Process control and yarn quality in spinning woodhead publishing-karthik

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
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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

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
6.2 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
7.2 Categories of yarn faults 197
vi Process control and yarn quality in spinning

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
9.2 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
10.2 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

11. Yarn faults 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
12.2 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

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

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

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

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

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

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

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

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.

• 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.

• 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

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.

– 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

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

1.5.1 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.

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

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:

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.

(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.

(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

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

• 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.

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.

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

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.

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.

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;

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.

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.

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.

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

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

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.

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

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

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.

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

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

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.

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

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.

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

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.

(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.

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.

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

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.

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)

• 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

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.

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...

of poor fibre
quality –
cotton price
Consequences of
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...

of poor fibre
quality –
cotton price
Consequences of
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...

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