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Process control and yarn
quality in spinning
© 2016 by Woodhead Publishing India Pvt. Ltd.
Process control and yarn
quality in spinning
G. Thilagavathi
and
T. Karthik
WOODHEAD PUBLISHING INDIA PVT LTD
New Delhi
© ...
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
Woodhead Publishing Ind...
Contents
Preface		 ix
Acknowledgement	xi
	 1.	 Quality management	 1
	 1.1	 What is quality?	 1
	 1.2	 Quality as input–ou...
3.5	 Cotton fibre engineering	 52
	 3.6	References	 67
	 4.	 Control of wastes in spinning	 69
	 4.1	 Yarn realization	 69...
7.3	 Unevenness (Um%)	 199
	 7.4	 Mass CV (Coefficient of Variation Cvm
%)	200
	 7.5	 Yarn imperfections	 216
	 7.6	Refere...
11.	 Yarn faults	 325
	 11.1	Introduction	 325
	 11.2	 Distinction between frequent and seldom-occurring 	 328
yarn faults...
Preface
Changes are taking place very fast all over the world in all fields, such as
technological developments, the livin...
spinning industry for the production of consistent yarn quality. Chapter 4
presents the various control points and remedia...
Acknowledgement
We would like to thank the Management and the Principal of PSG College
of Technology for providing us the ...
Abstract: This chapter discusses about the various definitions and dimensions
of quality and their significance on process...
2	 Process control and yarn quality in spinning
user or government and industry. It can be guidelines or characteristics f...
Quality management	 3
1.4	 Seven tools of quality
Quality pros have many names for these seven basic tools of quality, fir...
4	 Process control and yarn quality in spinning
When to use a Fishbone Diagram
•	 When identifying possible causes for a p...
Quality management	 5
average, an upper line for the upper control limit and a lower line for the lower
control limit. The...
6	 Process control and yarn quality in spinning
•	 When determining whether a process is stable (in statistical control).
...
Quality management	 7
•	When determining whether the outputs of two or more processes are
different.
•	When you wish to co...
8	 Process control and yarn quality in spinning
the points will hug the line. For example, yarn strength may depend on twi...
Quality management	 9
	 –	When determining whether two effects that appears to be related both
occur with the same cause.
...
10	 Process control and yarn quality in spinning
When to use stratification
•	 Before collecting data.
•	When data come fr...
Quality management	 11
1.5.1	 Problems faced by the textile industry in India
Spinning mills today face various challenges...
12	 Process control and yarn quality in spinning
1.5.3	 Reasons for poor quality in spinning industry
•	 Lack of top manag...
Quality management	 13
testing as well as the organisation, documentation and release procedures
which ensure that the nec...
14	 Process control and yarn quality in spinning
Chairman/managing director
General manager
Factory manager Human resource...
Quality management	 15
(ii) Machinery auditing
Generally, maintenance gang will be doing auditing of the machine at the
ti...
16	 Process control and yarn quality in spinning
(ii) New product development
By studying the market requirements, new pro...
Quality management	 17
1.6.2	 Ways to achieve optimum quality and cost
conditions
Quality management system should serve t...
18	 Process control and yarn quality in spinning
	 •	 Optimum selection of the raw material for the required quality
	 •	 ...
Quality management	 19
	 9.	 Duties and Responsibilities of Quality Control Staff in a Spinning Mill (1996), SITRA
Focus, ...
Abstract: This chapter discusses the significance of statistical quality control in
textile industry. The basics of statis...
Application of statistics in textiles	 21
for interpreting the test results necessitates the use of appropriate statistica...
22	 Process control and yarn quality in spinning
2.2	 Measures of central tendency
A measure of central tendency is a sing...
Application of statistics in textiles	 23
2.2.2	 Weighted arithmetic mean
The arithmetic mean, as discussed earlier, gives...
24	 Process control and yarn quality in spinning
hence it will not be easily distorted by abnormal values. A major disadva...
Application of statistics in textiles	 25
2.2.6	 Harmonic mean
The harmonic mean is a measure of central tendency for data...
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...
Application of statistics in textiles	 27
Table 2.2  Mean and standard deviation of sample
Sample Mean Standard Deviation
...
28	 Process control and yarn quality in spinning
curve which is also the reason why such a distribution is called as ‘norm...
Application of statistics in textiles	 29
lie between these limits and consequently 32% outside these limits. Similarly,
i...
30	 Process control and yarn quality in spinning
2.4.3	 The confidence range
The means, standard deviations and other stat...
Application of statistics in textiles	 31
	 From statistical tables,
		 t (for s = 95%, f = 4)	 =	 2.78
		 Q95%
	=	
2.78 3...
32	 Process control and yarn quality in spinning
	 In other words, mean values of distributions which are not normal can b...
