due to evaporation of part of the moisture content in the cotton and the escaping of short fibres and dust at various stages of processing of cotton such as micro dust, flies etc. are called as invisible loss. Invisible loss = 100 – packed yarn production% – packed waste% (including micro dust and sweeping waste). In practice, however, the invisible loss is the total unaccounted loss and consequently reflects immediately any mistake in record keeping. A relatively small inaccuracy in the waste or production records would cause a large proportionate change in the invisible loss. 4.1.5.1



1. Short fibres and fluff escaping from departments 2. Weighment errors in cotton purchased and wastes sold 3. Excess giveaway of yarn and inaccuracies in the estimates of stock held in process 4. Differences in moisture content between cotton and yarn 5. Pilferage 6. Inaccuracies in the estimates of stock held in process 7. Improper accounting of waste produced

4.1.5.2



Reasons for invisible loss

Control measures for invisible loss

1. Ensure the moisture content in yarn is equal or little higher than the moisture content in mixing. The loss due to this would be invariably very insignificant and the mills should keep a check by weighing a few bales at random after a lapse of 3–4 months and compare the same with the weight at the time of purchase. Normal moisture content in mixing – 6–7%

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Process control and yarn quality in spinning

Normal moisture content in yarn:   Before yarn conditioning – 4.5–5.0%   After yarn conditioning – 6.0–6.5% During the process of fibre to yarn conversion, the decrease of 1–2 percentage happens which would not only affect the yarn quality, but also the invisible loss. The yarn conditioning process increases the moisture content by 1.0–1.5% (by restoring the yarn’s natural regain). The increase in moisture content in yarn after conditioning would reduce the invisible loss and in some cases it would result in ‘invisible gain’. 2. Maintain the relative humidity at 65% in winding 70% in packing departments 3. Condition the yarn at least for 12–16 hours in humidified atmosphere before packing 4. Use yarn-conditioning plant, if necessary. The objective of yarn conditioning system is to restore the natural properties of yarn like moisture content and to improve the strength and elongation and to produce a balance yarn (twist setting). So due to this the invisible loss is compensated. 5. Accuracy of balances used in weighment plays a crucial role since any under estimation in weight would be a financial loss to the mill and over estimation would lead to market complaints. Hence balances used in cotton godown, cone winding packing departments must be calibrated as per schedule and cross-checked periodically with standard weight. 6. Tare of different packs viz., bags, cartons and pallets must be checked every week 7. Saleable wastes like blow room droppings, card waste, comber waste, etc., should be weighed shift-wise and proper records should be maintained. Reconciliation of weight of wastes should be done during selling. Yarn issued to workers (for cleaning purpose) must be weighed and recorded. 8. A high invisible loss can arise due to excess bundle weight in reeling, either due to coarser count or longer length. The quality control department should keep a periodic check of the net weight after conditioning and length of yarn in cones and knots.

4.1.6

Control of hard waste in spinning mill

A high incidence of yarn waste, apart from leading to a loss of Rs 6–15 per spindle per year for every 0.1% waste, is an indication of poor machinery



77

Control of wastes in spinning

condition and maintenance, and inappropriate work practices of operatives. The norms for hard waste for different machines are given in Table 4.4. Table 4.4  Norms for hard waste in spinning and post-spinning machines (SITRA) S. no.

Good

Average

Poor

Conventional cone winding •  Mechanical slub catcher •  Electronic clearer

0.10 0.10

0.15 0.15

0.20 0.20

2

Reeling

0.10

0.15

0.20

3

Doubler winding •  Cop feed •  Cone feed

0.15 0.04

0.25 0.06

0.30 0.08

4

Ring doubling

0.05

0.08

0.10

5

Two-for-one twister

0.03

0.05

0.06

6

Auto coner •  Savio •  Muratec •  Padmatex 138 •  Schlafhorst 238

0.40 0.50 0.50 0.30

0.60 0.75 0.75 0.45

0.75 1.00 1.00 0.60

7

Open end spinning

0.01

0.02

0.03

8

Ring spinning

0.02

0.03

0.04

1

Department

The incidence of hard waste in any process is influenced by the following three factors: • End breaks and feed package replacement • Work practices and • Other causes such as quality of feed packages, housekeeping and material handling. 4.1.6.1

Control of hard waste in ring frame

Causes of hard waste Since the ring cops are the feed packages for single yarn winding, the quality of ring cops must be maintained at good level. Whenever there is a count change in ring frame, the cop quality should be checked. Proper quality of cop ensures higher winding efficiency. The cop quality is checked as per the parameters listed in Table 4.5.

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Process control and yarn quality in spinning

Table 4.5  Quality parameters to be checked in a ring cop S. no.



Parameter

Standard

Impact

1

Bobbin diameter

Ring diameter: 3 mm

•  Cop content •  Hairiness

2

Chase length

Cop diameter × 1.2

Less chase: •  Slough off •  Bobbin rejection •  Bunch in cone

3

Bobbin hardness

60–70° cotton

4

Winding and binding 2:1 to 3:1 length

Slough off

5

Bobbin empty (Top)

12 mm

Lower setting at top: •  Slough off •  Low yarn content

6

Bobbin empty (bottom)

10 mm

Lower setting at bottom: •  Bottom spoiled

7

Back winding

1.5 to 2

Hard waste

8

Under winding

3–5 layers

•  Hard waste •  Startup breakage

9

Tail end

Min. possible

Tension break at bottom

shore

for Less hardness: •  More slough off

(a) Cop content: Depending on the spindle lift and ring diameter, the cop content (in grams) should be as given in Table 4.6

Table 4.6  Relationship between lift, ring diameter and cop content (g) Spindle lift

Ring diameter 38 mm

170 mm 180 mm 190 mm



40 mm

42 mm

48–52 62–65 68–70

(b) Diameter of the cop: The ‘actual cop diameter’ must be checked against ‘standard cop diameter’. The standard cop diameter depends on the ring diameter as shown in Table 4.7. Standard cop diameter = Ring diameter – 3 mm



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79

Table 4.7  Relationship between ring diameter and cop diameter



Ring diameter

Cop diameter

38 mm

35 mm

40 mm

37 mm

42 mm

39 mm

(c) Back winding: The number of back winding coils should be around 1.5–2.5 and the maximum length of back winding should not be more than 80 cm. (d) Under winding: The number of under winding coils should be around 2–3 and the maximum length of back winding should not be more than 20 cm. As the under winding and back winding increases, more time is wasted to open them up before feeding in the magazine and also hard waste is increased. (e) Top clearance: The clearance from bobbin tip to yarn body of a full cop should be approx. 10 mm. If the top clearance is too less, it may cause slough off at the start of the bobbin unwinding. (f) Bottom clearance: The clearance from bobbin bottom to yarn body of should be approx. 10mm. If the bottom clearance is too less, it may cause bottom spoiled bobbin. (g) Yarn length per chase: The length of yarn per chase should be around 3.5–5.5 m. If the length is too long, it may lead to slough off during high speed unwinding. (h) Bobbin hardness: The bobbin hardness should be around 50° to 55°. Soft bobbins results slough off. Besides the above-mentioned points, the cops should be also checked for long tail end, deshaped bobbin, kirchi & lapetta, ring cut, overfilled and bottom spoiled bobbin to ensure high production efficiency in winding. In ring frames, poor work practices of workers and poor maintenance of machinery affect the quality of cops which in turn increases the end breaks, slough off, cop rejection etc., in the post spinning process ultimately leading to high hard waste. Some of the wrong work practices which affect the quality of the cop are double gaiting, over-end piecing, upward and downward ratcheting, not engaging the pawl on the ratchet wheel while starting the frame after doffing, using empties with remnants, not stopping the frame properly for doffing thus leading to more backwind coils, etc. Improper maintenance of builder motion, poor spindle and lappet gauging, etc., produce poor quality cops. The average cop content is about 15% lower than that expected for the lift and ring diameter used by the mills. Some of the parameters which influence the cop content besides the maintenance of

80

Process control and yarn quality in spinning

machines are as follows: low utilization of tube length, frequent ratcheting, not providing cop bottoms, improper ratchet wheel and lifter wheel combination, more chase length etc. The quality of yarn should also be maintained at good level. The incidence of high hard waste in ring frames is due to the following causes: 1. High end breaks 2. Removing more yarn unnecessarily while attending defects in cops 3. Taking more length of yarn from cops while piecing 4. Removing the cops roughly without stopping the spindle and making slough off 5. Poor doffing practice – doffing and donning separately 6. More frequent wrapping (for count checking) Measures to reduce hard waste 1. Maintain low breakage rate in all post-spinning operations by improving the parent yarn quality 2. Improve the quality of cops by reducing the defects like ring cuts, slough off, over filled cops, double gaiting, etc. 3. Ensure high cop content for the given package size 4. Impart training to workers for correct work methods 5. Maintain the machinery in good condition 6. Maintain the number of backward coils / underwind coils in the cop 7. Adopt good material handling practices such as use of plastic crates for transporting cops, trolleys, etc. 8. Improve housekeeping. Keep cop stocks in cone winding with proper covers and full cones should be stocked in raised platform. 4.1.6.2

Control of hard waste in cone winding The various measures required to reduce the hard waste level in winding department are given below: 1. Keep the functioning of stop motions in cone/cheese winding in good condition 2. Maintain the cop rejection in autoconers below 10% by improving the cop quality. The various reasons of bobbin rejection are as follows: • Bobbin quality – Long tail end, kirchi / lapetta, deshaped bobbin, overfilled bobbin, bottom spoiled bobbin, ring cut bobbin, soft bobbin, sick bobbin • Bobbin feeding in magazine •  Top bunch transfer failure •  Fault in winding unit and yarn quality •  Double gaiting / over piecing in ring frame •  Insufficient suction in the gripper arm





Control of wastes in spinning

81

3. Wind all the rejected bottoms from auto coners in conventional cone winding machines with slow speed (after removing the defects, if any) instead of cutting them using knife 4. Attend to red light immediately in auto coners 5. Feed only the minimum amount of yarn in the suction of autoconer while creeling the cops 6. Avoid using damaged empties 7. Maintaining proper yarn tensioning 8. Minimum splicing length. 9. Maintain proper records 10. Reconcile the hard waste recorded in production departments with that of godown figures periodically

4.2

Control of blow room waste

Raw cotton contains various kinds of trash, such as leaf, bark, and seed coat particles. The content of each of those trash categories is highly depending on the origin of the cotton and its harvesting method. Trash content from bale to sliver should decrease through the opening. In one hand, the requirements of sliver quality impose that the cotton must be intensively cleaned during ginning, spinning mill and carding. On the other hand, the amount of those contaminations provides useful information for finding more efficient cleaning processes and predicts the quality of the finished products.

4.2.1

Need for opening

The term ‘opening’ in the technological sense, means while number of fibres remaining constant volume of the flock is increased, i.e. the specific density of the material is reduced. Opening is usually the first step in the spinning process and includes removal of the fibres from the bale by plucking followed by further opening using pinned cylinders and pinned lift aprons. Opening to a fine degree is normally performed using a feed roll/feed plate combination to restrain the cotton whilst it is opened into very small tufts by wire wound cylinders, pinned beaters or blade beaters. At each stage of opening a cleaning operation can be performed. Cotton has to be opened more than once because trash is removed only from the surface of tufts and multiple opening actions are needed to expose all the trash. In the blow room cotton tuft size vary from 5 mg to not more than 150 mg. Throughout the processing steps in the spinning plant the density of the fibre assembly changes as shown in Table 4.8.

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Process control and yarn quality in spinning

Table 4.8  Density of fibres at different processing stages Processing step

Density

In the bale

0.30–0.70 g/cm3

In mixer or blender

0.10 g/cm3

In the sliver can

0.10 g/cm3

On the roving bobbin

0.25–0.35 g/cm3

Yarn on the bobbin

0.50 g/cm3

The intensity of opening of cotton in blowroom depends upon on raw material, machines, machine speed and ambient conditions. All these factors have to be considered before optimizing the opening and cleaning of the cotton in the blowroom. The influence of number of machines in the blow room line is shown in Fig. 4.2.

Figure 4.2  Influence of number of machines on opening intensity in blow room



Control of wastes in spinning

83

The fourth or fifth cleaning step in the blow room line on the over-all cleaning efficiency is marginal, but the contribution to fibre loss and quality reduction is considerably higher. Hence shorter cleaning lines with only two or three cleaning points are preferred in modern blow room lines. With appropriate machinery design, one pre-cleaner and one or two fine cleaners per line are sufficient.

4.2.2

Need for cleaning

The term ‘cleaning’ in the technological sense means that it is the process of releasing the ‘imprisoned’ impurities from the bale cotton. The available possibilities for cleaning of natural fibres can be classified into three groups’ viz. wet cleaning, chemical cleaning and mechanical cleaning. Since mechanical cleaning is the cheapest, easiest and fastest method, in mechanical processing of cotton, this method is widely accepted and used in the blowroom. In the mechanical cleaning the dirt particles on the surface of the flocks are removed by the use of one or combination of the striking, scrapping, suction, combing or ejecting.

4.2.3

Factors influencing cleaning in blow room

Normally trash is separated from the cotton by centrifugal force. The material is moved at high speed in a circular motion. The trash tends to sling out from the fibre surface during the passage in the pipeline. Grid bars are provided in the machines to allow the trash to escape and separate from the fibre that passes over the grid bars. The grid bars are adjustable to vary the angle and open space allowing more, or less trash to be removed. With an increase in the grid bar opening, there is an increase in the amount of good fibre that goes into the waste with the trash. Good fibre in the waste is normally kept to a minimal amount. However, if maximum cleaning is required there will be good fibre in the waste. New tuft surfaces must be created continuously to facilitate cleaning. Larger, heavier particles are relatively easy to remove. Beating devices tend to break large trash particles making them smaller and more difficult to remove. Large trash should be removed at the beginning of the cleaning process. For this reason, cotton removed from the bales should be in small tufts for effective cleaning. Very small trash particles tend to be carried with the cotton in the transport air and it is difficult to obtain separation. Condensers and fibre separators help with the removal of dust sized particles. The various trash liberating forces and their principle of operation is given in Table 4.9.

84

Process control and yarn quality in spinning

Table 4.9  Change of intensity of trash liberation and separation Way to change intensity Liberation by (i) Centrifugal force (ii) Impact (iii) Pneumatic force (iv) Frictional force Separation by (i) Gravity (ii) Suction (iii) Buoyancy (iv) Magnetic

Rotational speed of opening element, diameter of drum or roller, velocity of air flow and radius of curvature of bend in duct Speed, setting between feed nip to line to action of opening elements Suction fan speed Sharpness of grid bars, angle of inclination, closeness of interacting surfaces

Size of slot, setting between grid bars Size of screen perforations, aim discharge rate Velocity of cross air, location of separation edge Magnetic power, location

As cleaning demands both liberation and separation good cleaning is only possible if both are highly effective and appropriate to the nature of trash/ foreign matter we intend to extract. As an example the principle to be used and intensified for liberating and extracting large and heavy trash particles would be quite different than that to be used for dust or seed coat fragments.

4.2.4

Degree of cleaning in the blow room

The trash content of cotton varies greatly, from 1% to as high as 15%. It is much more difficult to remove trash from cleaner cotton than it is from dirty cotton. Table 4.10 shows how much trash is normally removed from cottons with different trash levels. Table 4.10  Relationship between cotton trash and trash removal in blow room Original trash content

Quantity of trash removed

< 1.2%

40%

Very good

30–40%

Good

20–30%

Average

10–20%

Bad

90%

Very good

80–90%

Good

70–80%

Average

60–70%

Bad

20%

< 26 mm

No optimum level. Increasing the noil percentage continuously improves quality

< 18%

> 26 mm

0.5 × short fibre %

Triangular

Flat Flat

4.4.1

Influence of preparation of comber lap on noil%

For a given mixing and count, the noil percentage to be removed in comber depends not only on the comber machine settings and process parameters but also on the comber lap preparation. The following parameters have to be considered in preparation of lap: 1. Parallelization of the fibres in the batt 2. Batt thickness (weight) 3. The disposition of the hooks 4.4.1.1

Parallelization of the fibres in the batt

From the viewpoint of both economics and quality, the degree of parallelization has a very great influence on the result of the combing operation. Lack of longitudinal orientation, i.e. noticeable fibre disorder, leads, as already explained, to elimination of longer fibres together with the noil. Loss of good fibres owing to fibre disorder is reinforced to the extent that the circular combs are overloaded during passage through a disordered batt, so that they pluck and tear at the stock, thereby carrying away bunches of fibres. The same happens with an excessively thick batt.



Control of wastes in spinning

109

Figure 4.21 Dependence of noil elimination on the degree of parallelization of the fibres in the feedstock (degree of parallelization corresponding to the draft). A: noil percentage. B: draft between the card and the comber.

With constant machine settings, the quantity of noil decreases linearly with increased parallelization of the fibres (Fig. 4.21) and with a decrease in batt thickness (below the optimum, of course). It therefore does not always follow that more noil is automatically associated with better yarn quality. The correct goal is always a predetermined waste elimination level. 4.4.1.2

Batt weight

The self-cleaning effect of the batt exerts a considerable influence on the combing operation. This effect arises from the retaining power of the fibres relative to impurities, which depends not only on the disorder of the fibres but also on their quantity. A thick batt always exerts greater retaining power than a thin one. At least up to a certain level, the clamping effect of the nippers is also better with a higher batt volume. Adversely, a thick batt always exerts a heavy load on the comb and this can lead to uncontrolled combing. In this case, the fibre farthest from the circular combs (upper side of the nipped web) may escape the combing operation, since the combs are no longer able to pass through the whole of the layer. Depending on staple length (and Micronaire

110

Process control and yarn quality in spinning

[g/m] 82 80 78 76 74 72 70 68 66 64 62 60

1 1 8/32 1 8/16 1 3/32 1 7/8 1 5/32 1 3/16 1 7/32 1 3/16 1 7/32 1 7/4 1 9/32 1 5/16 1 11/32 1 5/16 1 11/32 1 3/8 1 13/32 1 7/16 1 16/32 1 1/2 1 17/32 1 9/16 1 19/32 1 5/8 1 21/32 1 11/16 1 23/32 1 3/4

value), the ideal batt weight lies between 72 and 80 Ktex for short and medium staple cotton, and between 64 and 74 Ktex for long staple cotton > 1 1/4 (Figs. 4.22 and 4.23).

[inch]

Maximal achievable Figure 4.22  Batt weight in relation to staple length Numbers of fibers in cross-section [x 1000] 3

700

3.5

600

4 4.5 5 5.5

500 400 300 200

54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 Batt weight [g/m] Figure 4.23  Batt weight in relation to fibre mass (Micronaire value and number of fibres in the cross section are decisive)



Control of wastes in spinning

111

It is observed that a heavier lap is an essential requirement for preventing buckling of the combed fringe during its forward movement. In addition, it also results in a better constraint to hold back the neps and trash particles during detachment. The orientation of fibres and the lap weight are considered together by taking the lap strength. The effect of lap strength on quality of combing is shown in Fig. 4.24.

Figure 4.24  Effect of lap strength on cleaning effect

4.4.1.3

Disposition of hooks

The fibres must be presented to the comber so that leading hooks predominate in the feedstock. This influences not only the opening out of the hooks themselves, but also the cleanliness of the web. If the batt is fed in the wrong direction, the number of neps rises markedly. It also increases the noil and loading of top combs and circular combs, and finally the neppiness. 4.4.1.4

Effect of pre-comber draft

Higher the pre-comber draft, the better will be the parallelization of the fibres and also more will be the hook removal. This in turn is expected to reduce the noil% for the same comber settings. In other words for the same comber noil%, it is expected that the yarn quality will improve with increased precomber draft.

112

Process control and yarn quality in spinning

4.4.2

Influencing factors in comber on noil%

4.4.2.1

Feed amount moved per cycle

This has a noticeable influence on • noil percentage, • the quality of the combing operation, and • the production rate A high feed amount increases the production rate but causes deterioration in quality, especially in the cleanliness of the web. Hence, the feed amount per cycle must be set lower, the higher the quality requirements, and this correlates – not exactly but approximately – with the fibre length. Figure 4.25 serves as an indication in selecting the feed amount. mm 7

6

5

4 1

11/6

11/8

13/16

11/4

15/16

13/8

17/16

11/2 B

Figure 4.25  Typical values for the feed amount per cycle. A, feed amount per cycle in mm; B, corresponding staple length of cotton in inches

4.4.2.2

Type of feed

Forward feed used to be chosen for higher production rates when quality requirements were not too rigorous, mainly for upgrading with noil percentages of up to 12% (max. 14%). When higher demands were made on quality, backward feed had to be used with noil percentages in the range of 12–25%. With modern high-performance the situation has changed. Forward feed is mostly used over all staple ranges for achieving noil levels from 8 to 18%. One main reason is the better “self-cleaning effect” during detaching and top combing action by generating higher retaining forces. Fibre rear ends and the hooks are more extended. Disturbing impurities (husk particles, dust and trash, leaf and husk remnants, fibre neps and seed coat fragments) and



Control of wastes in spinning

113

short (floating) fibres are hold back by the top comb during detaching and are combed out by the next circular combing cycle. 4.4.2.3

The detachment setting

This refers to the distance between the clamping line of the nippers and the nip line of the detaching rollers when these parts are at their closest spacing. The detachment setting provides the chief means for influencing the level of noil elimination. A wide detachment setting results in a high level of noil elimination; a closer setting is associated with a lower noil level. Spinning mills must find the optimal setting for their own conditions. If the detachment setting is increased, starting from a certain optimum, there will be hardly any improvement in quality except in relation to imperfections. The detachment setting normally lies in the range of 15–25 mm. If the noil percentage varies for no reason while the machine settings (including the detachment setting) are kept constant, the cause lies not in the machine but in the raw material (variability of the raw material characteristics, e.g. short fibre content). 4.4.2.4

Depth of penetration of top comb

Noil extraction can also be influenced by the depth of penetration of the top comb. Lowering of the top comb by about 0.5 mm is followed by an increase in noil of about 2%. The main improvement due to this procedure has to be seen in the elimination of neps. Initially the top comb can be set to +0.5. In case of extracting less than 10% noils the top comb can be set to ‘0’ or (–1). For higher waste% and when quality requirement is high it can be set to (+1). As always, the optimum setting must be established, since excessively deep penetration of the top comb disturbs fibre movement during piecing. The result is deterioration in quality. 4.4.2.5

The number of points in the comb

Comb segments on older machines had a clothing of needles. Both the point density and the fineness of the needles were adapted to the raw material. Top combs are still clothed in this way or are equipped with teeth. Clothing of circular combs has changed in recent decades: a saw tooth clothing is used today. In comparison with needles, the new type of clothing is more robust, needs less maintenance and is more universally applicable. Since the combs are called upon to perform the main work of the machine, their influence on quality is considerable. Needles on the top comb have a flattened cross-section and are formed with a bend. Usually they are used with a point density in the range of 23–32 needles per centimetre.

114

Process control and yarn quality in spinning

Fewer needles are used when higher production is needed together with lower waste elimination. More needles produce more noil. An important fact about neps that is significance in the context of optimizing comber waste is as follows. The proper place for controlling neps is carding and not combing. Firstly, the quality of carding influences the level of neps considerably and secondly, it is often more economical to run cards at somewhat low production rates than to take out extra comber waste for keeping the level of neps in yarn at the desired low level. The nep removal at combers is expensive because the comber needles cannot positively comb out neps, which are smaller in dimension than the spacing between the needles of even the top comb. The neps are in fact removed along with the clusters of fibres which go into the waste. Thus, large amount of wastes will have to be incurred for increasing the nep removal at combing.

4.4.3

Reasons for comber waste variation

Reasons for variation in waste% between heads and machines are: 1. Variation in mixing time to time 2. Variation in blending of different cotton 3. Variation in unsuitable and unproportional mixing of soft waste 4. Variation in lap weight 5. Insufficient draft in lap preparation 6. Bad mechanical condition of lap machine like bent weighting hooks, defective top rollers and variation in top roller pressure 7. Bad condition of comber machine parts like brush, unicomb, top comb. 8. Variation in unicomb to nipper gap between heads and top comb penetration 9. Poor nipper grip and bent nipper 10. Variation in feed ratchet gear; count change gear and tension change gears between combers The head-to-head variation can be controlled to the level ±1.5% and the comber-to-comber variation can be controlled to the level of ±0.5%.

4.4.4

Procedure for control of comber waste

Two important parameters such as “overall comber waste%” and “head-tohead variation” in waste are to be controlled during combing process. Both the head wise as well as overall comber waste can be determined accurately by collecting and weighing the head wise noils and sliver made during five minutes of production.



Control of wastes in spinning

115

Loaded circular and top combs are known to cause a slight increase in the waste percentage. Depending on the preparation given, it rises by 0.5– 1% in the first 20 minutes and then remains more or less constant. To obtain comparable measurements, therefore, before every measurement, the circular and top comb must be cleaned properly; i.e., the machine must run in slow speed for some time. However the waste percentage is determined afterwards at normal speed. Both head-wise as well as over all comber waste can be determined accurately by collecting and weighing the head-wise noils and the combined sliver from the cans made during 5 minutes.

Overall comber waste % =



Head wise waste % =

Weight of noil from heads Weight of sliver + Weight of noil from all head Weight of noil from heads ‘X’

1/n (Weight of sliver + Weight of noil from X head)

× 100

where n = number of heads on the comber. Estimating the waste percentage, longer periods more than an hour and measuring individual head noils and sliver produced has close relationship with the above easy method. Estimating the waste% less than 5 minutes does not give accurate results as well as longer duration more than 5 minutes do not improve the accuracy also. The waste% of comber has to be checked and reset at least once in 15 days. The comber which is mechanically sound and properly set the waste% will vary time to time. Such natural variation must be allowed before taking up the comber for resetting. For combers in good condition, the coefficient of variation of comber waste based on a 5 minute test is about 4%, while that for the waste from individual head is about 6%. For these values of CV, Table 4.19 gives the limits within which there is no need for readjusting the comber. Table 4.19  Limits of comber waste Normal waste %

8

10

12

15

18

20

Comber to comber

(+)

0.6

0.8

1.0

1.2

1.4

1.6

Head to head

(+)

1.0

1.2

1.4

1.8

2.2

2.4

Stepwise procedure for controlling the comber waste is given below: 1. Determine the optimum level of comber waste (0.5 × short fibre%) to be extracted from cotton/mixing. 2. Find out the variability of comber waste by taking at least 20 waste readings of 5 minutes each over a period of 10–15 days. If the CV% of head waste and comber waste are higher than 6% and 4%

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Process control and yarn quality in spinning

respectively, attempts should be made to bring down by looking into the mechanical condition of the combers. 3. Take a 5 minute test, collecting the sliver from the can and the waste from each head 4. Calculate the head-wise and the total comber wastes. 5. Adjust each head where the waste falls outside the limits and recheck the waste by a second test.

4.4.5

Improvement in mean length on combing

Combing efficiency is generally assessed by the improvement in fibre length that achieved in sliver in relation to that of the input material. Theoretically, the comber should remove all the fibres in the lap which are shorter than the detachment setting, i.e. which are shorter than the minimum distance ‘d’ mm between the nippers and the grip of the back detaching rollers. Also, if ‘f’ mm is the lap feed per nip than all fibres longer than d + f should always go into the sliver. Fibres of intermediate length (i.e., those longer than d but shorter than d + f) would go either into the waste or into the combed sliver depending upon their position in the feed cycle. Even a perfectly maintained comber, therefore, cannot remove short fibres as selectively as could be expected from its mechanism since it is not possible to feed all fibres in the lap as straight fibres without any hooks and parallel to the length of the lap, also the fibre movement should not be influenced by any frictional contact between the neighbouring fibres. The words fractionating efficiency refer to the degree to which a comber succeeds in removing all the fibres shorter than the detachment setting d without losing any fibres longer than the detachment setting plus feed, d + f. Combing efficiency (%) per 1% noil extraction is given by = 50% span length of comber sliver-50% span length of comber lap



50% span length of comber lap

×

100 % noil

A periodic check on the fractionating efficiency of combers serves two purposes: (i) it permits the assessment of improvement in fibre length in relation to the comber waste percentage; and (ii) it helps indirectly to judge the mechanical condition of the combers.

4.4.6

Factors influencing the combing efficiency

4.4.6.1

Fibre length distribution in staple diagram

The combing efficiency not only depends upon the amount of waste extracted but also has a greater bearing on the amount of short fibre content in the feed



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117

material. For example in the triangular-type fibre distribution the increase in noil% improves the combing efficiency. However in the more like rectangular (i.e., flat) type staple diagram the extraction of noil% beyond the short fibre content does not improve the combing efficiency. 4.4.6.2

Amount of waste extracted

Under good working conditions, for every 1% increase in comber noil, yarn lea strength will increase by 1% and the yarn evenness will improve by 0.15U%. Apart from machinery condition and process parameters used, the combing efficiency also depends on the short fibre content of feed lap. Higher the short fibre content better will be the combing efficiency. The norm for combing efficiency is given in Table 4.20. Table 4.20  Norms for combing efficiency Rating

Combing efficiency (%) per 1% noil extraction Up to 12% noil extraction

13% to 20% noil extraction

0.6 0.5 0.4

0.8 0.7 0.6

Good Average Poor

4.4.6.3

Comber lap preparation

The combing efficiency may slightly improve with better lap preparation. However the type of fibre distribution in the staple diagram is the major contributor. 4.4.6.4

Comber machine settings

The following settings have influence on the combing efficiency.

1. Top comb penetration



2. Unicomb to nipper gap



3. Nipper grip



4. Brush and wind protecting plate setting



5. Detaching roller loading



6. Detaching distance setting



7. Timing of unicomb



8. Selection of control wheel index setting

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4.4.7

Process control and yarn quality in spinning

Influence of modern developments on comber waste

4.4.7.1 Concentric synchronise movement of the nippers

The combing will be better if the bottom nipper lower surface remains at fixed set distance from the bottom comb during the entire circular combing. This is achieved by using the standing pendulum principle (Fig. 4.26). The Rieter E65/E 75 comber, Trützschler Comber TCO 1, Marzoli Comber CM600N are equipped with concentric synchronise nippers movement.

Figure 4.26  Concentric nipper assembly

4.4.7.2

Reducing the clamping distance

By reducing clamping distance, long fibres going into the waste can be avoided (Fig. 4.27). If this distance is wide, fibre control during combing is hampered, thus deteriorates combing efficiency. Rieter comber has designed this area to keep this distance minimum.



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Figure 4.27  Clamping distance in Rieter comber

4.4.7.3

Self-cleaning top comb

The top-combs, during operation, get loaded with short fibres and impurities. Thus, the machine is stopped intermittently for cleaning and results into production loss. Trützschler comber is equipped with self-cleaning top combs (Fig. 4.28). An extremely short compressed air blast of a few milliseconds purges the needles from top to bottom and detaches the adhering fibres. The cleaning frequency can be adapted to the respective degree of soiling. Similarly Rieter also provides ‘Ri-Q-Top’ top-comb with high selfcleaning effect as shown in Fig. 4.29. The flat teeth combined with wedgeshaped wire profile minimize the wrapping tendency of fibres in the top comb wires.

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Process control and yarn quality in spinning

Figure 4.28  Self-cleaning topcomb from Trützschler

(a)

(b) Figure 4.29  (a) Conventional top comb; Vs (b) Rieter Ri-Q-Top

4.5

Contamination removal techniques

In today’s circumstances, contamination playing a vital role in deciding the quality of cotton apart from essential properties such as length, strength, fineness. Contamination of raw cotton can take place at every step, i.e. from the farm picking to the ginning stage. Contamination, even if it is a single foreign fibre, can lead to the downgrading of yarn, fabric or garments or even the total rejection of an entire batch and can cause irreparable harm to the relationship between growers, ginners, merchants and textile and clothing mills. An International Textile Manufacturers Federation (ITMF) reported that claims due to contamination amounted to between 1.4% and 3.2% of total



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sales of 100% cotton and cotton blended yarns. All this makes it important to find the most effective solution to combat foreign matter in cotton. Many foreign fibre problems are only detected after finishing, and the spinner is finally made responsible for the damage. Therefore, the costs for such claims can be considerable, and provisions have to be made to absorb such claims if the spinning mill does not have a quality management system to eliminate or minimize the number of foreign fibres in yarns.

4.5.1

Contamination level in cotton bale

Extraneous contaminants are found in cotton from all origins, without any exception. Not one single cotton shipment was found to be totally free from contamination in the last eight years. The perceived degree of contamination in all growths steadily increased from 1989 to 2007. Indian cotton always have high contamination level due to poor work practices right from picking to finishing stage. The raw cotton bales from different countries differ in the degree of contamination. Broadly, cottons from 15 origins can be classified into 3 groups – group I with low contamination, group II with medium levels of contamination, and group III with high level of contamination – as can be seen in Table 4.21. Table 4.21  Extraneous contamination by origin (2006–07 average) Origin

% of bales contaminated

% of fibrous contaminants

Amount of contamination

Group I (Low contamination)

Australia Brazil China Mexico United States

10–20%

60–75%

1–3 g/ton

Group II (Moderate contamination)

Mozambique Paraguay Uzbekistan West Africa Zambia

60–80%

75–85%

4–12 g/ton

Group III (High contamination)

India Pakistan Syrian Arab Republic Uganda Zimbabwe

90–100%

80–90%

20–100 g/ton

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4.5.2

Process control and yarn quality in spinning

Types of foreign material in cotton

Mixing of foreign material/matter with main product at any stage of collection, production, handling, storage, processing in the yarn manufacturing process is termed as contamination. The International Textile Manufacturers Federation (ITMF) investigates the contamination of cotton bales on a global scale. The classification of foreign material in bales is given in Fig. 4.30.

Figure 4.30  Classification of contamination in bale

4.5.3

Effect of contaminations on process efficiency

The influence of various types of contaminations on process efficiency and the remedial measures are given in Table 4.22.



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Table 4.22  Effect of contaminants on further process S. no.

Source of contamination

1

Strings/fabrics of jute/hessian

2

String/fabrics of • Poor quality of yarn/cloth cotton due to coloured fibres

• Automatic transportation • Manual picking • Education/Training

3

Strings/fabric of • Differential dye pick-up woven plastic/ • Very poor yarn/fabric plastic film quality • Damage to machinery

• Avoid usage of plastic material • Better house-keeping and practices

4

Organic • Damage to machinery matters: leaves, • Increased waste at feathers, paper spinning leathers etc

• Use of pre-cleaners at ginning • Better house-keeping and practices

5

In organic matter: (a) Sand dust

• Damage to machinery • Increased waste at spinning

(b) Inorganic matter metal/wire

• Damage to machinery

• Use of pre-cleaners at ginning • Better house-keeping and practices • Better house-keeping and practices

Oily substances: (a) Stamp colour (b) Grease/Oil

• Mars yarn/fabric appearance

7

Hair-human

• Increased end breakage rate at ring/rotor spinning • Poor yarn/fabric appearance • Differential dye pick-up

• Use of caps • Automatic transportation • Education/Training

8

Stones

• Damage to machinery

• Better house-keeping and practices

9

Seed-coats

• More waste at spinning • Poor yarn/fabric appearance

• Use pre-cleaner and post cleaner ginneries

10

Pouches-Gutka • Damage to machinery • Poor yarn appearance

6

Effect • Increased end breakage rate at ring/rotor • Poor yarn appearance • Differential dye pick-up

• Mars yarn/fabric appearance

Remedies • Avoid use of jute/hessian for transportation at farms and ginning • Use of cotton cloth for cotton bales

• Avoid usage of stamp colour • Use of printed/pre-marked cotton cloth/paper • Better house-keeping • Replace single roller gin by double roller gin

• Education/training • Better practices

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4.5.4

Process control and yarn quality in spinning

Size and appearance of foreign matter in spinning mills

In order to convert a fibre into yarn, cotton passes through a large number of processes in a spinning mill. A large number of machines mechanically reduce the size of most foreign matter clusters into a large number of individual foreign fibres. These fibres can remain undetected under normal mill processing conditions and only become noticeable when the production process is interrupted, by a spinning end break or when the yarn is used to make up fabric and the fabric is subjected to normal quality control inspection. If foreign material cannot be eliminated prior to the card the foreign material is cut into pieces by the card. A piece of plastic can result in a number of individual foreign fibres after the card. As these fibres are mostly colored fibres, the cluster of foreign fibres can easily be recognized in the card sliver (Fig. 4.31). Often in some spinning mills some of the foreign fibres are added accidentally through human ignorance, waste recycling, etc., which contaminate the cotton fibres during the spinning process. For such fibres the clearer as a monitoring system at the last stage of the spinning process is the only tool which can eliminate such fibres. The foreign fibres which cannot be eliminated during the spinning process will show up in the yarn and have to be eliminated by the yarn clearer either on the winding machine or OE rotor spinning machine.

Figure 4.31  Effect of contamination in bale on yarn

4.5.5

Appearance of foreign fibres in spinning mills

In order to understand the appearance of foreign fibres in spinning mills we have to consider that foreign fibres which exist as clusters in the card sliver



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125

are drawn in the spinning process. The more steps in the spinning process the more increases the distance from foreign fibre to foreign fibre in the yarn. Therefore, the distance between two foreign fibres is longer in a ring spinning operation with combers than in an OE rotor operation as shown in Fig. 4.32. Assumption: Plastic film prior to card of 2 cm2. Resulting cluster: 400 individual foreign fibres in the card sliver. In Fig. 4.32, the processing steps and the drawing ratios are shown for the 3 most important spinning processes. It can be seen in the figure that the distance between two foreign fibres is short for short spinning processes and long for spinning processes with many steps.

Figure 4.32  Influence of spinning techniques on contaminated yarn appearance

4.5.6

Causes of contamination in picking and ginning process

Most contamination arises from impurities being incorporated into the bale as a result of human interaction during harvesting, ginning and baling as shown in Fig. 4.33. The following are the some of the reasons behind high contamination level in cotton:

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Process control and yarn quality in spinning



Hand picking method Reuse of fertilizer bags Lack of infrastructure and awareness Improper maintenance work methods Iron tap for bale packing Label

1. 2. 3. 4. 5. 6.

Figure 4.33  Contaminants collected during ginning process

4.5.7

Effects of contamination

1. Contamination of cotton causes it to become sticky that creates obstruction in rollers. 2. It causes wastage of dying material and requires extra efforts at cleaning process that unnecessarily inflates cost. 3. Even after cleaning leftover embedded pieces of contamination in yarn affect its quality and value. 4. Contaminants such as stones, metal pieces, etc… causes disturbance to material flow especially in spinning preparatory process which affects production as well as quality of the process. 5. Metal pieces tend to cause fire accident which leads to severe machine and material loss. 6. Fabric appearance produced with contaminated yarn will be poor and prone to rejection (Fig. 4.34)



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Figure 4.34  Polypropylene contaminants in knitted fabric



7. Dyeing affinity of contamination is different from dyeing affinity of fabric which leads to uneven fabric coloration.

4.5.8

Methods to eliminate contamination in cotton

Figure 4.35 shows the domains of foreign material removal systems and the frequency of foreign material. It is obvious that the frequency of foreign material increases considerably in the area of fine foreign matter (human and animal hair, plastic fibres, fragments of strings, seed coat fragments). It is evident that the type and frequency of foreign matter require an effective system to combat this problem.

Figure 4.35  Methods to eliminate foreign material in cotton and foreign material frequency

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Process control and yarn quality in spinning

Over the years spinning mills used the following methods to eliminate disturbing foreign matter in order to keep the defects within acceptable limits: 1. Cotton selection 2. Manual labour to pick foreign matter in cotton prior to the opening 3. Contamination removal devices prior to the card 4. Foreign fibre clearers in winding 5. Installation of ultraviolet (UV) lights in the packing and inspection section In some cases, especially in vertically integrated textile mills, the mending of defects after finishing the fabric is also common practice, but only part of the foreign fibres can be extracted. 4.5.8.1

Cotton selection

It makes sense in a spinning mill to know the growth areas with low foreign material contamination. It must be the aim to order cotton from areas with a low number of foreign material content to keep the risk of remaining foreign fibres low and to improve the efficiency of the removal systems both human and electronic. Further, they help to keep the number of foreign fibre cuts with the clearer on a low level. This is especially valid for end customers who ask for “zero foreign fibres” as a mandatory requirement, and a significant premium is paid for such a high value addition. If the premium which the spinner can realize is not significant, choosing low contamination cotton can often lead to other issues seriously affecting profit margins. This may be cotton with higher nep content, higher short fibre content and higher cotton prices. Further, cotton supply contracts in general do not include contamination level as a dispute clause, with the result that losses cannot be recovered in case contamination expectations are not met. 4.5.8.2

Manual labour

Spinning mills situated in countries where labour costs are comparatively low employ large numbers of people to patrol the bale lay down and remove contamination from bales before cotton is fed into the blow room line by the bale opener as shown in Fig. 4.36. A small number of spinning mills are able to manually check and remove contamination from every bale of cotton before it is repacked and released for processing in the mill. This manual sorting is either done directly from the bale or the bale is first opened using a bale opener with a spiked lattice to open the cotton prior to manual sorting. The cost for this manual cleaning is estimated at 3.1 to 4.4 US cents per kg of lint depending on the level of contamination



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Figure 4.36  Manual removal of contamination in spinning mill

The number of people or the work load employed varies from mill to mill and the end use. Estimates from spinning mills in China show between 1 person per 1 to 3 bales depending on the quality demand. Therefore, in an average size spinning mill with 30,000 spindles the number of employees who do these jobs vary from 60 to 180 people. 4.5.8.3

Contamination removal systems prior to the card

There are various contamination removal systems available today prior to the card. In general such devices are important to eliminate the foreign matter of a size greater than 1 sq cm to avoid further disintegration into finer fibres and to increase cuts in the final inspection by the yarn clearers. However, such systems do not help to fully meet the quality targets of the end user since the size and the number of ejections makes it practically impossible to eliminate the single foreign fibres which constitute the highest amount of disturbing defects in the final yarn or fabric. Further, the location of the system and the size of the tuft play a decisive role for the detection efficiency. Removal systems in the blow room line focus on the bulk of the contamination. Such systems have not been designed to detect and remove small particles (in the range of several millimetres such as individual fibres). The use of only yarn clearers can only be considered in the event of very clean cotton. Normal cotton (such as Asian and African cotton) will contain so much contamination that it cannot be removed without an excessive number of clearer cuts i.e. without a loss of machine efficiency which cannot be accepted.

