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COTTON GRADING – THE APPROACHES FOR TEXTILE VALUE

CHAIN EFFICIENCY

Presenting and communicating author: Debashish Banerjee – CEO, Blackstone

Synergy Consulting Group Limited, Nairobi-00604, Kenya

db@blackstonesynergy.com

The HVI and the AFIS and related instruments have been used extensively for grading

cottons from all over the world for quite some time now. However, the relationship of the

quality variables with the performances in the process in the textile value chain have not

evolved to higher quality thresholds owing to a lack of application in integrating the

cotton variables in the process itself.

The paper strives to work in the grey areas of process engineering wherein the

variables of the cotton in the intermittent stages of the process like that after the

carding, the pre comber drawing, the comber, the noils, the draw frames and the roving

need to be analyzed to evaluate the impact of the machinery parameters on the process

and found the decisions in the process to manage quality and productivity.

The interpretations of data have always been skewed leading to incorrect evaluation

and decisions. The scope of the paper includes predicting solutions founded on the

detailed analysis of the in-process quality data of the cotton and correlating with the

mechanical and energy data of the process lines to create a decision tree for higher

order process control techniques.

Introduction

Cotton and other natural fibers have a perpetual enigmatic aura around itself causing

the technicians and the engineers in the realm of fibers to be baffled in their search for

lasting solutions to the processing problems in both the yarn and the fabric industry. A

host of parameters have been identified after high resolution imaging and segmentation

of the properties of cotton have been achieved using state of the art digital technologies

the world over during the course of the decades preceding the current times.

However, the challenges yet stem from the interpretation of data and integrating into the

process knowledge domains to enable the engineers to predict fiber behavior and the

machinery settings commensurate with the changes in the fiber properties.

The processing of the cotton through the yarn and fabric manufacturing technologies

throws up several challenges and the purpose sought to be achieved through this

research paper is to evaluate the technologies and the interpretation modules to arrive

at lasting solutions. The paper is more on data modeling and interpretation using

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advanced statistical and predictive analytic techniques that have great relevance in the

contemporary technologies right through the textile value chain.

Conceptual design

The key determinants of the cotton testing as in the HVI include the fiber length at

different population densities and the micron values for thickness apart from the

standard tensile properties like the elongation, breaking stress, the primary and

secondary creep values.

However, the test parameters are greatly influenced by the quality of the substrate or in

the context of natural fibers, the degrees of freedom enjoyed by the fiber clusters. The

honey dew and sugar content in the cotton as also the distribution of the fiber

coordinates in the clusters would define the resistance to slide and hence shall

determine the degrees of fiber freedom.

The test results pertaining to the tensile properties are distorted by the substrate

characteristics and so is the evaluation of the microns. Hence, the paper serves to

explore the effect of the different stages of the fibers in the processing value chain and

the reproducibility of the test results with the implications on the processing parameters.

An experimental design was adopted for bulk test results for a period of three months

with 50,000 test results on the HVI for cotton bales used in the lay down, the mix after

the beating points in the Trutzschler blow room typically having the CVT3, the mix in the

aero feed of the carding – TC-03 and then the slivers at all the satges in the process

inclusive of the carding, the comber after the pre-comber drawing, the combed sliver

and the draw frame.

Important derivations include the following:

1. The microns at different stages of the process are reproducible with time and

sampling domains.

2. The tensile properties are also compatible with the varying process conditions within

the lay down and between lay down and closely follow the inferential statistics at all

stages.

3. The color and reflectance characteristics closely follow the carding and comber

processing conditions and are reproducible in the value chain culminating in the knitted

fabrics; both at the greige and piece dyed stages and also in the woven structures.

4. Modeling of the data is statistically significant and interpretation can be arrived at with

higher reliability. The process conditions at the carding and the combing have greater

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influence nodes in the value chain and have significant bearing on the dye uptake and

color fixation properties on piece dyeing.