Application of statistics in textiles	 33
2.5.1	 Comparison of two means for large samples
To determine whether there is s...
34	 Process control and yarn quality in spinning
	 Since tcal
is greater than tsf
, there is significant difference betwee...
Application of statistics in textiles	 35
2.5.4	 Difference between the standard deviation of two
large samples (n  30)
Wh...
36	 Process control and yarn quality in spinning
2.6	 Quality control within the spinning mill
The quality of yarn obtaine...
Application of statistics in textiles	 37
is a means for detecting when there has been a significant departure from the
us...
38	 Process control and yarn quality in spinning
	 Upper Warning Limit = X 2+ s
		 Upper Action limit = X 3+ s
		 Lower Wa...
Application of statistics in textiles	 39
conclude that the process is out of control. To take account of this line
of rea...
40	 Process control and yarn quality in spinning
	 (c)	 A sequence of points sometimes occurs in which all the points lie
...
Application of statistics in textiles	 41
	14.	 Tippet, L.H.C. (1930), Statistical methods in textile research – Part 1, J...
Abstract: This chapter discusses about the significance of raw material selection
in a spinning industry for the productio...
Cotton fibre selection and bale management system	 43
variability. These mixed cottons were then rebaled and fed to the op...
44	 Process control and yarn quality in spinning
Such control is economically important because cotton cost and related mi...
Cotton fibre selection and bale management system	 45
	 •	 Stickiness
	 •	 Colour and grade
	 •	 Contamination
	 These fib...
46	 Process control and yarn quality in spinning
Figure 3.3  Effect of cotton fibre properties on rotor-spun yarn strength...
Cotton fibre selection and bale management system	 47
Table 3.2  Consequences of poor fibre quality
Fibre property Descrip...
48	 Process control and yarn quality in spinning
Fibre property Description Ideal range Consequences
of poor fibre
quality...
Cotton fibre selection and bale management system	 49
Fibre property Description Ideal range Consequences
of poor fibre
qu...
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  1. 1. Process control and yarn quality in spinning © 2016 by Woodhead Publishing India Pvt. Ltd.
  2. 2. Process control and yarn quality in spinning G. Thilagavathi and T. Karthik WOODHEAD PUBLISHING INDIA PVT LTD New Delhi © 2016 by Woodhead Publishing India Pvt. Ltd.
  3. 3. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 Woodhead Publishing India Pvt. Ltd. 303, Vardaan House, 7/28, Ansari Road Daryaganj, New Delhi – 110002, India © 2016 by Woodhead Publishing India Pvt. Ltd. Exclusive worldwide distribution by CRC Press an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20150824 International Standard Book Number-13: 978-93-80308-18-0 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reason- able efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www. copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organiza- tion that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com For information about WPI Publishing visit their website at http://www.woodheadpublishingindia.com © 2016 by Woodhead Publishing India Pvt. Ltd.
  4. 4. 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 3.4 Spinning Consistency Index (SCI) 50 © 2016 by Woodhead Publishing India Pvt. Ltd.
  5. 5. 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 vi Process control and yarn quality in spinning © 2016 by Woodhead Publishing India Pvt. Ltd.
  6. 6. 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 253 and V-L curve 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 Contents vii © 2016 by Woodhead Publishing India Pvt. Ltd.
  7. 7. 11. Yarn faults 325 11.1 Introduction 325 11.2 Distinction between frequent and seldom-occurring 328 yarn faults 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 Index 401 viii Process control and yarn quality in spinning © 2016 by Woodhead Publishing India Pvt. Ltd.
  8. 8. 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 statisticalqualitycontrol in textileindustry. Chapter 3 converses about the significance of raw material selection and bale management in a © 2016 by Woodhead Publishing India Pvt. Ltd.
  9. 9. 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. x Process control and yarn quality in spinning © 2016 by Woodhead Publishing India Pvt. Ltd.
  10. 10. 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  11. 11. 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–Itdenotesauniformsetofmeasures,agreements,conditionsor specifications between parties, i.e. between buyer and seller or manufacturer– 1 Quality management © 2016 by Woodhead Publishing India Pvt. Ltd.
  12. 12. 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 Process parameters Input parameters (Raw material specification & quality) (Product specification) (QC department, marketing & consumer) Feed back Textile production system Output parameters 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 Product Usage Performance, aesthetic, functional, cost Technology, machine, process parameters Material specification Figure 1.2  Quality feedback cycle © 2016 by Woodhead Publishing India Pvt. Ltd.
  13. 13. Quality management 3 1.4 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 atTokyo 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-and- Effect Diagram. Eccentric gear Improper meshing Periodic variation Missing of teeth in gear Eccentric rollers Causes Effect Figure 1.3  Cause-and-Effect Diagram for periodic variation in yarn © 2016 by Woodhead Publishing India Pvt. Ltd.