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Process control and yarn quality in spinning

Depending on the quality requirements one system in the blow room line may, however, be sufficient particularly where manual picking is still used during the opening process. In case of stringent requirements and of heavily contaminated cotton both measures are recommended: The sorting machine in the blow room will act as a coarse filter removing the bulk of the contamination, the yarn clearer focusing on the remaining particles which can be eliminated with an acceptable number of cuts. Effective position of contamination clearer in blow room line The degree of opening of the cotton tufts is the controlling parameter for selecting the optimal position in the bow room line. The better the degree of opening, the smaller the risk that contamination is hidden within the tufts. Experience shows that a system at the beginning of the blow room line will detect only about 20% of the particles which can be detected at the end of the line. The degree of opening is important not only in respect to the detection performance but also in respect to the loss of good fibres. If the cotton is not well opened, larger cotton tufts will have to be removed. The loss of good fibres at the beginning of the line is about 5 times higher than the loss at the end of the blow room line (about 3–8 grams per removal cycle as compared to 0.5–1.0 gram). In case of a system at the beginning of the line, it will be necessary to reduce the loss of good fibres by subsequently manually sorting the removed material.

Figure 4.37  Position of contamination clearer in blow room line



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131

Blow room lines frequently start with one bale plucker and are afterwards divided in two lines. In such cases two removal systems would be necessary at the end of the line whereas only one system would be needed at the beginning as shown in Fig. 4.37. Contamination detection by optical means All systems detect contamination by optical means. Yarn clearers and the Sorter of Loptex use photo sensors and detect the contamination as being darker than the cotton. Other sorting machines use colour cameras and detect contamination as being different in colour. The difference in practical performance is insignificant. Cameras, however, are more delicate and costly (for repairs and replacements) and, if they fail, will cause a total breakdown of the system. The default of a photo sensor will not lead to a total break down, but only slightly reduce its performance. Since the photo sensors are arranged on separate modules, the replacement will be easy and cost effective. In some cases cameras will not monitor the fibre flow directly but only indirectly through mirrors. Mirrors attract, however, dust and need to be cleaned. The critical point, however, is that an optical sensor can only see what is visible, meaning that it cannot detect contamination which is hidden within the cotton tufts. To compensate this handicap most systems, use two optical sensors each positioned at the opposite side of the pipe. This permits to detect contamination which is located on the back of a cotton tuft. In most cases this will be sufficient. However, if the opening of the raw material is poor, it can happen that the contamination is hidden inside of the tuft contamination which offers no contrast to the cotton i.e. colourless contamination or contamination of the same shade. Unfortunately one of the most harmful contaminations, namely packing material in polypropylene, comes in whitish shades which do not offer a sufficient contrast to the raw material. Contamination detection by ultrasonic means Everyone knows the phenomena of echo. Louds will be reflected by a rock wall. The degree of reflectance of acoustic waves depends on the surface structure of the object in their path. It detects contamination independent of it colour on the basis of its surface structure. Most contamination has a denser surface structure than loose cotton tufts, in particular plastics. The sensor consists of a number of emitters of ultrasonic therefore not hearable waves. The receiver will receive waves which are reflected by the contamination contained in loose cotton. If no contamination is present, the ultrasonic waves will be absorbed in the absorber box located on the other side of the pipe. Contamination detection with ejection by pneumatic valves In case of the detection of a contamination being by the optical or the acoustical system the electronic control will activate pneumatic valves. It will take into

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Process control and yarn quality in spinning

account the transportation speed of the raw material and release the air blow after the necessary delay. The number of valves which will be activated is variable. It depends on the size of the detected contamination. The air blow will be targeted since only the valves are activated which are located in front of the passing contamination. The contamination will be deviated through an opening in the pipes into the waste container of the machine. Loptex Optosonic Sorter Optical detection of colored contamination doubled with ultrasonic detection of colourless material. The raw material will first be presented to the acoustic sensor and thereafter to the optical sensor as shown in Fig. 4.38.

Figure 4.38  Loptex optosonic sorter

The acoustic sensor will emit ultrasound waves. A contamination with compact surface structure like plastic will reflect these waves into the receiver. The receiver will thereafter trigger the ejection device. The optical sensor consists of standard fluorescent light tubes and custom made photo sensors array. A colour contamination reflects less light to the photo sensor array which thereafter will trigger the ejection device.



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133

Only the pneumatic valves placed in front of the contamination will be activated. The duct opposite the valves, presents an opening through which the contamination is blown into the waste container. Rieter’s Visionshield Detecting module consist of two CCD line colour camera with associated lighting system, rapid deflection system for ejecting the detected contaminants. Twelve long-life fluorescent tubes arrangement in front and back side of tuft for illumination. The CCD camera require small gap between the fluorescent tubes for optimum illumination as shown in Fig. 4.39.

Figure 4.39  Rieter Visionshield (grey scale image)

Trutzschler’s Securomat The dedusting function is taken on by a modified material separator (1) by means of perforated plates. The dusty exhaust air is not led to a filter, as would be usual, but is used to dispose of the foreign parts (2). This saves filter capacity and all costs involved. Apart from the distribution flaps, the material separator has no moving parts and thus distinguishes itself by low energy consumption and is very easy to maintain. From the material separator, the dedusted material gets into a reserve trunk (3).

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Process control and yarn quality in spinning

Figure 4.40  Trutzschler Securomat

This reserve trunk represents a sufficiently large material buffer directly in front of the cards and makes the continuous feeding of the cards much easier. With varying production amounts, the delivery of the SCFO changes and the preceding cleaner always works at an even production rate. The feed mechanism (4) of the SECUROMAT SCFO feeds the material into the working area of a needled opening roll (5) as shown in Fig. 4.40. This roll guarantees an even, high opening of the cotton tufts. By the way, the fans for feeding and material suction of the SECUROMAT SCFO as well as for feeding the cards, which are driven by frequency-controlled motors, are “onboard” the machine so that a compact design of the blow room installation can be realized.



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135

The surface of the rotating needle roll, which is covered with fibres, is permanently monitored by a CCD colour line camera (7) with 2048 pixels per line as shown in Fig. 4.41. Camera and lighting system (8) are accommodated in a dust-tight room. The needle roll cover (9) can be easily removed to clean the window. An intelligent evaluation unit on the basis of a powerful computer system detects all foreign particles which are different in colour from the metallic background of the needle roll and the fibres transported on it that is also pastel-colored, e.g. yellow, foreign particles, which would not give a sufficient contrast against the background of a compact cotton tuft. Due to the high opening of the fibre material and the good presentation of the objects on the needle roll even tiny foreign particles can be safely detected.

Figure 4.41  (a) Compressed air nozzles with opening roll; (b) CCD colour-line camera

The separation of the foreign parts is effected by means of 32 compressed air nozzles (10) distributed over the total working width of 1600 mm, which can be individually controlled by pneumatic valves, in fact exactly at the point where a foreign part is located on the surface of the needle roll. The compressed air stream aiming at the roll in a tangential direction is activated for only a few milliseconds. Therefore, the consumption of compressed air can be neglected even with high separation rates. This form of selectively blowing out the foreign particles results in a minimal loss of good fibres of only 1–2 grams of fibres per blow-out. A low good fibre loss is the necessary precondition for adjusting a high sensitiveness and separating even tiny foreign particles.

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Process control and yarn quality in spinning

The waste disposal concept of the SECUROMAT SCFO can only be called progressive. Instead of collecting the foreign parts and fibre tufts in a separate container that must be manually emptied in regular time intervals, the separated material of the SECUROMAT. SCFO can be directly fed to the filter. Where this solution is not possible or is not desired because the separated objects shall be examined later, a condenser on a stand with a collecting container below can be delivered.

4.5.8.4

Foreign fibre clearers in winding

Foreign fibre clearers are by far the most efficient systems to solve the contamination problems. Today in the rest of the world (excluding China) over 75% of delivered clearers are with foreign fibre functionality. Since the clearers are integrated in the automatic winder, they are in a position to make the final inspection and monitor every millimetre of yarn. Further, the clearers are today capable of detecting the finest defects not clearly visible to the naked eye. This includes white and transparent polypropylene defects. The clearer can replace each disturbing defect with a splice, thereby eliminating the defect from the final package to the end user. The foreign material removal systems prior to the card have little influence on the cut rate of the clearers, because most of the foreign fibres which are eliminated by the clearers cannot be recognized by systems prior to the card. It also has to be taken into consideration that the automatic foreign material elimination systems prior to the card eject a considerable amount of cotton together with the foreign materials which must be separated manually from the “real” foreign materials to keep the waste on a reasonable level. Table 4.23 shows the influence of remaining foreign fibres in yarns on subsequent processing stages in the textile chain. Table 4.23  Experience values / end breaks in beaming, weaving, knitting caused by foreign matter Process

Benchmarks for end breaks

End breaks caused by foreign matter

Beaming

0.2–0.3 per 10,00,000 meters

Up to 50%

Weaving

1–2 per 1,00,000 picks

Up to 50%

Knitting

1–2 per hour

Up to 40%

Classification of foreign fibres with the USTER® QUANTUM 2 Uster Technologies has developed a classification matrix for foreign fibres. This matrix is shown in Fig. 4.42.



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Appearance A4

B4

C4

D4

E4

A3

B3

C3

D3

E3

30%

F

20% A2

B21 B22

C2

D2

E2

C12

D12

E12

C11

D11

E11

10% 7%

B13 B14 no counts

5% 0



B11 B12 1.0 1.4

2.0

3.0

5.0

7.0 cm Length

Figure 4.42  Uster classification matrix for foreign fibres (grey scale image)

This matrix was developed in a similar way as Uster Technologies designed the matrix for thick places and thin places. A considerable amount of foreign fibres are located in the B1 class. Therefore, the B1 class (B11 to B14) serves as a benchmark for recognizing the degree of contamination of the raw material. The experience values are the given in Table 4.24. Table 4.24  Benchmarks for foreign fibres Yarn type

Low degree of contamination per 100 km

Heavily contaminated per 100 km

Combed yarns, 100% cotton

10

150

Carded yarns, 100% cotton

20

300

Worsted yarns, 100% wool

20

100

Figure 4.43 shows an investigation for a large European knitter. It is a comparison of foreign fibre content in yarns from the supplier with the least contaminated and the most contaminated raw material. 4.5.8.5

Installation of ultraviolet (UV) lights in the packing and inspection section

Installing Ultra Violet lights in the packing and inspection departments to detect chemical/oily substances and foreign fibres such as polyester and other synthetic manmade fibres and defective packages are rejected manually.

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Process control and yarn quality in spinning

Figure 4.43  Foreign fibres shown in classification matrix, two yarn suppliers

4.5.9

Measures to reduce contamination



1. Introduction of standardized picking storage and marketing of raw cotton. 2. Dissemination of awareness through mass media to the targeted segment. 3. Cloth bags instead of jute and fabric must be provided by farmers and ginning factory owners to pickers. 4. Cotton should be stored on clean and proper floors. 5. Metal body open trolleys should be used for quick transportation of cotton from field to factories. 6. Sheds and platforms should be built properly in the market. 7. Bags should be opened by unsewing instead of cutting twine in to small pieces. 8. Bags should not be beaten on heap. Instead it should be done separately and obtained cotton should be cleaned properly to be added in heap. 9. Conveyers can greatly facilitate. 10. Plastic strips are used for strapping bales to avoid contamination by rust. 11. Bale packing should be graded and create awareness to improve bale packing.

4.6 References 1. Artzt P. (1985). Melliand Textilberichte, Influence of Various Card Clothing Parameters on the Results Obtained in High-speed Carding on Cotton, E789-E796 / 701–712, English Edition.



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2. Bhaduri S.N. (1959). Effect of openness of cotton on subsequent processing, Proceedings of ABS Joint Technological Conference, p.13. 3. Bhaduri S.N., Subramanian N. and Patel S.M. (1956). On reducing card waste without deterioration in yarn quality, ATIRA Research Note, 6, 3. 4. Bogdan J.F. (1955). How to reduce card waste, Textile World, 105, No. 3, p.87. 5. Chattopadhyay R. (2001). Process Control in Spinning, Department of Textile Technology, Indian Institute of Technology, Delhi. 6. Cotton Contamination Survey, ITMF, 1997. 7. Garde, A.R., and T.A. Subramanian (1987). Process Control in Spinning, ATIRA, Ahmedabad. 8. Grover, J.M. (1983). Some studies at blowroom and cards in relation to waste extraction and lint loss, Proceedings of ABNS Joint Technological Conference, pp. 3–9. 9. Gupta A K, and Patel R S, and Subramanian T A (1985). Some studies on the removal of seed-coat fragments from cotton to control blemishes on ring spun cotton yarns, Journal of Textile Institute, 76, No. 6, p.40. 10. Gupta A K, Garde A R and Grover J M (1978). Cleaning at blow room and cards in relation to the nature of trash in cotton: Part I – Assessment of trash content, Indian Journal of Textile Research, 3, No. 2, p.29. 11. Gupta A K, Garde A R and Grover J M (1978). Cleaning at blow room and cards in relation to the nature of trash in cotton: Part II – Generation of trash in cotton and optimization of processing parameters, Indian Journal of Textile Research, 3, No. 2, p.36. 12. Gupta A K, Garde A R and Grover J M (1978). Cleaning at blow room and cards in relation to the nature of trash in cotton: Part II – Generation of trash in cotton and optimization of processing parameters, Indian Journal of Textile Research, Vol. 3, No. 2, p.36. 13. Gupta, A.K. (1985). Some studies on the removal of seed coat fragments from cotton to control the blemishes in ring spun cotton yarns, J. Text. Inst, 76, p.407–412. 14. Gupta, A.K., Shah, P.H. and Subramanian, T.A. (1986). Spinning fault free cotton yarns: from a dream to reality, 18th International Cotton Conference, Bremen. 15. Klien, W. and Schneider, U.T. (1992). The blowroom – Key to quality and economy, International Textile Bulletin, 2, pp. 3–7. 16. Mariappan, S. and Shanmuganandam, D. (2011). How to improve yarn realization in a spinning mill? SITRA Focus, 28, pp. 1–8. 17. Measures to reduce cotton contamination in ginneries, SITRA Focus, Vol. 28, No. 5, January 2011. 18. Mehtani, J.G. (1998). Contamination in Indian Cottons: Status and Remedies, 39th Joint Technological Conference, NITRA. 19. Operating instruction of C60 carding, Rieter, Winterthur, Switzerland. 20. Operating instruction of TC11 carding, Trutzschler, Germany.

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21. Patel R.C. (1959). How to get the best out of blow room machinery, Proceedings of the BTRA Technical seminar on ‘Practices and Experience in processing cotton to yarn. 22. Patel, D.I. and Shah, P.H. (1994). Towards contamination free and cleaner lint through improved ginning, 39th Joint Technological Conference, NITRA. 23. Ratnam, T.V. and K.P. Chellamani (1999). Quality Control in Spinning, SITRA, Coimbatore. 24. Shanmuganandam, D. (2009). How to improve yarn realization and control wastes? The South India Textile Research Association. 25. SITRA Focus (1985). Yarn realization and process waste control, 3, No. 4. 26. SITRA Focus (2011). Measures to reduce cotton contamination in ginneries, 28, No. 5. 27. Sreenivasan, J. and Shanmuganandam, D. (2013). Hard waste control in automatic cone winding – an analysis, SITRA Focus, 31, pp. 1–8. 28. Sreenivasan, J. and Shanmuganandam, D. (2014). How control invisible loss in spinning mills? SITRA Focus, 31, pp. 1–8. 29. Subbarayudu D., and Subba Rao V.N. (1961). Behaviour of cotton in blowroom, Proceedings of the Technical Seminar on Spinning, Textile Association of India, pp. 10–16. 30. Wakankar V.A. and Bhaduri S.N. (1963). Effect of fibre configuration in feed on comber waste, Textile Research Journal, 33, p. 239. 31. Wakankar V.A. and Bhaduri S.N. (1963). Effect of fibre configuration in feed on comber waste, Textile Research Journal, Vol. 33, p. 239.

5 Control of neps and fibre rupture

Abstract: This chapter provides insight into the types of neps, their measurement, and control in blow room, carding and combing processes. The fibre and process parameters influencing the nep generation in blow room and their control have been dealt in this section. Further, the influence of process parameters in carding and comber on nep removal efficiency has been discussed here. Further, the fibre and process parameters influencing the fibre rupture in blow room and carding is also discussed in detail in this section. The effects of modern developments on improvement in quality of the intermediate products in these machines have also been dealt. Key words: nep, fibre rupture, NRE, setting, speeds, wire clothing, grinding

5.1 Introduction The ASTM (ASTM, 1994, 1995) defines a nep as, “one or more fibres occurring in a tangled and unorganized mass”. Neps are created when fibres become tangled in the process of harvesting, ginning and other operations. They can cause difficulty in processing and detract the appearance of yarns and fabrics. Over the years neps have been classified in several ways. A distinction is made between two basic types of neps (Fig. 5.1): • Fibre neps are small knots of entangled fibres, often with immature fibres at their core. • Husk or seed coat neps consist of tangled fibres attached to a fragment of seed coat. Pearson (1933) discussed a “nep classification” including various nep-like structures and tangled knots of fibres. His classification of neps was based on four groups of fibres: thick walled fibres, medium-walled fibres, thin-walled fibres and fuzz fibres. From these 4 fiber types, 15 nep categories were defined according to the types of fibres that are in the tangle. Later he discussed the following four structures: naps, neps, motes and seed-coat fragments. The distinction between naps and neps is based on a difference in size. Neps, in contrast to naps, are very small tangles of fibres. Naps are large tangles of fibres which are visible when the lint is viewed as a whole. The Bureau of Agricultural Economics considers neps to include all fiber tangles up to those that are twice the size of a pinhead.

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(a) Fibre neps

(b) Seed coat neps Figure 5.1  Types of neps

A more recent classification defines neps as biological or mechanical. Mechanical neps are entirely composed of fibres and are created during mechanical processing. Biological neps contain foreign materials, such as seed, leaf, and grit, with fiber attached. There is a general agreement that neps are affected by most mechanical processing stages in the cotton production chain. Souther (1954) observed that the majority of neps were created in the ginning process. Subsequent operations such as opening and cleaning almost doubled the amount of neps. Mangialardi (1985) found that the gin machinery affected nep content, with the gin stand and saw cylinder lint cleaners being the major contributors to the formation of neps. Many fibre properties such as elongation, fineness, length, maturity, strength, and short fibre content, along with contaminants such as stickiness and seed coat fragments have been cited as possible predictors or as related to nep formation. Mangialardi & Meredith (1990) found that among varieties, neps were highly correlated with maturity-fineness measurements. They also found significant correlations between nep counts and fibre strength, elongation, mote counts, and funiculi. Van der Sluijs (1999) reported a study where micronaire emerged as the most significant fibre property in terms of determining nep levels and nep size. Van der Sluijs (1999) also reported that the number of neps per grams decreased as micronaire, length uniformity ratio, strength, and span length increased. The review of literature (Van der Sluijs, 1999) for neps extensively covers the occurrence of neps and processes for neps removal, yet rarely does the literature deal with the prevention of nep formation. In general, cotton neps are associated with fine fibres, but in particular with immature low micronaire cotton.



Control of neps and fibre rupture

143

The susceptibility to neps is typically with: • micronaire 0.5–1.0 • micronaire 4.5–5.0 • micronaire 2.5–4.0 Investigations of causes of neps in general showed that: • 60% due to immature fibres • 35% due to normal fibres • 5% due to seed coat fragments However it should be pointed out that certain cotton varieties and growing regions produce a cotton seed that has a relatively fragile coat. The coat breaks off with the cotton fibre during the ginning operation. Owing to the design of blow room machinery and the nature of fibres, the nep count progressively increases up to the card and is then reduced considerably. During the rest of the preparation processes the nep count increases slightly with the exception of the combing process in which again the neps are removed. The increase or decrease in nep level after each process are given in Fig. 5.2.

A – Bale; B – Card mat; C – Card sliver; D – Ribbon lap; E – Comber sliver; F – Finisher sliver; G – Roving Figure 5.2  Nep level in each processing stage

5.2

Guideline values for neps in bale as per Uster

The guideline values for neps in bales for short, medium and long staple cotton fibres are given in Tables 5.1 and 5.2.

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Process control and yarn quality in spinning

Table 5.1  Guideline for neps in short and medium staple cotton Fibre neps/gram

Seed coat neps/gram

Cotton rating

Less than 100

< 10

Very low

101–200

11–20

Low

201–300

21–30

Medium

301–450

31–45

High

Above 451

> 45

Very high

Table 5.2  Guideline for neps in long staple cotton Fibre (Neps/gram)

Seed coat (Neps/gram)

Cotton rating

Less than 100

26

Very high

5.3

Evaluation of machine efficiency

In order to express the degree of increase and removal of neps respectively DNi (%) and DNr (%), the theoretical relation between the cotton ability and machine efficiency the elimination of impurities was derived. Degree of increasing and removal of neps, respectively, DNi (%) and DNr (%) can be defined by the following formulae: DNi (%) = CNi (%)/ MNi DNr (%) = MNr × CNr Where, CNi = Increase ability of neps CNr = Removal ability of neps MNi = Neps increase efficiency MNr = Neps removal efficiency Thus, the more the cotton has a tendency towards the formation of neps (CNi is big) the more DNi (%) is raised. But better the conditions of the machine (MNi is big), the less is a formation of neps. So, DNi (%) is reduced. Whereas, the more the neps are easily eliminated (CNr is big) and the card is good because it eliminates more neps (MNr is big), the more DNr (%) is better. Therefore, yarn nippiness is a result of quality, processed raw materials and the technological process.



Control of neps and fibre rupture

145

The degree of increase and removal of neps respectively DNi (%) and DNr (%) can be determined by the following expressions: Nbneps out – Nbneps in DNi (%) = Nbneps in

DNr (%) =

Nbneps in – Nbneps out Nbneps in

Nbneps in = Number of neps per gram at the input of machine Nbneps out = Number of neps per gram at the output of machine

Table 5.3  Classification of DNi (%) Class (%)

Interpretation

> 80

Very bad

60–80

Bad

40–60

Average

20–40

Good

< 20

Very good

According to the Uster, it is possible to classify the degree of increase and removal of neps as indicated in Tables 5.3 and 5.4. Table 5.4  Classification of DNr (%) Class (%)

Interpretation

> 90

Very good

80–90

Good

70–80

Average

60–70

Bad

< 60

Very bad

A new method is presented to determine the neps increasing ability (CNi) and neps removal ability (CNr) for cotton fibres by the determination of the neps number in the case of raw cotton after passage on two standard cleaners by adapting two methods: 1. The laboratory apparatus Shirley Analyzer permits to value the neps increase ability, because it eliminates trash while increasing the number of neps in the cotton. This device behaves as a blow

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Process control and yarn quality in spinning

room machine. Indeed, the CNi factor can be determined by the ratio between the number of neps increased after passage of the cotton on Shirley Analyzer (Nbneps out i) and the number of neps/g at the input of this device (Nbneps in i). So, the more CNi (%) shows that cotton has a strong tendency towards the formation of neps.

CNi (%)

Nbneps out i × 100 = Nbneps in i

2. The laboratory apparatus “Microdust Dust and Trash Analyzer 3 (MDTA3)”, allows the determination of the neps removal ability, because it eliminates trash and neps in the cotton. This device simulates the carding and combing action. The CNi factor can be determined by the relation between the number of neps eliminated after passage of the cotton on MDTA-3 (Nbneps out r) and the number of neps per gram at the input of this device (Nbneps in r). So, a high CNr factor means neps reduction by the card becomes an easier process.



5.4

CNr (%) =

Nbneps out r Nbneps in r

× 100

Control of nep generation and fibre rupture in blow room

Nep generation and fibre rupture are necessary evils in the process. For tuning the blow room, we have to give more importance for increase in SFC (n) than nep generation. In general, the increase in nep should be 100% and increase in SFC (n) at least by 1%. Optimum level of nep generation and SFC (n) increase will be there to ensure proper opening. Insufficient opening will lead to overloading of carding. A well-tuned blow room should have the following quality values: • Neps generation – 90–130% in Neps/gm • Fibre rupture – Below 2% • Cleaning efficiency – 50–60% • Waste percentage – Equivalent to trash% in cotton • Lint ratio – Lint presence 30–40% Nep generation in blow room can be measured by the following formula:





Control of neps and fibre rupture

Nep generation % =

Neps/gm in delivery – Neps/gm in feed Neps/gm in feed

147

× 100

The guideline values for some of the blow room machines are given below: • Bale plucker / Unifloc / Blendomat – 10–20% • Vario clean / Uniclean – 15–20% • Unimix / Mulitimix – 20–25% • Flexi clean – 20–30% Fibre rupture in the process can be assessed on two aspects: • Amount of length reduction in UQL(w) • Increase in short fibre content in the delivery Fibre rupture % =



UQL (w) feed – UQL (w) in deliver UQL (w) in feed

× 100

Guideline value for the fibre rupture can be given in the following manner: • Fibre rupture should be less than 2%; 2.5% span length should not drop by more than 4%. If the uniformity ratio drops by more than 4%, then it is considered that there is fibre rupture. • Increase in short fibre content (number) must be within 2% in absolute value

5.4.1

Contribution from machinery parameter

(1) Number of beating points The number of beating points (no. of blow room machines) depends upon the fibre parameters like maturity, fineness and trash level of the cotton. More number of beating points or aggressive opening will lead to higher nep generation and fibre rupture. For example, for DCH Cotton, 1.5 Beating Point is enough and for Cotton like J34, the beating point varies 2.5 Beating Point because of higher trash content. (2) Beater speed Higher beater speed again lead to higher nep generation and fibre rupture. For Indian cotton, beater speed 450–600 RPM works well. (3) Type of beater Proper design of the preparatory line and regular condition monitoring of the beaters is key to consistent yarn quality and good fibre yield. Fibre opening

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Process control and yarn quality in spinning

is a key to good yarn spinning. Good, gentle opening ensures maximum retention of fibre strength by minimizing fibre rupture, reducing the level of neps, effective trash removal and minimal amounts of micro dust and lint. In modern blow rooms, four types of beaters are primarily used – disc beaters, peg beaters, pinned beaters and saw tooth beaters; each having its own function and suitability to certain requirements and conditions. As disc and peg beaters are used exclusively for beating and pre-opening of cotton fibres. It was not until the 1960s that blow room cleaning machines embraced metallic wire as an option to bladed, porcupine, vertical or pin beaters. Production limitations of traditional blow room lines of around 300 kg/h gave way to metallic wire wound cylinders enabling dramatic increases in production. This provided more intense opening and cleaning, however, not without quality concession. Metallic wire in many applications results in fibre damage and requires frequent maintenance. Typical Indian mills producing fine ring spun yarn up to the 1980s used up to seven cleaning points in their blow room lines and no metallic wire was used. Though in modern blow room lines, it is claimed that less cleaning points are required which helps reduce fibre damage at high productions rates, what is actually done is that multiple cleaning points are provided in a single blow room beater or card. However, even if one or two cleaning points are actually reduced, use of metallic wire leads to aggressive opening and consequent fibre rupture, which affects ring spun fine yarns quality in particular. It may be prudent to replace the wire wound rolls with pinned rolls to reduce fibre damage, particularly for fine count, ring spun applications. Most super high production lines are suited for open end spinning operations where quality demands are more forgiving. Ring spun yarns, on the other hand, are very sensitive to speeds, over beating, and aggressive cleaning. Machine makers are thus increasingly adopting the pin technologies to overcome some of the issues posed by aggressive metallic wire cleaning. Engineering improvements, such as use of long lasting alloy steel pins, extruded alloy aluminium lags and special pin profiles, along with proven advanced methods of pin fixing that provides pin-point accuracy, have dramatically improved the performance of pinned beaters, which was never achieved in wooden products. The advantages of pinned beaters are the following: • Pins provide extended product life. • Pins are kind to fibres – reduced fibre rupture leading to yarn strength improvements. • Pins provide fibre process flexibility.



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149



• Pinned products are easily repairable (cost savings). • Extruded aluminium tubes and profiles are lightweight alternatives of most metallic wire applications with cast or steel base rollers. • High tech pin fixing methods provide pinpoint accuracy without grinding. • Pin designs provide improved fibre transfer/doffing. Choice of beaters and sequence of opening depends on the nature of fibre and the process requirements. Long staple cotton with low trash would require lesser beating and more opening than short staple, trashy cotton. Synthetic fibres require no beating and only gentle opening. Though not preferred, saw tooth wires can be used for opening polyester or nylon fibres. However, they can cause several problems if used for opening soft fibres like viscose since such fibres have a tendency to disintegrate under stress. In saw tooth wire, the opening action is done by the knife-edges of the saw tooth, which tend to cut open the fibres (Fig. 5.3a). This leads to fibre rupture and lint generation. This tends to increase the percentage of short fibres and the level of neps. The trash contained in the fibre supply also tends to disintegrate into micro dust due to the saw tooth action. In comparison, the pin has a smooth round surface and a spherical tip, which opens the fibres through a gentle untangling action (Fig. 5.3b). It is obvious that fibre rupture would be minimized as well as the consequent generation of micro dust and lint would also be reduced considerably with the use of pins. The round profile of pins also has another significant advantage that of higher performing life and more consistent quality of opening.

(a)

(b) Figure 5.3  Action of (a) Saw tooth wire (b) Pinned wire

As the lifetime of saw tooth beater increases, the knife-edge gets rounded thus reducing opening action significantly. Moreover, small cuts or crevices

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Process control and yarn quality in spinning

develop on the leading edge of the saw tooth, which tends to ‘Catch’ the fibres and create neps. Usually, the edge of the saw tooth loses its sharpness in the first few months itself, thus causing a rapid deterioration in the fibre opening action. This is partially mitigated by grinding the roller so that the teeth regain their sharpness, but this lasts for much less duration before the deterioration occurs again. Another significant effect of this loss of sharp edge is that the saw tooth begins to push fibres rather than open and carry them forward. This obviously results in an increase in fibre rupture, leading to a loss of rich fibre and lowering of fibre yield. Since the pin has a rounded tip, it retains its opening ability much longer. Additionally, the wear all around the tip causes a new tip to be formed as the old one is eroded, though the pin length gets slightly reduced. This results in several significant benefits: increase in life of pins, more consistent opening action, thus ensuring a consistent sliver quality and a higher fibre yield as compared to saw tooth wires. Key process conditions and requirements influencing selection of beater designs are: (a) Type of fibre – As mentioned earlier, synthetic fibres need to be opened using a different process than cotton. Even the type of cotton or synthetic fibres has a bearing on the preparatory process. (b) Cleaning – The percentage of trash in cotton will determine how many opening points and what intensity of beating are required. (c) Fibre rupture – There is a trade-off between better cleaning and fibre rupture so a delicate balancing act is required. What achieves better cleaning can also cause higher fibre rupture, if not carefully managed. (d) Micro dust – Saw tooth beaters are generally easier to use but in case of trash content being high, micro dust generation can be a problem with such beaters. (e) Lint – Low micronaire cotton tends to generate more lint with saw tooth wires. (f) Neps – Generally, neps level tends to increase with the increase in short fibre percentage, though this is not always the case. The preparatory system design has to consider the causes of neps to minimize them. (g) Production rate – What works in a low production line may not do so for high production rate. Consistency of quality becomes a primary factor when designing a high production preparatory line. (h) Desired quality of yarn – Preparatory requirements differ for fine counts and coarse counts, with fibre rupture being a much more significant quality factor in case of higher counts as is the levels of lint and neps.







Control of neps and fibre rupture

151

(i) Maintenance cost – This becomes a very sensitive issue in high production lines as breakdowns, repairs and reconditioning mean loss of production, increase in operating expenses and inconsistent quality. (j) Fibre yield – Sometimes there is a trade-off between higher fibre yield and a better-opened and cleaned fibre; which one is more important depends on the quality of yarn produced.

Design factors in beaters (i) Diameter – Increase in diameter helps reduce the rpm of the beater and also helps increase production. This is an option open only to machine manufacturers, though. (ii) Opening points density; projection and angle of points; and tip profile – They influence intensity of opening and cleaning, operating life, reduction in neps and fibre rupture. (4) Beater to feed roller setting Wider setting results in lesser rupture and also less fibre opening (5) Beater to grid setting The influence of this setting on fibre rupture is less. But too closer setting (Grid setting of 1) will lead to fibre rupture. (6) Fan speed It is advisable to run the fans at optimum speeds. Higher fan speeds will increase the material velocity and will create turbulence in the bends. This will result in curly fibres which will lead to entanglements. The influence of ventilator fan speed of material transport is lesser. But too high fan speed lead to higher fibre rupture.

5.4.2

Contribution from raw material

The influence of fibre properties on neps and fibre rupture in blow room are given in Table 5.5. Table 5.5  Influence of fibre properties on blow room performance Parameter

Influence

Maturity ratio IFC Moisture content

Lesser the MR, higher Nep and SFC (n) generation higher Higher IFC, higher Nep and SFC (n) generation Lower the MC, higher SFC (n) generation Higher the MC, higher Nep generation Lower the strength, higher SFC (n) generation

Low strength

152

5.5

Process control and yarn quality in spinning

Control of neps and fibre rupture in card

It is normal for the nep count in the card matt to be double the count in the raw cotton. The card nep removal rate can be between 80 and 90 percent of the neps in the matt. A well-tuned carding should have the following quality values: • Neps removal efficiency – 75–85% • Fibre rupture (SFC(n)) – No rupture • Trash in sliver (including micro dust) – 0.04–0.1% • Waste percentage – It is decided by trash level, micro dust, SFC and end yarn quality • U% / 5 m CV% – 3 to 4.5/ below 2.5% A decisive factor is not only the number of neps but also the size. Small neps are often not visible in rotor yarn or in coarse ring yarns, but are of major concern in fine-combed yarns. Small neps are difficult to remove even by the comber. Neps may be reduced in two ways: by removing them or by opening them. It has demonstrated that 75% of all neps can be opened, but normally it is no more than 60%. The majority of the unopened neps either passes into the sliver or is removed by the flats. A small percentage is removed in the waste.

5.5.1

Influencing factors of neps in card

Nep removal efficiency (NRE) in card can be expressed in percentage and calculated as

NRE% =

(Neps/Gm in feed – Neps/Gm in delivery) Neps/Gm in feed

× 100

Opening/removing of neps at the card is primarily accomplished by: • Close settings of the clothing • Sharp clothing • Low doffer speeds • Light sliver weight The factors influencing nep removal in card can be categorized as 1. Contribution from machinery parameter and 2. Contribution from material parameter The contribution from carding machinery parameters and material parameters on nep removal efficiency is given in Table 5.6 and 5.7, respectively.



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153

Table 5.6  Influence of carding machine parameters on NRE Parameter Type of card clothing wire Wire sharpness of card clothing Flat-to-flat height variation and flat to cylinder setting SFD setting Cylinder RPM Waste percentage Relative humidity Productivity

Influence (%) 20 40 50 10 15 20 20 Directly related with increase in production

Table 5.7  Influence of material parameters on NRE Parameter

Influence

Micronaire

Lower the micronaire, higher neps in sliver

Type of trash in the mixing

Higher low weight trash like leaf bits, higher neps in sliver

Maturity of cotton

Lower the maturity level, higher sliver neps

Neps in feed material

Nep generation is high with rupture in BR will lead to higher neps

Honey dew content

Higher honey content, higher neps in sliver

5.5.2

Control of neps in card

For reduction of neps in carding we have to consider the following factors: 1. Wire sharpness of card clothing 2. Flat to flat height variation 3. Flat to cylinder setting 4. Cylinder speed 5. SFD setting 6. Relative humidity 7. Waste percentage and 8. Production (doffer speed & sliver hank) (1) Wire sharpness of card clothing The guideline for wire population and speeds for producing quality sliver are given in Table 5.8. The primary indication that the clothing needs to be ground is that the nep count in the sliver has reached the upper tolerable limit. The wire condition

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Process control and yarn quality in spinning

should be visually checked using a magnifying glass of 30 to 50x powers. The tips of worn clothing appear rounded with no carding leading edge. It is then necessary to grind the clothing until a clean sharp leading edge is visible over the whole of the clothing. The recommended grinding interval and life of the different card clothings are given in Table 5.9. The grinding should be performed carefully and in small steps to prevent the formation of “burrs” on the front edge of the teeth. Table 5.8  Guideline for speeds and wire density of different components for processing different fibres Fibres

Very fine yarn combed ring

Med-fine yarn combed Nm 100

Coarse yarn carded / OE

Yarn count

> Nm 80 ( >Ne 48 )

Nm 40 …. 100 (Ne 24 …. 60 )

< Nm 35 (< Ne 20 )

Licker-in - PPSI

41

41–61

41–61

Manmade > 1.0 dtex – 3.3

Manmade < 1.0 dtex – 0.6

32

36 pin roller

- angle

10 deg.

10 deg.

10–15 deg.

0–5 deg.

58 deg.

- rpm

900–1300

1000–1450

1300–1700

800–1100

1430

Cylinder - PPSI

800–1080

800–1000

600–865

450–650

720

- angle

30 deg.

40 deg.

30 deg.

30 deg.

30 deg.

- rpm

350–450

400–500

450–600

400–450

400–450

Flat clothing

550

480

410

350

430

Doffer - PPSI

340–365

340–365

280–340

280–340

340–365

- angle

30 deg.

30 deg.

30 deg.

30 deg.

30 deg.

Table 5.9  Recommended guideline for grinding interval of wires in card Life (Tones) Cylinder wire

450

Flat tops

400

1st sharpening

2nd sharpening

3rd sharpening

4th sharpening

5th sharpening

No Sharpening is needed. If the flat tops are changed at 350 tones, the wire may give life up to 700 tonnes 100

180

260

340

Replace

For sharpening the flat tops, full width emery roller to be used. Once in alternate grinding, full width ceramic stone is used for leveling. For synthetic tops, there is no sharpening needed Licker-in wire

120–150

No sharpening for licker-in. Life depends on trash level/fibre stickiness in cotton

SFD

450

No sharpening for SFD

SFL

120–150

No sharpening for SFL

Doffer

450

Sharpening is need based or once in 100 tonnes



Control of neps and fibre rupture

155

(2) Flat-to-flat height variation / flat to cylinder setting To increase the nep removal efficiency in card, the flat wire height variation should be minimum to ensure uniform setting between flat to cylinder at all points. Flat height variation has to be measured with reference to cylinder. If it is measured in a jig, toe area to be measured Within flat variation – 0.02 mm Flat-to-flat variation – ±0.02 mm Overall range of variation can be max of 0.05 mm. The factors influencing the flat height variation are: (a) Method of Flat Clipping & Leveling – To get a perfect clipping, flat is to be clipped only after pasting. After loading the new flat, it is better to go for one ‘On the machine’ flat levelling. (b) Off machine Wire Sharpening is not recommended. (c) Resting of Heel & Toe on flexible bend is to be ensured. If the flats are lifted up, the chain may be reason. Proper lubricating of the Chain & proper torque tightening of Flat bolts to be ensured. (d) Twist in the flat, which has to be corrected during re-clipping. (e) Wear out of Heel & Toe Area – New Clipping is to be done only after end Milling if the wear out is noticed. (f) Fibre accumulation in the flat guide pulleys will tilt the flats. The aggressive opening action depends upon the cylinder to flat setting. Every fibre depending upon its fineness, dust level and tenacity has an optimum setting. Over the entire flat zone, the setting is gradually reduced in the material flow direction in order to gradually increase the intensity of opening. A too close setting would lead to intensive opening of fibre clusters with liberation of dust and trash but neps and short fibres may increase due to high level of stress acting on fibres. A wide setting will cause insufficient opening and nep disentanglement. As a result short thick place in yarn and nep level might increase. (3) Cylinder speed In any card, the highest cylinder speed attainable is limited by mechanical design considerations. Today, though a speed of 600 rpm is attainable on modern card, usually it runs in the range of 300–500 rpm. In the transfer of fibres on to the doffer, speed plays a significant role in addition to geometry and population density of wire points of respective surfaces and closeness of their approach. The influence of cylinder speed and its consequences are given in Table 5.10.