Analysis of data design

ANALYSIS OF THE LAY DOWN FIBER QUALITY - 1

Sampling Analysis

Structural Analysis of the cell

Maturity Analysis Length distribution Analysis

S. No. Micronaire 2 50% SL 85% SL 75% SL

2.5% SL

Slope - Gradient

1 4.3 0.25 16.32 8.33 8.93 29.35 0.30

2 4.4 0.15 14.87 4.67 8.85 31.22 0.23

3 4.3 0.11 15.25 5.73 7.96 30.56 0.16

4 4.6 0.09 15.35 5.78 8.94 31.22 0.13

5 4.3 0.22 14.97 6.15 9.21 30.76 0.31

6 4.4 0.27 13.93 5.97 9.23 29.88 0.38

7 4.5 0.17 14.77 4.88 8.75 30.56 0.25

8 4.3 0.18 15.55 5.32 9.15 31.22 0.26

9 4.2 0.09 14.97 5.36 8.88 30.88 0.13

10 4.3 0.13 15.33 5.43 7.95 31.26 0.19

11 4.2 0.12 14.98 5.33 8.92 30.77 0.17

12 4.5 0.23 15.43 5.37 8.85 29.95 0.32

13 4.4 0.22 15.45 5.66 9.23 30.25 0.30

14 4.2 0.18 15.66 4.99 9.15 29.87 0.25

15 4.1 0.15 14.99 4.37 8.75 28.99 0.22

16 4.3 0.17 15.37 4.85 9.14 29.23 0.24

17 4.5 0.11 15.88 4.86 8.88 32.04 0.16

18 4.3 0.13 14.99 5.17 8.92 31.86 0.20

19 4.3 0.19 14.96 4.93 9.02 29.84 0.27

20 4.2 0.22 15.76 5.29 7.99 29.77 0.32

21 4.2 0.15 15.47 5.39 7.86 31.22 0.22

22 4.3 0.16 15.83 4.76 8.53 30.55 0.23

23 4.3 0.16 15.39 4.83 9.11 30.65 0.23

24 4.3 0.15 14.95 5.61 8.85 29.97 0.21

25 4.3 0.16 14.95 5.97 8.94 31.22 0.23

26 4.4 0.17 15.32 6.21 9.21 32.08 0.24

27 4.3 0.17 14.97 6.22 9.17 31.33 0.24

28 4.3 0.15 15.53 4.87 8.79 30.43 0.22

29 4.3 0.15 15.38 5.31 8.94 29.85 0.21

30 4.3 0.15 14.96 4.97 8.96 29.65 0.21

Standardization -0.94 -0.91 -1.54 -0.92 -0.19 -0.54 -0.90

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The data array is drawn in from the lay down wherein sampling size is a minimum of

thirty for a normalized distribution with representation done randomly to represent the

fair homogeneity.

The HVI tests represent the micronaire as a fundamental function of maturity as

described by the variance in the population and then the length population across

different span lengths is analyzed for comparisons and determination of the slope

summation. The calculation is done by pegging the 2.5% SL and comparing with the

various SLs for approximating the slope configuration – an important definitive approach

for understanding the maturity of the cotton independent of the genetic attribute of

micronaire.

The major derivatives of the lay down analysis for the key behavioral traits are:

1. The variance of the micronaire determines the probability of barre and differences in

color absorption.

2. The slope range across the lay down and between the lay down define the basic

consistency of the cotton being processed; the process conditions remaining the same,

the output at each stage shall be expected to be conforming to narrower bandwidths.

3. The standardization process is simply the evaluation of the distances of the data

points from the claimed standard and the discrete nuclear distance is crystallized

therein.

4. The nuclear distances of each of the parameters in the evaluation matrix contribute to

the computation of the quality score.

5. All of these are attributes of the quality that are essentially defining the maturity of the

cotton.

Similarly attributes cluster-2 have the following derivations:

1. The tensile properties of elongation and breaking force at rupture are evaluated.

2. The work done to rupture and the determination of the creep points as also the

inflexion points for secondary creep give insights for understanding the resilience of the

cotton and more importantly the strain characteristics.

3. The stress-strain configurations can be understood from the creep coordinates;

greater the values, lower is the resilience available in delaying the curve of rupture and

vice-versa.

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ANALYSIS OF THE LAY DOWN FIBER QUALITY -2

Tensile properties of the structure

Elongation at break Force at break Work done to rupture Secondary creep coordinates

Elongation

%

2

Force

to

break

(cN/tex)

2

Work done

to rupture

2

(determined by the ratio

=time to reach secondary

creep / total time for rupture)