  14. 14. 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 Warp breaking Weft breaking Shuttle trap Shuttle change Slack weft Faulty transfer No pim transfer Miscellaneous 23 26 14 40 20 13 27 38 Number of defects 5 10 15 20 25 30 35 40 45 50 Total 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  15. 15. 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 392 346 132 141 344 170 164 14 10 2 6 2 7 0 3.6 2.7 1.5 4.2 0.6 4.1 0 8 7 6 5 4 3 2 1 0 1 2 %Defective UCL LCL X 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  16. 16. 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 (non- routine 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 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. 50 45 40 35 30 25 20 15 10 5 0 No.offaults Longthick Longthin Neps Foreigncuts Shortthick 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  17. 17. 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 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. 50 45 40 35 30 25 20 15 10 5 0 No.offaults Neps Shortthick Longthick LongThin Foreigncuts 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  18. 18. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  19. 19. 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 YolkeFrontBackSleeves Under front Collar department Assembly of parts Join shoulder seam Join collar to shirt Set sleeve Cuff attachment Button attachment Finishing Packing Cuff department Figure 1.9  Flow chart of manufacturing process for shirt © 2016 by Woodhead Publishing India Pvt. Ltd.
  20. 20. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  21. 21. Quality management 11 1.5.1 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 ?? Higher demand for consistent quality Globalized competition Volatile raw material prices Increased energy costs Problems Faced by the Textile Industry in India 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  22. 22. 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  23. 23. 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 (RD) General Manager (RD) 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 (RD). His responsibilities are: © 2016 by Woodhead Publishing India Pvt. Ltd.
  24. 24. 14 Process control and yarn quality in spinning Chairman/managing director General manager Factory manager Human resources manager General manager finance Godown keeper Shift supervisors Quality control manager Workers Filters Time keeper Accounts manager Purchase manager Sale manager Costing manager Assisant Assisant Assisant AssisantCashier Canteen Store keeper 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  25. 25. 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 Adetailed 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  26. 26. 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 RD 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  27. 27. Quality management 17 1.6.2 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  28. 28. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  29. 29. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  30. 30. 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 2 Application of statistics in textiles © 2016 by Woodhead Publishing India Pvt. Ltd.
  31. 31. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  32. 32. 22 Process control and yarn quality in spinning 2.2 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 X = X N Σ 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 = 156 121 150 114 130 156 144 130 139 130 10 + + + + + + + + + = 1370 10 = 137 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  33. 33. Application of statistics in textiles 23 2.2.2 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 WX = WX W Σ Σ Where ‘ WX ’ 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. WX = ( ) ( )(60 3.5) 30 4.0 10 2.8 500 100 100 × + × + × = = 5 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; © 2016 by Woodhead Publishing India Pvt. Ltd.
  34. 34. 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 Mode Mode Median Median Mean Right skewed Left skewed 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 = 1 2 3 nN X X X ......... X 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 log GM = 1 2 n 1 (logX logX ... log X ) N + + + Therefore, GM = Antilog 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  35. 35. Application of statistics in textiles 25 2.2.6 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. HM = 1 2 N N 1 1 1 1 1 .... X X X X =  + + Σ    For instance, the harmonic mean of 20 and 40 is calculated as follows: HM = N 1 X   Σ    = 2 2 80 1 1 3 3 20 40 40 = = + = 26.67 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 = 2 (X X) N 1 s − − 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  36. 36. 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 (X X)Σ − = 1404.50 Standard Deviation s = 2 (X X) 1404.5 N 1 9 Σ − = − = 12.49 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  37. 37. 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. CV1 = 12.5 100 136.5 × = 9.16 CV2 = 12.5 100 136.5 × = 7.82 CV3 = 12.5 100 134.3 × 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  38. 38. 28 Process control and yarn quality in spinning curve which is also the reason why such a distribution is called as ‘normal’ (Fig. 2.3). 13.6% 2.1% 2.1% 0.1% –3s –2s –1s 1s 2sµ 0.00.10.20.30.4 0.1%13.6% 34.1% 34.1% 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 = x(x µ) 2 1 e 2 − − s s π 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  39. 39. 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. f(r) = –µ r e .µ 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  40. 40. 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  41. 41. Application of statistics in textiles 31 From statistical tables, t (for s = 95%, f = 4) = 2.78 Q95% = 2.78 3.15 5 × ± = ±39 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 / n − s 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  42. 42. 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 = 1 2 3 4 5X X X X X 5 + + + + 8 5.4 6.4 6.8 7 5 + + + + = 6.72 Standard Deviation (of the means) = 0.94 Confidence Range X ± X95% = t.s X n + 6.72 ± (2.78 0.94) 5 × = 6.72 ± 1.17 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 10 × = 11.25 ± 0.78 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  43. 43. Application of statistics in textiles 33 2.5.1 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 = 1 2 2 2 1 2 1 2 | X X | S S n n × + Where 1X = Mean of first test series 2X = 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., 2 2 1 2 1 2 s s n n + represents the standard error of the difference of the two means. If the number of readings in both the test series is the same, the equation simplifies to tcal = 1 2 2 2 1 2 n | X X | X S S × × 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 = 2 2 30 |16.5 17.2 | X (0.78 ) (0.85) + + = 3.32 tsf = (s = 95%f = (30 – 1) + (30 – 1) = 58) = 2.00 © 2016 by Woodhead Publishing India Pvt. Ltd.