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Process control and yarn quality in spinning

Table 5.10  Influence of cylinder speed Expected outcome

Consequences

Intensive opening action are train More individualization, higher stress on between cylinder and flat, cylinder and fibres, more liberation of trash particles, carding segments seed coats and short fibres Higher centrifugal force on fibres and • More liberation of trash particles, seed trash particles as well coats and short fibres • Increase in transfer efficiency with concomitant decrease in cylinder load. This will lead to better fibre opening

From the quality point of view, the following things can be expected with the increase in cylinder speed. • Increase in cleaning efficiency especially removal of finer dust • Reduction in neps • Reduction of fibre clusters • Increase in flat strip • Generation of short fibres especially with long and finer fibres With higher cylinder speed, slight reduction in the Neps/gm and Nep Size can be noticed. However, in the process, the following negative impact on quality of sliver, which we need to be observed and corrected. (i) Increase in waste% (ii) Increase in SFC (n)% With the lower cylinder speed (Say 360 rpm), the AFIS results may be good with respect to Neps/Gm & SFC(n), but yarn quality results may not be good. This is due to less opening at carding due to lesser cylinder speed. Hence Optimum fibre opening must be ensured at Card. Other than DCH, No cotton will give quality results with cylinder speed of 360 rpm. (4) SFD setting With the closer post carding segment settings, the NRE can be increased slightly due to better opening and cleaning action of already well opened fibres from the flat zone. For low production less than 20 kg/hr, 0.225 mm setting and for Higher Production above 20 kg/hr, 0.25mm setting can be kept for better performance. (5) Relative humidity Relative humidity in the department plays an important role in nep reduction. Higher the RH in the department, lower the NRE and higher Neps/gm in card



Control of neps and fibre rupture

157

sliver due to lack of opening and sticking of fibres in the machine parts. For cotton like PIMA, the temperature must be 100–103 °F and RH% must be 42–45%. Normal recommendation of RH% in carding department is 50–55%. (6) Waste percentage Removal of higher waste in carding results in lower the Neps/gm up to some point of waste level. Flat strips are the main waste which is responsible for nep removal which can be increased either by closer flat setting or by increasing the flat speed. Beyond certain limit, Neps/gm will not come down as the waste percentage increases. It is better to take more waste in Comber than in card to have better quality (7) Productivity The optimum production rate of a card is mainly dependent on raw material quality, technological sophistication of the machine and quality of yarn. The production rate of a card can be changed by different modes such as: 1. Change in doffer and feed roller speed keeping sliver hank and cylinder speed constant 2. Change in sliver fineness keeping doffer and cylinder speed constant 3. Change in overall aped of the machine. The consequences of increase in production rate of card are given in Table 5.11. Table 5.11  Influence of production rate on carding performance Production enhanced by Doffer speed

Sliver fineness

Machine speed

Transfer efficiency Cylinder load

Increase Increase little

Decreases Increases

Increase Decreases

Total waste (%) Licker-in waste (%) Flat strip waste (%) Flat load

Reduces Reduces Reduces Increases

Reduces Reduces Reduces –

Reduces Increases Increases Increases

Cleaning efficiency

Reduces

Reduces

Increases

–

Increases

Increases

Poor

Poor

–

Increases Decreases

Increases Decreases

– –

Nep level Web appearance Major hook Minor hook

Productivity and neps in card sliver are directly related i.e. as the productivity (depends on doffer speed and sliver hank) increases, the Neps/gm

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Process control and yarn quality in spinning

in sliver will also increase due to lack of fibre opening and individualization in the flat region as the fibres will soon transferred to the doffer. The guidelines for the productivity level in card for better performance are given below: Up to 20’s count – Up to 55 kg/h 30’s – 40–50 kg/h 40’s – 30–40 kg/h 60’s and above – Depending on mixing and quality requirement

5.5.3

Influencing factors of fibre rupture in card

The factors influencing fibre rupture in card can be categorized as 1. contribution from machinery parameter and 2. contribution from material parameter The contribution from carding machinery parameters and material parameters on nep removal efficiency is given in Table 5.12 and 5.13, respectively. Table 5.12  Influence of carding machine parameters on fibre rupture Parameter

Influence %

Type of card clothing wire Cylinder RPM Relative humidity SFL setting Licker-in wire type Licker-in speed Licker-in to feed plate setting

20% 30% 20% 50% 20% 20% 15%

Table 5.13  Influence of material parameters on fibre rupture Effect

Major Contributing setting

Micronaire

Parameter

Lower the micronaire, higher the rupture

Licker-in to feed plate setting, licker-in wire angle and speed

Maturity

Lower the maturity level, higher rupture

Cylinder speed, licker-in to feed plate setting

Strength

Lower the strength, higher the rupture

SFL setting

5.5.3.1

Reduction of fibre rupture in card

For reduction of fibre rupture in carding, we have to consider the following factors: 1. Type of card clothing wire 2. Cylinder speed





Control of neps and fibre rupture

159

3. Relative humidity 4. SFL setting 5. Licker-in speed, wire type and feed plate to licker-in setting

(1) Type of card clothing wire Higher wire angle and higher wire population (PPSI) will give higher fibre rupture due to intensive opening. To overcome this for higher angle wire, we have to keep wider SFL setting to reduce the fibre rupture. The guideline values for pre-carding segment setting (SFL) are given below: 30° Angle – 0.3–0.35 mm 35° Angle – 0.325–0.375 mm 40° Angle – 0.35–0.4 mm If the production rate is more than 30 kg/h, another 0.025 mm has to be added with the above setting. (2) Cylinder speed With higher cylinder speed, the fibre rupture will increase which can be noticed from increase in short fibre content (SFC (n)) or reduction in fibre length before and after carding tested in AFIS instrument. Except for PIMA and J34, all other cotton will perform better with 430 rpm. For DCH having very fine micronaire value, the cylinder speed of 360 rpm will be optimum. (3) Relative humidity Relative humidity in the department plays an important role in nep reduction. Higher the RH in the department, lower the fibre rupture in card sliver. This has to be balanced in such a way that this should not affect the NRE. For fibres having low strength, higher RH in the department will be helpful to reduce the fibre rupture. (4) SFL setting The pre-carding zone setting plays a vital role in fibre rupture in carding. Closer SFL setting results in higher fibre rupture due to aggressive opening. This can be optimized only by trial and error method. (5) Licker-in speed and wire type This can be optimized only by Trial-and-Error method for different material and production rate depending on the fibre parameters like length, strength and fineness. Generally, we have to use 5 degree wire for processing longer fibres.

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Process control and yarn quality in spinning

Table 5.14  Influence of licker-in speed Expected outcome

Consequences

Intensive opening of fibre tufts

Greater scope for trash to get exposed

Reduction in tuft size on licker-in

Smaller tufts get passed for carding

Higher force on fibres as they teased out More liberation of trash particle with from feed nip possible danger of fibre damage Higher centrifugal force to be experienced Greater scope for escape of trash and by tufts on licker-in surface immature fibre clusters entrapped within tufts with associated danger of fibre loss Lessening of draft between licker-in and Difficulties in fibre transfer to cylinder cylinder at excessive high speed Aggressive opening action between Greater scope for trash liberation with combing bar segment and licker-in possibilities of fibre rupture

The influence of licker-in speed in carding is given in Table 5.14. Hence one can expect that higher speed would lead to better cleaning and carding at the cost of more waste generation and fibre damage. Difficulty in transferring the fibres to cylinder also can be experienced at excessive high speed. For adequate transfer, the draft between cylinder and licker-in should be around 1.5–1.7 for cotton and 2.5–2.9 for man-made fibres. Poor transfer of fibres may cause licker-in loading with serious consequence of unopened fibre tufts being passed on to a cylinder in an erratic manner and loss of good fibres as licker-in waste. Sliver quality is therefore expected to improve initially with the rise in speed with associated increase in waste. But beyond certain speed, the quality may in fact deteriorate. The exact optimum speed may vary from mixing to mixing, machine design and production rate. (6) Feed plate to Licker-in Setting This setting mainly influences the distance between the point of release of fibres from the grip of feed roller – feed plate to the line of action of the teeth of licker-in. The gentle teasing of fibres are away from the nip is required as they are released but not plucking of fibre tufts as a whole. The length of the fringe depends upon fibre length. If greater part of this fringe is brought in contact with licker-in teeth by having closer setting, more fibres with trailing ends still lying at the nip or beyond will be pulled out and therefore more aggressive the action would be. If on contrary, setting is increased only the tip of the fringe with the fibres leading end reach this point, many of them will have trailing end released from the feed nip. Since no resistance will be offered to their withdrawal from the fringe, they will be simply be plucked away without being teased. Hence intensity of opening will suffer.



Control of neps and fibre rupture

5.6

161

Control of neps and short fibre content in comber

The cotton sliver produced by the card contains several contaminants that interfere with the spinning of fine high quality yarns. There are trash particles, neps and up to 10% by weight of short fibres less than 0.5 inch (12.7 mm). Additionally, the fibres in the card sliver are entangled, hooked and generally not aligned. To further prepare these fibres for the spinning of yarns finer than Ne 36/1 on the ring spinning or on the high draft “Vortex” system, the combing system is needed.

Performance attributes of combing are:



• Neps removal efficiency – 60–70%



• Mean fibre length L(n) – Must increase



• SFC (n) – 2–3% reduction



• U% / 5 m CV% – 3–4/ below 2



• Web appearance – No web cut

5.6.1

Influencing factors of neps and SFC in comber sliver

5.6.1.1

Contribution from machinery parameter

Top Comb (a) Top comb depth – The deeper the penetration of the top comb, the cleaner the combed sliver will be. The noil level will also be increased. The short fibres and trash restrained by the top comb are primarily retained in the remaining batt fringe and then combed out by the next action of the circular comb. With an increased top comb penetration or with a higher number of needles/cm, the top comb tends to fill up with trash particles and short fibres and more frequent cleaning of the top comb is required. Top comb depth must be always +1 for better action of top comb and removal of neps and short fibres. Over deep penetration of top comb disturbs the fibre movement, with close detaching setting, it can also result in fibre breakage and creation of neps. Particularly top comb with higher density with deeper penetration results in creation of neps and also chocking of needles. A practical example of nep level at the yarn is given in Table 5.15.

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Process control and yarn quality in spinning

Table 5.15  Influence of top comb penetration on yarn quality Top comb penetration

+ 1.0

+ 0.5

U%

13.98

14.27

Thin/Km

147

179

Thick/Km

510

554

Neps/Km

431

557

Total imperfections/Km

1088

1290

In the above example the variation is only the top comb penetration and all the materials and the process parameters are same up to yarn stage. (b) Top comb setting – The top comb setting of 0.2 mm must be uniform between the heads for achieving better results. (c) Type of top comb – The number of needles in Top comb depends on the fibre micronaire, the lap weight and fibre parallelization in the lap. If the fibre Micronaire is less than 3.6, number of needles per cm in top comb can be 30. In general for fibres above 3.8 Micronaire, 26 needles per centimetre is used. The cleaning of the top comb is important in maintaining the quality of the combing action. It is not appropriate to increase the intensity of the top comb and reduce the necessary time between comb-cleaning cycles if the operators cannot perform the task in a timely manner. The top comb cleaning cycle should not be less than every four hours (the maximum cleaning cycle should be at least 8 hours to maintain good running conditions). Self-cleaning top comb is better for achieving better quality. Brush setting Brush setting plays an important role in cleaning of Unicomb for its better action. At 20 index position, the brush can’t be rotated by hand which has more influence on quality. Air Shield plate setting has to be checked after brush setting. The setting has to be checked frequently in all the heads to find out brush diameter and the difference between head to head should be rectified by closing the setting for better cleaning of Unicomb. Unicomb (a) Nipper to unicomb setting – The unicomb to nipper gap has greater influence on the yarn quality particularly the neps and imperfections. Closer the gap, better the nep removal and vice versa. The setting between the bottom nipper must be always within 0.275 to 0.325 at 5 Index position for better action of Unicomb on the fibre fringe. With too wide setting, there is a chance for the longer fibres to go as a waste. (b) Unicomb condition – The basic requirement for an efficient combout is the cleanliness of the circular comb clothing. The needle geometry and



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163

the needle surface also have a decisive influence. The combs are intensively cleaned at 30-minute intervals by reducing the speed of the machine to a slow speed for a few seconds while the clearer brush remains in high speed. Circular combs, which have a tendency to soiling, have a negative effect on the combing and yarn quality. Rough surfaces and sharp edges on the needles tend to load much more than smooth round needles. Unicomb wire points must be sharp enough for better combing. (c) Unicomb index – Unicomb index 36 will give better yarn quality by 10%. Nipper Variation in setting between head to head will lead to noil variation Nipper bend will lead to long fibre loss 5.6.1.2

Contribution from material and process parameter (1) Feed material (a) Feed lap weight – The self-cleaning effect of the lap sheet arises from the retaining power of the fibres relative to the impurities. This depends on the lap weight. If lap weight is more, the unicomb efficiency may not be good. But the nipper grip will be good for heavier lap weight. Therefore an optimum lap weight should be decided. It depends on fibre micronaire (no. of fibres in the cross section below 500,000). The relationship between number of fibres in comber lap, comber sliver and micronaire are shown in Fig. 5.4. For E7/4 comber, lap weight of 52–60 grams per meter can be selected to produce a fairly good quality yarn. In case of E-62 comber (latest from RIETER), it can range from 65 to 75 grams per meter to produce a fairly good yarn. Lesser the number of piecing in comber, better is the quality. Every piecing in comber is a defect. Therefore, it is better to increase the lap weight as high as possible. For modern lap preparation, it is around 20–23 kg/lap and for older lap preparation, it is around 12–13 kg/lap. (b) Length distribution – The fibre selectivity is an important criterion for assessing the separation of short and long fibres in the combing process. Higher the SFC (n) in feed, lower will be the quality of comber sliver. (c) Lap preparation – There are different types of lap preparation. The best combination is draw frame and unilap combination. Lap piecing will be less in this combination compared to sliver lap and ribbon lap combination. Every lap piecing is a major fault compared to sliver piecing. If number of lap piecing is less, top comb damages will also be less. The total draft for sliver lap and ribbon lap combination should be around 9. If Micronaire is less than 3.8, the lap licking tendency will be more. For such fibres, the total draft between card and comber should be kept as low as possible, i.e. around 8.5. For draw frame

164

Process control and yarn quality in spinning

Figure 5.4  Relationship between fibre fineness and number of fibres in sliver



Control of neps and fibre rupture

165

and unilap preparation the total draft can be from 9.5 to 11, depending upon the fibre and lap weight. Fibre parallelization in a lap should be reasonably good, to avoid long fibres in the noil. With the modern cards, the fibre parallelization is improved because of the stationary flats. Better fibre orientation lead to lesser long fibre loss and better mean fibre length in comber sliver. (2) Process parameter (a) Noil% – Plays major role in combing quality (b) Feed/mm – 5.3 for Coarse count up to 24’s – 4.7 for Medium count up to 60’s – 4.3 for Finer count above 80’s (c) Break draft – Break draft selection according to feed weight (d) Top roller load – 3.5 bar front roller /5 bar back roller (e) Calendar roller load – As minimum as possible (f) Table tension draft – Higher draft gives 5–10% IPI improvement

5.7

Influence of modern developments on nep removal

5.7.1

Developments in card

Now as before, the card is the one machine in the spinning mill which does have the greatest effect on the quality of the end product. However, to accomplish this as effectively as possible under the above-mentioned conditions, special emphasis was placed on the redevelopment of the setting elements and the advancement of online sensor technology on latest innovations. Settings Among the multitude of possible setting points which have an influence on quality and productivity, it is known that the setting of the carding gap between main cylinder and revolving flats is the most effective and important one for nep removal. Hence, the tiniest changes of even a few thousandths of an inch influence the card sliver quality. When considering, however, that this important setting is usually still carried out by subjective sensing of the distance via feeler gauges, it becomes clear that this is the most effective place to simplify the setting and improve the reproducibility of the carding quality. In the new high production card, the interaction of all elements of the revolving flats system was newly designed to meet these requirements. With 1. Flat bars made of high-precision aluminium extruded profiles (Fig. 5.5)

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Process control and yarn quality in spinning

Figure 5.5  Aluminium flats



2. Flexible bend, now with 6 instead of 4 adjusting spindles (Fig. 5.6)

Figure 5.6  Adjustment for flat setting

•  Flat drive with a separate variable speed drive • Non-contact flat distance measuring system flat control FCT (Fig. 5.7)



Control of neps and fibre rupture

167

Figure 5.7  Flat control (FCT)

• Newly developed precision setting device precise flat setting (PFS) (Fig. 5.8). The flat adjustment can be carried out in seconds without tools, reaching a new optimal level and up to now unknown precision.

1. 2. 3. 4. 5. 6.

Metal flexible bend Wear-resistant special plastic slide rails Setting lever High-precision aluminium flat bars Cam timing belt for the flat drive The setting can be read directly from a scale Figure 5.8  Precise flat setting (PFS)

168

Process control and yarn quality in spinning

• With the help of the FLATCONTROL print-outs (Figure 5.9), flat adjustment can be accurately carried out, for instance in two steps by 4/1000” each.

Figure 5.9  Flat adjustment with PFS

Sensor technology In the field of quality sensor technology, sensors to control and regulate the sliver evenness on the card are part of the standard equipment today. With help of these sensors and sophisticated closed-loop feedback control techniques – into which all drives that determine the material flow are gradually incorporated – it is possible today to produce card slivers at an excellent level of evenness and to continuously monitor their quality. Of same, if not even more importance is the removal of trash, neps and other interfering particles from the material by the card. A control of this important function, however, is still carried out by very expensive random tests in the laboratory within the scope of gravimetrical trash measurement and nep counting. The realization of online control for the interfering particles is therefore rightfully an item of top priority for the spinning mills. The development of the online nep sensor nep control provides a device which ensures that these requirements placed on high production cards are met. In this connection, a camera located below the stripper roll (Fig. 5.10) traverses in a hollow profile and detects the size and number of interfering particles by constantly providing sample images over the width and length of the web produced. A computer installed at the profile classifies the type of interfering elements as neps, seed-coat fragments and trash, and transmits the result to the card control. Afterwards the particle counts per gram can be shown on the card display. In addition to plotting neps over time, the nep distribution can also be established over the working width and automatically monitored for limiting values. By utilizing the NCT, the setting of the card to a constant nep level can for the first time be carried out in a quick and accurate way.



Control of neps and fibre rupture

169

Figure 5.10  Nepcontrol (NCT)

A digital camera films the web under the takeoff roll approx. 20 times per second as shown in Fig. 5.11. In doing so, the camera moves about the whole working width of the card in a special, fully closed profile. This optical principle copies the visual perception of a person, and is thus superior to indirect measuring methods. The high-performance computer, directly attached to the profile, evaluates the pictures with special analysis software, and discerns: • Neps • Trash particles • Seed coat fragments

Figure 5.11  The camera view of the web with trash particles (neps, seed coat fragments, trash parts)

170

Process control and yarn quality in spinning

Grinding Sliver quality is directly related to the sharpness of the clothing. The number of neps in the sliver is affected positively or negatively by the state of the card clothing. The IGS-System (Integrated Grinding System), has been developed by Rieter in order to maintain the clothing at a constant level of sharpness and hence to obtain even better carding sliver quality. IGS-Classic and IGS-Top add-ons are available for cards of type C 50 and C 51. During carding, the tips of the card clothing wear and must therefore be sharpened at regular intervals. Increased production throughput of the cards is accompanied by a decrease in the life expectancy of the card clothings which also become increasingly complex to sharpen. As a result, the cylinder clothings are sharpened more infrequently and in some instances are no longer sharpened at all. By contrast, with the IGS sharpening of card clothings could be done irrespective of time and personnel. The IGS is an integral part of the card; it is permanently installed in the lower zone of the cylinder. Sharpening of the cylinder takes place automatically without any interruption of production. The life of the clothing can sustain about 400 grinding cycles. Software which forms part of the IGS calculates when it is time for sharpening and automatically initiates the movement of the grindstone. During every grinding cycle only a minute fraction of the clothing is removed. Experience shows that this increases the average life of the clothing by up to 20%. However optimum carding necessitates regular grinding of the flat clothing and its regular adjustment to match the cylinder clothing. When grinding the new generations of clothings, practically no sparks are detectable any more. Maintenance of this type of clothing can only be carried out by very well trained operating personnel. But with IGS, grinding of the clothings is carried out precisely and automatically by the machine. This means that excessive or insufficient grinding of the clothings is a thing of the past. The IGS-classic cylinder grinding system It consists of an aluminium profile as carrier and a linear-directed grindstone stabilized by spring pressure. In the parked position (right-hand side of the machine) the flat belt is pushed upwards by clamp profiles so that no dust or particles of fibres can get inside the profile. The parameters necessary for the grinding operation can be entered on the card (Fig. 5.12). The program calculates the grinding schedule, distributing the fixed grinding cycles optimally over the lifetime of the cylinder clothing (270 and/or 400, to and fro = 1 cycle). The time between cycles is longer at the beginning of the schedule than at the end. On the way to the left-hand side of the machine the grindstone is lowered. Grinding occurs when the grindstone moves from the left to the right-hand side of the machine. This means a sharp wire all the time and thus constant quality (Fig. 5.13).



Control of neps and fibre rupture

171

Figure 5.12  IGS classic

Figure 5.13  Effect of IGS grinding on wire sharpness

IGS-TOP integrated grinding system A grinding brush is permanently installed behind the flat cleaning device (Fig. 5.14). Under the grinding brush and the one flat in contact with this brush a spring is provided that presses the flat bar against the brush. The flats are thus raised one by one and ground at this point. With the IGS grinding device grinding takes place for more than 100 cycles during the lifetime of the clothing.

Figure 5.14  IGS-top grinding

172

Process control and yarn quality in spinning

IGS-classic and IGS-top feature considerably more frequent but less aggressive grinding than takes place in manual clothing maintenance. This prolongs the service life of the clothing, and at the same time the tips always stay sharp. The success of this approach is reflected in the card sliver through high consistency in purity and low nep content (Fig. 5.15).

Figure 5.15  Quality improvement using IGS system

5.7.2

Developments in comber

Nippers geometry The nipper geometry for short and long-staple material has been optimized with respect to fibre guiding by reducing the clamping line distance (Fig. 5.16).



(a) Others

(b) Rieter

Figure 5.16  Nipper geometry with short clamping distance



Control of neps and fibre rupture

173

This has a positive effect on the fibre selectivity and ultimately results in a higher yarn quality. Additionally the nipper profile has been designed to give a double clamping action, which securely holds the batt without damaging the fibres (Fig. 5.17).



(a) Conventional nipper

(b) Modified nipper

Figure 5.17  Double clamping action of modified nipper profile

Synchronization of movements The coordinated interaction of the elements involved in the combing process has a considerable influence on the fibre selection and the purity of the combed sliver. One important parameter, for example, is the distance between nipper and circular comb during the combing action. During the entire combing process, the fibre fringe should thereby be guided as close as possible to the circular comb. Further, a more precise combing action is achieved by a concentric movement of the nipper with the circular comb (Fig. 5.18).



(a) Others

(b) Concentric nipper motion

Figure 5.18  Concentric nipper assembly in Rieter comber

174

Process control and yarn quality in spinning

5.8 References 1. Bar H P, Furter R, and Harzenmoser I (1990). Influence of autoleveling and on-line quality control on the quality of ring yarns, Textil Praxis, 45, p.362. 2. Chattopadhyay R (2002). Advances in Technology of Yarn Production, New Delhi, NCUTE Publications. 3. Dipali Plawat and A. R. Garde – Spinning Tablet II, Carding – The Textile Association (India). 4. Dr. H. V. Sreenivasa Murthy and Dr. A. K. Basu – Optimization of Opening, Cleaning and Blending at Blow room – NCUTE Pilot programme 30th and 31st Jan 1999. 5. Dr. R. Chattopadhyay – Quality consideration in blow room – NCUTE pilot programme 30th and 31st Jan 1999. 6. Garde A R and Subramanian T A (1978). Process Control in Cotton Spinning, 2nd Ed., Ahmedabad, ATIRA. 7. J. M. Grover and A. R. Garde – Spinning Tablet III, Drawframes – The Textile Association (India). 8. Klein W (1987). Short Staple Spinning Series, Vol 3: A practical Guide to Combing and Drawing, Manchester, The Textile Institute. 9. M. C. Sood and A. R. Garde – Spinning Tablet VI – Ring Frames – Part I: Yarn Quality and Productivity – The Textile Association (India). 10. Morton W E and Nield R (1953). The effect of lap preparation on waste extraction at the cotton comber, J. Text. Inst., 44, pp. T317–T334. 11. Operating instruction of RSB 851 drawframe, Lakshmi machine works, Coimbatore, India. 12. Piyush H. Shah and A. R. Garde – Spinning Tablet IV – The Textile Association (India). 13. Purushothama, B (2007). Linking exercises – A strong tool for Quality Auditing – Quality Update, Indian Society for Quality. 14. Ratnam T V, Seshan K N, Chellamani K P and Karthikeyan S (1994). Quality Control in Spinning , Coimbatore, SITRA. 15. Salhotra K R (2004). Spinning of Manmade and Blends on Cotton System, Mumbai, The Textile Association (India). 16. SITRA Focus (1983). Neps assessment and Control, 1, No. 4. 17. SITRA Focus (1985). Fibre damage during spun yarn manufacture, 9, No. 5. 18. Slater K (1986). Textile Progress: Yarn Evenness, Manchester, The Textile Institute. 19. van der Sluijs M H J and Hunter L (1999). Textile Progress: Neps in Cotton Lint, Manchester, The Textile Institute. 20. Vivek Plawat and Garde, A. R. Spinning Tablet I, Blow room – The Textile Association (India).

6 Control of count, strength and its variation

Abstract: This section provides insight into the control of variations in yarn count and strength. The main process/machine to look for the control of count variation is the draw frame. The two aspects for control of count variation such as with-in bobbin and between bobbin variation and their control are discussed in this section. Key words: count variation, strength variation, with-in bobbin, between bobbin

6.1 Introduction Quality of the product depends on three aspects: (1) Product meets the specifications as per the requirements of user (2) value addition of the product (3) fault free final product. The aim of spinner is to produce the actual count of yarn as close as the nominal count of yarn. It is the basic requirement because any change in the count will change other properties.

6.2

Control of count

The procedure for control of count should consist of three steps: assessing process capability, improving it when possible and using the improved value for specifying sample size and tolerance limits.

6.2.1

Assessing the process capability

Process capability is generally determined by the quality of raw material, the level of technology used for production, the mechanical condition of the machines and the competency of the personnel. In case of yarn count, the process capability is best measured in terms of the co-efficient of variation of weight of leas. Hence, the first step in setting up count control is, therefore, the determination of the coefficient of variation of lea count. For this purpose, wrappings are taken in different departments and pinion changes effected with a view to controlling the count. More often, the tolerance limits for effecting a pinion change are fixed arbitrarily and not determined on the basis of process variation and sample size.

176

Process control and yarn quality in spinning

Sampling – The number of samples to be taken in any department would depend on the variations in the product and the desired level of accuracy in the estimate of the average. Sampling error – It is very significant to note that even though the average count spun by a mill is 60s, the count in individual leas would vary anywhere between 55s to 65s. In other words, if one lea is tested at random the count may vary anywhere from 55s to 65s. When number of leas are tested, the average of sample of leas would be nearer to the actual count spun by the mills. The larger the sample size, the closer would be the sample average to the actual count spun by the mills. The difference between the actual count and the count estimated from the sample is termed on the sample error due to sampling. The standard error in the estimation of count from a sample of ‘n’ leas is ±2 S.D./√n at 95% confidence limits. For example, for the Count CV% = 4.2% (S.D. = 2.48); Sample size = 25; Nominal count = 60s 60 ± 2 × 2.43



Standard error =



Sample size – The estimated sample size is derived from the standard

25

= 60 ± 1.0

error € expressed as the percentage of mean e =

e =



n =

2 × S.D. n

×

100 Mean (X)

2(CV%) n 4(CV)2 e2

Where, n is sample size. The number of wrappings CV% and allowable error in different departments for the length of wrappings normally tested by the mills for the purpose of count control are shown in Table 6.1. Since the draw frame is the key point of count control and the CV% of the weight of 5m finished sliver is low at 1% the sample size could be so selected as to given an error of only 0.5% in the average weight. Practical considerations of time and material used will also govern the size of the sample and when variability is higher as in the case of speed frames and spinning frames, less precise estimates will have to be accepted.



Control of count, strength and its variation

177

Table 6.1  Number of wrappings in different departments Department Drawing 5 m sliver

C.V.%

% Error allowable (e)

No. of wrappings (n)

1.0

0.5

8

Fly frame 15 m wrapping

1.5–2.0

1.13–2.0

8

Ring spinning lea

2.0–5.0

0.64–4.9

25

Tolerance limits – The tolerance limit for affecting the pinion changes can be obtained from mean values ±3S/√n. If any sample falls outside these limits, it is indicative of the fact that the value of the product has significantly deviated from the standard and corrective action is warranted. While setting the control limits, the limitations imposed by mechanical factors should also be taken into account. Consider a ring frame with a draft change pinion of A. A change of pinion is advantageous only when the true deviation from the desired count exceeds C/2A. Based on this consideration, the limits for the average count beyond which a pinion change may be called for have been given below: C 3S + C+ 2A n

C–

C 3S – 2A n

To illustrate, if a mill spinning a nominal count of 60 gets a CV% of 5 and has a change pinion with 50T, for a test of 25 leas, the tolerances are ± (3 + 3/√25 + 60/100) = ± 2.4 counts. In other words, if the average count lies between 57.6 and 62.4, no change in pinion is warranted. Once the tolerance limits are based on these considerations and wrapping results show that the product is outside the limits, the possible causes are: 1. Hank of the feed material and 2. Variation in atmospheric conditions

6.2.2

Other considerations on control of count

6.2.2.1

Weighing equipment

The weighing equipment should be sensitive enough to make accurate estimates of the count or hank. For assessing yarn count, roving and sliver hanks, auto sorter with electronic balance may be used. Once in every month, the balances should be checked for their sensitivity as well as for proper weights. Weighing of all leas together will reduce the errors of weighment in addition to providing a check on weights. Care should be taken to ensure that the correct length is taken.

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Process control and yarn quality in spinning

6.2.2.2

Atmospheric conditions

The weight of a sample of cotton, sliver, roving or yarn is the combination of two weights, the weight of the cotton itself and the weight of the moisture. The latter is determined by the atmospheric conditions in the room. Thus, in any attempt to control the count, the amount of moisture should necessarily be taken into account. The most accurate method to estimate moisture regain is by the use of oven drying method but a figure only slightly less accurate can be obtained more quickly by an electronic moisture meter. It is advisable to keep the material in the wrapping room for 2–3 hours before testing and the average relative humidity and temperature of the room should be used for applying the correction. 6.2.2.3

Wrapping procedure

Routine wrappings for count control need to be taken only at ring frame and finisher drawing sliver. The suggested frequencies are given in Table 6.2. Table 6.2  Suggested wrapping procedure Department

Sample per count

Frequency of wrappings

Ring spinning

25 leas

(a) Once a week for counts 40 and finer (b) Twice a week for counts 18–39 (c) Daily for counts coarser than 18

Finisher drawing

8 wrappings of 5 m

Once in a shift

If very few ring frames are working for a specific count, select adequate bobbins from each ring frame in order to take the leas required. For the purpose of average count control, only one lea per bobbin needs to be taken. No regular wrappings are required at cards, breaker draw frames, combers and fly frames; only spot-checks needs to be done at the time of count change.

6.3

Control of count variation

Higher count variability invariably leads to higher strength variability. The weak patches in the yarn lead to frequent end breaks in further processing, which often leads to rejection of bobbins and cones. In latest autoconers, which have settings for rejecting bobbins with count of yarn exceeding beyond certain limits of the nominal, processing of yarns with even slightly high count variations becomes extremely difficult. Winding efficiency reaches unacceptably low levels with such yarns. Higher count variability especially of medium to long length range results in moiré-like appearance in fabric and increases warp way streaks and weft bars. Ring cuts and soiled



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179

ring packages is another problem with higher count CV. To overcome this, wider clearance is kept between ring diameter and full package leading to lower doff weights. With higher count variability, percentage of bobbins exceeding tolerance limits of nominal count increases, leading to sales rejections and market complaints. In shuttle-less looms, problem of weft tear is encountered during weaving when count of weft changes abruptly beyond certain limits at the time of pirn change. High count variations in weft are also a cause of warp way fabric creases in processed fabrics like dyed poplins. Dependence on length variability of count depends upon the length of the yarn used for estimating count. Though 120 yds. or lea is normally used for estimating yarn count, sometimes half leas are used especially in coarse polyester blend counts to keep strength measurement within the capacity of strength tester. In very fine counts like 120s, two leas are weighed together to estimate count to achieve better accuracy in weighment. The first pre-requisite for control of count variation is to test adequate leas (200 to 300), set the control limits and do the statistical tests of significance while interpreting data. The sample should be representative to cover all ring frames. If bobbins from a single doff alone are taken, a certain amount of count variation will result. If bobbins from different doff, perhaps over one day or one week is tested, a large variation in count will result. This is because there are slow long-term changes in count which hardly affect the count in a doffing. The data on count variation should therefore be collected preferably over a period of one week. It is well known that CV of count decreases with increase in length but the rate of reduction decreases with increase in length. CV of half lea will be 1.2–1.3 times the CV of full lea. Tracing the source of count variation location of source of count variability will be greatly facilitated if wrappings and estimate of CV from the same are based on corresponding wrapping lengths of material at different stages. Thus wrappings and CV of wrappings may be based on 5yd instead of the traditional 15 yd length. At draw frames, CV of wrappings based on 0.5 yd length will be more useful from the same consideration. Estimation of CV of such lengths can be obtained from modern evenness testers.

6.3.1

Categories of count variation

The count variation can be distinguished into two components, namely, within bobbin and between bobbin variations. What causes these variations can best be studied by relating equivalent lengths of each earlier product which, after drafting becomes one lea or one bobbin of yarn as the case may be.

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Process control and yarn quality in spinning

The equivalent lengths in 40s Ne carded count having a length of 5000 m in one cop for a mill having top-arm drafting in ring frame, single process fly frame, and two passages of drawing with 36 doublings are given in Table 6.3. Table 6.3  Length of material required for production of one lea and one bobbin Process

Draft given in each process

For one lea, 110 m (120 yd)

For one bobbin, 70g

Yarn

25

110 m

5000

Roving

10

4.4 m

200 m

Drawing II

6

44 cm

20 m

Drawing I

6

7.33 cm

3.33 m

100

12.22 mm

0.55 m

0.12 mm

5.55 mm

Cards Blow room

Variations in lengths less than those given in the second column of Table 6.3 will not have any added influence on count variation because all such variations occur within a lea and not between leas. Thus, these length figures could be taken as the minimum lengths which can cause lea count variation. Similarly, the figures in the fourth column of Table 6.3 are the minimum lengths for ‘between-bobbin’ variation. From Table 6.3, it appears that the identification of cause of high count variation may need a complete investigation starting from blowroom to ring frame. But logical analysis by seeing the table shows that in a large majority of cases the causes of high count variation, whether within-bobbin or betweenbobbin has to be looked in a few specific places only, specifically after draw frame because the control of variation in very small length is not possible. Variation in blow room lap or card sliver is not going to affect the variability in yarn count particularly within-bobbin variation. Further, the doubling process in drawframe will greatly reduce the effect of variability of lap and card sliver on within-bobbin count variation. Hence, if the within-bobbin count variation is found to be higher than the norms the causes are not to be looked for in the blow room or card sliver, nor in the raw material barring exceptional cases. The most important single cause of within-bobbin count variation is defective draw frame drafting and it is important to understand that only draw frame one to one correspondence is there in the material. The contribution to between-bobbin variation from the preparatory section is largely from longer lengths. Millimetre-to-millimetre variation in blow room lap and meter-to-meter variation in card sliver and breaker draw frame sliver influence the between-bobbin variation. In finisher draw frame



Control of count, strength and its variation

181

and roving frames, the corresponding lengths are much longer. Ring frame is also a source of count variation for both between and with-in bobbins.

6.3.2

Source of contribution to count variation

The relative contribution of the various sources to yarn lea count variation under very good working conditions is given for carded yarns in Table 6.4. Table 6.4  Contribution of different processes on count variation Source

Percent to total*

CV%

Blow room variation 1. Full lap weight 2. Meter to meter 3. Centimetre to centimetre

0.14 0.39 3.50

0.60 1.00 3.00

Card variation 1. Waste% 2. Sliver U%

0.39 9.80

1.00 5.00

Drawings I and II variation 64 Doublings

66.78

1.80**

Roving variation

9.25

–

Ring spinning variation 1. Within-bobbin 2. Between-bobbin

9.75 – –

1.18 1.62

100.00

2.00

Total

* Expressed as a percentage of the square of count CV% * For finisher head (0.5m)

The overall count CV% under good working conditions is 2.0%. The between-bobbin variations account for about 65% of the variance and the within bobbin variation for the remaining 35% under good working conditions. Blow room and cards accounts for about 14%, draw frames for about 67%, fly frames for 9% and ring spinning for 10% of the total variation.

6.3.3 6.3.3.1

Contribution from various processes

Blow room The starting point of the control of hank is blow room. Individual laps are weighed and the laps which fall outside certain limits are rejected or reprocessed. Likewise, weight variation within the lap is also checked, that is

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Process control and yarn quality in spinning

metre to metre variation. The following points are to be considered to control the lap weight variation. (1) Tuft size: The tuft size in the blow room is maintained by opening the material thoroughly and gradually and also synchronizing the production level. The mixing bale opener should run 80 – 85% time when comparing to the Scutcher running time. Theoretically, full lap weight and metre-to-metre variations within a lap can be expected influence the between-bobbin variation. However, their effect is much smaller than the experimental error associated with the estimation of count variation. The effect of centimetre-to-centimetre variation in lap is small at lap CV% of 3.5% provides the lap is very regular. (2) Material level in the Hoppers: The material level in all the hopper is to be ¾ level in all the time. Higher variation in hoppers, cause uneven feed, subsequently weight variation. (3) Piano feed setting: Piano feed should be set such that, the belt on cone drum should run always in the middle. Also the sensitivity of Piano feed is to be checked and should act effectively for feed variation. The condition of pedal links, pedal to feed roller setting are to be checked. (4) Material deposition on cage: Normally, the material deposition on top cage 2/3 and bottom cage 1/3. This can be achieved by adjusting cage side flap. In the case of poor lateral distribution of the material across the width of the lap, the adjusting flap is to be adjusted for allowing more or less air for even distributors of the material. Apart from the above, the physical condition of calendar roller, cage surface, condition of wheels, fan speeds, connection of air passage and material passage are to be checked. 6.3.3.2 Cards

The contribution to count CV from card sliver arises from stripping cycle, differences in the amount of waste extracted between cards and short-term variation in sliver. The presence of extremely ling-length variations in card sliver with wavelengths larger than the total length of the sliver in the creel of draw frame cannot be evened out by doublings. Variation in waste between cards is occasionally cited as a source of count variation, but its effect is microscopic. The reasons for higher short-term irregularity in carded sliver are 1. Improper feed roller weight 2. Eccentric cylinder, doffer, calendar roller and worm feed roller bearing 3. Improper licker-in speed 4. Undue slippage of lap from lap roll to feed roller





Control of count, strength and its variation

5. 6. 7. 8. 9.

183

Defective gears Improper under casing settings, allowing laps of waste to doffer Higher tension draft and different draft pinion used in different card Difference in lap weight Worn out clothing causes improper transfer of material

In case of chute line 1. Chute width gradually increased – each following chute 2 mm more 2. Separating nose to be set such that all the chute filled evenly. Pressure drop should be 2 mm WC from separator head to separator head 3. There should be minimum return material 4. Flock feed production and card production is synchronized. Flock feeder motor should run 40–60% of its speed. 5. Fan speed set such that material should move only in the middle of duct. 6. In the last card feed chute there must be static pressure of 20 mm WC. 6.3.3.3 Comber

The contribution by combed sliver U% and variation in sliver weight to yarn lea count variation would be of the same order as that of carded material in the case of carded counts. The short-term irregularity U% of the comber sliver has a significant influence on within-bobbin count variation, whilst long-term variation of about 0.15–0.3 m sliver affects between-bobbin count variation. Variation in waste between heads of a comber as well as between combers will not have any significant effect on yarn lea count variation. The causes for high comber sliver variations are 1. Eccentric top and bottom rollers 2. Misalignment and bent nippers 3. Broken or bent needles of unicomb and top comb 4. High head-to-head variations 5. Improper timing of combing and damaged/meshed gears 6. Aspirator system choked 7. Non-standard drafts, such as between lap roller and feed roller or detaching roller and calendar roller 8. Variations transferred from ribbon laps at different average levels 6.3.3.4

Draw frame

Major contribution to yarn count variation comes from drawframe. Primarily there are two sources of variability medium-term variations in the sliver and long-term variations in the average hank between frames and between shifts. Variation in the lengths 0.25–0.7 m depending on the count spun are the factors affecting within bobbin count variation. Variation in longer lengths of

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Process control and yarn quality in spinning

about 1.5–2.5 m in breaker head and 10–20 m in finisher head influences the between-bobbin count variation. The draw frame introduces much higher variation because of one or more of the following causes: 1. Roller slippage 2. High tension draft 3. Over-parallelization of fibres 4. Improper pinion changes 5. Improper roller settings 1. Roller slippage: The top roller slip is one of the major causes of draw frame sliver variation. This is due to damage pressure hose/membrane, hooks, pressure saddle, low air pressure, minimum top roller diameter, eccentric of top and bottom roller, too closer roller settings, etc. The length of the finisher draw frame sliver corresponding to one lea of yarn ranges from 0.25 m to 0.70 m. The lower end of this range corresponds to the wave lengths of the period that are introduced by roller slip in the finisher draw frame. Modern drafting system such as 4 over 5 and 3 over 5 give protection against roller slippage. 2. High tension draft: The tension draft at creel and delivery side is maintained according to the material processed. The variation of tension draft wheel among different frame also causes variation. Excessive web tension draft can be a cause for with-in bobbin count variation as shown in Table 6.5. Table 6.5  Effect of tension draft in drawing Web tension draft Sliver U% Lea count CV%

Normal

1.10

4.10 3.78

5.60 5.31

An increase of web tension draft from 1.02 to 1.06 however did not shown any appreciable increase in count variation. Thus, web tension draft would be of consequence only in case it is excessively high. The optimum web tension draft for the finisher head is 0.95 for the short cottons and 1.03 for fine combed cottons. 3. Sliver U%: Some of the causes for short-term irregularity of the draw frame sliver include bent or eccentric rollers, weight hooks or pins not acting properly, hollowness of bent roller, wrong settings, incorrect size of trumpet, improperly meshed or worn gears, excessive creel draft, broken or loose slides, eccentric pinion, gears bored eccentrically, gear wheel brackets broken or improperly secured, etc.