6.2 0.13 26.2 0.25 162 0.33 0.73

5.8 0.12 29.3 0.15 170 0.28 0.77

5.5 0.23 29.5 0.11 162 0.27 0.75

5.9 0.22 31.1 0.09 183 0.25 0.76

6.1 0.25 30.7 0.17 187 0.21 0.74

5.6 0.15 27.4 0.18 153 0.25 0.71

5.3 0.11 28.3 0.09 150 0.24 0.81

5.4 0.09 29.2 0.13 158 0.27 0.85

5.5 0.22 28.5 0.12 157 0.29 0.86

5.8 0.27 27.9 0.13 162 0.26 0.74

6.2 0.17 29.6 0.12 184 0.25 0.73

5.9 0.18 30.8 0.23 182 0.18 0.72

5.9 0.09 32.3 0.22 191 0.17 0.77

6.3 0.13 31.8 0.25 200 0.36 0.78

6.1 0.12 32.1 0.15 196 0.41 0.81

6.1 0.23 30.9 0.11 188 0.19 0.75

5.9 0.22 29.7 0.09 175 0.21 0.76

6.4 0.19 30.2 0.22 193 0.23 0.75

6.5 0.22 29.9 0.27 194 0.19 0.76

6.3 0.15 31.1 0.17 196 0.17 0.74

6.2 0.16 30.8 0.18 191 0.21 0.77

5.9 0.16 30.3 0.09 179 0.22 0.76

6.4 0.15 27.2 0.13 174 0.18 0.74

5.9 0.16 26.1 0.12 154 0.23 0.71

5.8 0.17 25.9 0.23 150 0.39 0.81

5.9 0.17 30.1 0.22 178 0.7 0.85

6.1 0.15 26.1 0.19 159 0.32 0.79

6.3 0.15 27.2 0.22 171 0.35 0.77

6.3 0.15 30.3 0.15 191 0.31 0.78

6.2 0.21 31.1 0.16 193 0.29 0.73

-0.64 -0.21 -0.11 -0.09 -0.43 -0.97 -0.25

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Inferences:

1. There shall be a mirror image of the progression in the fiber characteristics with

sampling population as the trends shall taper off so long as the parametric influences

are consistent. Absence of mirror images shall point to variances in the process; greater

is the influence of the electro-mechanical conditions of the card and comber than

anything else.

2. Threshold changes in the micronaire values reflect on the gradual improvements in

the quality extraction of immature and dead fibers in a balanced process.

3. Tracking the progression in the process on a continuum is the key determinant for

effective controls in the process.

3.6

3.8

4

4.2

4.4

4.6

4.8

5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Progressive analysis of micronaire

Micronaire - laydown

Micronaire - carding

Micronaire- pre comber DF

Micronaire - comber

Micronaire - DF

Poly. (Micronaire - laydown)

Poly. (Micronaire - carding)

Poly. (Micronaire- pre comber DF)

Poly. (Micronaire - comber)

Poly. (Micronaire - DF)

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Inferences:

1. The slope progressively closes in around zero – the proverbial ideal state for the

length distribution in the cotton at different stages in the process.

2. The effectiveness of parametric optimization in the process is a vital derivative of this

routine analysis. Absence of trends in the slope as we move progressively can imply

that the parameters and the state of the machinery are not optimum and call for

adjustments and corrections in the process.

3. The fundamental variances in the pre-comber stage is the rallying point since this

part of the process prepares the configuration of the fibers for an effective combing as

also defines the degree of freedom in the fiber clusters that are vital to actual extraction

of the immature fibers.

4. So long as the trends are reproducible across different lay down separated by

timelines, the consistency in the process is assured with respect to subsequent dye

affinity and migration properties in both the yarn and the fabric stages; both knitted and

woven structures.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Slope Analysis for length distribution with precomber DF as referenceSlope - Gradient - laydown

Slope - Gradient - carding

Slope - Gradient- precomber DF

Slope - Gradient Comber

Slope - Gradient - DF

Poly. (Slope - Gradient -laydown)

Poly. (Slope - Gradient -carding)

Poly. (Slope - Gradient-precomber DF)

Poly. (Slope - Gradient Comber)

Poly. (Slope - Gradient - DF)

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5. The utility of the analysis forms the foundation for AI (artificial intelligence)

approaches to yarn engineering.

Inferences:

1. The creep analysis is vital for generating insights in yarn performance for the weaving

and knitting applications; especially in composite yarns of high tensile behavior patterns.

2. The stress-strain behavior of the yarns can be predicted reliably and controls in the

process can prevent variation sin coloration while using colors exhibiting metamerism

and color composites having more than two color components.

3. The carding optimization should typically be done using these extrapolated process

curves of the cotton as tested in the HVI at different stages.

The author has extensively used these tools to optimize processes for reliability with

lower cotton quantum in the warehouse and at higher performance threshold.

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Secondary Creep Analysis with precomber DF as reference Secondary creep coordinates-

Laydown

Secondary creep coordinates -Carding

Secondary creep coordinates -Precomber DF

Secondary creep coordinates -Comber

Secondary creep coordinates - DF

Poly. (Secondary creep coordinates- Laydown)

Poly. (Secondary creep coordinates - Carding)

Poly. (Secondary creep coordinates -Precomber DF)

Poly. (Secondary creep coordinates - Comber)

Poly. (Secondary creep coordinates - DF)