The document discusses approaches for improving cotton grading and efficiency in the textile value chain. It analyzes data from cotton testing at various stages of processing, from the lay down to carding, combing, and drawing. The analysis shows that fiber quality metrics like micronaire and tensile properties remain consistent as cotton moves through processing, and can be used to predict fiber behavior and optimize machinery settings to manage quality and productivity. The paper models the data using statistical and predictive techniques to provide insights for higher order process control across the textile value chain.

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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)