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185

Appreciable increase in draw frame sliver irregularity shows only marginal increase in lea count variation in finer counts as shown in Table 6.6. Table 6.6  Effect of finisher draw frame sliver U% on count CV Count (Ne)

U% of sliver

CV% of yarn

20s

3.91 4.69 7.80

2.72 2.90 3.79

60s

3.54 6.65 7.66

2.96 3.07 3.81

80s

3.65 6.30 8.91

2.78 3.13 3.66

The higher lea count CV% in 60s and 80s can be attributed more to the increase in the variation in longer lengths of sliver along with the increase in sliver U%. This, however, does not mean that the U% of the finisher draw frame sliver is unimportant; its influence would be reflected in lengths shorter than a lea. 4. Over parallelization and doubling: More number of doubling in the draw frame will give better CV%. The draft applied in the draw frame is almost equal to the number of doubling. The amount of draft mainly depends upon the staple length of material. Shorter staple requires less draft and longer fibre more draft. If the fibre is over parallelized, sliver causes stretch and roller lapping. The sliver stretch is particularly high while combing short staple cottons. The problem of over-parallelization can be to some extent overcome by humidity control, heavier hanks, lower speeds and reduced drafts in draw frames. 5. Improper pinion changes: To reduce the overall count variation, particularly the day-to-day variation, pinion changes should be made on a scientific basis. Mills make frequent and unwarranted pinion changes, particularly in drawing, which in turn increases the overall count variation. 6. Improper roller settings: One of the purposes of drafting is to straighten the fibres by removing hooks and crimps. By effective removal of hooks and crimps, one could expect to reduce count variation, since count variation is nothing but mass variation between successive lengths of yarn, the length being 120 yards. Hook and crimp removal will be optimum only if the relationship between fibre length and roller settings is optimum.

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Process control and yarn quality in spinning

Roller setting adjustments based on the fibre length as presented to a particular drafting zone would be of practical significance in obtaining optimum results since fibre length changes as fibres processed through the various machines due to removal of hooks and crimps, probable fibre breakages, etc. Therefore, in order to exercise control over count variation ‘in-process length’ of fibres is to be measured and according to the ‘in-process length’ the roller settings are to be maintained. “In-process length” is the length of the fibres in the fibre fleece as they are coming out of a drafting zone before they are condensed in the form of sliver. AFIS and Digital Fibrograph are popular instruments to measure the “in-process length” of fibres in terms of span length. The recommended roller setting in draw frame based on AFIS 5% length is shown in Table 6.7. Table 6.7  Guideline for roller setting in draw frame Setting zone

Break zone setting

Front zone setting

Breaker draw frame

5% length + 4 mm

5% length

Finisher draw frame

5% length + 6 mm

5% length + 2 mm

Co-efficient of variation of yarn count is nothing but the weight variation between successive lengths of yarn, the length being 120 yards. Due to the maximum removal of hooks at settings based on span length concept, it is logical to expect a reduction in weight variation between successive lengths of yarn. 6.3.3.5

Speed frame

The contribution to with-in bobbin count variation can be from two sources: irregular drafting and irregular stretching. Of these the effect of irregular draft is not much. This is because irregular drafting will introduce variability between small lengths of roving. Such variability will be averaged out in a 5 m piece of roving which roughly correspond to a lea of yarn. The effect of irregular stretching caused by improper regulation of bobbin speed can introduce differences in the weight of roving over different layers of the roving bobbin. Ratching would affect both the within-bobbin and between-bobbin count C.V. Ratching within lengths of about 100 to 300 m of rove would produce a higher effect on within bobbin CV% while ratching over higher lengths would affect between bobbin CV% to a greater extent. The detection of ratching can be easily accomplished by wrapping each layer on a few bobbins. The position of cone drum belt at the time of full doff also gives an indication of ratching. If the belt is near its extreme position, the ratchet wheel is almost correct. It should be ensured that the tension draft in fly frames at the beginning of the doff should be less than 1%.



Control of count, strength and its variation

187

The tension difference between the initial build of the bobbin and the final build of the bobbin causes roving hank variation. This hank variation can be due to two sources: tension difference and material variation in the back process. The full bobbins are collected at the time of doff and then the bobbins with 2 initial layers are collected with the same spindle immediately. Hank of the initial layer – H1 Hank of the final layer – H2

Tension difference =

H1 − H 2 × 100 . This should not exceed 1% (H1 + H 2 ) / 2

The winding-on-wheel determines the initial bobbin speed and the ratchet wheel, its later speed. Incorrect winding-on-wheel is, however, more detrimental to count variation than an incorrect ratchet wheel. The effect of winding-on-wheel on CV% is shown in Table 6.8. Table 6.8  Effect of winding-on-wheel on CV% Winding-on-wheel

17T (Normal)

19T

22T

Weight per 5.5 m (g) Front row Back row

3.31 3.30

3.22 3.30

3.07 3.16

CV% of 5.5 m Front row Back row

2.52 2.26

4.08 3.18

6.70 6.67

Yarn lea count variation

3.14

3.54

6.91

It can be seen that an incorrect choice of winding-on wheel results in a very steep increase in count CV%. Variation in bobbin diameter also leads to ratching since the initial winding has to be adjusted to suit the smallest diameter. Thus, on the larger bobbins a slight amount of ratching is bound to occur. Apart from affecting the average hank, the higher bobbin diameter is also associated with a slight increase of 0.3 to 0.5 in CV%. The bobbin diameter should not deviate by more than 1% from the standard diameter. The number of coils per cm. should be taken into account; the one usually recommended being 5√hank. The differences in hank between rows are found to be generally small and are of not much practical significance. However, in a few instances, the front row bobbins are about 3% finer. This would mean a significant increase of 15% in the relative variance of yarn. Some of the remedial measures to minimize row-to-row differences are extended flyer tops on back row so that

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Process control and yarn quality in spinning

roving meets the hole at the same angle and different flyer top design on each row to give more effective twist in the front row. 6.3.3.6

Ring frame

Contribution to count variation from ringframe comes from variation in mechanical draft between frames, slippage of top roller, stretch of material in creel, variation in mechanical draft. Variations in mechanical draft come from the use of different change pinions on frames of the same make and drafting system. Some common causes for this defective practice are lack of sufficient stock of change pinions. Using change pinions differing by one tooth on the two sides of a frame, ostensibly to achieve an average count close to nominal. This should be discouraged as it increases variability in count between frame sides. Frames of different makes and drafting systems are used for spinning the same count but the same mechanical draft is not kept on them, slippage of strand under top rollers arises because of inadequate weighting or improper grip. Count variation is therefore reduced upon conversion of older version of top arms to later versions with higher pressures. Higher frequency for cot buffing, higher starting diameter for cot up to 30 cm is therefore helpful to reduce count variation. Disturbances to weighting also comes from worn out springs, leakage of air and improper seating of plunger on rib in pneumatic drafting, leading to count variation. One of the reasons for stretch of strand in the creel is low roving twist. The level of creel breaks can assess this. Misalignment of creel roving bobbin in relation to the creel roving guide is another contributory factor to stretch. Improper location of creel guide rod in relation to the bobbin can also cause stretch. If located too high or too low, stretch takes place when roving unwinds from top most or bottom most portion of roving bobbin.

6.4

Between-bobbin count variation

High correlation exists between count variation of between-bobbins and the total count variation (r = 0.7 to 0.95 in different counts). A positive correlation of a lesser extent is noticed between ‘Within-bobbin’ variation and total count variation (r = 0.35 to 0.70). This suggests that the high count variation in some of the mills is mostly due to high between-bobbin variation. Sources which can cause the average count to vary from one bobbin to another are: (i) differences in the average hank between creel bobbins, (ii) any pronounced trend in the hank over a creel bobbin, and (iii) differences in the effective draft between spindles of a group of ring frames spinning a given count.



Control of count, strength and its variation

189



Differences in the average hank of inter bobbins can originate from:



1. differences in blow room lap weight over such long periods that are unlikely to be evened out by subsequent doubling.



2. draft differences between groups of cards or combers, the sliver from which tend to get processed in isolation without inter doubling.



3. hank differences between draw frames, slivers from which tend to get channelized, draft differences between fly frames.



4. excessive hank differences between front and back row of bobbins on fly frames.



5. marked trend in hank over a fly frame bobbin caused by irregular control of bobbin speed.

At ring frames, it is necessary to make sure that the draft constant of a group of frames considered identical is kept the same. Where this is not possible it is necessary to make sure that suitable pinions are used taking into account the differences in draft constants. Adequate attention is not paid by mills to the proper feed of the creel bobbins. Sometimes, the creels are fed diagonally and beneath the roving bars, as a result of which undue stretch occurs. Damaged skewers, accumulation of lint in the creel and trumpet, mis-shaped trumpet, improper back zone draft or weighting of top roller can also cause roving stretch. The presence of creel draft variation can be checked by marking each rove end close to the bobbin. Excessive variation in tension between bobbins due to differences in spindle speeds, spindle-out of centre etc. can also result in between-bobbin variation. At this stage, it is worthwhile to consider the nature of corrective actions needed for controlling the between-bobbin as against the withinbobbin variation. The largest contributor to the within-bobbin variation is the unevenness of the draw frame sliver. The sliver unevenness is reduced essentially by means of technological actions such as change in drafts, pressure, etc. Once implemented, these changes ensure that the sliver quality will remain under control over fairly extended periods. In contrast to this are the corrective actions needed for controlling the between-bobbin variation. The most frequent causes of between-bobbin variations are poor control on lap weight in blow room and on wrappings at draw frames, use of incorrect draft pinions and also ratchet wheels on the fly frames at the time of count changes on individual machines and inadequate control on levels of wastes. Each of these three main causes can be controlled only by instituting a system of monitoring the processes frequently.

190

6.5

Process control and yarn quality in spinning

Control of variability of lea strength

The control of average lea strength is undertaken for one or more of the following purposes: (i) to see that the strength does not fall short of that expected from the quality of the mixing that is being used, (ii) to ensure that the leas strength is adequate for satisfactory weaving, and (iii) to meet the specifications for the tensile strength of the yarn or fabric. Strength variation could be largely dependent on count variation. It is observed that about 50% of the variation in strength as well as about 50% of the variation in strength CV between mills are explained by variation in count. It can be observed that the contribution of the CV of count to the CV of strength would be about 1.5 times the CV of count. A large part of the within-bobbin variability in strength has to be attributed to inherent testing errors and, therefore, this is not amenable to technological corrective action. A reduction in the within-bobbin count variation where this is very high is the only available means of bringing down the within-bobbin strength CV. The between-bobbin variation is affected by three factors, namely, the level of twist, the between-bobbin count variation and the between-spindle differences in the condition of the drafting system. Generally, high twist yarns have a very low strength variation. However, the level of twist is obviously a matter of specification and cannot, therefore, be considered as a means of controlling the lea strength variability. In case the between-bobbin variability of yarn strength is found to be high, action has to be taken to bring down the between-bobbin count variation. If the count CV itself is low, then the drafting system should be examined for differences between spindles in top arm pressure, cradle positioning, spacer used, diameter of cops, condition of cots and aprons, pressure between aprons, etc. In general, the preparatory processes of opening, carding and combing need not be considered in this context.

6.5.1

Factors affecting the yarn strength

The factors which affect yarn strength are listed in the order of their importance. 1. Quality of drafting at ring frame: The type of drafting system at the ring frame has a considerable effect on the strength of yarn. A modern top arm drafting system gives 8–10% more yarn strength than the systems of older systems. Given a drafting system of the top arm type, the parameters which have a critical influence on yarn strength are: the total draft, the break draft in relation to the twist in the rove and the apron spacer. The smallest possible apron spacer generally gives the maximum yarn strength and evenness, but can also result in working difficulties due to undrafted ends or formation of



Control of count, strength and its variation

191

crackers. It is, therefore, advisable to use an apron spacer just one size bigger than the one which results in undrafted ends. The mechanical condition of the drafting system is of great importance in getting the maximum leas strength from a given mixing. Defects like eccentric rollers can bring down the strength considerably. Another important factor is the top arm pressure. For polyester-blended yarns a higher pressure on the front roller is a good insurance against the formation of crackers. 2. Quality of carding: In the normal range of variability the production parameters (hank and delivery speed) and the machine settings have only a small effect on the yarn strength. More important, however, is the mechanical condition of all carding surfaces. The poor maintenance and grinding practices on cards, either with flexible fillet or with metallic wire clothing, lead to a loss in yarn strength of the order of 5–10%. Such a loss in yarn strength which is caused by poor carding does not get evened out by combing. 3. Quality of combing: The level of comber waste and the mechanical condition of the comber have a substantial influence on the improvement in yarn strength that is brought about by combing. For triangular fibre length distributions of the mixing, successive increases in the level of comber waste give rise to a similar increase in the lea strength; while for flat distributions, the lea strength does not increase when the comber waste is increased beyond a certain limit. Poor mechanical condition of cylinder and top comb needles and of the brush, and non-uniformity of settings often result in decreasing the advantage of combing. 4. Other processing factors: There are some processing factors which have a little or no effect on the lea strength of yarn. In blow room, changes in beater speeds or settings, or changes, within the possible limits, in the number of machines in the blow room sequence have no influence on any measured characteristic other than the number of neps and trash particles. The amount of waste extracted in card has also no perceptible effect on lea strength. The evenness of drawing sliver and rove does not have much effect on average strength. The direction of feeding of fibre hooks to the ring frame can influence the lea strength. When the major hooks (i.e., the trailing hooks at the card) are fed to the ring frame drafting system in the trailing direction, the yarn strength is found to be better than when they are fed as leading hooks. 5. Quality of mixing: Once the processing conditions have been ensured to be the best that are possible, the next important step in meeting the standards for yarn strength is the choice of a proper mixing. The bale management programmes has to be adopted to select the appropriate mixing to achieve the desirable strength of resultant yarn. In the context of meeting the requirements of yarn strength through appropriate mixing, quality, the effect of twist in the

192

Process control and yarn quality in spinning

yarn on its weavability needs also to be considered. When the fibre properties of the mixing are somewhat superior to the norm, lower twist multiplier can be used for obtaining the required strength; this lower twist multiplier has the advantages of higher production at ring frames and, possibly, of a better feel of the fabric.

6.6

Control of yarn elongation

The elongation requirements of 100% cotton yarn for weaving and knitting as specified by European buyers for different counts are given in Table 6.9. Hosiery yarns need about 0.5–0.8% higher elongation as compared to warp yarns. Table 6.9  Elongation requirements for weaving and knitting Type of yarn end use

Hosiery

Weaving

Count of yarn

30

34

40

Warp

Weft

Yarn quality Breaking Elongation (%)

Min. 5.8

Min. 5.6

Min. 5.5

Min. 5.0

Min. 5.0

Elongation generally has a CV of about 10% and elongation of yarn is generally estimated by taking 100 readings. Hence, for acquiring a minimum elongation of 5.0% (required for warp yarns) and 5.5% (required for hosiery yarns), the average elongation to be obtained is 5.15% and 5.67%, respectively. However, the average elongation values obtained for some of the popular counts of Indian yarns lies only in the range of 4–4.5% as given in Table 6.10. Table 6.10  Breaking elongation of yarns at different counts Count

Breaking elongation (%)

30s C

4.26

40s C

4.24

60s C

3.95

80s C

4.14

30s K

4.59

40s K

4.15

60s K

4.34

The inter-relation between fibre and yarn elongation, CV of fibre elongation and yarn irregularity is shown in Fig. 6.1. To enhance the elongation values of Indian yarns, our cottons need to be improved from the viewpoint of absolute elongation as well as CV of fibre elongation.



Control of count, strength and its variation

193



The low breaking elongation of yarns is partly due to: (i) low strength and elongation of Indian cottons (ii) over spinning of Indian cottons (as compared to the quality of cottons used in developed countries to spin a particular count) The average fibre strength and elongation of some of the imported and indigenous cottons are given in Table 6.11 for different length groups.

Figure 6.1  Interrelationship between fibre and yarn elongation, CV of fibre elongation and yarn irregularity Table 6.11  Average fibre strength and elongation Cotton Imported 1. African/Russian Cottons 2. American Pima 3. Egyptian Giza Indigenous 1. LRA/S4 2. DCH 32 3. Suvin



2.5% Span length (mm)

Fibre strength (g/tex)

% Elongation

27.5–28.5 33.0–34.5 34.5–36.0

21.0–23.0 30.0–32.5 31.5–32.0

5.5–6.0 6.9–7.1 6.5–6.8

27.0–28.5 34.0–35.5 36.0–37.5

20.0–22.5 23.0–25.5 30.0

5.0–5.5 6.0–6.2 5.2–5.5

In equivalent length groups, Indian cottons are (i) weaker by 5–20% (ii) have low elongation by 0.5–1.3% (absolute values)

194

Process control and yarn quality in spinning

Due to this, yarns spun from Indian cottons have low breaking elongation as stated earlier (by 0.5–1.2% in different counts). Hence, to produce yarns meeting European requirement for breaking elongation, two options are available: 1. Using imported cotton (which are known for the intrinsic higher breaking elongation) 2. Under-spinning while using Indian cottons Under-spinning helps to increase the obtainable RKm value of yarn and thereby the breaking elongation. The extent of under-spinning depends on the actual elongation required. For example, to produce a 40s C warp yarn of 5.5% elongation, under-spinning to the tune of about 25% may be required. In other words, the quality of fibre used to spin 40s C warp yarn with an average breaking elongation of 5.5% (meant for European market) should be better than that used to spin 40s C warp yarn (meant for local market) with an average breaking elongation of 4.5% by about 25%. This would mean that the RKM value of 40s yarn for the European market would be around 20.0 g/tex which meets Uster 10 to 15% (approximately) statistics for breaking tenacity.

6.6.1

Influence of fibre properties on yarn elongation

The performance of a yarn particularly in weaving is largely influenced by grey yarn elongation. In weaving, grey yarns with high initial elongation and a higher residual elongation after weaving preparatory processes such as winding, warping and sizing operation perform better than low elongation values. Trials conducted at weaving stages also show that there is a good correlation between weaving efficiency and residual yarn elongation. Elongation properties of yarns are mostly governed by the characteristics of fibres. The findings of the various studies to relate grey yarn elongation with fibre characteristics and yarn parameters are as follows: The most important fibre property influencing yarn elongation is fibre elongation, a fact concluded by many researchers. Fibre strength ranks second in importance as a contributor to yarn elongation. The fibre fineness influences yarn elongation only after fibre elongation and strength. Other characteristics such as span length, uniformity ratio, maturity, etc., do not contribute significantly to the yarn elongation. Yarn elongation increases with increasing twist. Coarser yarn has higher elongation than finer yarns. Yarn elongation decreases with increasing spinning tension. Breaking elongation of spun yarn is greatly influenced by the count spun, twist and cotton employed. On an average, increase of every 10 counts from a given cotton, reduces elongation by 0.43% (absolute values) and the



Control of count, strength and its variation

195

increase of every 1 TM (from the optimum value from the point of view of yarn strength) improves elongation by about 0.5%.

6.7 References 1. ATIRA, Ahmedabad (1968). Count control in spinning, Technical leaflet. No. 23. 2. ATIRA, Ahmedabad (1971). How to examine wrapping averages. Technical leaflet. No 33. 3. Bandyopadhyay, S. and Subramanian, T.A. (1953). Fabric streakincss: causes and preventive measures— Part II: Role of lea count variation. Proceedings of the ABS Joint technological conference, p.124. 4. Bandyopadhyay, S. and Subramanian, T.A. (1973). Fabric streakiness: causes and preventive measures— Part II. Proceedings of the ATIRA technological conference, 9th, 1973, p.26. 5. Bandyopadhyay, S. and Subramanian, T.A. (1973). Study of fabric streakiness— Part II: Role of count variation, Journal of Textile Association, 34, p.181. 6. Dakin, G, Foster, G.A.R and Locke, J. (1953). Roller slip and the irregularity of cotton and rayon staple draw frame slivers, Journal of Textile Institute, 44, p.544. 7. Norms for spinning mills, SITRA publication, T.V. Ratnam, et al, March 2010. 8. Quality management in textile laboratory, Application report, Uster Technologies 9. Ratnam, T.V. and Chellamani, K.P. (1999). Quality control in spinning, SITRA publication. 10. Ratnam, T.V. and K.P. Chellamani (1999). Quality Control in Spinning, SITRA, Coimbatore. 11. Subramanian, T.A. and Patel, S.M. (1958). Certain aspects of getting a uniform scutcher lap, Textile Digest, 19, p.7. 12. Subramanian, T.A., Patel, S.M. and Sreenivasan, H.E. (1961). Count variation. Indian Textile Journal, 71, p470. 13. Venkataraman, V. and Ahmed, N. (1933). Examination of a proposed relationship between the lea test and the single thread test results. Journal of Textile Institute, 24, p.235.

7 Yarn evenness and imperfection

Abstract: This chapter deals with the basic category of yarn faults such as unevenness and imperfections. The basic characteristics, definitions and their usefulness on evaluation of yarn quality have been discussed in this section. The concept of irregularity, the effect of doubling and drafting on irregularity of materials has been discussed with examples. The basic concept of measurement of thin, thick and neps by evenness testers, their definitions and various sensitivity levels are provided in this section. The common points in each process to troubleshoot the higher imperfections in yarns are also discussed. Key words: evenness, imperfections, U%, CV%, irregularity, thin, thick, neps

7.1 Introduction The task of a spinner of staple fibres, in particular natural fibres, is to transform a mass of millions of individual fibres with variable properties, tangled and containing unwanted foreign matter, into a yarn characterized by uniformity of weight per unit length, diameter, turns per inch, color, strength and so on. Most of these characteristics are interrelated and a detailed analysis of any of these parameters would give us a fair idea about the extent to which a yarn is regular or irregular. However, the approach which can be considered to be universal and which has found popularity over the years is to consider the variation in weight per unit length. One main advantage with this method is that it could be adopted not only for yarn but also for material in different stages of the spinning process sequence like sliver, roving, etc. Suppose if a strand of material is cut into short pieces of equal length and the weight of each consecutive length is found and plotted on a graph in a manner similar to that as shown in Fig. 7.1. By joining the points, a trace is produced; this shows the way in which the weight per unit length varies about a central or mean value. This basic information could be utilized in a number of different ways. The deviations from the mean could be determined, the mean deviation calculated, and the percentage mean deviation derived and used as a measure of irregularity. Alternatively, the deviations could be squared and the coefficient of variation is calculated.



Yarn evenness and imperfection

197

Figure 7.1  Variation in weight per unit length

The deviations from the mean are not always randomly distributed. Certain plots of variations in weight per unit length show definite sequences of thick and thin places. These thick and thin places also vary in terms of the cross-sectional size and the length. A spinner is therefore encountered with a plethora of yarn deficiencies which he has to clearly categorise and then take steps to control them.

7.2

Categories of yarn faults

A yarn which is not uniform is said to be irregular or to contain yarn defects or faults. These faults vary in their cross-sectional size and length. Figure 7.2 shows a plot of fault cross-sectional size against fault length.

Figure 7.2  Categories of yarn faults

198

Process control and yarn quality in spinning

In the above plot, three distinct categories of yarn faults are represented based on their size, length and their frequency of occurrence. • Unevenness or irregularity • Imperfections • Objectionable yarn faults

7.2.1

“Unevenness” or “irregularity”

In all staple spun material (yarn, rovings & slivers), the fibre distribution along the material varies. For example, if a yarn is cut at a number of places there is always a variation in the number of fibres present in the cross-sections. Consequently, a change in the cross section of the material within about ±40% is always present. This variation is also affected by fibre fineness, fibre fineness variations and material type. Even in an ideal fibre distribution, as in the case of a continuous filament yarn, though small, there exists certain irregularity because of fibre fineness variations. The mass per unit length variation due to variation in fibre assembly is generally known as “Irregularity” or “Unevenness” (in practice the Um% or CVm% value). It is the skill of the spinner to arrange all machine settings in such a way that all fibres are spread as even as possible over the length of the material.

7.2.2 Imperfections The extremes of variations, i.e., the thin places, thick places and neps, are usually referred to as “Imperfections”. These imperfections, although lying, in general, within the limit of ±100%, are normally few in number and must therefore be counted separately rather than grouped with the irregularity value Um% which they hardly influence. Under normal conditions, these imperfections range from a cross-sectional size of +30% to 100% based on the mean yarn cross-section with reference to the thick places and from −30% to −70% with reference to the thin places. The neps are measured based on a length of 1 mm. The length of the thicker places and thinner places is usually in the range of 1.5 times the staple length of the fibres in the yarn. These “imperfections” are determined according to a frequency figure or number per 1000 m or yards.

7.2.3

Objectionable yarn faults

If one now considers faults larger than +100% based on the mean yarn crosssection, one moves into the range of ‘yarn faults’ and correspondingly a further reduction in the frequency. Yarn faults have sizes from +100% and larger and lengths of 1 mm and longer.



Yarn evenness and imperfection

199

Their frequency is always referred to in terms of number per 1, 00,000 meters of yarn. The long thick places (spinners doubles) and the long thin places are extremely seldom-occurring, so that they are hardly referred to by means of a frequency figure. Of the type of faults discussed above, an ‘evenness tester’ is used to estimate the first two: unevenness and imperfections. While an evenness tester was first used only to measure and provide these parameters in the early days, successive developments in the field leading to the latest generation microprocessor based instruments have led to the availability of a considerable lot more information on the evenness characteristics.

7.3

Unevenness (Um%)

The Unevenness is the most common parameter used to express mass variations in a strand of fibres. In simple terms, it is the percentage mass deviation of unit length of material and is caused by uneven fibre distribution along the length of the strand.

7.3.1

Definition of Um%

To understand clearly the concept of the Unevenness Um%, a graphical explanation is shown in Fig. 7.3. Consider Fig. 7.3 to be a graphical trace obtained by plotting the mass of unit length of a fibre strand against the evaluation time.

Figure 7.3  Mass per unit length variation

Then U% is given by

U% =

Adev Atot

× 100

200

Process control and yarn quality in spinning

In graphical form, U% is the area of the mass signal of the tested material which deviates from the average value, expressed as a percentage of the total area (total area = full mass of sample).

7.3.2

Mathematical representation

In mathematical form, Um% is expressed as follows: T



Um% =

∫0 | x i − x | dt × 100 xT

The U-calculator of the evenness tester operates approximately to this mathematical method.

7.3.3

Characteristics of unevenness Um%

The graphical and mathematical representations of the Unevenness Um% bring out clearly two distinct characteristics.

• The unevenness Um% is proportional to the intensity of the mass variations around the mean value.



• The unevenness Um% is independent of the evaluating time or tested material length if the mass variations are homogeneously distributed. This is because, in the graphical representation, with an increasing evaluation time, not only the areas a1, a2, a3, etc. but also the area ‘A’ i.e., x.T will be larger.

7.4

Mass CV (Coefficient of Variation Cvm%)

The CVm is derived from the standard deviation, which is a statistically calculated value. Besides the old value U%, it is the common numerical value for describing the general evenness (or unevenness) of slivers, rovings and yarns. Because there are other CV types, such as optical diameter CV, tenacity CV, etc., one speaks of CVm when referring to the mass variation. The CVm value is used to determine or check the overall evenness of the tested material. Generally lower the CVm is, the more even the material is and the more even it will look in the end product. The example of higher yarn CV% and its influence on fabric appearance is shown in Figs. 7.4 and 7.5, respectively.



Yarn evenness and imperfection

(a) CVm = 17.74%

(b) CVm = 11.48% Figure 7.4  Comparison of lower and higher yarn CV%

Figure 7.5  Fabric appearance of low and higher yarn CV%

201

202

7.4.1

Process control and yarn quality in spinning

Definition of CVm

The mass variations can be considered to conform approximately to a normal distribution when a homogeneous fibre composition is available. The determination of CV% from the mass variation is shown graphically in Fig. 7.6.

Figure 7.6  Graphical representation of determination of CV%

A measure of the size of the these mass variations is the standard deviation s, which is expressed as n



s =

∑ ( xi − x )

2

i =1

n −1 The Coefficient of Variation CVm% is defined as the standard deviation expressed as a percentage of the mean.

7.4.2

CV =

s × 100% x

Mathematical representation

In mathematical form, CVm% is expressed as follows:

CVm% =

100 1 x x

T

∫0 (x i − x)

2

dt

The CV calculator of the evenness tester operates exactly as per this definition.



7.4.3

Yarn evenness and imperfection

203

Characteristics of the Coefficient of Variation (CVm%)

The representations of the coefficient of variation bring out a distinct characteristic. The larger deviations from the mean value are much more intensively taken into consideration in the calculation of CVm% rather than in Um% (due to the squaring of the term). For this reason, the Coefficient of Variation CVm% has received more recognition in modern statistics than the irregularity value Um%.

7.4.4

Expected mass deviations according to statistical probability

In the case of a normal random distribution of the mass variation, 1. 35% of all measured individual values will be outside ±CVm × 1 2. 5% of all measured individual values will be outside ±CVm × 2 3. 0.1% of all measured individual values will be outside ±CVm × 3 Interrelation between the mass diagram, CVm and mass distribution curve of a yarn with CVm = 14% is shown in Fig. 7.7. Each measured point on the mass diagram is one area unit on the histogram.

Figure 7.7  Interrelation between the mass diagram, CVm and mass distribution curve of a yarn

204

Process control and yarn quality in spinning

Example: A roving which will be drafted by factor 40 in ring spinning has a CVm of 9%. Which mass deviations are to be expected in the yarn? 5% of all tested points in the roving will probably deviate more than 2 × CVm = ±18% from nominal count, and 0.1% of all the points will deviate more than 3 × CVm = ±27% from nominal count. Each point would represent 1cm of the roving, since the cut length for the normal CV measurement is 1 cm. With a draft by factor 40, one can expect to have 5% of the produced yarn to have pieces of 40 × 1 cm = 40 cm or longer which deviate ±18% or more. For every 1 km of yarn, that would be a total of 20 m with maximum 50 events of randomly distributed thick and/or thin places of ±18% deviation (The above calculation is done under the assumption that the 1 cm or longer deviations in the roving would all be elongated equally). Other deviations which are truly exceptional and due to random or periodic irregularities in the production process cannot be predicted with the CVm value such as above.

7.4.5

Relationship between Um% and Cvm%

The Coefficient of Variation CVm is preferred more and more to the irregularity Um value. This is evident from the increasing number of standard methods of test which describes a coefficient of variation value for the standard mass variations, e.g., in Germany (DIN 53804), the Eastern European Countries, the recommendation of the IWTO (International Wool Textile Organisation), etc. Moreover, CVm is a parameter commonly recognised for variation at all cut lengths while Um is used to refer only to mass variations at the basic cut length (usually 1 cm). However, till such time CVm% gains universal and sole recognition over Um%, it is essential that a proper conversion is used from one to another. It can be considered that if the fibre assembly required to be tested is normally distributed with respect to the mass variation, then the two parameters of irregularity are related as follows:

CVm = 1.25U%

Figure 7.8 shows a typical case of a fault free fibre assembly. Here the mass variation is distributed symmetrically, is single peaked and tends towards normal distribution. Consequently, the conversion factor 1.25 can be used in this case.



Yarn evenness and imperfection

205

Figure 7.8  Distribution of a fault free fibre assembly

Where an ideal normal distribution cannot be assumed, the conversion factor of 1.25 will be subject to error. Such cases occur quite frequently in the textile industry. Some examples are discussed below.

(a) Distribution showing excessive



(b) Asymmetrical

(c) Distribution mean value variations

Figure 7.9  Asymmetrical distribution of faults

206

Process control and yarn quality in spinning

Figure 7.9 show mass variations respectively with two or more peaks (fibre assemblies with accentuated mean value variations) and with single peak but with asymmetrically distributed mass variations (assemblies with frequent and accentuated thick places). In such cases, the conversion relationship gets modified as follows.

CVm > 1.25U%

If strong periodic mass variations (Fig. 7.10) are available in a fibre assembly, the relationship is as follows:

CVm < 1.25U%

An example of such a type is shown in Fig. 7.10. Distributions of this type are symmetrical, but deviate from the normal distribution.

Figure 7.10  Distribution showing strong periodic variations

For a perfect sinusoidal curve, i.e. for a fibre assembly with only periodic variations, the conversion would be CVm = 1.11 Um. However, when the periodic variations are superimposed by small random variations, the conversion constant would be more than 1.11 but less than 1.25 provided the distribution is symmetric. If, therefore, a conversion has to be made from Um to CVm or CVm to Um, a decision must be made based on the diagram as to whether the conversion factor of 1.25 can be applied. A summary of the general guidelines for usage of the conversion factor is given in Table 7.1.



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207

Table 7.1  Relationship between U% and CV% Type of fibre assembly Normal Distribution (contains purely random variations, symmetric) Asymmetric Distribution, periodic and random variations present (faulty test material e.g., with long wavelength count variations, thick places, etc.) Symmetric Distribution with strong periodic variations

7.4.6

Conversion Factor 1.25 >1.25 250 m (1) Long-term autolevellers Only the mean count value is autolevelled. The actual evening out is undertaken in full by doubling. (2) Medium-term autolevellers Both the mean value and also to a large extent the complete variancelength spectrum is autolevelled. As only short-term variation remains in the materials, the complete variation spectrum is quite considerably reduced. The autoleveller takes over the normal function of doubling in the medium term range. (3) Short-term autolevellers In this case, besides the mean value, the complete variance length spectrum is to a large extent autolevelled and can in the ideal case also completely take over the function of doubling is applied together with doublings, then its advantage lies particularly in the correction of periodicities and other short disturbances. 8.1.1.2

Based on principle of operation

There are two types of autolevelling systems: • Open-loop system • Closed-loop system

228

Process control and yarn quality in spinning

(a) Open-loop autolevelling The open-loop system may generally be used for correction of short-term variations. The input material variation is measured using a measurement unit. A measuring sensor is provided in the region of the in feed for continuous detection of the actual value (volume) – mechanically, optically, pneumatically, or otherwise (Fig. 8.1). A regulator compares the result with the set reference value, amplifies the difference signal, and feeds it to an adjusting device (actuator), which then finally converts the impulse into a mechanical adjustment. Measuring

Correction

M

DV

A

TD

S

TG

Figure 8.1  Principle of open-loop autolevelling

M – Measuring zone DV – Desired value A – Amplifier TD – Time delay S – Speed adjusting unit TG – Tacho generator In open-loop system, there is no check on delivered sliver. In other words the changes in draft are solely based on the mass variation in the input material. Control by this chain of steps requires an additional element, namely a storage device. Since the material has to travel a certain distance between the measuring and adjusting points, and therefore arrives at the adjusting point with a time



Short-term irregularity

229

delay, the signal must be held back in the storage device until this instant. This additional requirement represents a second disadvantage of open-loop control in addition to the lack of self-monitoring. There is a third disadvantage, since very exact values of the adjustment are required at all times. Advantage – This system has got shorter time lag between detection and correction and hence can easily correct even much shorter variations. Since the detection is done at feed end itself it is possible to correct the fault exactly at the right location. Disadvantage – There is no way of ensuring that the variation detected has indeed been corrected. (b) Closed-loop autolevelling In the closed-loop system, is generally used for correcting long-term variation. The measuring sensor is usually arranged in the delivery region, i.e. downstream from the adjusting device (Fig. 8.2). The sliver coming out at the delivery end is constantly monitored for any variation from the standard value. Any deviation detected is fed to a correction system at the feed end which corrects the draft depending upon the variation detected. This corrected sliver is again monitored at the delivery and any variation detected is fed to the feed correction system thus ensuring closed-loop system.

Measuring

Correction

M

I

TG

S

A

Figure 8.2  The principle of closed-loop control

DV

230

Process control and yarn quality in spinning

M – Measuring zone DV – Desired value A – Amplifier TD – Time delay S – Speed adjusting unit TG – Tacho generator If too much material passes through the sensor, the regulating transmission receives a negative signal (i.e. reduce speed) until the actual and set values coincide again. Neither a positive nor a negative signal is produced when there is coincidence – the instantaneous speed is maintained. The principle is substantially simpler than open-loop control. However, this advantage, and the advantage of self-monitoring, must be weighed against a serious disadvantage, namely the dead time inherent in the system. The measured portion has already passed the adjusting point when the adjusting signal arrives. Compensation cannot be achieved in this measured portion; i.e. some of the long- and medium-term errors, and all of the short-term errors, remain in the product. It is therefore clear that closed-loop control is unsuited to compensation of irregularity over short lengths.

8.2

Autolevellers in carding

8.2.1

Rieter C-60 integrated draw-frame card

Figure 8.3  Rieter autolevelling concept



8.2.2

Short-term irregularity

231

Trutzschler card

The Trutzschler card contains elements for both short-term as well as long-term autoleveling. The short-term autoleveling is done at the feed point where the thickness of the feed is monitored with the help of a spring loaded measuring system as shown in the figure and the variations in sliver are controlled by carefully controlling the feed roller drive. As far as the long-term autoleveling is concerned the sensor is located near the funnel near the calendar rollers. These sensors are optical sensors and continuously monitor the sliver thickness and the signal from these sensors is used to change the speed of cylinder and feed rollers to obtain the required draft as shown in Fig. 8.4.

Figure 8.4  Trutzschler autolevelling concept

8.3

Autolevellers in draw frame

The doubling on draw frame in suppressing irregularity in card sliver is any way an integral part of the process. The doubling process has some limitations, such as • It cannot correct long-term variations • The periodic variation is also difficult to suppress and • It can reduce CV% only by the square root of total number of doublings.

8.3.1

Principle of Autoleveller (Rieter RSB-951)

The production speed of a draw frame is pretty high. The response characteristics of the sensor and inertia of the whole regulating drive system, earlier did not

232

Process control and yarn quality in spinning

allow very short-term control to be exercised even with open-loop principle. Today with the development sensor and actuator design, the correction length has been brought down to 3 cm depending upon the operating speed. For system designed on closed-loop principle, the correction length lies between 5–10 m of sliver length. A schematic view of an autoleveler used in Rieter RSB-951, RSB-D-30 high speed draw frame is shown in Fig. 8.5. This system is an electronic levelling system. The major components in the system are • scanning roller • signal converter • levelling CPU • servo drive (servo motor and servo leveller) • differential gear box (Planetary gear box)

Trumpet

Scanning rollers

Back rollers

Front rollers

1132 mm Differential gearing

Signal converter Electronic memory

Servo motor

Main motor

Set point stage

Figure 8.5  Drawframe Autoleveling (RSB-951)

8.3.1.1

Pre-autolevelling setting

Prior to adjusting the autoleveller, the drawframe has to be correctly set with the autoleveller switched off. The draft, roll settings, speeds, tension drafts and components have to be carefully optimized. The scanning rollers should be selected according to the amount of material being fed. The scanning roller distance, which corresponds to the cross-section of the total fed material, must be between 2.8 and 5.2 mm. Table 8.1 shows the recommended widths of the scanning rollers for various materials at different total sliver weights. The scanning rolls must not touch each other when the pressure is on with no material in place. The clearances between the tongue and groove should be 0.1 mm.



233

Short-term irregularity

Table 8.1  Scanning roller width recommendations Input sliver weight g/m Grains/yd

12 - 20

15–28

26–40

37–52

170 - 280

210–390

360–560

520–730

Material

Width of scanning roller (mm)

Fine flexible fibres, i.e. combed cotton, rayon, and polyester (cotton type)

3

5

6.5

8

Cotton carded

3

5

6.5–8

8–10

High crimped bulky fibres i.e. acrylic, polyester (wool type)

6.5

8–10

10

Synthetic of very high bulk

6.5

8–10

10–12

8–10

10–12

Coarse polypropylene

The sliver funnel should be clear of the scanning rollers with no material present and the pressure on as shown in Fig. 8.6.

Figure 8.6  Sliver funnel setting with scanning roller

The scanning roller pressure has to be adjusted using the regulator and pressure gauge, according to the material being processed. Recommended scanning roller pressures are shown in Table 8.2.

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Process control and yarn quality in spinning

Table 8.2  Scanning roller pressure recommendations Roller pressure daN (kg) Delivery speed up to 500m/min Material Fine flexible fibres, i.e. combed cotton, rayon, and polyester (cotton type)

Delivery speed 500 to 1000m/min

100 120 140 160 180 100 120 140 160 180 X

XX

X

Cotton carded

XX

High-crimped bulky fibres i.e. acrylic, polyester (wool type)

X

X

XX

X

X

XX

X

XX

X

XX

Synthetic of very high bulk

XX

X

XX

Coarse polypropylene

XX

X

XX

Cotton/polyester

8.3.1.2

XX

X

X

Working of autoleveller

The RSB drawframe works with the principle of open-loop control. The thickness of the incoming sliver is sensed by a pair of tongue and groove rollers called as scanning rollers (Fig. 8.7). One of the scanning rollers is moveable. These scanning rollers are loaded either by a spring loading system or a pneumatic loading system. Pneumatic loading is always better, because the pressure in kilograms will be always same (consistent), irrespective of the sliver feed variation. But in the case of spring loaded, the pressure on scanning rollers may vary depending upon the feed variation.

Figure 8.7  Scanning roller assembly in Rieter autoleveller



Short-term irregularity

235

The variations in sliver mass of the incoming slivers displace the scanning roller. The distance moved by the scanning is proportional to the sliver mass fed. The angular movement of the scanning roller is converted into voltages by means of displacement transducer. A plate is connected to the rollers and is moved into the electromagnetic field of the transducer. This movement of the plates cuts the flux and a voltage is induced which depends on the thickness of the material. This signal is transferred to the electronic memory, which then transmits it to the set point stage with a certain delay. The correction delay is determined by the pulses which can be set by FIFO (First in First out). The distance between the measuring rollers and the front draft zone is divided into 177–192 pulses. FIFO is a register with the first measured variation stored in the first register and so on. FIFO can be changed depending upon the drafting setting and the position of the guides. This system ensures the change in draft takes place exactly when the corresponding deviating length of the sliver passes the main draft zone. The set point stage uses the measuring voltage and the machine speed which are measured by two tacho generators to calculate the speed of the servo drive. At this stage a manual override is provided for any error in correction which can be exactly set right by either adding or subtracting voltage from the measured voltage. This efficiency of the autolevellers can be studied by using the evenness tester mass variation diagram and by taking count CV% readings. The middle roller is driven by a differential gear arrangement which has a constant drive from the front roller and variable speed from the servo motor. The servo drive which can rotate in both directions either to add or subtract the speed transmits this speed to the middle roller of the drafting system thus altering the draft. Speed of the variable speed motor is continuously measured and it does not correspond with the intended speed the machine is switched off. Accurate leveling is ensured by the high dynamic servo drive, so the correction times are of the order of few milliseconds and the correction lengths a few millimetres. The synchronization of the mechanical parts, the drive, the electronics and the software is therefore very decisive. High-performance draw frames with the appropriate devices and corresponding synchronization deliver a sliver with outstanding short-term, medium-term and long-term evenness. Leveling is performed exclusively by adjustment of the draft. Theoretically, there are two possibilities for such adjustment, namely via the break draft and the main draft, respectively. However, the main draft is always used because it is larger, and therefore finer adjustments are possible. Furthermore, use of the break draft would run the risk of entering the stick/slip zone. Draft variation can also be carried out by adjusting either the in-feed or the delivery speed. Adjustment of the in-feed speed is generally used, since

236

Process control and yarn quality in spinning

lower masses then have to be accelerated and decelerated at lower speeds. Furthermore, the delivery speed, and hence the production rate, remains constant. The adjustments taken place in various components after detection of variation are shown in Fig. 8.8.