  44. 44. 34 Process control and yarn quality in spinning Since tcal is greater than tsf , there is significant difference between the two means and the CV% 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 = 1 2 s 1 2 | X X | 1 1 n n − + Where ‘s’ is the pooled standard deviation given by S = 2 2 1 1 2 2 1 2 (n 1)S (n 1)S n n 2 − + − + − 2.5.3 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  45. 45. Application of statistics in textiles 35 2.5.4 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 t = 1 2 2 2 1 2 1 2 |S S | S S 2n 2n − + 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 = 2 2 21 1 22 2 S , where S S S 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) In this case, Fcal = 2 2 2 1 S S since 462 422 = 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  46. 46. 36 Process control and yarn quality in spinning 2.6 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  47. 47. 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 1 2 3 4 5 6 7 8 9 10 11 Time (sample no) UL LL 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  48. 48. 38 Process control and yarn quality in spinning Upper Warning Limit = X 2+ s Upper Action limit = X 3+ s Lower Warning Limit = X – 2s Lower Action Limit = X – 3s The limits are shown in Fig. 2.7. Upper action limit (UAL) Upper warning limit (UAL) µf +3.09s f µf +1.9s f µf –1.96s f µf –3.09s f µf 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  49. 49. 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) (b) (d) (d) TimeTime Time Time TimeTime 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  50. 50. 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,Areview 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  51. 51. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  52. 52. 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 3 Cotton fibre selection and bale management system © 2016 by Woodhead Publishing India Pvt. Ltd.
  53. 53. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  54. 54. 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) © 2016 by Woodhead Publishing India Pvt. Ltd.
  55. 55. 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 4 – Cleanliness Length and length uniformity Length and length uniformity 5 – – Friction Cleanliness 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 © 2016 by Woodhead Publishing India Pvt. Ltd.
  56. 56. 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. © 2016 by Woodhead Publishing India Pvt. Ltd.
  57. 57. 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 Consequences of poor fibre quality – spinning 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 UHML in excess of 1.125 inch or 36/32nds; For premium fibre 1.250 or 40/32nds Significant price discounts below 33/32nds 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 Short fibre content Short fibre content (SFC) is the proportion by weight of fibre shorter than 0.5 inch or 12.7 mm 8% No premiums or discounts apply The presence of short fibre in cotton causes increases in processing waste, fly generation and uneven and weaker yarns Uniformity Length uniformity or uniformity index (UI) is the ratio between the mean length and the UHML expressed as a percentage 80% Small price discounts at values less than 78; No premiums apply Variations in length can lead to an increase in waste, deterioration in processing performance and yarn quality Micronaire 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. 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. 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 Strength 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 The ability of cotton to withstand tensile force is fundamentally important in spinning. Yarn and fabric strength correlates with fibre strength Contd... © 2016 by Woodhead Publishing India Pvt. Ltd.
  58. 58. 48 Process control and yarn quality in spinning Fibre property Description Ideal range Consequences of poor fibre quality – cotton price Consequences of poor fibre quality – spinning 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 MID 31, small premiums for good grades Significant discounts for poor grades 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. Trash / dust Trash refers to plant parts incorporated during harvests, which are then broken down into smaller pieces during ginning Low trash levels of 5% High levels of trash and the occurrence of grass and bark incur large price discounts. 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. 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. 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 In dry crop conditions seed- coat fragments may contribute to the formation of a (seed-coat) nep. Low / none Moderate price discounts Seed-coat fragments do not absorb dye and appear as ‘flecks’ on finished fabrics. Neps Neps are fibre entanglements that have a hard central knot. Harvesting and ginning affect the amount of nep. 250 neps/ gram. For premium fibre 200 Moderate price discounts. Neps typically absorb less dye and reflect light differently and appear as light coloured ‘flecks’ on finished fabrics. Contd... Contd... © 2016 by Woodhead Publishing India Pvt. Ltd.
  59. 59. Cotton fibre selection and bale management system 49 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 Classing laboratories Textile mills Procurement Ware housing Mix selection Off-line process control Ware housing Marketing On-line process control On-line classing colour and leaf Off-line classing micronaire, strength and length 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, Contd... © 2016 by Woodhead Publishing India Pvt. Ltd.

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