Figure 8.8  Adjustment of components in autoleveller

8.3.1.3

Correction length If there is a sudden deviation from the set volume as the material passes through, a corresponding signal is sent to a regulating device to correct the fault. Owing to the mass inertia of the system, compensation cannot be effected suddenly, but must be carried out by gradual adjustment. A certain time (the correction time) in Fig. 8.9 (I) elapses before the sliver delivered has returned to the set volume. During this time, faulty sliver is still being produced, although the deviation is being steadily reduced. The total length that departs from the set value is referred to as the correction length (I). In closed-loop systems, the correction length is further increased by the dead time. In this case it depends upon the dead time (II) and the correction time (III). The correction length depends upon the system and the speed of operation, and therefore varies considerably. The term “correction length” is used to describe the efficiency of a leveling device. However, this term is used in different ways and sometimes also incorrectly. The current interpretation is: “the correction length is the length of the product which would be produced when leveling a rectangular



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237

deviation of the product.” The length therefore refers to amplitude of the fault of 1%. The term “correction length” is therefore a theoretical value, since in practice rectangular faults do not occur. As they cannot be checked in the spinning mill, the quality of the delivered sliver is usually taken as the standard of comparison, and sliver evenness can be determined by any evenness tester.

Figure 8.9  Correction length in open- and closed-loop autolevellers

The correction length depends upon • inertia of the regulating system and hence on its design • delivery speed • draft • extent of mass variation of sliver from set value • sense of change of mass i.e. whether it is from – normal level to lighter side or – lighter level to normal side – normal level to heavier side – heavier level to normal side If a system takes ‘t’ seconds to level a certain percent increase in mass variation of a sliver that is being delivered at V m/min, the correction length (l) would be 100 Vt mm l = 60

238 8.3.1.4

Process control and yarn quality in spinning Testing of autoleveler

Following are the two important parameters for quality levelling • Levelling action point ( time of correction) • Levelling intensity Levelling action point Both feed variation sensing and correction are being done when the machine is running (continuous process) at two different places (i.e. sensing is at one place and correction is at another place). Hence the calculated correction should be done on the corresponding defective material. This is decided by levelling action point. The time required for the defective material to reach the correction point should be known and correction should be done at the right time. Levelling action point depends upon • break draft • main draft roller setting • delivery speed The levelling action point is in the main draft zone and is influenced by several factors that include: • tension of the sliver entering the drafting zone (VE) • main draft roll setting distance (HVD) • break draft (V V) • vertical setting of the sliver guides • delivery speed • fibre characteristics Figure 8.10  shows the factors influencing the levelling action point.

Figure 8.10  Factors influencing levelling action point



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Table 8.3 lists the normal ranges of levelling action points (LAP) for some different materials: Table 8.3  Suggested value range of levelling action point

90 pulses

Material Carded cotton

88 pulses

Break draft

LAP values (mm)

LAP values (mm)

1.16/1.28

1005–1023

981–999

1.16

1017–1035

993–1011

Combed cotton > 1 1/8” Combed cotton < 1 1/8”

1.16

1011–1029

987–1005

Synthetic fibres

1.28 / 1.41

1005–1023

981–999

(a)

(b)

(c) Figure 8.11  Influence of leveling action point on sliver U%

Feed one meter of extra sliver with tapered end in the feed direction along with rest of the slivers. Produce a sliver and take its mass spectra by Uster evenness tester. The three situations are indicated in Fig. 8.11. Figure 8.11(a) shows the correct timing. Figure 8.11(b) shows late where as Fig. 8.11(c)

240

Process control and yarn quality in spinning

shows early timing. The reason for such behavior could be that the ‘delay’ essential for the autoleveler working on open-loop principle is incorrectly set. It has to be adjusted. An incorrect leveling start will lead to sliver joints with long wave, whereas with correct leveling timing, a short wave deviation ±½ sliver thicknesses would occur. Levelling intensity Levelling intensity is to decide the amount of draft change required to correct feed variation. The correlation between mass and volume for different fibres is not same. Therefore the levelling intensity may be different for different fibres. The levelling intensity setting ensures that the leveller will correct the sliver weight if there is a major swing in mass of the in feed material. To check and set the levelling intensity a “Sliver Test” has to be performed. The delivered sliver produced from the normal feed is compared with slivers produced from feeds of normal plus one sliver and normal minus one sliver. The % deviation is then corrected by changing the levelling intensity Wrapping of the delivered sliver should be checked with “n”, “n + 1”, “n − 1” sliver at the feeding side.  The format for determination of A% to determine the levelling intensity is shown in Fig. 8.12. Sliver weight (ktex) (n – 1) slivers

n slivers

(n + 1) slivers

1 2 3 4 Average A%

A% =

(ktex(n–1) – ktex(n)) × 100 ktex(n)

A% + = Overcompensation A% – = Undercompensation

A% =

(ktex(n+1) – ktex(n)) × 100 ktex(n)

A% + = Undercompensation A% – = Overcompensation

Figure 8.12  Sliver test for adjustment of levelling intensity



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241

The sliver weight of the delivered sliver should be same for all the three combinations or should be the minimum. This can be checked if the sliver is checked at UT 3(Uster) or premier tester 7000 for mass variations (U%). If Levelling correction point and levelling intensity is selected properly, then the cut length CV% of 1 meter will be less than 0.5, if the sliver is tested in UT-3 instrument.

8.4

Advantages of high performance leveling

8.4.1

In the spinning mill

• reducing count variations; • fewer short-term mass variations in the yarn (CV %); • improving the coefficient of variation of yarn strength (CV % cN/tex); • fewer yarn imperfections (IPI and Classimat); • improving the efficiency of roving frame and spinning machine by reducing the ends down rates; • fewer cuts on the winding machine.

8.4.2

In the subsequent process stages

• Reduction of ends down rates in weaving preparation and weaving; • Even appearance of the finished cloth; • Reducing the cost for claims by eliminating a remarkable number of faults.

8.5

Control of yarn evenness (U%)

The short-term irregularity in the processed material and yarn is generally determined by assessing the U%. The Uster U% is a measure of the variation in the weight of pieces of 20 mm in the case of sliver and 12 mm in the case of rove and 8 mm in the case of yarn.

8.5.1

Blow room

The reasons for variation in blow room lap weight are given below: • Insufficient opening of cotton and wide variation in tuft size. • Use of excessive soft waste in the mixing • Malfunctioning of the length measuring motion • Cage choked with dust and dirt on their surface • Ineffective working of feed regulating motion.

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Process control and yarn quality in spinning

Between lap CV% as per norms is 0.6. The tolerance set is directly related to CV%. So the tolerance should be set (i) by finding out the between lap CV% (ii) based on lap weight/metre. As per rejection theory laps weighing ±5% of the nominal lap weight need not necessarily be rejected. This CV% is calculated by weighing the laps individually and finding out the deviation from mean lap weight. The norms for within lap and between lap CV% are given in Table 8.4. Table 8.4  Norms for with-in and between lap CV% Particulars

Good

Average

Poor

Between laps

0.6

0.8

1.0

Within a lap (1 m length)

1.0

1.5

2.0

8.5.2

Card sliver

Control of card sliver irregularity has to be given much importance, since its effect on the overall yarn quality is marked. In fact, if the card sliver is very even, its contribution to the count variation is about 10%. The norms for card sliver U% are given in Table 8.5. Table 8.5  Norms for card sliver U%



Type of card

Unevenness (U%)

SHP HP VHP

4.0 3.5 3.0

The card sliver U% can be reduced by the following ways: 1. Doffer wire condition should be checked frequently. If needed doffer grinding can be done. 2. Under casing tongue setting has to be set according to the fibre processed for better fibre transfer from cylinder to doffer. 3. Optimum web tension draft to be selected for that particular speed in the transfer zone 4. Drive transmission to be perfected (no loose belts, no slippage, and minimum play between gears) 5. Optimum feed draft between lap-to-feed roller or chute-to-feed roller 6. Trumpet/condenser selection as per hank of sliver 7. Variation in flat speed between cards processing the same mixing 8. Obstruction in the movement of aprons during doffing in modern cards should be avoided





Short-term irregularity

243

9. Check the condition of back and front plates. Bent/damaged plates should be replaced 10. Difference in drafts between cards

8.5.3

Comber sliver

As in the case of card sliver, a high irregularity in comber sliver could have a detrimental effect on the yarn count variation. The norms for comber sliver U% are given in Table 8.6. Table 8.6  Norms for comber sliver U% Rating Good Average Poor

Sliver hank 0.12–0.16

> 0.16

3.0 3.5 4.0

3.5 4.0 4.5

The following points require careful attention to reduce the U% of the comber sliver. 1. Difference in waste extraction between heads 2. Variation in settings between back detaching roller and nipper 3. Unicomb choked with seed coats or immature cotton 4. Wider setting between unicomb and brush 5. Improper needle spacing, broken or bent needles 6. Variation in detaching roller diameter and improper timing of top combs 7. Bad maintenance of the machine 8. Bad lap preparation (too little draft in preparation) 9. Dirty lap tension roller, Nippers, fluted rollers, fleece guides, trumpets, etc. 10. Badly set drafting system 11. Bottom detaching roller running eccentric 8.5.3.1

Adjustment for evenness of sliver 1. Draw box setting: Closer draw box setting than recommendation will give lower U% but may leads reduction in yarn strength. Normally 5% Uster staple or hand pulled staple is the guideline for the above setting. 2. Correct the top rollers and top roller weighting: Top roller eccentricity, too smaller diameter, physical damages on the cots will influence the U%.

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Process control and yarn quality in spinning

3. Control wheel index: The control wheel index has greater bearing on the output sliver U%. The over lapping length depends on the length of the fibre processed as well as the draft employed in the draw box. Longer fibre requires longer over lapping and vice-versa.

8.5.4

Draw frame sliver

Control of draw frame sliver irregularity is one of the important points in reducing yarn count variation. Under good working conditions the short-term variation in draw frame sliver contributes about half of the lea count variation. The norms for assessing the draw frame sliver irregularity are presented in Table 8.7. Table 8.7  Norms for finisher draw frame sliver U% Sliver hank

Rating Good Average Poor

0.12–0.16

> 0.16

2.0 2.5 3.0

2.5 3.0 3.5

There should be a constant and continuous check on the draw frame sliver irregularity – each delivery should be tested once in a week. The draw frame sliver irregularity will have a significant influence on roving U%. The factors which affect the draw frame sliver U% are outlined below. 8.5.4.1

Setting between the rollers

Roller settings based on span length would be more meaningful as this measure considers the distribution of part length of the fibres as is present in the drafting zone. Such settings are known to confer improvements in the performance of preparatory and spinning machines as well as in sliver and yarn quality. Table 8.8 shows the guideline for roller settings. Table 8.8  Recommended settings in draw frames based on span length concept Setting zone

Break zone setting

Front zone setting

Breaker draw frame

5% length + 4 mm

5% length

Finisher draw frame

5% length + 6 mm

5% length + 2 mm

8.5.4.2

Web and creel drafts

The break draft is determined by a number of factors such as the fibre properties of raw material, type of draw frame, first or second passage, etc. The recommended break draft is given in Table 8.9.



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Table 8.9  Recommended break draft in breaker and finisher draw frame Draw frame passage

Carded count

Combed count

Manmade fibres

Breaker

1.7

1.3

1.7

Finisher

1.3

1.3

1.3

The web draft and creel tension draft should also be maintained at optimum level in order to ensure a low sliver irregularity. The web draft, which is governed by the type of material used, must be slightly lower at the breaker drawing than at the finisher. Excessive web draft would lead to an increase in the sliver irregularity as well as lea count variation. The normal web drafts recommended are given in Table 8.10. Table 8.10  Web draft for cotton and manmade fibres Count

Web draft

Cotton: Upto 24s 24s–36s Above 36s Manmade fibres

0.96–0.97 0.98–1.00 1.00–1.02 1.00–1.02

The creel tension draft should be as low as is practicable and in any case it should not exceed 1.02 to get satisfactory levels of sliver evenness. 8.5.4.3

Trumpet size The size of the trumpet is also a factor which would affect the sliver irregularity. The recommended sizes are given in Table 8.11. Use of a proper trumpet helps to obtain a sliver of sufficient compactness necessary for subsequent processing. Table 8.11  Diameter (mm) of trumpet hole for draw frames (carded counts) Sliver hank 0.25 and above 0.18–0.24 0.15–0.17 0.12–0.14

8.5.4.4

Carded counts Breaker

Finisher

3.0 3.0 3.5 3.5

2.5 2.5 3.0 3.5

Machinery condition

The mechanical condition of the draw frame is also an important factor determining sliver irregularity.

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Process control and yarn quality in spinning

8.5.5 Roving

Under normal working conditions, roving process contributes for about 15% of the yarn irregularity. The norms for rove U% are given in Table 8.12. Table 8.12  Norms for roving U% Sliver hank

Rating

1.2–1.6

> 1.6

3.5 4.0 4.5

3.8 4.3 4.8

Good Average Poor

Periodic irregularity in roving affects lea strength variation adversely. The effect is more pronounced in fine counts. Short-term irregularity in roving (U%) influences medium-term variation in yarn which is mainly responsible for end breaks in ring spinning. The factors which affect the roving unevenness are explained below. 8.5.5.1

Setting between the rollers

Fly frame roller setting depends on the fibre length distribution in the roving and the hank of roving. In case of 3/3/ drafting arrangement, the front and middle zone settings are fixed and the back zone settings are arrived by 2.5% span length + 12–15 mm allowance. 8.5.5.2

Total draft and break draft

The total draft and break draft on the machine are some of the major parameters influencing the amount of irregularity added in the roving process. They are to be decided by factors like type of drafting system, quality of back material and condition of the machine. General guidelines are given in Table 8.13. Table 8.13  Drafts to be used on fly frame with top arm drafting system Count (Ne)

Draft in fly frame

Count (Ne)

Draft in fly frame

20s

9.0

70s

13.5

30s, 40s and 50s

10.0

80s

14.5

60s

12.0

90s and 100s

15.0

Break draft in simplex must be maintained between 1.2 (finer hank) and 1.4 (coarser hank) for satisfactory performance.



Short-term irregularity

8.5.5.3

247

Wrong size of sliver guides

Selection of sliver guides of proper size helps to condense the sliver effectively and reduce uneven rate of feeding. The guideline for sliver condensing guides and floating condensers are given in Table 8.14 and 8.15, respectively. Table 8.14  Size of sliver condensing guides in fly frame Hank of sliver (Ne) Inlet condenser (mm) Middle condenser (mm)

0.09–0.12

0.121–0.140

0.141–0.20

16 × 4

15 × 3

12 × 2.5

11 or 14

9 or 11

6 or 9

Table 8.15  Size of floating condensers in fly frame Hank of sliver (Ne)

Hank of roving (Ne)

Size of floating condenser (mm)

Size of spacer (mm)

0.09–0.12

0.5–1.0

11–18

6–9

1.1–1.6

9 –16

5.5–8

1.7–2.5

7.5–14

5 –7

0.6–1.0

11–16

6 –9

1.1–1.6

9–14

5.5–8

1.7–2.5

7.5–11

5–7

0.7–1.2

9–14

5–8

1.3–1.6

7.5–11

5–8

1.7–3.0

6–9

5–7

1.0–1.6

6–9

5 –7

1.7–3.0

6–7.5

5–7

0.121–0.14

0.141–0.17

0.171–0.2

8.5.5.4

Slipped or missing aprons

Spindles running without bottom aprons create uneven yarn because the materials are being stretched in loose state and without any guidance. Irregularity of roving produced thus could be higher than that produced with bottom aprons by as much as 2–2.5 U%. 8.5.5.5

Top roller loading and shore hardness

In the case of fibres with high inter fibre cohesion, the drafting force required for a given draft would be higher. Under such circumstances, chances for top roller slip will be more if the loading on the top roller is not adequate. Recommended top roller pressures on fly frames are given in Table 8.16.

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Process control and yarn quality in spinning

Table 8.16  Top roller pressure (kg) in fly frame Material

Position Front line Middle line Back line

Cotton

Manmade

22 12- 13 12 -13

25.0 16.5 16.5

If the shore hardness of the roller is too low (lower than 70°), then they will wear out at a faster rate. On the other hand if the hardness is too high, load will not be properly applied on the fibres. Therefore, the shore hardness of top roller cots must be maintained at an optimum level. A shore hardness of 80° proves to be ideal both for cotton as well as for man-made fibres. 8.5.5.6

Machinery condition

The mechanical condition of the fly frame is also an important factor determining roving irregularity.

8.5.6

Yarn unevenness

The yarn unevenness can be assigned to three causes, namely, cotton quality, processing parameters in ring frame – type of drafting, draft and mechanical condition and roving irregularity. An expression relating these three factors and yarn irregularity derived by SITRA is given below.

U2 = 29.4 ( F / L ) Ne 2

d −1  d − 1 2 + a  + a ( d − 1) + U r d d  

Where U = Yarn unevenness Ne = Yarn count d = Ring frame draft F = Fineness Ur = Roving unevenness (U%) a = Contribution of the ring frame The major process parameters that affect the yarn irregularity are outlined below. 8.5.6.1

Roller setting

In order to avoid the creation of drafting waves and to reduce short-term irregularity (U%) of yarn, proper roller settings must be adopted. The guideline for back zone setting is given in the Table 8.17.



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249

Table 8.17  Recommended back zone setting for ring frame Count group

Back zone setting (mm)

Upto 20s 21s to 60s 61s and above

55 60 65

8.5.6.2

Top roller pressure and shore hardness

Insufficient loading of top rollers leads to erratic movement of the fibres due to fibre slip between the drafting rollers. This, in turn, will lead to high level of short-term unevenness of yarn. The recommended top roller pressure in ring frame is given in Table 8.18. Table 8.18  Recommended top roller pressure in ring frame Position

Pressure (kg)

Front line Middle line Back line

16–18 10–12 12–14

Use of softer cots (shore hardness of 70° to 75°) generally improves the yarn quality by reducing slip between the cot and the bottom-fluted roller. Softer cots with a top roller pressure of 18 kg in counts below 50s and 15 kg in counts finer than 50s will result in improved yarn quality. 8.5.6.3

Draft distribution

The total draft and break draft employed in spinning influence the amount of irregularity added in spinning and they depend on the quality of roving and condition of the ring frame. The optimum levels of total draft that could be adopted in ring frame for different counts are given in Table 8.19. Table 8.19  Recommended draft level in ring frame Count (Ne)

Draft

Count (Ne)

Draft

10s 16s 20s 26s 30s 40s 44s

10 14 17 21 21 22 23

50s 60s 64s 70s 80s 90s 100s

24 26 26 26 27 28 29

250

Process control and yarn quality in spinning

Break draft in ring frame is mainly to break the mild twist in the roving. Higher the break draft, greater will be the fibre breakage at the back zone. If the twist multiplier in the roving is higher, then comparatively higher break draft could be employed. While using the higher break draft, the back zone settings should be wider to obtain optimum performance. The recommended levels of break draft for different twist levels in roving are given in Table 8.20. Table 8.20  Recommended levels of break draft in ring frame

8.5.6.4

TPI in roving

Break draft

1.38 1.94 2.35

1.2 1.3 1.4

Apron spacing

Cradle opening in ring frame contributes to the tune of 60–80% on the incidence of thick and thin places and slubs in the yarn. Wider cradle opening, lesser will be the control of fibres between aprons leading to thin places in the yarn. Narrower the cradle opening, greater will be the strain to the fibres between the aprons, leading to increased fibre damage and resistance to drafting which result in undrafted ends in the yarn. The recommended spacer for different counts in ring frame is given in Table 8.21. Table 8.21  Recommended spacer for different counts in ring frame

8.5.6.5

Count (Ne)

Apron spacer (mm)

Upto 20s 21s to 40s 41s to 80s Finer than 80s

4.0 3.5 3.0 2.5

Roving twist

There is a high degree of interaction between apron spacing, break draft and roving twist. In general, it can be said that a closer spacing between the aprons is to be attempted for improving yarn irregularity. But closer spacing is not always achievable because it may lead to uneven drafting and hard ends in certain cases. Under such circumstances, it will be useful to explore the feasibility of increasing the break draft and reducing the twist in the input roving. Higher twist level than the optimum is not preferable at roving because of these considerations.



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8.6 References 1. Bandyopadhyay, S., Garde, A.R., Subramanian, T.A. and Raj, B.S. (1972). Imperfections in cotton yarns: assessment and causes, Journal of Textile Association, 33, p.65. 2. Bandyopadhyay, S., Garde, A.R, Subramanian, T.A. and Raj, B.S. (1971). Imperfections in cotton yarns: Assessment and causes. Proceedings of the all India textile conference, p.35. 3. Bar H.P., Furter R. and Harzenmoser I. (1990). Influence of autoleveling and on-line quality control on the quality of ring yarns, Textil Praxis, 45, p.362. 4. Bhaduri, S.N. and Purohit, J.N. (1955). Effect of processing on neps: study in the pattern of their incidence and a description of experiments in controlling them. Textile Digest, 16, p.154. 5. Bhaduri, S.N. and Purohit, J.N. (1955). Effect of processing on neps: A study in the pattern of their incidence and description of experiments in controlling them. Proceedings of the all India textile conference. 6. Bragg, L.O. (1958). Effect of bottom front roll run out in spinning on yarn quality and processing performance. Textile Research Journal, 28, p.520. 7. Caveny, B., Foster, G.A.R. and Anderson, S.I. (1955). Irregularity of materials drafted on cotton spinning machinery and its dependence on draft, doubling and roller setting Parts : I and II, Journal of Textile Institute, 46, p.529. 8. Chattopadhyay R. (2002). Advances in Technology of Yarn Production , New Delhi, NCUTE Publications. 9. Chattopadhyay R., and Rengasamy, R.S. (1999). Spinning: Drawing, combing and roving, New Delhi, NCUTE Publications. 10. Foster, G.A. (1950). Causes of the irregularity of cotton yarns, Journal of Textile Institute, 41, p.357. 11. Foster, G.A.R. and Martindale, J.G. (1946). Form and length of the drafting waves in cotton rovings, Journal Textile Institute, 37. 12. Foster, G.A.R. (1950). Causes of the irregularity of cotton yarns, Journal Textile Institute, 41, p.357. 13. Further R. (1982). Evenness testing in yarn production: Part I, The Textile Institute, Manchester. 14. Further R. (1982). Evenness testing in yarn production: Part II, The Textile Institute, Manchester. 15. Garde A. R. and Subramanian T. A. (1978). Process Control in Cotton Spinning, 2nd Ed., Ahmedabad, ATIRA. 16. Garde, A.R., Bandyopadhyay, S. and Subramanian, T.A. (1972). Influence of yarn unevenness and thread density of fabric appearance: A quantitative assessment, Journal of Textile Association, 33, p.197. 17. Gupta, A.K., Shah, P.H. and Subramanian, T.A. (1986). Blemishes in fine count yarns: Raw material and process contributions. Proceedings of the ABNS joint technological conference, p.15.

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Process control and yarn quality in spinning

18. Klein W. (1987). Short Staple Spinning Series, Vol 3: A practical Guide to Combing and Drawing, Manchester, The Textile Institute. 19. Martindale, J.G. (1945). New method of measuring the irregularity of yarns with some observations on the origin of irregularities in worsted sliver and yarns, Journal Textile Institute, 36, p.35. 20. Martindale, J.G. (1950). Review of causes of yarn irregularity, Journal of Textile Institute, 41, p.340. 21. Martindale, J.G. (1950). Review of causes of yarn irregularity, Journal Textile Institute, 41, p.340. 22. Nutter, W. (1958). Regularity and machines. Textile Mercury and Argus, 139, p.16. 23. Operation manual. Rieter spinning system, Drawframe RSB 851. 24. Ratnam, T.V. and K.P. Chellamani (1999). Quality Control in Spinning, SITRA, Coimbatore. 25. SITRA Focus, (1986). Control of short-term irregularity of draw frame sliver, 14, No.2. 26. SITRA Focus, (1987). Roving unevenness – Contributing factors & control measures, 5, No. 4. 27. SITRA Focus (1988). How to control short-term irregularity of yarn, 5, No.5. 28. Souther, R.H. (1954). Influence of processing on nep formation, Textile Research Journal, 24, p.495. 29. Temmerman, R. and Hermanne, L. (1950). Application of the index of irregularity to the study of spinning on the cotton system, Journal of Textile Institute, 41, p.411.

9 Interpretation and analysis of diagram, spectrogram and V-L curve

Abstract: This chapter provides an insight about the various irregularity charts from the evenness testers such as normal diagram, spectrogram and V-L curves. The basic principle, characteristics and application of these charts for the analysis of yarn faults are discussed in detail with suitable examples. The basic concept and application of deviation rate on evaluating the yarn and consequent fabric appearance were also discussed in this section. Key words: Diagram, spectrogram, variance-length curve, deviation rate

9.1 Introduction Irregularity charts provide easy analysis possibilities as well as providing more complete information than the numerical estimates. With graphical representations, long-term trends of changes in mean value, sporadic deviations, periodicities in faults, etc., can be easily identified. Also elaborate statistical calculations are generally not required for interpreting graphical representations. The following irregularity charts are taken from the latest generation evenness testers. • Spectrogram • 3D spectrogram • Variance-length curve • 3D variance-length curve • Normal diagram • Cut-length diagram • Histogram

9.2

Measuring principle of mass evenness

The principle of measurement of mass variation in an evenness tester is shown in Fig. 9.1. The sensor for measuring the evenness of slivers, rovings or yarns is a capacitive measuring sensor. A high-frequency signal field is generated in the sensor slot, between a pair of capacitor plates. If the amount of the material between the capacitor plates changes, the high-frequency signal is

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altered and the electrical output signal of the sensor changes, accordingly. The result is an electrical signal variation proportional to the mass variation of the test material passing through. That analog signal is then digitized (converted to data bytes), stored and processed directly by the computer.

Figure 9.1  Capacitive measuring principle

9.3

Normal diagram

The mass diagram directly shows the mass variation of the test material in graphical form. A normal diagram actually contains the entire information from which the other mass test results (CV, Spectrogram, IPI, etc.) are derived. The use of the mass diagram is to determine whether any random gross mass deviations or increased variation occur along the tested material.

Figure 9.2  Normal diagram

The normal mass diagram obtained from evenness tester is shown in Fig. 9.2. The vertical axis shows the (+) and (−) mass deviations of the test material as it had passed through the sensor’s measuring field. The zero line

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along the middle represents the average value of the material. This average value has been determined over the first 15 seconds of material running through the sensor. The horizontal axis represents the length of the material (in meters or yards) which has been tested. The reference length for the mass values is the basic measured length of 1 cm. The following are some of the information which can be usefully applied for process control from the normal diagram. 1. Seldom-occurring events 2. Long-term variations 3. Periodic mass variations with wavelengths which are longer than 100 m and therefore cannot be confirmed by the spectrogram 4. Extreme thick and thin places 5. Randomly occurring thick and thin places which tend to occur in batches slow changes of the mean value 6. With periodic faults, it can be determined whether the fault is permanently present or occurs only in batches 7. With measurements “within” a bobbin, seldom-occurring events can be found and changes in the mean value taking place over a number of kilometres can be confirmed.

9.3.1

Cut-length mass diagram

If a Cut length other than “normal” is selected, the diagram has a different appearance. The short term variations and peaks vanish and the long term variations become more noticeable. This is due to the % change is less when cut length is increased. The use of the cut length diagram is to detect medium and long mass deviations along the tested material. The principle of the cut length diagram has been graphically explained in Fig. 9.3.

Figure 9.3  Principle of different cut-length diagram

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A small balance continuously measuring short lengths of the material passing over it would show more variations than a larger balance continuously measuring longer lengths of material. It is the same case as the material passing through an imaginary measuring slot of 1 cm length would show much more variation than through an imaginary measuring slot of 10 cm length, where the shorter variations would be averaged out. An example of different cutlength diagrams of the same material are shown in Fig. 9.4.

(a) 10 m cut-length diagram

(b) 1 m cut-length diagram

(c) 1 cm cut-length diagram (= Normal diagram) Figure 9.4  Difference in diagram of different cut-lengths of same material

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9.3.1.1  Application of cut-length diagrams

(1) Checking the correct functioning of a finisher draw frame autoleveller The method to check whether the draw frame is producing an output sliver with variation less than 1 metre. If any mass deviations were detected over a 1m cut length, then the result would be the same magnitude of count deviation in very long stretches of yarn produced from such a sliver. (2) Forecasting yarn clearer cuts with the help of Normal Diagram The yarn clearer settings in spinning mills are usually arrived at by testing the yarn in an off-line instrument for objectionable faults. This testing would also indicate the number of clearer cuts expected during winding which is a valuable input in deciding the yarn clearer settings. In mills which do not have such instruments, the information provided by the evenness testers could also be used for determining the number of short thick places and consequently the expected number of cuts. This is made possible by the option of choosing a special scale (+400% / −100%) for the diagram. The short thick places are easily seen as significant peaks in the diagram. A physical count of the number of such peaks would provide an estimate of the number of short thick places.

9.4 Spectrogram The mass variations present in a yarn are very serious if they are periodic in nature. A fault is said to be periodic if it repeats continuously in a yarn at fixed lengths. Such faults occur quite frequently in the spinning process either due to mechanical deficiencies or due to improper process parameters. The numerical values such as Um% or CVm% are not influenced by the periodic variations. The normal diagram just plots the mass variations as they are detected during the course of testing and any specific repetitions are not identified. Hence, it is usually very difficult to detect the periodic variations from any of the numerical values or the normal diagram.

9.4.1

Comparison of the diagram and spectrogram

Periodic variations are extremely frequent not only in the products prior to spinning, but also in yarns, because defective card clothing, out-of-centre running rollers in draw-boxes, defective aprons, etc., can all produce periodic mass variations. It is unfortunately not possible in most cases that one can recognize and analyze this type of fault from the diagram. For this purpose, the spectrogram is available. The spectrogram is a representation of the mass variation in the frequency domain. In other words, a spectrogram shows how many times a mass variation repeats itself in a tested length of yarn, whereas the diagram is a representation in the time domain.

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As seen from Fig. 9.5, whenever a periodic fault with a certain frequency say f1 is detected, it is represented by means of an increased height of the spectrogram at the particular frequency. In a textile mill, however, for fault analysis purposes, the frequency spectrum is not convenient and hence, the representation is usually made with reference to the wavelength. The wavelength of a spectrogram directly indicates the distance over which the periodic fault repeats.

Figure 9.5  Comparison of diagram and spectrogram

Figure 9.6 shows the plot of a yarn cross-section with periodic mass variations along its length. The wavelength and the amplitude of the faults are also represented therein.

Figure 9.6  Periodic variations in yarn cross-section

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Between frequency and wavelength there is a simple relationship: f=



9.4.2

V l

f = Frequency (s–1) λ = Wavelength (m) v = Material speed (m/s)

Advantages of the spectrogram with respect to the diagram

• In the diagram, various types of periodic faults can also be recognized, but the proof of these is much more difficult without the spectrogram • Two or more periodic faults in the same fibre assembly can hardly be recognized in the diagram, whereas they are clearly evident in the spectrogram • The proof of periodic faults in the diagram requires, in many cases, a drastic reduction of the testing speed, whereas the spectrogram can be traced out with the highest testing speed.

9.4.3

Arrangement of the spectrogram

A typical spectrogram is given in Fig. 9.7. The x-axis represents the wavelength. In order to cover a maximum range of wavelengths, a logarithmic scale is used for the wavelength representation. The y-axis is without scale but represents the amplitude of the faults in yarn. The wavelength spectrum is theoretically a continuous curve. But technically, it is not possible, without considerable costs to investigate each and every separate frequency (or wavelength). Therefore a finite number of frequency ranges are chosen which are determined by electronic filters. These filters can easily be recognized in the form of separate steps in the spectrogram. As the numbers of filters are increased, the smoothness of the spectrogram improves.

Figure 9.7  Spectrogram

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A frequency range is determined by an electrical filter. The arrangements of filters in the evenness tester are shown in Fig. 9.8.

Figure 9.8  Principle of spectrogram

The number of filters/channels for the various types of evenness testers is: Conventional GGP: 35 channels USTER® TESTER 1: 54 channels USTER® TESTER 2: 55 channels USTER® TESTER 3: 80 channels USTER® TESTER 4 SE: 80 channels USTER® TESTER 4 SX: 160 channels The distance from filter to filter is 15%. With the USTER® TESTER 4 SX, the distance between the channels is only 7.5%. As each filter provides a certain part of the spectrogram, the separate filters can also be recognized in the spectrogram as separate steps.

9.4.4

The ‘significance’ zone

The spectrogram consists of shaded and non-shaded areas as shown in Fig. 9.9. These areas indicate the significance or otherwise of a periodic fault detected during testing. The significance is decided by the number of repetitive occurrences of a fault. For a periodic fault to be present in the shaded area, such faults should have passed through the measuring head for a minimum of 25 times.

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Figure 9.9  Significance zone in spectrogram

Wavelength ranges which are not statistically significant are not shaded. In this range the faults are displayed but not hatched. This happens when a fault repeats for about 6–25 times within the tested length of the material. Faults which occur for less than 6 times are not drawn out at all. When a series of faults are noticed in a spectrogram, it is recommended that action is first taken on those faults in the significance zone. As far as those faults in the unshaded area are concerned, it is recommended to first confirm the seriousness of the fault before proceeding with the corrective action. This can be done by testing a longer length of yarn (by increasing either the evaluation time or the testing speed).

9.4.5

Three-dimensional spectrogram

Evenness testers also provide the possibility of viewing or printing out the spectrograms of a number of tests simultaneously (Fig. 9.10).

Figure 9.10  3D spectrogram

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This 3D spectrogram may be used to identify whether the fault is present in a single output/delivery or common to all samples. If the samples are from the same machine and all the spectrograms indicate the same fault, then it can be concluded that the fault is due to a common problem such as main drive elements.

9.4.6

Normal spectrogram and ideal spectrogram

If a fibre material should achieve the limiting irregularity, its spectrogram would be ideal. Spectrograms of ideal fibre assemblies will be referred to in the following as ideal spectra. Under normal spectra one understands, on the other hand, spectrograms of fibre assemblies which can be realized technically and are free of faults. The index I can be considered as the ratio between the height of the normal spectrum and that of the ideal spectrum. The spectrogram of a fault-free yarn consisting of natural or variable length fibres has the following shape. The spectrogram of a fault-free yarn consisting of all fibres with equal length (e.g. staple fibre yarn) will be as shown in Fig. 9.11. The maximum amplitude in the fault-free spectrogram of cotton yarn lies at a wavelength of 2.82 × Mean fibre length.

Figure 9.11  Spectrogram of fault-free cotton yarn

Figure 9.12  Spectrogram of fault-free synthetic yarn

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The fault-free spectrogram of synthetic fibre yarns are shown in Fig. 9.12. Here, the spectrogram maximum is at a wavelength of 2.7 × Mean Fibre Length. At a wavelength equal to the fibre length, the spectrogram is at zero position. Under practical conditions, such a case of perfectly equal fibre length will hardly be found. Even in the case of yarns with constant cut length e.g. synthetic fibres; there is a chance that fibre rupture during the spinning process results in fibres with variable length. In such a case, although the length will be recognizable in the spectrogram, no zero position will be indicated. Depending on the fibre length and length distribution, different basic spectrogram shapes will result after testing (for each type of material) as shown in Figs. 9.13–9.16.

Figure 9.13  Combed cotton yarn, maximum at ~7 cm

Figure 9.14  OE cotton yarn, maximum at ~5 cm

Figure 9.15  Wool yarn, maximum at ~22 cm

Figure 9.16  Cut staple yarn, maximum at ~9 cm, dip at ~3.5 cm

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9.4.7

Influence of periodic faults on the spectrogram

If a periodic mass variation with the wavelength λ appears in the fibre material, a peak is recorded in the spectrogram at the position λ. The height of this peak is a measure of the intensity of the periodic fault. In Fig. 9.17, the wavelength λ is 20 m and the corresponding cut-length diagram is shown in Fig. 9.18.

Figure 9.17  Spectrogram with a 20 m chimney, i.e. 20 m periodic fault

Figure 9.18  Cut-length mass diagram of the same material as in the top spectrogram

Figure 9.19  Ideal base wave for the chimney at 20 m

The ideal base wave (Fig. 9.19) of the periodicity and an amplified illustration of how the test materials corresponding mass variation would look are drawn alongside the diagram. The spectrogram is used to check the test material for any abnormally high periodic or systematic mass variations. In most cases, those variations are due to dirty or defective cylinders or wrongly set preparation and spinning machinery. The source of the periodic fault can be located in a previous material processing. In that case, the fault will be in a longer wavelength range, such as in the above example.

Interpretation and analysis of diagram, spectrogram and V-L curve

9.4.8

265

Distinguishing disturbing periodic faults from tolerable faults

Generally, the end use of a yarn or fibre compound has to be known in order to predict which sizes and wavelengths of periodic yarn faults will be acceptable and which disturbing. Nevertheless, there are some commonly used rules for distinguishing severe or disturbing material faults from tolerable faults. An example for analysis of seriousness of faults is shown in Fig. 9.20. 1. For wavelengths shorter than ~2 m: A peak 50% or more higher than its surroundings can be regarded as disturbing. 2. For wavelengths longer than ~2 m: A peak double or more as high than its surroundings can be regarded as disturbing.

Figure 9.20  Analysis of seriousness of periodic fault in spectrogram

If the height of the peak (P) above the basic spectrogram at any wavelength equals or oversteps by 50% of the height of the basic spectrum at that wavelength, then it can be considered to be sufficiently serious warranting immediate corrective action i.e., in Fig. 9.20, if P > B/2 the fault is serious and to be attended immediately. Other periodic faults may appear disturbing in the spectrogram but will not affect the end product directly, such as chimneys in card slivers. Nevertheless, it is important to pay attention to those cases as well, since the faults can be a sign of deterioration of machinery. One may save costs by intervening in time to avoid any damage to the respective machinery parts.

9.4.9

Spectrograms with pronounced periodic faults

There are two types of spectrograms as far as the fault types are concerned. They are chimney-type and hill-type spectrograms as shown in Fig. 9.21.

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Figure 9.21  Types of spectrogram fault

A chimney-type spectrogram, consisting of one or more ‘peaks’ or ‘chimneys’, is normally due to a mechanical fault such as eccentric roller/ gear, improper meshing, missing of teeth in gear wheels etc. A hill-type spectrogram, where several adjacent peaks are noticed, is normally due to drafting waves caused by factors such as improper settings in the drafting zone, improper pressure applied by the top rollers, too many short fibres in the material, etc. The wavelength of the peak is specific in chimney. But in hill-type fault the wavelength varies over a range. Whenever there is an occurrence of a mechanical fault, it would result in a shooting up of a particular channel in the spectrogram. However, not all faults result in deterioration in the fabric quality. This is because, the extent of influence of a periodic mass variation on the fabric quality is not only dependent on the amplitude of the spectrogram peak but also on the width and type of the woven fabric, type of fibre, yarn count, etc.

9.4.10

Neighbouring peaks in the spectrogram

The spectrogram consists of a limited number of channels. If a periodic fault occurs at a wavelength which is exactly between two channels, then both the channels will be actuated as shown in Fig. 9.22. In such a case the height of the peaks are likely to be reduced. For analysis on the seriousness of the fault, the heights of the individual peaks have to be summed up and the total height should be taken into consideration.

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Figure 9.22  Neighbouring peaks in a spectrogram

9.4.11

Shortening of the wavelengths of periodic faults

Defective and worn-out bearings at the spindles of ring spinning machines and speed frames, eccentricities of such spindles, eccentricities between spindles and flyers of the speed frame and eccentric running spinning bobbins lead to periodic mass variations whose wavelengths correspond to the circumference of the package produced. The peaks in the spectrogram move correspondingly in the direction of the shorter wave-lengths, when the material is wound off from the package. If, therefore, the peak moves to the “left” as material is drawn-off from the package, this must refer to a fault as referred to above, and cannot, for instance, have been produced by the drafting elements. Figure 9.23 shows a roving bobbin with a 12.5 cm outside diameter. The diameter of the bobbin tube is 5 cm.

Figure 9.23  Effect of bobbin diameter on periodic fault

An eccentricity between the flyer and the bobbin has resulted in the fact that, with a measurement of the full bobbin, a peak will appear at approx. 40 cm (12.5 × π = 40 cm), but with a measurement on a practically empty bobbin, the peak will appear at approx. 16 cm (5 × π = 16 cm).

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9.4.12

Process control and yarn quality in spinning

Effect of doubling on the height of spectrogram peaks

Two or more strands of material are combined to improve the regularity of the mass along its length. Typical examples are the combing and drawing stages. Any longitudinal strand of material is bound to have some mass variations along its length. As a representation of the most popular situation and for simplicity, let us assume that these variations are more or less sinusoidal in nature. Consequently, every thick place is followed by a thin place and vice versa. When such strands of material are combined, then one of the following is likely to happen: 1. The thin places and the thick places of one strand may align perfectly with the respective thin places and the thick places of the other strands (Fig. 9.24); in such a case, the resulting periodic variation is again sinusoidal but double in size. However, since the mass of the sliver is also doubled, the spectrogram shows a periodic fault of the same intensity and at the same wavelength as would be the case with a single strand.

Figure 9.24  Effect of doubling on spectrogram when perfect matching of thick places



2. The thin and the thick places of the two strands are slightly offset and as a result the periodic mass variations compensate each other; in such a case, the resulting periodic variations become considerably less significant when compared to those in the single strand (Fig. 9.25).

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Under normal practical situations, the probability of occurrence of the first case is rare and the second case could be considered to be the normal situation resulting in an improvement in the quality whenever doubling takes place. The above aspect can be effectively applied in the combing process for optimization of the parameters in order to achieve perfect staggering of the piecing waves from the four or eight individual heads of the comber.

Figure 9.25  Effect of doubling on spectrogram when compensation of thick and thin places

9.4.13

Influence of drafting on the wavelength of periodic variations

A periodic fault which occurs at some stage in the spinning process is lengthened by subsequent drafting in the next stage. The wavelength of the periodic fault is multiplied by the total draft given from the source point to the stage where the material is checked. This means that, if a fault is noticed in the spectrogram at any stage, the source of the fault could either be in the same machine or in the earlier stages and the exact source can be identified with the help of the drafts in different stages. Let us consider an example of a spinning process with the drawframe, speed frame and ring frame as successive processing stages. The drafts these sudden stages are 8, 10 and 25, respectively (Fig. 9.26).

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Figure 9.26  Effect of drafting on periodic fault

In isolation, let us consider the situation of a periodic fault caused by the eccentricity of front roller in a draw frame. If the diameter of the concerned roller is 40 mm, then the eccentricity introduces a periodic fault with a wavelength

λ1 = π × 4.0 = 12.56 cm

At the output of the fly frame, the wavelength of the above fault will be increased by the draft at fly frames, i.e. the fault would occur at a wave length

λ2 = λ1 × 10 = 1.26 m

Similarly, to calculate the wavelength of this fault at the yarn stage, the value is to be further multiplied by the draft given in the ring frames. Therefore the fault would appear at a wavelength λ3 = λ2 × 25 = 31.5 m The resultant spectrograms at the three stages are represented in Figs. 9.27–9.29. Therefore, working backwards from the wavelength of faults at any particular stage, the exact source of faults could be identified.

Figure 9.27  Spectrogram of draw frame sliver

Interpretation and analysis of diagram, spectrogram and V-L curve

Figure 9.28  Spectrogram of roving

Figure 9.29  Spectrogram of yarn

9.4.14

Effect of eccentric rollers

Figure 9.30  Effect of eccentric and oval rollers

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A drafting roller which is eccentric affects the drafting process in a known way. A periodic fault is introduced at the wavelength equal to the circumference of the roller. So far, only eccentric-running rollers and spools have been referred to where, for each full revolution, a periodic fault is produced. Worn-out and badly ground rollers can also, however, be oval. In this case, per full revolution of the roller, a faulty draft is produced twice. Figure 9.30 shows the effect of an eccentric roller on the mass variation of a fibre assembly. The eccentricity results in a sinusoidal mass variation whereby one period L corresponds to a full circumference of the roller. With one complete revolution of an oval roller, a sinusoidal mass variation also results, but two periods occur.

Figure 9.31  Spectrogram of eccentric roller

Figure 9.32  Spectrogram of oval roller

If one assumes that this fault occurs at the front roller of a ring spinning machine, then the following peaks will appear in the spectrogram. As can be seen in Fig. 9.30, the rollers have a diameter of 25.4 mm. With an eccentric roller (Fig. 9.31), the peak would appear in the spectrogram at a wavelength equal to the circumference of the roller (2.54 × π = 8 cm), and with an oval roller (Fig. 9.32) at half this circumference (½ × 2.54 × π = 4 cm).

9.4.15

Hill-type spectrogram

Unlike a true periodic defect, a drafting wave repeats over a range of several wavelengths. Because of its nearly periodic nature, a drafting wave appears as

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several wavelengths. A fault of this type leads to an increase in the spectrogram at the corresponding wavelength range and not to a distinct peak. Drafting waves can cause very serious thick and thin places that result in poor yarn performance and unacceptable fabric appearance. In contrast to the chimney-type periodic faults, which are caused mostly due to specific mechanical defects of components, the hill-type faults are usually caused by drafting waves which may result from raw material deficiencies or improperly optimized draft zone parameters (swimming fibres) and in the range of synthetic filament yarns, the nearly periodic vibration of the filaments in the quench air duct (as a result of the cooling air). A spectrogram of this fault group is shown in Fig. 9.33. With staple fibres drafting waves are mostly known in cotton, as the short fibres can only be guided inadequately in the draw box. Drafting waves do not cause distinctive patterns in the end product, as shown for the periodic faults, but produce more a “cloudy” appearance.

Figure 9.33  Hill-type of spectrogram

Depending on the origin of the faults, the wavelength varies. For a hilltype fault, since the height of a number of channels increase simultaneously, the channel with the maximum height (λmax) is taken for consideration for identification of source. A defect in the drafting zone in any of the departments introduces a hill-type fault with a λmax value equal to ‘K × Mean Fibre Length’. The constant K varies depending on material type as given in Table 9.1. Table 9.1  Constant value drafting wave determination for different materials Material

K

Yarn

2.80

Roving

3.20

Sliver

3.50

For example, if λmax matches 3.5 times the mean fibre length, then the problem is with the drafting zone in draw frames. The λmax of the hill-type

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fault also gets extended by the drafts in subsequent stages as in the case of chimney-type faults. Example: During the spinning process of carded cotton, drafting waves caused by “swimming” fibres are produced in the main drafting area of both the speed frame and the ring spinning machine. At which wavelength will these faults be indicated in the spectrogram? Overall draft at the ring spinning machine: 23.5 Mean fibre length lW = 2.2 cm Wave length of the drafting waves coming from the ring spinning machine: λv1 = 2.8 × lW = 2.8 × 2.2 = 6 cm Wavelength of the drafting waves which result from the drafting elements of the speed frame: λv2 = 3.2 × 2.2 × 23.5 = 1.65 m These two drafting waves are shown schematically in Fig. 9.34.

Figure 9.34  Hill-type spectrogram of the resultant yarn

9.4.16

Influence of the sporadically occurring periodic faults on the spectrogram

In the textile industry, various types of mass variations will occur which can only be considered as periodic during a certain period of time. Faults of this type are produced by vibrations transmitted through the machine supports which appear from time to time and then disappear. Furthermore, with a given speed and a certain number of windings (resonance), a non-circular rotation of the package will also produce strong periodic mass variations. This type of fault also results in a quite distinct peak in the spectrogram, whereby the height of this peak is approx. the mean value of the periodic mass variation with respect to the period of time being considered. A fault of this type can be seen in Fig. 9.35.

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Figure 9.35  Sporadically occurring periodic faults

The diagram begins with a normal irregularity. At about 200 m a periodic mass variation occurs which disappears later on.

9.4.17 Harmonics In many cases, a single periodic material fault produces multiple peaks. Multiple peaks are the result of a periodic yarn mass variation which is not evenly shaped, i.e. not sine-shaped. The reason for the appearance of multiple chimneys lies in the behaviour of wave signals. Mathematically, it is complex (Fourier transformation), but graphically, it becomes quite evident.

Figure 9.36  Behaviour of base sine waves for the formation of harmonics

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Illustration of how a new wave shape (in this case a square wave) is created by adding sine waves of the base wavelength λ and further shorter wavelengths (harmonics of λ) of decreasing amplitudes is shown in Fig. 9.36. But, the rectangular waveform is not actually prevalent in textile practice. A multiple periodic fault consists of a base wavelength and of so-called harmonic wavelengths. The harmonics are usually to be found at factor 1/2, 1/3, 1/4, etc., of the base wavelength. Multiple periodic faults may be difficult to recognize on the diagram chart, but they are easily visible on the spectrogram. When analyzing the periodic variation drawn with the bold line (Fig. 9.37), besides the basic wave A1 (wavelength λ), there are upper harmonics A2 and A3 (λ/2, A λ/3). Thus, there are upper harmonics in the spectrogram which will be in an integral relationship to the basic wave (Fig. 9.38).

Figure 9.37  Periodic variation with harmonics in yarn assembly

Figure 9.38  Harmonics and corresponding yarn appearance

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Whenever harmonics are shown in the spectrogram, if the action is taken for the fundamental wavelength then the harmonics also will be taken care of.

9.4.18

Common periodic fault types in textile practice

The signal shape of each of the above examples is contained in the mass diagram, but most often the shape is hardly visible or not visible at all. In a normal diagram, the length scale is too large to see short variations. Also, the high amount of random diagram peaks covers up the wave shape of the periodic fault. In some cases, such as with very short periodic pulses, the fault would be visible in the diagram as high signal peaks, and the material itself would contain regularly appearing thick places which would clearly stand out. The periodic faults commonly occurring in textile materials are given in Fig. 9.39.

Figure 9.39  Common periodic faults in textile materials

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9.4.19

Influence of periodic mass variations on woven and knitted fabric

In each case according to the width of the woven or knitted material and according to the wavelength of the periodic fault, thick or thin places can appear at regular intervals in woven and knitted fabrics. These result in an unacceptable patterning and, in many cases, downgraded finished fabric. In warp it gives streaky appearance and in weft diamond and block bars can arise in case of shuttle weaving machines. The conditions for diamond bars: W = (R + x) × λ Where, λ = Wave length of periodic variation R = An integral multiple of ½ x = Value less than ¼ This means, to cause a “diamond bar” a weft must have a periodic variation whose wave length is less than twice the fabric width. λ < 2W

Figure 9.40  Effect of periodic variation in shuttle loom fabric

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For example, if the effective cloth width of a certain fabric is 15” and that the weft has a periodic variation with a wavelength of 18” i.e. 9” sections of thick and thin places. Figures 9.40 and 9.41 are the representation of the way in which weft could position itself in the fabric in case of shuttle and shuttleless loom fabrics respectively.

Figure 9.41  Effect of periodic variation in shuttleless loom fabric

Figure 9.42 shows three possible fault patterns in a woven or knitted material caused by periodic mass variations.

Figure 9.42  Fault patterns in fabric due to periodic variation in yarn

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The fault patterning referred to as Moiré is the most frequent, whereas the other two patterns are exceptional cases. Nearly periodic faults result in an uneven appearance in the finished fabric. One speaks in this case of “cloudiness”. The type of disturbance, whether in a woven or knitted fabric, is dependent primarily on the wavelength λ of the fault; in this respect, one differentiates between short-, medium- and long-term periodic mass variations. 9.4.19.1

Short-term periodic mass variations (λ = 1–50 cm)

Periodic mass variations in the range of 1–50 cm are normally repeated a number of times within the woven or knitted fabric width, which results in the fact that periodic thick places or thin places will lie near to each other. This produces, in most cases, a Moiré effect as shown in figure. This effect is particularly intensive for the naked eye if the finished product is observed at a distance of approx. 50 cm to 1 m. 9.4.19.2

Medium-term periodic mass variations (λ = 50 cm to 5 m)

Periodic mass variations in the range 50 cm to 5 m are not recognizable in every case. Faults in this range are particularly effective if the single of double weave width, or the length of the stretched out yarn for one circumference of the knitted fabric, is an integral number of wavelengths of the periodic fault, or is near to an integral number of wavelengths. In such cases, it is to be expected that weft stripes will appear in the woven fabric or “rings” in the knitted fabric. 9.4.19.3

Long-term periodic mass variations (λ = longer than 5 m) Periodic mass variations with wavelengths longer than 5 m can result in quite distinct cross-stripes in woven and knitted fabrics, because the wavelength of the periodic fault is longer than the weave width or the circumference of the knitted fabric.

9.4.20

Finding the source of periodic faults in spectrogram

Every genuine periodic fault (chimney) visible in the spectrogram has its origin in the production machinery. An exception to that rule can be a periodic fault due to material handling, for example the scraping of a roving bobbin surface, etc. When searching the origin of the periodicity, the first step is to remember that the fault is caused by a moving machine part, usually a rotating one. It can be directly touching the material (rollers, coiling, etc.) or in the machine drive (gears, pulleys, etc.). If the part is running smooth and perfectly uniform, it will not cause any periodic faults, of course. If it is running irregularly and/or has an uneven surface, the same uneven points of

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281

the rotating part will periodically produce a fault in the material passing out of it once per revolution, i.e. once per its circumference. Example (Fig. 9.43):

Figure 9.43  Periodic fault in yarn due to eccentric front roller

In the above example, the circumference of the top roller would have to be approximately 7.7 cm ÷ π = 7.7 cm ÷ 3.14 = 2.45 cm = 24.5 mm. Normally, a top roller in a cotton drafting system has an initial diameter of around 27 mm. Probably, the spinning contraction and/or repeated grinding of the roller surface are responsible for the slight difference between the theoretical and the actual roller diameter. 9.4.20.1

Systematic fault search A good method to find the faulty part causing a periodic mass variation, visible as one chimney or an array of chimneys in the spectrogram, is following procedure:

1. Divide the chimney’s wavelength (λ) by π. If there are multiple chimneys, check if they belong together by looking for ratios of λ/2, λ/3, λ/4, etc. Then divide the main wavelength λ, the furthermost right one, by π.

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2. Check if the result corresponds to any diameter of a moving part touching the material directly at the output of the machine. If there is a part with such a diameter, then it is probably the faulty element. If the result of λ/π is much larger than any diameter at the output, continue with step 3. If λ/π is smaller or only slightly larger than any diameter at the output, go to step 4. 3. Divide the result λ/π by the drafting ratios present in the machine(s) until finding the diameter of a part touching the material further back in the machine or in a previous passage. If there is a part with such a diameter, then it is probably the faulty element. If no such part can be found, continue with step 4. 4. Look for a possibly faulty part in the machine’s drive: Using the correct gear plan, calculate the gear ratios backwards from the delivery cylinder of the machine. When a gear ratio is found that, when multiplied by λ/π, results in the diameter of the delivery cylinder, then one has probably found the faulty area in the machine drive. In a machine with several deliveries or spindles, gear faults will usually produce the same chimney height on all samples taken from a machine at the same time. or

λchimney = Diameter × π × Draft ratio Diamter =

λchimney π × Draft ratio

For gear faults: λchimney = Diameter × π × Gear ratio

For certain types of machines such as cards or very complex gear boxes, it can be necessary to use an auxiliary method to find the fault source: A faulty (rotating) part can be searched in a machine while it is running, by flashing onto the suspected fault source with a stroboscope. This auxiliary method is not generally recommended because of the danger involved when searching a fault on a running machine! Instead, where available, the RPM (rotations per minute) displays of the machine should be read.

Auxiliary general formal:

λchimney =

Out speed [m/min] RPMfaulty element [1/min]

Interpretation and analysis of diagram, spectrogram and V-L curve 9.4.20.2

283

Calculation examples of periodic faults originating in drafting systems (Fig. 9.44)

Figure 9.44  Example for analysis of spectrogram

(1) Yarn Spectrogram chimney at λ ≈ 8.2 cm (Fig. 9.45):

Figure 9.45  Yarn spectrogram chimney at λ ≈ 8.2 cm

Diameter d = λ ÷ π = 8.2 cm ÷ 3.14 = 2.61 cm ≈ 26 mm = Diameter of exit top roller of ring frame (2) Yarn Spectrogram chimney at λ ≈ 3.6 m (Fig. 9.46)

Figure 9.46  Yarn spectrogram chimney at λ ≈ 3.6 m

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Process control and yarn quality in spinning

Diameter d = λ ÷ π ÷ draft factor = 360 cm ÷ 3.14 ÷ main draft (30) = 3.82 cm • No such part exists in the middle of the ring frame’s drafting system. 3.82 cm ÷ break draft (1.2) = 3.18 cm • No such part exists at the entry of the ring frame’s drafting system or the exit of the roving frame’s drafting system. Solution: 360 cm ÷ main draft (30) = 12 cm = apron length L of cracked apron (the apron length is already a circumference). (3) Yarn Spectrogram chimney at λ ≈ 2.9 m (Fig. 9.47)

Figure 9.47  Yarn spectrogram chimney at λ ≈ 2.9 m

(a) Diameter d = λ ÷ π ÷ draft factor = 290 cm ÷ 3.14 ÷ main draft (30) = 3.08 cm •  No such part exists in the middle of the ring frame’s drafting system. (b) 3.08 cm ÷ break draft = 3.08 cm ÷ 1.2 = 2.57 cm ≈ 26 mm = diameter of top input roller of ring frame (4) Yarn Spectrogram chimney at λ ≈ 3.4 m (Fig. 9.48)

Figure 9.48  Yarn spectrogram chimney at λ ≈ 3.4 m



(a) Diameter d = λ ÷ π ÷ draft factor = 340 cm ÷ 3.14 ÷ main draft (30) = 3.61 cm •  No such part exists in the middle of the ring frame’s drafting system. (b) 3.61 cm ÷ break draft = 3.61 cm ÷ 1.2 = 3.01 cm ≈ 30 mm = diameter of top exit roller of roving frame

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(5) Yarn Spectrogram chimney at λ ≈ 32 m (Fig. 9.49)

Figure 9.49  Yarn spectrogram chimney at λ ≈ 32 m

(a) Diameter d = λ ÷ π ÷ draft factor = 3200 cm ÷ 3.14 ÷ total draft ring frame (36) = 28.31 cm • No such part exists at the entry of the ring frame’s drafting system or the exit of the roving frame’s drafting system. (b) 28.31 cm ÷ total draft roving frame (8) = 3.54 cm ≈ 35 mm = diameter of top exit roller of drawframe 9.4.20.3

Calculation example of a periodic fault caused in the machine’s gear sector (Fig. 9.50)

Figure 9.50  Gearing diagram of machine

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(1) Spectrogram with chimney at λ = 3 cm (Fig. 9.51)

Figure 9.51  Yarn Spectrogram chimney at λ ≈ 3 cm

Calculation:

(1) Diameter λ1 = πd = 2.54 cm × 3.14 = 7.97 cm

• The wavelength produced by the gear 85T is also 0.96 cm. since it is fixed in the shaft of the roller.

(2) Wavelength corresponding to 160T gear wheel

λ2 =

No. of teeth in driving gear ( or wheel diameter of driving roller ) No. of teeth in driven gear ( or wheel diameter of driven roller )

× Wavelength

160 × 7.97 85 = 15 cm

λ2 =

(3) Wavelength corresponding to 32T and 140T gear wheel

32 × 15 160 = 3 cm

λ3 =

(4) Wavelength corresponding to 100T and 35T gear wheel

100 ×3 140 = 2.14 cm

λ4 =

Solution: The fault’s origin is the 32-tooth gear or it is on the same axis than that gear. Note: If a yarn spectrogram periodicity such as above is at a very short wavelength, it is quite possible that the fault is generated in the machine’s gear sector. The fault would then be apparent on all samples tested from the same machine side. Of course, gear faults can also be of longer wavelengths than the shaft diameter × π.

Interpretation and analysis of diagram, spectrogram and V-L curve 9.4.20.4

287

Drafting faults

Another type of irregularity which is clearly visible in spectrograms is a drafting fault. It is an exaggerated crest (hill) which results from poor fibre control in a drafting zone. Example of the origin of drafting faults (Fig. 9.52):

Figure 9.52  Yarn Spectrogram hill-type fault

Spectrogram of a ring-spun yarn with typical pointed crests resulting from both bad pre- and main draft settings. The main draft factor is the ratio of the 2 wavelengths at the hill crests: 1 m 15 cm: 6.5 cm ≈ draft factor 17.7 Drafting faults are created and influenced by non-optimal settings of one or several of the following factors: • Gauge distance between the drafting rollers (Nip) • Roller pressure • State of the roller’s surfaces • Humidity of material and surrounding climate When searching to eliminate drafting faults, one would look for the main cause in one of those factors first. In many cases though, a compromise has to be found, since certain materials are more critical. Example: Combed cotton drawframe slivers, where the fibres are highly parallel and thus slippery and difficult to draft optimally at a reasonable speed.

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A drafting fault hill is to be found at a wavelength of about 2.8 × Average fibre length. If the drafting fault hill does not lie around 2.8 × Average fibre length, one has to divide the wavelength λ of the hill crest by 2.8 × Average fibre length in order to get the approximate draft factor back to the origin of the fault.

Draft ratio =

λ hill crest 2.8 × Average fiber length

Examples of drafting faults originating in different process stages (Fig. 9.53)

Figure 9.53  Example for analyses of hill-type faults

Drafting faults originating in (1) the main draft zone of the ring frame (pressure set too low) and (2) the break draft zone of the finisher drawframe (gauge open too wide) 1. Yarn Spectrogram showing a drafting fault at λ ≈ 6 cm (Fig. 9.54)

Figure 9.54  Yarn spectrogram with hill at λ ≈ 6 cm

Average fibre length ≈ 6 cm ÷ 2.8 ≈ 2.14 cm = 21.4 mm. In this case, the draft factor is 1, since the main draft of the ring frame output is bad.

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289

2. (a) Sliver Spectrogram of the same material lot as the yarn with drafting fault at λ ≈ 32 cm (Fig. 9.55)

Figure 9.55  Yarn spectrogram with hill at λ ≈ 32 cm

Draft from faulty zone to sliver: Draft factor ≈ λ ÷ (2.8 × average fibre length) = 2 cm ÷ (2.8 × 22 mm) = 32 cm ÷ 6.16 cm = 5.19 ≈ 5.22 • The calculated draft nearly coincides with the draw frame’s main draft; therefore the fault lies in the break draft zone. 2. (b) Yarn Spectrogram showing a drafting fault at λ ≈ 90 m (Fig. 9.56)

Figure 9.56  Yarn spectrogram with hill at λ ≈ 90 m

Draft from faulty zone to sliver: Draft factor ≈ λ ÷ (2.8 × average fibre length) = 90 m ÷ (2.8 × 22 mm) = 9000 cm ÷ 6.16 cm = 1460 ≈ [36 × 8 × 5.22 = 1503] The calculated draft nearly coincides with the draft factor from the ring frame output back to the middle of the draw frame’s drafting system. The factor is [Total draft ring frame × Total draft roving frame × Main draft drawframe]. Therefore, the fault lies in the finisher draw frame’s break draft zone.

9.4.21

Special spectrogram faults

In some cases, there are chimneys and hills in the spectrogram which come from causes other than the classical ones described in the previous pages. The

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Process control and yarn quality in spinning

following cases are some of the most common types of additional faults found in staple spinning: 9.4.21.1

Coiler periodicity

When the sliver is led into the can, it is wound like a coil. At the part of the lay toward the centre of the can, the sliver is twisted by overturning (Fig. 9.57). This twist shortens the sliver and increases the cross section temporarily. The longer the sliver is fed into a can, the greater the pressure becomes. In this way, the twist is retained and remains in the sliver even after it is taken away from the can. Periodic thick places thus produced are detected with the spectrograph and are drawn as a strong peak in the spectrogram. Such variations are not sinusoidal and so the second and third upper harmonics will appear in many cases. The can effect is reduced in the subsequent process and the fault will disappear. A fault like this, therefore, is a false periodic variation.

Figure 9.57  Periodic fault due to sliver coiling

If a faulty coiler head rotation is provided, a periodic fault whose periodicity corresponds to the circumference of a coil is produced as shown in Fig. 9.58. The fault thus produced is a real periodic variation. In order to obtain correct measured results, it is recommended that the sliver be directly taken from the machine for testing, without feeding into the can, or carryout the test after the subsequent process to give drafts in those areas. Example: A coiler period in a sliver is often not regarded as a serious fault, since in most cases it will vanish after the next processing stage, i.e. roving (or finishing). Often, the coiler period is due to a moisture difference between the material exposed to air in the middle of the can and the stacked layers of sliver windings which cover each other. If that is the case, it is recommended to test a can directly from the machine with as little delay as possible. Bad coiler periods however, which are due to truly defective sliver coilers, will be visible as long wavelength periods in the spectrograms of the subsequent processing stages.

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291

Figure 9.58  Sliver spectrogram with a coiler periodicity chimney; λ = each winding of the can sliver

9.4.21.2

Protection twist periodicity

In a roving frame, a certain twist is given to the roving in order to protect the fibres from slipping apart. If the twist is too hard, the material becomes harder to draft evenly, and the above twist periodicity can occur. If the twist is too soft, the fibres fray open easily and tend to form more fly in ring spinning and laps on rollers. Even when the roving twist is optimal, certain machine settings can worsen the twist periodicity, such as the roving frame twisting tension, the ring frame roller pressure and apron pressure, the type of apron distance plates, etc. These settings can also influence the amount of long thick and thin places in the yarn. Example: (Figs. 9.59 and 9.60)

Figure 9.59  Yarn spectrogram with roving periodicity chimney at λ ≈ 44 cm

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Process control and yarn quality in spinning

Figure 9.60  Roving with a twist length of 1.2 cm



λ roving period = Roving twist length [cm] × Total draft ring frame = 1.2 cm × 36 = 43.2 cm ≈ 44 cm (Fig. 9.43) Where twist length [cm] = 100 ÷ twist /m or 2.54 ÷ twist/inch

9.4.21.3

Faults caused by autolevellers (Figs. 9.61 and 9.62)

Figure 9.61  Autoleveller in draw frame

Figure 9.62  Drawframe sliver spectrogram with draft-type fault at λ ≈ 35 cm

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293

Often, spectrogram faults due to a badly adjusted autoleveller have a nearly identical shape and wavelength range as draft faults as shown in Fig. 9.62. 9.4.21.4

Comber periodicity

In combing machines, the fibres are soldered, i.e. joined together periodically at a certain length interval. If the adjustments at each combing position and on the machine table are not optimal, a strong periodic fault can arise due to the soldering. Example (Figs. 9.63 and 9.64): Qty. of laps = Lap = Soldering = Table sliver = Output sliver =

8 64.0 kTex 4.0 mm 4.0 kTex 4.5 kTex

Figure 9.63  Comber machine parameters

Figure 9.64  Comber spectrogram with a chimney at λ ≈ 45 cm

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Process control and yarn quality in spinning

Calculation example: λ combing period = S  oldering length × Draft lap to table × Draft table to output sliver = 0.4 cm × (64 kTex ÷ 4 kTex) × (4 kTex × 8 ÷ 4.5 kTex) = 45.51 cm ≈ 45 cm 9.4.21.5

Card faults

In a modern short-staple or worsted spinning mill, card spectrogram faults will not be visible anymore at the end of the preparation line, i.e. after the output of autoleveler draw frames. Nevertheless, it is important to search the source of any chimneys or high areas in those spectrograms, which can be due to mechanical misalignment or dirty elements. In such cases, secondary negative effects such as fibre damage or an increased amount of neps and impurities in the output sliver can be the result. When searching spectrogram faults in cards, it is not necessary to search the diameters and draft ratios of the carding elements in the calculation. The cylinder, doffer, etc., which run with different surface speeds and fibre material density on them, do not actually draft the material in the conventional sense.

The formula used in this case is:

λcard chimney =

Out speed [m/min] RPMfaulty element [1/min]

In modern cards, the output speed and RPM indication of the important carding elements can be read off a display. Output speed = 80 m/min Doffer RPM = 1300 1/min Cylinder RPM = 450 1/min Licker-in RPM = 850 1/min Example (Figs. 9.65 and 9.66):

Figure 9.65  Carding machine parameters

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295

Figure 9.66 Card  sliver spectrogram with periodic fault at λ ≈ 18 cm

λ = Output speed ÷ RPM = 80 m/min ÷ 450 m/min = 0.177 m = 17.7 cm ≈ 18 cm • The cylinder drum is the cause of the spectrogram chimney and would have to be checked.

9.5

Variance-length curve

The coefficient of variation at different cut lengths provided by the evenness testers provides invaluable information with regard to the variations prevalent at the specific cut lengths. Therefore independently, the short-, medium- and long-term variations could be studied by estimating the coefficient of variation of the required cut length. However, such numerical values cannot directly provide complete information on the source of faults. The spectrogram provides a possibility of localizing the source of fault but with a spectrogram, only faults of periodic nature could be identified and that too, in most cases, only if proceeded by some other means of identifying the machine/processing stage responsible for the fault. When the variations prevailing at different cut lengths are simultaneously represented graphically, it provides the possibility of segregating cut lengths at which abnormal variations occur and consequently identify the process stage which is most likely to be responsible. This is made possible by the ‘variance-length curve’ which is a standard feature of most evenness testers.

9.5.1

The variance-length curve

In simple terms, a ‘variance-length curve’ is a graphical representation of the coefficient of variation (CV%) against the reference cut length. The representation is explained with a simple hypothetical example as shown in Fig. 9.67.

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Figure 9.67  Representation of different cut lengths

Consider a longitudinal strand of fibres cut up into several equal segments of length l1. For purposes of explanation let us further assume that the length of the segment corresponds to the measuring field length of the evenness tester. Ideally, for obtaining mass variations at different cut lengths, the measuring field length should be increased. For construction reasons, it is not possible to physically lengthen the measuring field of the evenness tester beyond a certain length. Therefore, the extension of the measuring field has to be simulated by the instrument according to electronic means. For constructing the variancelength curve, the measuring field length is taken as the basic cut length at which the CV is calculated and plotted. For variations at other cut lengths, the mass of successive portion of material are added up and the CV calculated.

Figure 9.68  A typical variance-length curve

Accordingly, referring to Fig. 9.67, the variations of mass at cut length l2 is estimated by summing up the mass values of two successive portions of material with length l1 and calculating the coefficient of variation. Similarly

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297

CV of mass at cut length l3 would be estimated by summing up the mass values of 4 successive l1 lengths of material or 2 successive l2 lengths of material and calculating the CV. When all these CV values are plotted against their respective cut lengths, we get the variance-length curve as in Fig. 9.68.

9.5.2

The shape of a variance-length curve

The shape of a typical variance-length curve can be explained as follows. A typical yarn exhibits variations in mass from the mean value. Such deviations will be present both on the positive as well as the negative side of the average mass value. These variations are more intensive at shorter cut lengths when compared to longer cut lengths. At longer cut lengths, the variations on the positive and the negative side tend to even out to a certain extent resulting in lower values for the CV%. The decreasing trend when drawn out on double logarithmic paper results in a straight line. A variance-length curve can be set out in quite a simple manner by cutting a fibre assembly into pieces and determining gravimetrically the mass of these pieces. The CV value is then calculated from each of these separate values. If this procedure is repeated for various cut lengths and the CV value drawn out, one obtains the variance-length curve.

Figure 9.69  Variance-length curve as determined by cutting and weighing (draw)

The figure shows the variance-length curve of a good yarn, when printed out on a double logarithmic scale, results in a straight line (Fig. 9.69). One can easily comprehend that the curve for the same raw material and same ideal processing conditions will always be a straight line with an unchanged angle φ of inclination. Deviations from the straight lines must therefore indicate problems caused by the machine or the raw material.

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Strictly speaking, the variance-length curve is a straight line on double logarithmic paper only in the medium length range of approx. 1 cm to 100 m. For cut lengths longer than 100 m, the variance-length curve tends to become flatter. This is because, as the cut length becomes longer and longer, the shortterm variations compensate each other more and more, and the difference between successive lengths will be dependant only on the intensity of longterm variations.

9.5.3

Identification of a faulty yarn

The explanation given in the earlier sections makes it clear that the variancelength curve for an ideal fault-free yarn would be a straight line. When a yarn becomes faulty either due to an inferior raw material or due to improperly optimized process or due to faulty machinery then the coefficient of variation becomes higher at cut lengths corresponding to the source of the fault. Therefore the variance-length curve of a yarn with faults always lies above the curve for the ideal fault-free yarn (Fig. 9.70).

Figure 9.70  Ideal and faulty V-L curve

The point of maximum deviation from the ideal yarn represents the most serious fault, and the source of such faults can be detected from the cut lengths at which the maximum deviation occurs.

9.5.4

Three-dimensional variance-length curves

Different levels of long-term mass variation, which are also clearly visible in the corresponding cut-length diagrams, appear as different inclinations of

Interpretation and analysis of diagram, spectrogram and V-L curve

299

the variance-length curves. In a variance-length curve array it is very easy to spot exceptional samples. In the case of normal diagrams, such differences are hardly visible. In spectrograms, faults such as in the above example are also not visible because the origins are non-periodic mass variations. With the evenness tester, a three-dimensional variance-length curve of a whole measuring series can be presented (Fig. 9.71). This makes it possible to compare all variance-length curves of one measuring series with each other. Bobbins, which are differ from each other, like for example bobbin 1/3 in figure, are thus quite easy to recognize.

Figure 9.71  Three-dimensional variance-length curve (grey image)

9.5.5

Location of sources of deviation

A fault created at any process stage creates higher CV values at longer cut lengths. The cut length at which the maximum deviation occurs depends on the stage at which the fault originates since subsequent drafting increases the cut length at which the deviations are noticed. This provides us with the possibility of identifying the process stage creating the variations. For this purpose, the cut length ranges corresponding to the various departments should be identified. This is done by the following procedure. First, an initial cut length needs to be calculated. This is done by using the following formula: Initial Cut Length l (cm) = Mean fibre length × K where K is given by 1 + CV2 (where CV – Coefficient of variation of fibre length) and approximates to 1.18 for Cotton and 1.27 for wool and 1.00 for synthetic fibres. If the mean fibre length is not available, it can be estimated from the spectrogram, since the highest point of a spectrogram is given by λmax = l × 2.82 (where, l – Mean Fibre Length)

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Consequently l =

lmax 2.82

Now, once the initial cut length l is calculated, the cut length range of the various process stages can be easily calculated. Let us assume that, in a cotton spinning mill, yarn is produced with draft D in different processing stages as per the following Fig. 9.72.

Figure 9.72  Draft distribution in a spinning mill



Assuming a mean fibre length of 2.5 cm, Initial cut length l (cm) = 2.5´1.18 = 2.95 cm Cut Length range of the speed frame = l × D4 = 2.95 × 30 = 88.50 cm Cut Length range of the 2nd draw frame = l × D4 × D3 = 2.95 × 30 × 10 = 8.85 m Cut Length range of the 1st draw frame = l × D4 × D3 × D2 = 2.95 × 30 × 10 × 6 = 53.1 m Cut Length range of the card = l × D4 × D3 × D2 × D1 = 2.95 × 30 × 10 × 6 × 8 = 424.8 m

Figure 9.73  Variance-length curve showing a faulty roving process

Interpretation and analysis of diagram, spectrogram and V-L curve

301

Now all these cut length ranges can be located on the V-L curve to indicate the cut length range of the individual process. When a deviation in the variance-length curve is noticed, by identifying the cut length at which the maximum deviation occurs, the department responsible for the variation can be identified. The variance-length curve corresponding to the example given above is shown in Fig. 9.73. It is quite clearly recognizable that the variance-length curve deviates from an ideal curve in the cut length ranges of the speed frame. Having identified the department, the next step is to locate the actual cause of the fault by analyzing the numerical and graphical results at the speed frame stage.

9.6

Deviation rate

The deviation rate was developed in Japan by the Japan Spinner’s Inspecting Foundation. Especially spinners supplying Japanese mills are often forced to provide the deviation rate in their reports. The DR describes by what percentage a mass deviation exceeds or falls below a certain limit. The cut length factor (in m) averages out the shorter, higher deviations.

9.6.1

Definition of deviation rate (DR)

DR= total relative length in (%) of all deviations of the mass signal which surpass the limit ± x% over a total test length of L meters, with the cut length of the curve being y meters (Fig. 9.74). The standard DR used for yarns is 1.5 m cut length t a ±5% limit. The application of the DR is similar to that of the CVm values. One has to take into consideration that the DR is based on threshold values and changes more significantly than CV values when higher mass deviations over long stretches of test material arise.

Figure 9.74  Determination of deviation rate

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Process control and yarn quality in spinning

Formula: (L1 + L 2 + .... + L n ) ×100 Llot



DR(xy) =



l DR(x, y) = ∑ i × 100% L tot

The Deviation Rate is calculated by comparing all the deviations of the positive range with the whole test length Ltot. The same is valid for all deviations in the negative range. As the zero-line corresponds to the median, the Deviation Rate can reach a maximum of 50%.

9.6.2

DR curves (Fig. 9.75)

The DR curves are actually mass histograms with a single-logarithmic scale. If the scale were linear, the outermost curve would have the same bell-shape as a normal mass histogram. The logarithmic scale permits a closer look at the larger deviations. The outermost curve always represents the deviations of the cut length 1 cm. The cut lengths of the inner curves, which are the cut lengths set in the Uster Tester. At 0% mass deviation (horizontal scale) the deviation rate is always 50% (vertical scale), since 50% of the entire measured material length is below and 50% above the average value.

Figure 9.75  DR Curves of cut lengths “-“(=normal)/ 0.1 m / 1.5 m / 10 m

The vertical extent of the inner curves depends on the tested yarn length: Within a yarn test of 1000 m, there are a maximum of 100 pieces of 10 m samples. That means that the resolution of the cut length 10 m (the innermost

Interpretation and analysis of diagram, spectrogram and V-L curve

303

curve in the above example) can be maximum 1/100 = 1% on the vertical scale. With the normal cut length of 1 cm (outermost curve) 1% of the total yarn length (vertical length) surpasses +40% mass increase (horizontal scale). In other words: 1% of all 1 cm pieces are 40% heavier than the average yarn weight. With a cut length of 1.5 m (2nd curve from inside), 2% of the total yarn length (vertical scale) surpasses +9% mass increase (horizontal scale), i.e. 2% of all 1.5 m pieces are 9% heavier than the average yarn weight. 9.6.2.1

Interpretation of the DR curves

Just as in the histogram, the narrower and more symmetrical the DR curves, the evener the material. The angle between the outer and inner DR curves changes in accordance with the degree of long-term irregularity. The DR curves can be regarded as an alternative report to the LVC (lengthvariance curves), since both curve types indicate the degree of the material’s variations over greater lengths. For comparisons between sub-samples, it is advisable to use the LVC curve reports (combined graph of all sub-samples), since exceptions are more clearly visible there. 9.6.2.2

Achievable level of DR ratio as per SITRA

DR% = (3.56 × Micronaire + 2.79 × SFC(w) % + 0.43 × Ring Frame Draft + 0.744 × Spindle Speed (RPM) + 0.218 × Count(Ne)) – 47.3

9.6.3

Influencing factors of DR ratio



1. Higher SFC(w) in combed Sliver, higher DR ratio



2. In coarser count, the effect of SFC(w) is more than finer count



3. Finer the fibres, lesser DR ratio



4. Higher the draft, higher DR ratio in all counts



5. Higher spindle speed, higher DR ratio

9.6.4

Significance of DR ratio%

Higher the DR ratio, poorer is the fabric appearance.

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9.6.5

Guidelines for DR% as per SITRA Norms (Table 9.2)

Table 9.2  Guideline for DR% Count

5% Level

25% Level

50% Level

20’S

11.5

13.0

16.0

30’S

13.0

16.0

17.5

40’S

14.0

18.0

19.0

60’S

14.5

18.0

19.5

80’S

16.0

18.5

21.0

100’S

17.0

19.5

21.5

9.7

Histogram of mass variations

The histogram is a distribution diagram of the mass variation. It shows in condensed graphical form information on all measured mass deviations over the whole test length. Graphic illustration of the relation between diagram and histogram is shown in Fig. 9.76.

Figure 9.76  Relationship between histogram and diagram

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305

Each measuring point on the diagram is one value which is transferred horizontally to the histogram. Each individual scan point in the diagram represents one value in the histogram, at the same deviation relative to the average value 0% on the diagram.

9.7.1 Application Mass deviations are approximately normally distributed. Deviations of the histograms compared to normal distribution indicate disturbances in the manufacturing process (asymmetrical distributions, two-peaked distributions, etc.) especially in the production of filament yarns, histograms react very quickly to changes in the production process.

9.8 References 1. Amin A. E., El-Geheni A. S. and El-Hawary I. A., El-Beali R. A. (2007). Detecting the fault from spectrograms by using genetic algorithm techniques, AUTEX Research Journal, 7, p.81. 2. Application handbook for evenness testers of the Uster type; Determination of periodic mass variations (spectrum). 3. Balasubramanian, N. (1963). Contribution to the Study of the B−L Curve of Cotton Yarns, Textile Research Journal, 33, p.697. 4. Balasubramanian, N. (1969). A Study of the Irregularities Added in Apron Drafting, Textile Research Journal, 39, p.155. 5. Bandyopodhyay, S.B., Guha, S.R., and Bhattacharji, S.S. (1956). Variance-Length Curves for Jute Yarns, Fibres, 17, p.198. 6. Booth, J.E. (1996). Principle of Textile Testing, A Butterworths Publication. 7. Foster, G.A (1950). Causes of the irregularity of cotton yarns, Journal of Textile Institute, 41, p.357. 8. Further R. (1982). Evenness testing in yarn production: Part I, The Textile Institute, Manchester. 9. Further R (1982). Evenness testing in yarn production: Part II, The Textile Institute, Manchester. 10. Garde A. R. and Subramanian T. A. (1978). Process Control in Cotton Spinning, 2nd Ed., Ahmedabad, ATIRA. 11. Jain, A.K., Das, D.K. and Ray, P.K. (1985). A Microprocessor-Based System for Obtaining Variance-Length Curves of Jute Yarns, Textile Research Journal, 55, p.372. 12. John D. Tallant and Myles A. Patureau (1968). Harmonic Response of the Uster Spectrograph, Textile Research Journal, 38, p.208. 13. Klein W. (1987). Short Staple Spinning Series, Vol 3: A practical Guide to Combing and Drawing, Manchester, The Textile Institute.

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14. Mishu I. Zeidman, Moon W. Suh and Subhash K. Batra. (1990). A New Perspective on Yarn Unevenness: Components and Determinants of General unevenness, Textile Research Journal, 60, p.1. 15. Nienhuis, W. A. (1963). A Simple Method for Determining the CB(L) Curve by Cutting and Weighing, Journal of Textile Institute, 54, p.353. 16. Nute, M. E., Pelton, W. R., and Slater, K. (1972). The Variance Between Ultra-short Lengths of Yams, Journal of Textile Institute, 63, 212. 17. Operation manual, Rieter spinning system, Drawframe RSB 851. 18. Pelton, W.R., and Slater, K. (1973). A comparison of Variance-Length curves derived by different methods, Journal of Textile Institute, 64, p.454. 19. Premier-Evolvics, Evenness Testing- Application handbook, Premier Publication, 2002. 20. Ratnam, T.V. and K.P. Chellamani (1999). Quality Control in Spinning, SITRA, Coimbatore. 21. Robert Williams and Donald W. Lyons (1977). Dynamic Response Characteristics of the Uster Evenness Transducer, Textile Research Journal, 47, p.9 22. Slater, K. (1986). Yarn Evenness, Textile Progress, 14, No. 3/4. 23. Tottman, J.A., ans Slater, K. (1981). The use of microcomputers in deriving variance –length curves, Journal of Textile Institute, 72, p.103. 24. Uster Application hand book, Evenness Testing, Uster publication, Zellweger Uster. 25. Van Zwet, C. J. (1955). A Method for the Calculation of the CB(1) Curve, Journal of Textile Institute, 46, p.794. 26. Vitor Carvalho, Jose G. Pinto, Joao, L. Monteriro, Rosa M. Vasconcclos, Filomena O. Soares (2004). Yarn parametrization based on mass analysis, Sensors and actuators, 115, p.540. 27. Chellamani, K.P., Ravindran, M.P.S. and Vittopa, M.K. (2008). Variance-length curves and deviation rate curves, Spinning Textiles, p.12.

10 Control of yarn hairiness in spun yarns

Abstract: This chapter discusses about the control of yarn hairiness, effect of raw material, various process and machine parameters in preparatory and ring frame on yarn hairiness. The influence of yarn hairiness on fabric appearance and further processes are also discussed in detail. Key words: Hairiness, Zweigle hairiness distribution, ring frame, spindle speed, traveller

10.1 Introduction Yarn hairiness is a complex concept, which generally cannot be completely defined by a single figure. The yarn hairiness depends on the fibres from the outer layer of the yarn that do not directly adhere to the body of the yarn. Some of the fibres have an end in the core of the yarn gripped by the other fibres, whereas others, because of the mechanical properties of the fibre (rigidity, shape, etc.) emerge to the surface. During the twisting of the yarn, other fibres are further displaced from their central position to the yarn surface, their ends being nipped in the core. The fibre spiral and the yarn twist can affect the dimensions of the loop that is formed. The majority of the emergent fibres belong to the peripheral layers. The wild fibres are those for which the head alone is taken by the twist while the tail is gripped by the front drafting rollers. They are the marginal fibres that originate from the edges of the bundle of fibres (edge of the spinning triangle) submitted to drafting in the spinning frame, particularly if some of the fibres have been separated slightly from the body of the fibre bundle. In such circumstances, the bottom front rollers prevent the migration of the fibres and produces yarn hairiness. If the width of the fibre web in the drafting field is large, the contact and friction with the bottom rollers reduces the ability of the fibres to concentrate themselves (inward migration) and hairiness occurs. This effect is more accentuated in coarse yarns with low twist. Hairiness is generally regarded as undesirable because of the following reasons.

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• It adversely affects the appearance of yarns and fabrics. Hairiness is one of the factors that determine the appearance grade of the yarn. Higher hairiness downgrades the appearance grade. Hairiness in yarns leads to fuzzy and hazy appearance of fabric. According to Uster, 15% of fabric defects and quality problems stem from hairiness. Warp way streaks and weft bars are caused by high hairiness and variation in hairiness. Periodic variation in hairiness has been traced to be a cause for alternate thin and thick bands in fabrics • It affects performance of yarn in subsequent stages. Adjoining warp threads cling together in the loom shed because of long hairs in yarn, which in turn resist separation of sheet during shedding. This leads to more warp breaks and fabric defects like stitches and floats. • Excessive lint droppings in sizing, loom shed and during knitting are encountered with hairy yarns because of shedding of hairs and broken hairs. • In printed goods, prints will be hazy and lack sharpness if yarn is hairy. • In sewing, breakages will be high with hairy yarns and removal of hairiness by singeing is invariably practiced. • Pilling tendency will be more with higher hairiness. Pilling is a major problem with knitted goods, polyester blend fabrics. • Increases air drag on rotating packages and balloon. Four conditions of fibre have been specified in the spinning zone: (i) The leading end is grasped by the convergence point. (ii) The leading end is free and has no tension. (iii) The trailing end is under control of lateral friction with the fibres around it. (iv) The trailing end is free. A fibre satisfying condition (i) and (iii) would migrate towards the core of the yarn. A fibre satisfying condition (ii) and (iii) would form a leading hair. If the fibre satisfies condition (i) and (iv), it will appear as trailing end. A fibre satisfying the condition (ii) and (iv) is likely to become a wild hair, wrapped around the yarn. In a Z-twist yarn, most of the trailing hairs are from the left-hand fringe in the spinning triangle. For a Z-twisted yarn, fibres in the right hand fringe of the ribbon can fold-over freely towards the left at the point of yarn formation. But fibres in the left hand fringe are not similarly free to fold-under towards the right because of obstruction by the top breast of the bottom drafting rollers; thus they are likely to be concentrated in the outer zone of the yarn. Lack of fibre tension as a fibre leaves the front roller nip, insufficient fibre length to build up tension in short fibres and dynamic



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buckling of fibres in the spinning triangle are the main reasons for the formation of trailing hairs. When a fibre emerges from the front roller, it needs enough radial force from the surrounding fibres to collect and transport it into the yarn formation point. Otherwise it moves straight ahead and misses the convergence point. In a Z-twisted yarn, if the said fibre is from the left hand-fringe of fibres, the rotating action of the yarn may manage to catch it and wrap most of them around itself, and possibly the tip of the fibre would not be tucked into the body of the yarn. Thus a leading hair is formed. If a fibre is stiff, it tends to spring up on emerging from the front roller and project out of the plane of the spinning triangle and ends up as leading hair. Fibres that are not grasped at both ends would become wild fibres. This situation happened mostly to very short fibres. The percentage of wild fibres is very small considering the small percentage of very short fibres. According to Klein, when the spinning triangle is short, the fibres from the edges must be strongly deflected to get bound in the yarn structure. This results in more fly and hairiness. However it is pointed out that longer the spinning triangle, further the distance the fibres have to travel before they are bound into the body of the yarn and therefore the easier it is for them to escape. When the spinning triangle gets smaller with increasing spinning tension, the hairiness is less. The ratios of the height to the base of the spinning triangle and the base of the spinning triangle to the width of the fibre in the main draft zone, the area of the spinning triangle with respect to the number of fibres and the absolute values of the base and height of spinning triangle in relation to fibre length have to be considered in analysing the results.

10.2

Parameters influencing the generation of yarn hairiness

Parameters at ring frame, preparatory processes and post-spinning operation have marginal to profound influence on the generation of hairiness of ring spun yarns. There are discrepancies on the outcome of the results possibly due to the conditions of the machine parts in their experiments and the method and accuracy of the evaluation of hairiness.

10.2.1

Fibre parameters

Various studies have shown the influence of fibre parameters on the yarn hairiness values. The influence of fibre parameters on the yarn hairiness with their correlation signs is listed in Table 10.1.

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Table 10.1  Influence of fibre properties on yarn hairiness Fibre parameters

Effect

Fibre length

Shorter the length, higher the hairiness – Major

Short fiber content

Higher SFC, higher hairiness – Major

Micronaire

Coarser micronaire, higher the hairiness

Length uniformity

Lower the uniformity ratio, higher the hairiness

Stickiness

Lower the stickiness, higher the hairiness

Fibre length, short fibre content, fineness and rigidity are the most important properties of fibre that influence hairiness. A significant correlation is found between hairiness and fibre length and uniformity ratio. Number of fibre ends per unit length increases as fibre length reduces and as each fibre end is a potential source of hairiness, yarns from shorter and variable cottons are more hairy. As a result any process from picking to ginning to opening of cotton those results in fibre breakages will increase hairiness in yarns. Hairiness increases with coarseness of fibre, because of higher resistance to twisting. For the same reason yarns from fibres with higher flexural and torsional rigidity have higher hairiness. Surface fibres in the yarn mostly contribute to hairiness. In blends made on ring spinning, shorter and coarser fibre constituent occupies preferentially the surface and so will contribute more to hairiness.

10.2.2

Yarn parameters

The yarn parameters influencing the hairiness are listed in Table 10.2. Table 10.2  Influence of yarn properties on yarn hairiness Yarn parameters

Correlation sign with respect to yarn hairiness

Linear density Twist level Diameter No. of fibres in yarn cross-section

Positive Negative Positive Positive

Count and twist have considerable influence on hairiness. Coarser yarns have more hairiness than finer yarns because of higher number of fibres in cross-section in the former as shown in Fig. 10.1. Yarn count has the maximum influence on hairiness. Yarn hairiness chart therefore bears a close correspondence with irregularity chart, with coarser regions having more hairiness than finer portions.



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Figure 10.1  Relationship between the yarn count and hairiness

Hairiness reduces with increase in twist because of shorter spinning triangle and more effective twisting in of surface fibres into yarn as shown in Fig. 10.2. With firmly bound fibres chances of release due to abrasion at traveller/ring junction is minimized. Hairiness is therefore more in hosiery yarns, which have low twist.

Figure 10.2  Relationship between the twist level and hairiness

10.3

Influence of ring frame parameters on yarn hairiness

Hairiness is produced at two zones in ring spinning. 1. At the delivery point of front roller 2. In the ring/traveller junction. A small amount of hairiness is also made at lappet and separator.

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Selvedge fibres in the strand do not get fully integrated into yarn, as twist does not flow right up to the nip because of spinning triangle. The effect is more for the trailing portion of fibre, as the tension in the fibre drops to zero, the moment trailing end leaves front roller nip. Trailing portion of majority of selvedge fibres therefore show up as hairs. The leading portion of fibres at the extreme end of selvedge may also project as hair, because of their nonintegration into strand. Some of the loosely bound leading as well as trailing portion of fibres will develop into hairs because of abrasion at traveller/ring junction.

10.3.1

Roving linear density/main draft at ring frame

For a constant linear density of yarn, hairiness increases with the increase in roving linear density or the main draft at ring frame due to high fibre spread at front roller nip. Yarn spun from double roving will have more hairiness than the yarn spun from single roving. This is due to the increased number of fibres in the web and due to higher draft required to spin the same count. Drafting waves increase hairiness. Irregularity arising from drafting waves increases with increasing draft. Yarn hairiness also may be accepted to increase with yarn irregularity, because fibres protruding from the yarn surface are more numerous at the thickest and least twisted parts of the yarn.

10.3.2

Presence of condenser at the drafting and predrafting fields

The use of correct size of condensers in the drafting fields in relation to the volume of fibres processed would reduce the ribbon width of fibres, thereby reducing the hairiness substantially. Research workers have found that the insertion of condenser in the pre-drafting zone had only a slight influence on the hairiness of yarn. A decisive improvement in hairiness has been observed, when the condensers were placed in the drafting field. The use of mechanical condensers in the main draft region of ring frame is highly effective in condensing the fibre flow. However, the action of these condensers is very harsh, cause excessive frictional forces among fibres, which result in drafting irregularities. The pneumatic condensers used in compact ring spinning systems are very effective in reducing the width of fibre flow in the main draft field, yet they are found to be gentle in their action, thus fewer disturbances to fibre flow.

10.3.3

Spindle speed

Higher spindle speed is generally found to increase hairiness. This is because of the larger balloon at higher speed as shown in Fig. 10.3. With larger balloon,



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traveller tilt will be more and this will reduce the space available for yarn passage and there will be chafing and abrasion of yarn. Twist flow at lappet will also be reduced.

Figure 10.3  Relationship between the spindle speed and hairiness

When yarns are spun at different spindle speed, the centrifugal force acting on fibres in the spinning zone will increase in proportion to the square of the spindle speed, causing the fibres ends as they are emerging from the front rollers to be deflected from the yarn surface to a greater extent. Further, at high spindle speed, the shearing action of the traveller on the yarn is likely to become great enough to partially detach or raise the fibres from the body of the yarn. As against the above factors, at higher spindle speeds the tension in the yarn will increase in proportion to the square of the spindle speed, and consequently more twist will run back to the roller nip, so that it is natural to expect that better binding of the fibres will be achieved.

10.3.4

Doff position

Doff position and chase positions have a significant influence on hairiness. When comparing the hairiness of yarns at different doff positions in mill and it was found that hairiness is higher at cop bottom position as shown in Fig. 10.4. This is because of larger balloon found at cop bottom, which increases traveller tilt and causes dashing of yarn against separator. The increased balloon size, more residence time of the yarn in the balloon region and the associated increase in the intensity and duration of rubbing of the yarn against the separators while winding the yarn at the base of the cop are responsible for the increase in the yarn hairiness.

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Figure 10.4  Relationship between the yarn position and hairiness

10.3.5

Chase position

Hairiness is more at the shoulder and reduces progressively towards the nose of the chase. The balloon is bigger at shoulder and traveller tilt is more. Yarn also dashes against separator. Both these factors increase hairiness. The periodic variation in hairiness in the chase, thus caused, is sometimes a source of hairiness.

10.3.6 Traveller Weight, profile and type of cross-section of traveller have critical influence on hairiness. 10.3.6.1 Weight

Heavier traveller up to a limit reduces hairiness because of improved flow of twist to front roller nip. As a result pilling of knitted material reduces. Higher tension associated with heavier traveller will also help to firmly twist the surface fibres into yarn. It is shown that with increasing spinning tension from 2 to 4 cN/tex for 60s Ne cotton yarn, the hairiness (2 mm) decreases by 50%, whereas the thin places and elongation of the yarn deteriorates. It is to be noted that a high spinning tension tends to increase the length and reduce the width of spinning triangle (long triangle), which may help in better consolidation of edge fibres. 10.3.6.2

Wire profile

Elliptical traveller has a low bow size and as a result limited space is available for passage of yarn. Chafing of yarn will therefore be more resulting in



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increased hairiness. ‘C’ shape traveller has a high bow size, which provides ample space for passage of yarn. Hairiness will be least with this traveller. But as centre of gravity is higher with ‘C’, it results in unstable flight and traveller fly especially at higher speeds. 10.3.6.3 Cross-section

Traveller with round cross-section compared with cross-sections with sharp edges (square, half round and rectangular) and traveller with a matching profile to the contact region of ring are better in avoiding the generation of hairiness because of reduced frictional resistance to yarn movement by the former. 10.3.6.4

Lubrication of traveller

Application of specially developed lubricant to the traveller has been found helpful in reducing hairiness by 20–30%. The reduction is more prominent immediately after application of lubricant and gradually reduces with passage of time. 10.3.6.5

Coated travellers

Travellers with coatings, such as silver and ceramic coating and chromium plating, are available for reducing traveller wear and for extending travellerchanging frequency. Because of their smooth finish, friction between yarn and traveller is reduced, which brings down hairiness. 10.3.6.6

Traveller changing frequency

Hairiness is found to increase over the traveller replacement cycle because of traveller wear. With traveller wear traveller flutter occurs during flight, which leads to increased abrasion. For hosiery, sewing threads and P/C blends, where low hairiness is desired, traveller replacement frequency has to be kept low.

10.3.7 Ring Flange number, type and wear influence hairiness considerably. 10.3.7.1

Flange number

Higher flange no. gives more space for passage of yarn and will reduce hairiness. But traveller wear will be more and higher speeds cannot be achieved in finer counts. Normally No. 2 flange should be used up to 20s count and No. 1 flange should be used for counts 30s and above. For bringing down the hairiness No. 2 flange may be used in counts of border range.

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10.3.7.2

Wear and tear

Worn out ring is a major cause of hairiness and variation in hairiness in mills as shown in Fig. 10.5. When wear is pronounced, the bobbins are highly hairy and exhibit whisker-like defects. When rings are more than 3-year old, hairiness starts increasing. Replacement of rings will bring significant reduction in hairiness.

Figure 10.5  Relationship between life of ring traveller and hairiness

10.3.8

Spindle eccentricity

An increase in spindle eccentricity increases the hairiness. Small eccentricity influences hairiness relatively lower, but from 0.5 mm onwards, the hairiness increases almost exponentially with eccentricity. The impact of spindle eccentricity on hairiness is also influenced by the diameter of the ring and of the bobbin, the shape of the traveller, the yarn tension, the balloon etc. On spindles, where centering is disturbed hairiness is found to be higher and upon accurate centering, hairiness comes down significantly. When spindle is not centred, traveller movement is not smooth because of peak tensions in yarn. With spindle eccentricity, the amplitude of spinning/yarn tensions is magnified. At very low spinning tension, consolidation of peripheral fibres of spinning triangle would be difficult. At very high yarn tension, the tucking out of surface fibres of yarn by the traveller would be high.

10.3.9

Eccentricity of lappet

Abrasion against lappet is a source of hairiness. This gets aggravated when lappet is grooved or is worn out. Height of lappet above the ring bobbin has



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to be optimized to reduce not only end breaks but also hairiness. If lappet to bobbin tip distance is high, balloon will be longer. This will reduce twist flow and also increase area of contact between yarn and lappet. As a result hairiness will be higher. Hairiness increases with the thread guide eccentricity. The eccentricity of lappet influences the variation of spinning tension for every revolution of the traveller. In addition, the amplitude of movement of yarn over the inner surface of the lappet increases and this beating action dislodges the surface fibres.

10.3.10 Balloon control ring The presence of balloon control ring reduces the balloon size (diameter). This in effect reduces the air drag and centrifugal forces acting on the yarn. Due to these, the dislodging intensity of surface fibres from the yarn core decreases and hence lower hairiness.

10.3.11 Balloon separators At high spindle speeds, the intensity of rubbing of the yarn over the separators would be severe, increasing the yarn hairiness. Plastic separators will increase hairiness because of static generation. Disturbed, slanting and bent separators generate hairiness because of excessive dashing of balloon on separator.

10.3.12 Life of ring and traveller The progressive increase of hairiness with the running time of the travellers has been well proved. Hairiness increases substantially with the working life of a ring. Rings not replaced for a long time, develops more wear and tear and micro welding from traveller. The chances of fibre dislodging from the yarn surface would be higher when yarn passes over this ring.

10.3.13 Relative humidity/static generation Recommended humidity in ring frame department is 55–60%. Hairiness increases in a dry atmosphere of ring frame department. At higher humidity levels, fibres tend to stick to drafting rollers resulting in protruding hairs and loops. At low humidity levels static generation causes repulsion of fibres, particularly with P/V and P/C blends, leading to more hairiness. RH% between 46 and 86 influences hairiness slightly.

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10.4

Influence of preparatory process on yarn hairiness

10.4.1

Number of draw frame passages

It is reported that the greater the number of passages, lesser the yarn hairiness. The reason attributed to this was reduction in the number of hooks due to better parallelization of fibres.

10.4.2 Combing The combed yarns have more protruding ends of 3 mm, than the carded yarns. Further it is reported that the combed yarns are less hairy than the carded ones. However, hairiness of combed yarn is less with higher noil extraction as shown in Fig. 10.6.

Figure 10.6  Relationship between comber noil % and yarn hairiness

10.4.3

Roving twist/compactness of roving

The more the roving twists, the less is the yarn hairiness. Roving produced with a high false twist level at fly frame is more compact. A compact roving by use of front zone floating condenser at speed frame will bring down hairiness, as this will reduce strand width at ring frame. Floating condenser can be used behind front roller at speed frame without any working problems in hanks 1.4Ne and finer but with coarser hanks from short staple cottons choke up of condenser is encountered.



10.5

Control of yarn hairiness in spun yarns

319

Effect of Post Spinning Operations on hairiness

Each operation increases the yarn hairiness due to the rubbing action of yarn surface over various machine elements. The rate of increase in yarn hairiness is initially high, and then reduces as the number of operation goes up. The percentage increase in the number of hairs, for hairs of 1 mm and 3 mm after first winding are 63 and 149 respectively for a cotton yarn. For a polyester yarn, these values are 129 and 373. Yarn wound at two-for-one twister seems to have high hairiness values either the feed package is too small or too big. With a small package, the yarn takes a sharp bend around the flyer and for a large package, the chances of outgoing yarn touching the inner surface of the protection pot increases. These may be the reasons for the high hairiness of yarns.

10.5.1 Winding Hairiness increases in winding. This is because of abrasion of yarn against tension disc, guide eyes, balloon breakers and winding drum. Extent of increase varies from 50% to 150%. Extent of increase in hairiness increases with winding speed as shown in Fig. 10.7. A very interesting finding of practical significance is that initial level of hairiness in ring yarn has considerable influence on the extent of increase in hairiness in winding. Short length hairs increase by 4–4.5 times with winding with ‘less hairy’ yarns. But with ‘more hairy’ yarns, short length hairs do not increase with winding. Long length hairs however show an increase with winding with both ‘less hairy’ and ‘more hairy’ bobbins.

Figure 10.7  Relationship between the winding speed and hairiness

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Process control and yarn quality in spinning

Longer hairs being on the surface of yarn more likely to come in contact with tension disc and also get pulled out because of frictional resistance. This is the reason why they increase with both type of yarns. With ‘more hairy yarns’ the surface of yarn body and short length hairs are well buried under long length hairs and therefore do not come into contact with the tension disc. There is therefore no generation of short length hairs at tension disc and short length hairs therefore do not show an increase with winding with such yarns.

10.6

Control of hairiness of ring spun yarns

The control of hairiness of ring spun yarns through proper selection of fibre parameters is in line with the requirement of fibre properties to get good uniformity characteristics and strength for yarns. A mill producing yarns that are good in the above-mentioned properties does not have much scope in reducing the hairiness, as fibre parameters are already optimized. Regarding the yarn parameters, the selection of twist level should be based on maximum yarn strength. It should be noted that the number of protruding hairs is independent of twist and the number of loops decreases with the increase in the yarn twist. Selecting a twist level that is more than optimum (based on yarn strength) would increase the snarling tendency of the yarns, which is highly undesirable. The consideration of having more number of fibres in the yarn cross-section is appropriate for getting high yarn strength, rather than having less number of fibres to minimize the hairiness. Lowering the draft at ring frame using finer roving involves installation of many speed frames, which would be uneconomical. The selection of spindle speed is always based on getting higher production, subject to the end breaks and traveller burnt-out. The selection of ring size should be based on the critical parameter viz., end-breaks and power consumption rather than the hairiness. With the prevailing high tariff for power, controlling of power consumption is assuming great significance. With introduction of splicing technology and high-speed winders, it is more economical to go for smaller rings at ring frames. The latest trend is to go for smaller ring. Balloon separators cannot be dispensed with coarser yarns. The relative humidity at the ring frame department has only marginal influence on yarn hairiness, but has greater effect on the end breaks. Roving produced form the latest speed frames is already compact as a result of high degree of false twist imparted to it while twisting at speed frame. Further increase in the compactness of roving with high roving twist introduces more drafting irregularity while drafting the roving at the ring



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frame. Extraction of more waste at comber would reduce the yarn hairiness, but, it is highly uneconomical and the trade largely dictates it. Monitoring of hairiness in relation to the life of the ring and replacing the ring at appropriate time would help in controlling the hairiness. The selection of right mass of traveller in relation to hairiness and end breaks, and right combination of traveller and ring would be useful to some extent in controlling the yarn hairiness. There are only limited options available to the spinners in reducing the yarn hairiness by controlling the above-mentioned parameters. In a good spinning mill, all these parameters are well taken care off. Even after all the necessary measures taken right from fibre selection to ring frame parameters, the hairiness of ring spun yarn increases considerably (particularly the long hairs) during winding operation. These lead to a conclusion that new approaches have to be thought off and techniques have to be evolved to reduce the hairiness of ring spun yarns. Traditionally, either sizing or singeing in the short staple sector or twofolding in the long staple sector has reduced yarn hairiness. Of late, several technologies have been explored to reduce the hairiness. These methods can be classified in to the following: (a) Using air-nozzles below the spinning triangle on a ring frame (b) Pneumatic condensation of fibre flow in the front drafting field of ring frame using compact spinning technology (c) Winding the spun yarn with an air-jet attachment or false twisting rollers The first two methods are found to be reducing the hairiness of asspun yarns. The behaviour of these spun yarns in winding and in generating hairiness is not studied yet. But these methods may be better than the limited options envisaged earlier, solely from the point of reducing the hairiness and not on the basis of the technological complications.

10.6.1

Use of air-jets below the spinning triangle of a ring frame

Studies have revealed the effect of a converging nozzle placed 4 cm below the front roller nip of a ring frame, under various spinning conditions and air pressure supplied to the nozzle. The design indicates that the airflow from the nozzle is upward, i.e. towards the front roller nip. The number of hairs 3 mm was measured using Shirley Yarn Hairiness Tester. The hairiness was found to be lowest around an air pressure of 0.5 kg/cm2. The above study has shown that the hairiness of as-spun yarn can be reduced by 9–55%, depending on the yarn count and spindle speeds. The yarn (while taking the twist) is

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compressed by the pressure of the air inside the nozzle and it is likely that the protruding hairs are folded back into the regular structure of the yarn. Similar works done elsewhere using Jet Ring Spinning has shown that hairiness can be considerably reduced with nozzles positioned below the twist triangle. They argued that the reduction in hairiness might be most efficient if the air vortex induces an axial air flow that suppresses the majority of protruding fibre ends, as there is a majority of trailing ends for ring spun yarn during spinning. This technology has the drawback of piecing the threads when an end break occurs.

10.7

Influence of hairiness on subsequent processing

10.7.1

Fabric appearance

Yarns with varying hairiness values, when woven into a fabric, could cause stripiness which is generally clearly visible in the case of dyed fabrics. The reason for this is that the protruding fibres take up more dye and consequently look darker.

10.7.2

Pilling tendency

A fabric woven with yarns having severe hairiness has a greater pilling tendency than a fabric woven with less hairy yarn.

10.7.3

Processing problems

Since a yarn is required to pass through a variety of narrow machinery components in the subsequent processing stages like heald wires, knitting needles, weft insertion devices, etc, a greater number of protruding fibres in the yarn naturally means processing problems such as jamming of warp and weft threads in modern high speed weaving machinery.

10.8

References

1. Atlas, S. and Kadoglu, H. (2006). Determining fibre properties and linear density effect on cotton yarn hairiness in ring spinning, Fibres and Textiles in Eastern Europe, 14, p.48. 2. Barella, A. (1981). Yarn Hairiness, Textile Progress, 13(1). 3. Barella, A. (1992). The hairiness of yarns, Textile Progress, 24(3). 4. Barella, A. (1993). The hairiness of yarns, Textile Research Journal, 63, p.431.



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5. Barella, A. and Manich, A.M. (2006). Yarn hairiness update, Textile Progress, 26 (4). 6. Barella, A. and Vigo, J.P. (1974). Effect of repeated winding on hairiness of open end and conventional cotton and viscose rayon yarns, Journal of Textile Institute, 65, p.607. 7. Basel, G. and Oxenham, W. (2007). Comparison of properties and structures of compact and conventional spun yarns, Textile Research Journal, 76, p.567. 8. Celik, P. and Kadoglu, H. (2000). A research on the compact apinning for long staple yarns, Fibres & Textiles in Eastern Europe, 12, p.27. 9. Chang, L., Tang, Z.X. and Wang, X. (2003). Effect of yarn hairiness on energy consumption in rotating a ring yarn package, Textile Research J., 73, p.949. 10. Chaudhari, A. (2003). Effect of spindle speed on properties of ring spun yarn, IE(I) Journal, 84, p.10. 11. Cheng, K.P.S. and Li, C.H.L. (2002). Jet Ring spinning and its influence on yarn hairiness, Textile Research Journal, 72, p.1079. 12. Cheng, L. and Wang, X. (2004). Relationship between hairiness and twisting principles of solo-spun and ring spun yarns, Textile Research Journal, 74, p.27. 13. Kothari, V.K, Ishtiaque, S.M and Ogale, V.G. (2004). Hairiness properties of polyestercotton blended fabrics Indian J. of Fibre and Textile Research, 29, p.30. 14. Krifa, M. and Hequet, E. (2006). Compact Spinning- Effect on cotton yarn quality Interaction with fibre characteristics, Textile Research Journal, 76, p.398. 15. Krishnaswamy, R., Paradkar, T.L. and Balasubramanian, N. (1989). Some factors affecting hairiness of polyester blend yarns, BTRA Technical Report No 04.2.8. 16. Krishnaswamy, R., Paradkar, T.L. and Balasubramanian, N. (1990). Some maintenance measures to control hairiness of polyester blend yarn, Journal of Textile Association, p.297. 17. Krishnawamy, R., Paradkar, T.L. and Balasubramanian, N. (1990). Influence of winding on hairiness: Some interesting findings, BTRA, p.8. 18. Lang, J., Zhu, S. and Pan, N. (2004). Change of yarn hairiness during winding process – Analysis of trailing end, Textile Research Journal, 74, p.905. 19. Lang, J., Zhu, S. and Pan, N. (2006). Change of yarn hairiness during winding processAnalysis of protruding ends, Textile Research Journal, 76, p.71. 20. Miao, M. and Wang, X. (1997). Reducing yarn hairiness with an air-jet arrangement in winding, Textile Research Journal, 67, p.481. 21. Morton, W.E. (1956). The arrangement of fibres in single yarns, Textile Research Journal, 26, p.325. 22. Nikolic, M., Stjepanavic, Z., Lesjak, L. and Skritof, A. (2003). Compact spinning for improved quality of ring-spun yarns, Fibres & Textiles in Eastern Europe, 11, p.43. 23. Parthasarathy, M.S. (1966). Factors affecting hairiness of Terene cotton yarns, Proceedings of 8th Jt. Tech., Conference, ATIRA, Ahmedabad, p.28.

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Process control and yarn quality in spinning

24. Peykamian, S. and Rust, J.P. (1992). Yarn hairiness and the process of winding, Textile Research Journal, 62, p.685. 25. Pillay, K.P.R (1964). A study of yarn hairiness in cotton yarns- PartI Effect of fibre and yarn factors, Textile Research J., 34, p.663. 26. Pillay, K.P.R. (1964). A study of yarn hairiness in cotton yarns- Part II Effect of processing factors, Textile Research Journal, 34, p.783. 27. Rengaswamy, R.S., Kothari, V.R., Patnaik, A., Ghosh, A. and Punekar, H. (2005). Reducing yarn hairiness in winding by means of jet: optimization of jet parameters, yarn linear density and winding speed, Autex Textile Journal, 5, p.128. 28. Salem, S.S. and Azam, M. (2004). Impact of air jet nozzle pressures and winding speed at autocone on imperfections and hairiness of 20s cotton yarn, Pakistan Textile Journal, p.18. 29. Tang, Z., Wang, X., Wang, L. and Fraser, W.B. (2006). The effect of yarn hairiness on air drag in ring spinning, Textile Research J., 76, p.559. 30. Tyagi, G.K. (1999). Hairiness of viscose OE rotor spun yarns in relation to test speed and process parameters, Indian Journal of Fibre and Textile Research, 29, p.35. 31. Usta, F. and Canoglu, S. (2002). Influence of traveller weight and coating on hairiness of acrylic yarns, Fibres & Textiles of Eastern Europe, p.20. 32. Wang, X. and Chang, L. (1999). An experimental study on effect of test speed on yarn hairiness, Textile Research Journal, 69, p.25. 33. Wang, X. and Chang, L. (2003). Reducing yarn hairiness with a modified path in worsted spinning, Textile Research Journal, 73, p.327. 34. Wang, X., Huang, W and X. Huang (1999). A study on the formation of yarn hairiness, Journal of Textile Institute, 90, p.555. 35. Wang, X., Miao, M. and How, Y.L. (1997). Studies in jet ring spinning Part I Reducing hairiness with jet ring, Textile Research Journal, 67, p.253. 36. Zeng, Y. and Yu, C.W. (2004). Numerical and experimental study on reducing yarn hairiness with jet ring and jet wind, Textile Research Journal, p.74. 37. Zhu, R.Y. and Ethridge, M.D. (1997). Predicting hairiness of ring and rotor spun yarns and analyzing impact of fibre properties, Textile Research Journal, 67, p.694.

11 Yarn faults

Abstract: This chapter provides information about the various types of yarn faults created by the raw material, preparatory process and ring frame. The classification and analysis of seldom occurring faults and the effect of various processing stages on classimat faults are also discussed in detail. The common ring yarn faults with their causes and remedial measures are also provided in this section. Key words: Yarn faults, classimat faults, seldom occurring faults, ring yarn faults

11.1 Introduction Despite the progress and many years of experience in spinning technology, it is still not possible to produce a fault-free yarn. Depending on the raw material and state of the machinery park, there are about 20–100 events over a length of 100 km yarn, which do not correspond to the desired appearance of the yarn. These kinds of yarn faults are places, which are too thick or too thin (Fig. 11.1). Foreign fibres or dirty places in the yarn are also counted as yarn faults.

Figure 11.1  Yarn faults

326

Process control and yarn quality in spinning

Yarn faults cause disruptions in the subsequent process stages, which affect production and quality. Yarn faults, which get in the woven or knitted fabric, can only be removed at very high costs or not at all. Therefore, the yarn processing industry demands a fault-free yarn from the yarn producer. The spinner has to fulfil these demands; otherwise he could not sell the yarn covering his costs. The spinner can fulfil these demands by a combination of two measures: 1. Prevent the origin of yarn faults by adequate measures 2. Remove yarn faults by the aid of yarn clearers The measures to avoid the origin of yarn faults are numerous, mainly the choice of the raw material, the maintenance of the machines, and cleanliness in the spinning mill. During the spinning process, a card sliver with about 20,000 to 40,000 fibres in the cross-section is drawn to a yarn with about 40–100 fibres in the cross-section. During the spinning process, it is not possible to keep the number of fibres in the cross-section constant at every moment. This leads to random variations of the mass. Only spinning mills with a permanent improvement process are able to keep these random variations within close limits. These variations are measured by the evenness tester in the laboratory. They are a measure for the unevenness of the yarn and are called imperfections. They occur so frequently that they are not eliminated from the yarn (Fig. 11.2). Their number is generally given per 1000 m. In contrast to the frequent yarn faults, there are also the seldom-occurring yarn faults. The difference between the frequent yarn faults and the seldom-occurring yarn faults is mainly given by the larger mass or diameter deviation and size. As these faults occur only seldom, their number is given per 100,000 (Fig. 11.2). These faults are monitored by the yarn clearer installation on the winding.

Figure 11.2  Frequently occurring vs. seldom-occurring faults



Yarn faults

327

Seldom-occurring yarn faults are classified in the classification matrix of the USTER® CLASSIMAT. Up to a length of 8 cm seldom-occurring yarn faults are counted and/or eliminated if they exceed the limit of 100%. Table 11.1 shows the classification matrix and the related definitions and terminologies. Table 11.1  Uster classimat faults Characteristics

Description

Fault classification

Fault lengths

A: Shorter than 1 cm B+TB: 1–2 cm C+TC: 2–4 cm D+TD: 4–8 cm E: Longer than 8 cm F+H: 8–32 cm G+I: Longer than 32 cm

Fault sizes

0: +45 to +100% 1: +100 to +150% 2: +150 to +250% 3: +250 to +400% 4: Over +400% E: Over +100% F+G: +45 to 100% TB1/TC1/TD1/H1/I1: −30% to −45% TB2/TC2/TD2/H2/I2: −45 to −75%

Contd...

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Process control and yarn quality in spinning

Contd... Characteristics

Description

Fault channels of the clearer

N channel for very short thick places S channel for short thick places L channel for long thick places T channel for long thin places C channel for count deviations

11.2



Sensitivity

Reference length

N channel:

+100% to +500%

S channel:

+50% to +300%

1 to 10 cm

L channel:

+10% to +200%

1 to 200 cm

T channel:

−10% to −80%

10 to 200 cm

C channel:

±5% to ±80%

12.8 m

Distinction between frequent and seldomoccurring yarn faults

Figure 11.3  Positions of the frequent versus the seldom-occurring yarn faults

Figure 11.3 shows the position of the frequent yarn faults (imperfections) in comparison to the position of the seldom-occurring yarn faults in the classification matrix. It becomes clear that both types of yarn faults differ



Yarn faults

329

from each other clearly by their size, and thus cannot be compared with each other. In addition, the areas of the clearer settings N, S, L, T, CCp and CCm are indicated in Fig. 11.3. This shows where the settings are effective.

11.3

Causes for seldom-occurring yarn faults

The causes for seldom-occurring yarn faults can be divided in three groups: 1. Caused by raw material and card 2. Caused by the processes before spinning 3. Caused by the spinning process The distribution of the faults can be found in the classification matrix (Fig. 11.4) as follows:

Figure 11.4  Causes for seldom-occurring yarn faults in the classification matrix

11.3.1

Yarn faults caused by raw material and card

These faults depend on the quality of the raw material. For natural fibres, they depend mainly on the physical properties such as fibre fineness, length and short fibre content. For synthetic fibres, the faults depend mainly on the disentanglement of single fibres. Insufficient disentanglement can lead to felted single fibres, which might be caused by softeners, oil additives, lubricants or climatic conditions.

11.3.2

Yarn faults caused by processes prior to spinning

These faults are characterized by extreme diameter variations or poor friction of the fibres. Often, it is a matter of fibre packages, which are not caught in the draw-box of prior processes and were not drawn apart. Therefore, they show a big increase of the mass or diameter in the yarn.

330

Process control and yarn quality in spinning

11.3.3.

Yarn faults caused in spinning

Most yarn faults are caused by spun-in fly in the area of the spinning machine and by fibre residues, which cling to the draw-box or other parts of the spinning machine and which are swept away from time to time and are spun into the yarn. Furthermore, it is possible that different setting possibilities of the ring spinning machine, as e.g. draft or distance settings of the draw-box, have an influence on the number of seldom-occurring yarn faults.

11.4

Standard settings in classimat

The following standard settings should assist when setting of clearer for short staple yarns and their blends.

11.4.1

Standard settings for the capacitive clearer

11.4.1.1

Thick and thin place settings (Fig. 11.5)

Figure 11.5  Standard settings for thick and thin places

11.4.1.2

Yarn faults

331

Standard settings for yarn count deviations and pearl chain (Fig. 11.6)

Figure 11.6  Standard settings for yarn count deviations

11.5

Analysis of classimat faults

Figure 11.7 shows the typical scatter diagram from Uster Classimat Quantum.

Figure 11.7  Scatter diagram of classimat faults in Uster Quantum

332

Process control and yarn quality in spinning

The factors influencing the Classimat Faults in the different departments are given in Tables 11.2–11.7. Table 11.2  Causes of classimat faults in blow room

High influence



Can influence Machine

Influencing factors Blow room Beating point

 

 

 

Overall Classimat  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lower pressure will make the fibre stay in the beating zone and will create A1 Higher suction Will not allow the fibres to open in the   pressure due beating zone to higher fan speed

 

 

   

Beater speed Beater type Traverse bar to beater RH and temperature Lower suction pressure

Effect

A

Increase in one beating point can influence up to 30%, if there is a rupture Higher speed, if rupture is there, will influence the faults 10/10 wire will lead to higher fiber rupture Closer than 10% of the fiber length, will lead higher faults Higher RH will lead to more faults

B

C

D

E,F,G

H,I

Table 11.3  Causes of classimat faults in carding Influencing factors

Effect

A

B

C

D

E, F, G

H, I

Overall Classimat

Higher card productivity

Higher the productivity, higher Classimat

 

 

 

 

Flat to cylinder setting

Wider the setting, higher Classimat

 

 

 

 

Flat to flat height variation

More the variation higher Classimat

 

 

 

 

SFD

Wider the setting, higher the Classimat

 

 

 

 

Too close SFL

Closer the SFL setting, due to fiber rupture, higher faults

 

 

 

 

Cylinder speed

If there is no fiber rupture, higher cylinder speed , low Classimat

Trash / neps in sliver

Direct impact on faults

Wire type & Condition

File No.: P Cardnep File 16

 

 

 

 



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333

Table 11.4  Causes of classimat faults in comber Machine Comber

Influencing factors

Effect

Top comb penetration

0.5mm Depth reduction can influence up to 15%

Top comb type Unicomb to nipper setting

Can influence up to 20% More the gap, higher the Neps

Brush height

Lesser the height, higher IPI

A

B

C

D

E,F,G H,I

Overall Classimat

A

B

C

D

E,F,G

H,I

Overall Classimat  

High coiler calendar Influences up to 10% roller load Web cut Influences up to 15% Head to head noil Variation Drafting draft distribution

Higher the variation, higher the IPI. Up to 25% Higher the draft, higher the clearer cuts

Width of the fleece guide

Influences 15%

Table 11.5  Causes of classimat faults in draw frame Machine Draw

Influencing factors Roller setting

Closer setting by 2 mm can create up to 40%

 

 

 

Wider setting by 2 mm

 

 

 

 

 

 

If the hexagonal nut for fixing the break draft tensioner is loose

 

 

 

 

 

 

 

Delivery speed More than 350, for combed counts, the Classimat will go up

 

 

 

 

 

 

 

Web tension draft

10% effect

 

 

 

 

Auto leveller timing

If CP setting is set with wide variation,   it can totally damage the yarn quality

 

 

 

frame    

Break draft belt slippage

     

Effect

No of doubling

Table 11.6  Causes of classimat faults in speed frame Machine Speed frame          

Influencing factors Roving stretch Top arm load Creel draft Wrong selection of winding wheel and tension wheel Floating condensers quality Uniformity in top roller setting

Effect Will influence thin

A  

Will have effect thin faults as well as A1 Higher the draft will give higher   thin Higher tension will lead to higher   long thin This can influence 10% of A1   faults Setting variation in the front zone top roller can influence 10%

B  

Overall Classimat  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C

D

E,F,G

H,I

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Process control and yarn quality in spinning

Table 11.7  Causes of classimat faults in ring frame Machine Ring frame        

Influencing factors

Effect

A

B

C

Front top roller Setting disturbance and wider by 0.5 setting mm can affect the Classimat by 10%

 

 

 

Higher roving TM

 

 

 

Lower TM

 

 

 

Higher BD

 

 

 

Lower BD

 

 

 

Department Fly and Department Temperature

Higher roving TM can create slubs in the yarn

D

E,F,G

H,I

Overall Classimat  

 

 

 

Higher the temp will create more fly in   the department. Lesser the no. of air changes will create A1

 

 

Closer spacer

 

 

 

Ring condition

 

11.6

Common yarn faults in ring yarn

11.6.1

Slub yarn

   

 

 

   

 

It is an abnormal thick place of length 1–4 cm and about 5–8 times larger than average yarn diameter showing less twist at that place of the yarn (Fig. 11.8). The various causes and remedial measures for the same are given in Table 11.8.

Figure 11.8  Slub yarn Table 11.8  Causes and remedial measures for slub yarn Effects

Causes

Remedies

• More end breaks in the ensuing process • Damaged fabric appearance • Shade variation in dyed fabrics

• Poor individualization of fibers in the card • Lack of adequate fiber control in the rafting region • Accumulation of fly and fluff on machine parts • Improperly clothed top roller clearers • Defective RF drafting and bad piecing • Too close roller setting

• Better opening and cleaning in BR and Carding • Replace defective aprons • Optimum top roller pressure and back zone setting at RF to be maintained • Excessive roving twist • Broken teeth in gear wheel to be avoided and proper meshing to be ensured



Yarn faults

11.6.2

335

Soft yarn

Yarn which is weak indicating lesser twist (Fig. 11.9). The various causes and remedial measures for the same are given in Table 11.9.

Figure 11.9  Soft yarn

Table 11.9  Causes and remedial measures for soft yarn Effects

Causes

Remedies

• More end breaks in subsequent processes

• Cord slippage on spindle wharves

• Shade variation in dyed fabrics

• Improper bobbin feed on the spindles

• Vibration of bobbin on the spindle should be avoided

• Less twist in the yarn • Bad clearing at the traveller

11.6.3

• Proper yarn clearing should be ensured • Periodic replacement of worn rings and travellers

Oily slub

Slub in the yarn stained with oil (Fig. 11.10). The various causes and remedial measures for the same are given in Table 11.10.

Figure 11.10  Oily slub

336

Process control and yarn quality in spinning

Table 11.10  Causes and remedial measures for oily slub yarn Effects

Causes

Remedies

• More end breaks in the subsequent processes • Damaged fabric appearance • Shade variation in dyed fabrics

• Accumulation of oily fluff on machine parts • Improper lubrication • Negligence in segregating the oily waste from process waste

• Yarn contact surfaces to be kept clean • Proper lubrication and segregation

11.6.4

Kitty yarn

Presence of black specs of broken seeds, leaf bits and trash in yarn (Fig. 11.11). The various causes and remedial measures for the same are given in Table 11.11.

Figure 11.11  Kitty yarn (grey colour image)

Table 11.11  Causes and remedial measures for kitty yarn Effects

Causes

Remedies

• Damaged fabric appearance

• Improper ginning i.e. seeds broken during ginning

• Good ginning practices

• Production of specks during dyeing • Needle breaks during knitting • Poor performance during winding

11.6.5

• Ineffective cleaning in blow room and cards • Use of cotton with high trash and too many seed coat fragments

• Cleaning efficiency of BR and cards should be improved • Optimum humidity in the departments should be ensured

Foreign matters

Metallic parts, jute flannel and other similar foreign matter spun along with the yarn (Fig. 11.12). The various causes and remedial measures for the same are given in Table 11.12.



Yarn faults

337

Figure 11.12  Foreign matters (grey colour image) Table 11.12  Causes and remedial measures for foreign matters Effects

Causes

Remedies

• Breaks during winding

• Improper handling of travelers

• Removal of foreign matters should be ensured during preparation of mixing

• Formation of holes and stains in the cloth • Damaged fabric appearance

11.6.6

• Improper preparation of mixing

• Installation of permanent magnets at proper places in BR lines

Cork screw yarn

Double yarn in which one yarn is straight and the other is coiled over it (Fig. 11.13). The various causes and remedial measures for the same are given in Table 11.13.

Figure 11.13  Cork screw yarn

Table 11.13  Causes and remedial measures for corkscrew yarn Effects

Causes

Remedies

• Breaks during winding

• Feeding of two ends in the RF

• Tenters are to be trained in piecing practice

• Lashing in of ends in RF

• Pneumafil ducts to be kept clean and properly set.

• Causes streaks in the fabric

338

11.6.7

Process control and yarn quality in spinning

Snarl/over twisted yarn

Yarn with kinks (twisted onto itself) due to insufficient tension after twisting (Fig. 11.14). The various causes and remedial measures for the same are given in Table 11.14.

Figure 11.14  Snarl yarn Table 11.14  Causes and remedial measures for snarl yarn Effects

Causes

Remedies

• Entanglement with adjacent ends causing a break • Damaged fabric appearance • Shade variation in dyed fabrics

• Wrong selection if twist wheel • Wrong selection of spindle driving pulley • Presence of too many long thin places in the yarn

• Optimum twist to be used for the type of cotton processed • Drafting parameters to minimize thin places in yarn to be adopted • Yarn should be conditioned • Correct tension weights and slub catcher settings should be employed at winding

11.6.8

Oil stained yarn

Oil or stain marks present on the yarn (Fig. 11.15). The various causes and remedial measures for the same are given in Table 11.15.

Figure 11.15  Oil-stained yarn Table 11.15  Causes and remedial measures for oil-stained yarn Effects

Causes

Remedies

• Damaged fabric appearance • Occurrence of black spots in the fabric

• Over flowing of oil from spindle bolster • Excessive oil on ring with intention of running the traveller smoothly • Improper material storage and handling • Piecing made with oily or dirty fingers

• Proper oiling and greasing • Proper material storage and handling



Yarn faults

11.6.9

339

Spun-in fly

Fly or fluff either spun along with the yarn or loosely embedded on the yarn (Fig. 11.6). The various causes and remedial measures for the same are given in Table 11.16.

Figure 11.16  Spun-in Fly (grey colour image) Table 11.16  Causes and remedial measures for spun-in-fly yarn Effects

Causes

Remedies

• More breaks in winding

• Accumulation of fluff over machine parts (captured between front drafting rollers and pigtail guide) • Fanning by workers • Failure of over head cleaners • Malfunctioning if humidification plant

• Machinery surfaces is to be kept clean by use of roller pickers • Fanning by workers is to be avoided • Performance of overhead cleaners and humidification plants should be closely monitored

11.6.10 Bad piecing Unduly thick piecing in the yarn caused by over end piecing (Fig. 11.17). The various causes and remedial measures for the same are given in Table 11.17.

Figure 11.17  Bad piecing Table 11.17  Causes and remedial measures for bad-piecing Effects

Causes

Remedies

• More end breakages in subsequent processes • Increase in hard waste

• Wrong method of piecing • Proper piecing and over end piecing •S  eparators should be • Twisting the ends provided instead of knotting • Excessive end • Too close roller setting breakages in spinning should be avoided

340

Process control and yarn quality in spinning

11.6.11 Crackers Very small snarl like places in yarn which disappear when pulled with enough tension or yarn with spring like shape (Fig. 11.18). The various causes and remedial measures for the same are given in Table 11.18.

Figure 11.18  Crackers Table 11.18  Causes and remedial measures for crackers Effects

Causes

Remedies

• More end breaks in winding • More noticeable in P/C blended yarns

• Mixing of cotton of widely differing staple lengths • Closer roller settings • Eccentric top and bottom rollers • Over spinning of cottons • Non optimum temperature and RH in the spinning shed • Long fibers (bridge the nip line in drafting system and disrupt the process)

• Optimum top roller pressure • Optimum roller setting • Use of properly buffed rollers free from eccentricity to be ensured • Mixing of cottons varying widely in fine length to be avoided

11.6.12 Neppy yarn Very short fault of more than 200% of the yarn diameter (Fig. 11.19). The various causes and remedial measures for the same are given in Table 11.19.

Figure 11.19  Neppy yarn (grey colour image) Table 11.19  Causes and remedial measures for neppy yarn Effects

Causes

Remedies

• Damaged fabric appearance • End breaks in subsequent process

• Ginning • Improper opening in BR • Poor carding due to improper settings • Use of low micronaire cottons

• Correct settings and speeds in BR and cards to be maintained • Grinding schedules to be maintained strictly • Avoid immature cotton • Use of long cotton



Yarn faults

341

11.6.13 Hairiness Protrusion of fibre ends from the main yarn structure (Fig. 11.20). The various causes and remedial measures for the same are given in Table 11.20.

Figure 11.20  Hairiness in yarn

Table 11.20  Causes and remedial measures for hairiness Effects

Causes

Remedies

• More end breaks in winding • Uneven fabric surface • Beads formation in the fabric in the case of P/C blends

• Use of cottons differing widely in the properties in the same mixing • Worn rings and lighter traveler • Low RH • Closer roller settings • Very high spindle speeds

• Traveler of correct size and shape • Use of rings in good conditions • Periodic replacement of traveler • Correct roller settings • Maintaining optimum RH • Wide cotton mixing should be avoided

11.7 References 1. Application handbook for Uster Quantum; Determination of periodic mass variations (spectrum). 2. Booth, J.E. (1996). Principle of Textile Testing, A Butterworths Publication. 3. Classification of yarn faults & optical yarn clearing, Loepfe Brothers Ltd, Switzerland. 4. Garde A.R. and Subramanian T.A. (1978). Process Control in Cotton Spinning, 2nd Ed., Ahmedabad, ATIRA. 5. Garde, A.R. (1980). Faults in polyester blended yarns, Proceedings of the all India textile conference, p.74. 6. Grover, J.M., Bhargava, A.M., Purandare, M.J. and Subramanian, T.A. (1974). Some causes of slubs in polyester cotton yarns, Proceedings of the ABS joint technological conference, p.10. 7. Grover, J.M., Bhargava, A.M., Purandare, M.J. and Subramanian, T.A. (1973). Some causes of slubs in polyester cotton yarns. Proceedings of the ATIRA technological conference, p.23.

342

Process control and yarn quality in spinning

8. Kumaraswamy, K. and Sharieff, I. (1979). Infrequent yarn faults: their incidence, causes and removal. Journal of Textile Association, 40, p.213. 9. Origin and frequency of thick places in yarn (1973). International textile bulletin, spinning, 4, p.429. 10. Pillay, K.P.R. and Hariharan, R. (1983). Effect of processing factors on incidence of yarn faults in spinning (sitra) 28. 11. Pillay, K.P.R. (1983). Influence of yarn faults on knitting performance and properties of knitted fabrics, (sitra) 28. 12. Ratnam, T.V. and K.P. Chellamani (1999). Quality Control in Spinning, SITRA, Coimbatore. 13. Sharieff, I. and Garde, A.R. (1980). Mechanism of formation of yarn faults. Proceedings of the abs joint technological conference, p.27. 14. Uster Application hand book, Uster quantum: Analysis of yarn by a sophisticated classifying system, Uster publication, Zellweger Uster. 15. Yarn faults and package defects, (1995). SITRA publications, India

12 Productivity of a spinning mill

Abstract: This chapter deals with the various factors influencing the productivity of the ring spinning. The various productivity indices with their definition and application for finding out the mills productivity for comparison are also provided in this section. The mechanism of end breakage, types of end breaks in ring spinning and the control of end breakage in ring frame are also discussed. The effects of atmospheric conditions and process parameters on end breakage rate are also provided. Key words: Productivity, HOK, OHS, SH, end-breaks

12.1 Introduction All spinners wish that the spinning productivity of their mill (ring frame production in grams/spindle shift) has the optimum level of efficiency. Though there are many aspects that limit the actual production like ring diameter and its life, lift, life and make of the ring frame, its maximum mechanical speed, type of spindle drive, lot size, fluctuating production program, poor control on RH, lower HP of main driving motor, greater percentage of untrained workers, impoverished technical knowledge of subordinates, etc. Today, there is a pressure from the management to decrease the conversion cost to its lowest possible level because of cut throat competition in both the local and export markets. Ring spinning contributes approximately 70% to the total conversion cost. Hence it is possible to speed up the ring frames to its maximum speed mechanically possible considering that spinning preparatory can feed ring frames at high speed. Also, neither the spinning performance nor the yarn quality is adversely affected by such speeding up of the ring frames. Currently many spinning mills in India are capable of managing their ring frames at actual great speeds quite successfully counts 30s to 40s at 20,000–22,000 rpm and finer counts – 60s to 76s at up to 24,500 rpm and yet maintaining identical breakage rate of 2–3 breaks per l00 spindle hours that they were earlier performing at 15,000–16,000 rpm. Also, the yarn quality has not been affected.

344

Process control and yarn quality in spinning

12.2

Productivity indices

The definitions of the various productivity parameters are given in Table 12.1. Table 12.1  Productivity parameters in spinning Parameter

Definition

HOK

Operatives hours to produce 100 kg of yarn

Adjusted HOK

The HOK (that is, the operative hours engaged to produce 100 kg of yarn) is adjusted to a common count of 40s by multiplying the actual production in ring frame in different counts be relevant conversion factors. The production so converted is termed as “Standardized Production”. Thus the adjusted HOK is calculated from: Operative hours = × 100 ∑ Count-wise standardized ring spinning production in kg

Conversion factors for HOK

The conversion factors are the ratios of the HOKs of individual counts to that of 40s count under given conditions: •  HOK for a given count •  HOK for 40s count The count-wise HOKs are estimated taking into account countwise production rates, work assignments, etc., that correspond to those of standard mill. The conversion factors are different for different departments, and for the count of 40s, it is unity for all departments. Another major practical advantage of the method is that it is not required to obtain a break-up of the operatives according to each count.

Standard HOK

HOK for 40s count under the specified conditions. Standard HOKs for different categories of operatives are given in Table 12.2 and the conditions under which they can be attained are given in the footnote to this table. As can be seen, the total standard HOK up to ring frames is 12.

Composite Productivity Index (CPI)

A measure of productivity calculated by expressing the standard total HOK of 12 as a percentage of the mill’s total actual HOK adjusted to 40s count and reflects the effect of both labor and machines.

P

Production per spindle per 8 hours shift adjusted to 40s count in grams. The overall production per spindle in ring frames, adjusted 40s count, can be obtained by using the conversion factors. =

∑ Count-wise standardized ring frame production in kg Total spindle shifts (of 8 hours each) corresponding to the above production

× 1000

Contd...



Productivity of a spinning mill

345

Contd... Parameter

Definition

Conversion Factor for P

These conversion factors are the ratios of the standard production per spindles in 40s count to the standard production per spindle in the given count. That is: 110 g Std. production per spl per 8 hour (g) in the given count

OHS

Number of operatives employed per 1000 spindles adjusted to 40s count. P × HOK = , where HOK and P are adjusted to 40s count 800

OHSAM

OHS modified to allow for a valid comparison of a mill’s OHS with the standard OHS of 1.65 taking into account the deviation of the mill’s production per spindle from the standard production per spindle. 165 × OHS = [1.65 – 0.0065 (110 – P)]

SH

A measure of spindle utilization expressed in terms of number of hours worked per spindle per day. It is calculated by dividing the total spindle hours worked per day by the installed spindles. The spindle utilization is also expressed as percentage, i.e. SH 24

MPI

× 100

A combined measure of production per spindle and spindle utilization. It is calculated by expressing the product of production per spindle (P) adjusted to 40s count and spindle utilization (SH) as a percentage of 2640 (110 × 24). The index would be reduced by one-seventh if the mill works for only 6 days a week. =

P ×SH 110 × 24

× 100

The norms for HOK and OHS in different departments are given in Table 12.2.

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Process control and yarn quality in spinning

Table 12.2  Norms for HOK and OHS in different departments Categories of operatives

HOK

OHS

Mixing attendant Blow room tenter Card tenter (chute feed) Draw frame tenter Fly frame tenter Fly frame doffer Ring frame tenter Ring frame doffer Ancillary operatives*

0.6 0.2 0.2 0.3 0.4 0.5 3.1 2.7 4.0

0.08 0.03 0.03 0.04 0.06 0.07 0.42 0.37 0.55

Total

12.0

1.65

Production per spindle per 8 hours adjusted to 40s (g)

110

Note: 1. Level of modernization assumed is as follows: Chute feed blow room line, cards fitted with automatic can changer, automatic waste evacuation system and auto leveller, draw frames fitted with automatic can changer for 1st passage, draw frames fitted with automatic can changer and autoleveller for 2nd passage, high speed fly frames and long length ring frames. 2. For work assignments, ancillary operatives as well as production rates assumed in various departments, reference may be made to SITRA publication ‘Norms for Spinning Mills’, 2010 edition. 3. HOK for combers and preparatory tenters and maintenance operatives may be taken as 1.0 (40s count). 4. 40s or 40s count always refer to 40s cotton carded yarn, unless otherwise qualified. Also, the phrase ‘adjusted to’ or ‘conversion to’ or ‘standardized’ or ‘conversion’ are used interchangeably

12.3

Control of end-breakage rate in ring spinning

One basic way to increase profit and quality in the ring spinning process is to keep the end breakage rate to a minimum level. The end breakage rate is a critical spinning parameter that not only affects the maximum spindle speed but may also indicate the quality of yarn, the mechanical condition of the machine and the quality of raw materials. Therefore, it is an important parameter which determines the overall working of a spinning mill.



12.3.1

Productivity of a spinning mill

347

End breakage mechanism in ring spinning

The mechanism of end breakage in the ring frame is significantly different from the failure mechanism of yarn in a tensile tester. In ring spinning, the end breakage occurs due to the imbalance in the tension imposed on the yarn and the yarn strength at the weakest portion. It is an observed fact that almost all end breaks in the ring frame take place just after the delivery from the front nip in the spinning zone, i.e. between the front rollers’ nip and the thread guide. Therefore, an end will break when the spinning tension exceeds the strength of the weakest portion of the yarn in the spinning zone. The end breakage phenomenon in ring spinning is absolutely slippage-dominated, i.e. there is no evidence of fibre breakage. The strength of yarn at the spinning zone is significantly less than the yarn strength obtained by a tensile tester. In general, the spinning tension is considerably greater than one-third of the single thread strength. In fact, a very thin portion of yarn just after the delivery from the front nip causes an end breakage in ring spinning.

12.3.2

Classification of end breaks

The end breaks can be classified into four categories in respect of their causes: catastrophic end breaks, end breaks due to gross faults, end breaks due to avoidable imbalance in the strength–tension interaction in spinning and end breaks due to unavoidable imbalance in strength–tension interaction. The occurrences of catastrophic breaks are due to the traveller flying off, collision between the balloons, floating fluff, failure of suction clearers, and so on. Torn aprons, faulty cradles, etc., which seriously disturb the drafting are examples of gross faults in spinning that causes end breaks. End breaks can also occur, when the yarn strength is not adequate to withstand the prevalent tension. The occurrence of such end breaks would depend upon the instantaneous tension and twist in the yarn as well as on the distribution of mass along the length of yarn. Some of the occurrences of the strength–tension imbalances are avoidable and are caused by mechanical faults in the machinery or by incorrect choice of spinning parameters which give rise to unfavorable levels of tension or to a markedly irregular yarn. Examples of these deficiencies are: vibrating or outcentre spindles and vibrating bobbins which result in frequent occurrences of peak tensions; or an improper choice of traveller weight or interval for traveller replacement or of the ratio of bare bobbin diameter to ring diameter which give rise to a high level of tension or twist; or eccentric rollers or inappropriate drafting parameters which results in numerous potential weak places in the yarn.

348

Process control and yarn quality in spinning

Obviously, even in the absence of these deficiencies in the drafting system, there will always be a certain minimum number of places of potential break in the yarn. Such places are the result of unavoidable irregularities introduced during drafting. In most situations, the incidence of catastrophic breaks accounts for only a small fraction of the total breaks. More attention, therefore, needs to be paid to causes which lead to end breakages due to gross faults and avoidable imbalances in strength–tension. An end will fail in spinning when the tension in the balloon exceeds the strength at the weakest point in the yarn. The yarn is at its weakest at the point of twist insertion, before the fibres are fully twisted together. When a very weak place or a gross fault, such as a slub, occurs in the yarn, the balloon tension may be, or may become, greater than the strength at the twist triangle. In a well-made roving there should be no slubs, nor the potential for slubs to form in the spinning operation, although slubs can arise from poorly made joins in the slivers fed to the roving frame, for example. Very weak places, however, can occur when even the best spinning practice is followed, because of the random distribution of fibres and the quasi-periodic variations induced by drafting; these are the major cause of end breaks. These very weak places are generally very thin places.

12.3.3

Balloon tension and end breaks in spinning

The primary factors determining the tension in the spinning balloon are spindle speed, balloon diameter, yarn linear density and traveller weight. The balloon tension defines the maximum strength of a weak place that will break in spinning. The maximum local linear density of a thin place at a break could be estimated if the instantaneous strength of the yarn at the point of twist insertion could be related to the instantaneous linear density. Although yarn strength at the point of twist insertion cannot be measured directly, the maximum linear density of a thin place which might break during spinning can be estimated from actual measurements of the spinning performance.

12.3.4

Effect of atmospheric conditions on end breakage

The atmospheric conditions with respect to temperature and humidity play very important part in the manufacturing process of textile yarns and fabrics. The properties like dimensions, weight, tensile strength etc. of almost all textile fibres whether natural or synthetic are influenced by climatic conditions. The importance of atmospheric conditions in the processing and testing of cotton textiles is well known. There is evidence that the ability of cottons to resist damage during mechanical treatment is directly related to the moisture content of the cotton. Any level of RH within the range 46–55% is adequate to ensure low-end breakage rate. There will be a rapid increase in end breaks with



Productivity of a spinning mill

349

increase in temperature of the spinning room 70°F to 90°F at below 48% RH. At 70°F and RH% above 62% caused difficulties by way of end breaks, roller lappings, etc., due to mechanical adhesion of fibres to the film of water on the rollers. At these levels, the number of end breaks significantly decreased when the temperature of the spinning room was increased from 70° to 77°F. The combinations of (1) high temperatures with low humidities and (2) low temperatures with high humidities adversely influence the end breaks. The difficulty under the first condition may be the lack of good cohesion between the fibres in the drafted rovings, and their consequent rupture at the spinning zone under the influence of spinning tension. On the other hand, in the second case, trouble might arise from the particles of water deposited on the rovings, rollers, and other machine parts. Under these conditions, the Pneumafil system of the spinning frame becomes less effective, and there is mechanical adhesion of fibres to the film of water on the rollers. Further, at high humidities, the friction between rings and travelers also increases, with a consequent increase in spinning tension. The adverse effects of extreme conditions can be avoided by employing a sufficiently low temperature and relative humidity in the spinning room. For attaining the optimum temperature and relative humidity, air conditioning of the spinning room might be necessary.

12.3.5

Effect of spinning process parameters on end breakage rate

The number of end breaks systematically increased with increase in spindle speed, the rate of increase being higher for higher speed levels. It is well known that twist in yarn has a significant influence on the number of end breaks during spinning. On an average, there will be decrease in end breaks with increase in TM from 4.2 to 4.8. An increase in count would be expected to increase the number of breaks due to the smaller number of fibres in the cross section of the yarn and the greater draft required to for spinning the yarns. The percent changes in end breaks due to these parameters are sometimes influenced by changes in temperature and relative humidity of the spinning room. It may be that high temperatures, combined with low TPI, reduce the cohesion between fibres and increase the number of end breaks at the spinning zone. The lack of cohesion between fibres at high temperatures may also be contributing to the higher number of end breaks with increase in spindle speed. There will be an increase in end breaks due to increase in spinning draft. There is a consistent decrease in the percent change of breaks due to draft with increasing relative humidity at all temperature levels. On an average, the percent increase in breaks decreases rapidly for a change in RH% from

350

Process control and yarn quality in spinning

lower (35%) to higher (70%). This implies that high relative humidity levels permit the use of higher spinning drafts. Low drafts and high humidities lead to unsatisfactory drafting conditions. It may be that at high humidities the cohesion between fibres is great and the surface friction between fibres increases, thereby requiring a higher draft to draft the roving evenly.

12.3.6

Effect of mechanical condition of machine on end breakage

The cleanliness and mechanical condition of the machinery play a major factor on end breakage rate in ring spinning. Eccentric rollers, worn-out arbors, inadequate pressure on rollers, worn-out cradles, grooved aprons, under-size cots and such other deficiencies in the drafting system; and vibrating bobbins and spindles, eccentric spindles, worn-out and tilted rings and such other deficiencies in the twisting-winding mechanism were often seen to account for a considerable proportion of end breaks. The mechanical condition of machinery at carding and combing was also a contributing factor for end breaks in ring spinning.

12.3.7

Possibilities of reducing end breaks in ring spinning

To reduce end breaks, the following aspects should be taken into consideration: • Since end breakage in ring spinning is related to slippage of fibres at the spinning triangle as a result of peaks occurring in the spinning tension fibre, the grip at the front drafting rollers should be increased by having a higher top roller pressure. The use of softer cots also enhances the grip at the front rollers. If the total pressure on the rollers cannot be increased, the grip at the front rollers’ nip can be improved by reducing the width of the cots. • A reduction in friction between ring and traveller could reduce the peak tension during the rotation of the traveller. • Measures should be taken to reduce the mass irregularity of yarn straight after carding. • The width of the drafted ribbon at the front roller nip should be reduced.

12.4 Control of end breaks in ring spinning 12.4.1 Carding

• Proper feeding of material and chute filling • Proper working of chute photo cell sensors • Wire condition of cylinder and doffer





Productivity of a spinning mill

351

• Proper air cleaning of machine • Teflon coated tongue and R.D. roller under cover should be cleaned regularly • RH% should be maintained 50–55% to avoid web falling and web transfer problems • Under casing should be smooth and clean • There should not be any side-fly in the card • All timing belt tension and chain tension has to be ensured • Proper seating of stripper in the crush roller. There should not be any gap between the stripper and crush roller which leads to crush roller lapping • Web doffing unit should be kept clean and smooth. During piecing the sider has to clean the lay down sheet and clean crush roller strippers • Delivery and feed sensor calibration should be done as per schedule to avoid unnecessary stoppage of machine • Coiler calendar roller area and friction ring area should be clean • The condition of condenser, scanning roller and coiler calendar roller should be ensured • Proper selection of tension draft

12.4.2 Drawing

• • • • • • • • • • •

Proper selection of trumpet size and web guide tube is to be ensured Web guide tube setting should be proper Free rotation of creel rollers is to be ensured The strippers should be cleaned periodically Correct sliver path in drafting Optimum top roller pressure should be maintained Quality of piecing should be good in creel Top rollers should be cleaned and interchanged with periodic intervals Top roller cots changing and buffing should be done as per schedules Proper suction at draft zone to be maintained All the belts and running parts condition should be good

12.4.3 Comber

• • • • •

Ensure uniform web Detaching roller pressure to be maintained as per recommendation Stripper setting in draw box should be correct Suction in draw box should be maintained Detaching and draw box top roller cots changing/buffing should be done as per recommended schedule

352

Process control and yarn quality in spinning

• RH% should be maintained in the range of 50–55%. Detaching roller lapping should be avoided • Correct size of trumpet to be selected • Periodic cleaning of the draw box, detaching top roller cots to be ensured • Proper brush setting to be ensured • Periodic cleaning of the top comb to be ensured • Top comb, Unicomb, brush changing should be done as per recommendation • Use of top combs and clearer rollers

12.4.4 Simplex

• • • • • • • • • • •

Proper stop motions in creel area Optimum roving tension Adequate TPI Condition of false twister has to be ensured Optimum break draft, roller settings and spacer Condition of top and bottom aprons Ensuring proper working and condition of the clearer cloth Top arm load to be checked and corrected as per recommendation Drafting zone cleanliness is to be ensured Flyers should be cleaned with air regularly Minimum variation in bare bobbin diameter

12.4.5

Ring frame

The types of breaks in ring frame and the reasons for the same are given below:

Un-drafting: • Higher roving TM • Lesser Break draft • Improper spacer size • Low top arm load • Cots buffing frequency • Higher RH% • Shore hardness of cots / cots diameter • Top roller setting • Old bottom aprons • Low total draft • Worn out gears / bearing failure



Productivity of a spinning mill



Creel breaks: • Free rotation of bobbin holders • Low roving TM • Bigger bobbins



Traveller fly: • Lighter traveller • High clearance traveller/wrong traveller profile • Ring life • Mix-up of travellers



Yarn accumulation in lappet hook: • Indication of balloon collapse/low tension • Lighter traveller • Improper lappet setting/squaring • Improper traveller profile



Fluff accumulation inside the traveller: • Higher cop diameter • Improper ring centering • Improper ring rail play • Fluff accumulation in department • Traveller burning



Fluff accumulation outside the traveller: • Traveller clearer setting • Fluff accumulation in department



Fluff accumulation in eyelet: • Undrafting • Over head clearer wastes falling on eyelets • Blowing points in OHTC • Reserve bobbin roving falling over eyelet • Fluff accumulation in department



Tension breaks: • Heavier/lighter traveller • Ring rail jerking /ring centering • Lappet height setting • Higher chase length • Higher winding length



Repeat breaks: • Rogue spindles • Defective bobbins

353

354

Process control and yarn quality in spinning

Thin yarn: • Low spindle tape tension • Less TPI

12.5

Effect of climatic conditions on spinning process

12.5.1

Blow room

For the cotton process, excessive humidity impairs trash extraction particularly the micro dust. There is less risk of fibre damage. Rayons display opposite behavior. On the other hand, with too low humidity severe dust incidents fly liberation and fibre damage results. Also too dry atmosphere generates more static electricity. For the synthetic process, higher relative humidity is more useful than low humidity. Low humidity causes bulky laps, split and licking laps and too much static electricity.

12.5.2 Carding The cleaning efficiency of carding with cotton process goes down with higher RH%. Sticking web, cylinder loading, etc. are noticed with high RH%. With too low RH% fiber damage, more fly liberation, web splitting, etc are observed. With high RH% the tinted and dyed synthetic material may lead to sticking problems due to deposition of tint or dye material on card surface. Too low humidity causes generation of static electricity leading to web splitting and uncontrolled fibres.

12.5.3

Preparatory and spinning

The effect of high RH% with cotton process in drafting is reflected in roller lapping and difficulty in drafting. Drafting requires more fleece, wider settings and higher drafts. Too low RH% on the other hand leads to more fly liberation, uncontrolled fibres and loss of twist. For processing synthetics, higher RH% is more useful than low RH% because of static electricity problems.

12.5.4 Winding Higher RH values of about 60% are required for processing the yarn in winding department. The higher rate of winding speed requires more strength in yarn and minimum fly generation to avoid breakages. With the above information it is clear that the climatic conditions have complex effect on textile material and therefore, optimum levels have to be maintained.



Productivity of a spinning mill

12.5.5

355

Recommended levels of conditions (Table 12.3)

Table 12.3  Norms for atmospheric conditions in various departments Department

Temperature °C

RH%

Blow room

28–30°C

45–60%

Carding

28–30°C

50–55%

Preparatory

25–30°C

50–55%

Ring spinning

25–30°C

50–60%

Winding

25–35°C

60–65%

12.6 References 1. Bhaduri, S.N., Paltwal, M.C., Sharma, R.S. and Subramanian, T.A. (1967). Towards increased productivity: The need for quantitative thinking. Proceedings of the All India Textile Conference, p.11. 2. Bhaduri, S.N., Subramanian, T.A., Sharma, R.S., Ghosh, G.C. and Raj, B.S. (1967). Scope for improving productivity and quality in spinning with existing machinery, Proceedings of the ATIRA Technological Conference, p.1. 3. ATIRA, AHMEDABAD (1971). Break spinning: Part II—Techno-economic studies of drum spinning, Monograph. 4. Srikantaiah, G. and Ramachandran, N. (1973). Conditions for high productivity in spinning. Proceedings of the ABS Joint Technological Conference, p.147. 5. Owalekar, R.G. and Nerukar, S.K. (1974). Getting higher productivity with high speed ring frames: Parts I and II. Journal of Textile Association, 35. 6. Mahajan, S.D. and Krishnaswamy, R. (1976). Study of some of the factors controlling end breaks on speed frames, Proceedings of the ABS Joint Technological Conference, p.35. 7. Garde, A.R. (1976). Scope for improving productivity in spinning and weaving, Proceedings of the ABS Joint Technological Conference, p.89. 8. How to assess spinning mill’s productivity? (2010). The South India Textile Research Association, 55, p.1. 9. Garde A.R. and Subramanian T.A. (1978). Process Control in Cotton Spinning, 2nd Ed., Ahmedabad, ATIRA. 10. Ratnam, T.V. and K.P. Chellamani, (1999). Quality Control in Spinning, SITRA, Coimbatore. 11. Nilesh P Patil, (2011). Improving productivity of ring frames, Indian Textile Journal, p.22. 12. Steiger, J.U. (1947). Some factors affecting end breakage in ring spinning, Journal of the Textile Institute Proceedings, 38, p.561.

13 Yarn quality requirements for high-speed machines

Abstract: The yarn quality requirements for hosiery, shuttleless weaving and for export are discussed in this section. The yarn quality requirements for hosiery applications like elongation, lint shedding, hairiness and other properties are given in this section. Further, the yarn qualities required for the application in shuttleless weaving and for export are also provided in this section. Key words: Hosiery, shuttleless weaving, export, lint shedding, warping, sizing

13.1

Yarn quality requirements for hosiery yarns

In high speed circular knitting, the occurrence of sudden loads are very common. Yarns with good recovery from stretch are ideal for high speed knitting. Among other things, the elastic recovery of yarns is influenced by the breaking elongation. Higher the breaking elongation of yarns better will be the elastic recovery and vice versa. Yarns with breaking elongation >5% will be good for high speed knitting. The tendency of a yarn to shed fly or lint during any mechanical process is termed as lint shedding. It is more predominant in knitting as the yarns are not sized. Staple yarns have a characteristic hairiness that consists of fibre ends and loops protruding from the surface. During knitting many of these fibre ends and loops are either pulled or sheared from the yarn and accumulate around the knitting elements, guides and other machine parts. These fibre accumulations are picked up by the incoming yarn; they jam the needles, break the yarn or the needles and deteriorate the fabric appearance. Around 25% of all the faults occurring during knitting process can be traced directly back to the incidence of lint/fibre fly. This excludes faults such as needle stripes and dropped stitches. Hence, the lint shedding propensity (LSP) of yarns used for knitting must be as low as possible to avoid the attendant problems. Among other things, the fibre and yarn parameters which influence the LSP of cotton yarns are: 1. Short fibre content in cotton 2. Yarn hairiness and 3. Twist employed in spinning



Yarn quality requirements for high-speed machines

357

An important aspect of fabric quality that determines its visual rating is the uniformity of cover between small areas of the order of 5 sq. cm. A lack of this uniformity gives rise to what is commonly called a “patchy” fabric. It is a common practice for the textile mills to assess the variation in mass per unit length of the yarns by capacitance type evenness tester. Besides the CV% of mass, the extent of variation is expressed by imperfections, which consists of thin places, thick places and neps.

The imperfections are measured at the following sensitivity levels:



1. Thin places: −50%



2. Thick places: +50%



3. Neps: +200% (+280% for O.E. Yarns)

However, the studies have shown that, extra sensitive imperfections i.e. thin places measured at (−40%) and (−30%) sensitivity levels, thick places measured at (+35%) sensitivity level and neps measured at (+140%) sensitivity level have a better correlation with yarn appearance as well as fabric appearance, as compared to that of normal imperfections. Hence, extra sensitive imperfections need to be maintained as low as possible to produce fabrics with good consumer appeal. Random periodic hairiness variation in the yarn results in a cloudy appearance of the knitted fabric after dyeing/finishing. Twist variation in yarn is another important factor having a significant influence on fabric barre (visual phenomenon and any yarn property which makes the yarn look different from the adjacent yarn in a fabric cause this defect.). In a study on performance related characterization of cotton yarns for high speed knitting, it was found that objectionable faults (A3, A4, B3, B4, C2, C3, C4 and D2, D3 and D4), long thick faults and long thin faults measured by classimat system affect breaks on high speed weft knitting machines significantly. In contrast to weaving yarns, the strength requirement of hosiery yarns, is secondary as the loading placed on the yarn during knitting is lower than that with a high speed loom. The ability of the yarn to be guided easily through various elements of the knitting machine is important for a hosiery yarn. This would call for low co-efficient of friction (yarn to metal). The friction coefficient (µ) for a hosiery yarn should be around 0.15. The moisture content in the yarn should be evenly distributed. Yarns in a c1imatised condition provide better running properties and a better appearance of the finished fabric. Requirement profile of different counts of 100% cotton combed yarns for knitwear is given in Tables 13.1–13.7.

358

Process control and yarn quality in spinning

Table 13.1  Yarn quality requirements for hosiery yarns Count (Ne) (cone yarn)

Quality parameter

30s CH

40s CH

60s CH

Unevenness U%

9.5

10.5

11.5

Yarn count CV (%)

1.0

1.2

1.4

Rkm (gms/tex)

17.0

17.5

19.0

CV of tenacity (%)

3.0

3.5

4.5

Elongation (%)

5.0

4.8

4.5

8

8

8

A1/B1/C1/D1 per 100 km

Mean 50

Mean 50

Mean 100

A3/B3/C2/D2 per 100 km

Mean 2

Mean 2

Mean 3

E per 100 km

Max 0.2

Max 0. 2

Max 1.0

H2/I2 per 100 km

Max 1.0

Max 1.0

Max 2.0

CV of elongation (%)

Table 13.2  Norms for normal imperfections/km (cone yarns) Normal imperfections per km Count (Ne)

Thin places (−50%)

Thick places (+50%)

Neps (+200%)

Total

20s KH

0

55

100

155

30s KH

1

125

224

350

40s KH

4

200

500

704

30s CH

1

18

40

59

40s CH

2

30

60

92

60s CH

7

50

90

147

Table 13.3  Norms for extra sensitive imperfections/km (cop yarns) Extra sensitive imperfections per km Count (Ne)

Thin places (−40%)

Thick places (+35%)

Neps (+140%)

Total

30s KH

150

650

750

1550

40s KH

300

1300

1400

3000

30s CH

30

250

300

580

40s CH

100

350

350

800

60s CH

200

500

750

1450



359

Yarn quality requirements for high-speed machines

Table 13.4  Norms for Uster Hairiness Index (H) (cone yarns) Count (Ne)

Uster Hairiness Index (H)

Carded 30s H 40s H

6.5 6.0

Combed 30s H 40s H 60s H

5.5 5.0 4.0

Table 13.5  Norms for Zweigle hairiness (S3 values) (cop yarns) Count (Ne)

Zweigle hairiness (S3 values)

Carded 30s H 40s H

400 500

Combed 30s H 40s H 60s H

20 250 500

Table 13.6  Guideline values for foreign fibre cuts/1 00 km in auto winders Count (Ne)

20s KH 30s KH 40s KH 20s CH 30s CH 40s CH 60s CH

Clearer cuts/100 km

10

15

15

20

15

20

Table 13.7  Guideline values for lint shedding of cotton yarns Count (Ne)

Lint shedding (mg/g of yarn)

20s KH

300

20s CH

200

30s CH

150

40s CH

275

50s CH

300

60s CH

200

100s CH

850

30

360

Process control and yarn quality in spinning

Lint Shedding is measured by using Constant Tension Transport (CTT) instrument by Lawson Hemphill Inc., USA. For hosiery yarns, lint shedding is about 20% higher as compared to warp yarns.

13.1.1

Yarn faults influencing some of the knitted fabric defects (Table 13.8)

Table 13.8  Knitted fabric fault caused by yarn fault Type of yarn fault

Knitted fabric defect

Relatively low strength

Holes and cracks

High mass unevenness

Cloudy fabric

Insufficiently waxed yarn

Drop stitches and cloth fall out

Improper dyeing

Horizontal stripes

High level of hairiness

Diffused stitch appearance and fluff build-up

Periodic faults

Fabric with stripes

13.1.2

Polyester/viscose blended yarns for hosiery applications (few considerations)

Hosiery fabrics from 100% cotton as well as P/C and C/V are quite common as nightwear garments, T-shirts and other inner and outer garments. P/V fabrics are more comfortable when compared with P/C due to higher moisture regain of viscose. Studies have shown that the “non-acceptability” of P/V knitted fabrics by the consumers is due to “high level of pilling” in these fabrics. Pilling propensity of a fibre is influenced, among other things, by the flexural rigidity. The specific flexural rigidity of some of the popular fibres is given in Table 13.9. Table 13.9  Flexural rigidity of various fibres Fiber type

Specific flexural rigidity (mN.mm2/tex2)

Silk

0.6

Cotton

0.53

Polyester

0.30

Wool

0.24

Acrylic

0.45

Viscose

0.35

Nylon

0.20



Yarn quality requirements for high-speed machines

361

As compared to cotton, viscose has low flexural rigidity to the extent of about 35%. Lower the flexural rigidity, higher will be the pilling propensity and vice versa. The higher pilling tendency of viscose is the main reason behind the poor acceptance of P/V knits as compared to P/C knits. “Low Pill” viscose fibres (similar to low pill polyester fibres that are already available in the market) need to be developed if P/V knitted fabrics is to be acceptable in the market. The low pilling tendency of acrylic which is attributed to the relatively high flexural rigidity is responsible for 100% acrylic knitted fabrics being quite popular in the market.

13.2

Yarn quality requirements for export

Requirement profile of different counts of 100% cotton combed yarns for export is given in Table 13.10. Table 13.10  Yarn quality requirements for 100% combed yarn for export Count (Ne) 20s 30s 40s 50s 60s 80s

13.3

RKM (cN/Tex) Count Total CV% Imp. Hosiery Warp 1.2 1.3 1.3 1.4 1.4 1.6

16.5

>17 >17.5 >18 >18.5 >18.5 >18.5

Rkm CV%

Obj