fabric defects cotton mix
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A Fabric Defect is any abnormality in the Fabricthat hinders its acceptability by the consumer
A Fabric that exhibits a consistentPerformance
Within the boundaries of human use & humanview
A Fabric that exhibits a consistent AppearanceWithin the human sight boundaries
What is a Fabric Defect?
What is a Defect-Free Fabric?
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Machine-Related Factors:
Failure of spinning preparation to eliminate or minimize shortand long-term variation
Failure of opening and cleaning machines to completelyeliminate contaminants and trash particles
Failure of the mixing machinery to provide a homogenous blend Excessive machine stops particularly during spinning Excessive ends piecing during spinning preparation Poor maintenance and housekeeping
Weaving-related defects Knitting-related defects Dyeing and Finishing-related defects
What are the Factors that could lead to
Fabric Defects?
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Material-Related Factors:
Fiber contaminants Excessive neps and seedcoat fragments Excessive short fiber content Excessive trash content High variabil ity between and w ithin-mix Clusters of unfavorable fiber characteristics Weight variation Tw ist variation
Excessive Hairiness
What are the Factors that could lead to
Fabric Defects?
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At Auburn University Testing Laboratory, we have a very sound
sample analysis program in which we perform systematic Fabric& Yarn defect-diagnostic analysis and provide complete reports.
Our laboratory has state-of-the-art Testing and Diagnosticsystems including optical and scanning M icroscopic systems,and all advanced physical & chemical testing techniques offibers, yarns, and fabrics.
Since 1989, we have handled over 3000 disputes for over 28
companies with a feedback rate down to few hours dependingon the case in hand.
Now , we have a Diagnostic-Expert Software program which assist
in speeding up diagnostic fabric defects analysis using a largeimage-base & an image-recognition & comparison system.
Examples from the image-base bank we have are shown below
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Fabric Barr
Material or machine related
Mixing is often a prime suspect
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Fabric Barr
Raw-Material
Excessive Between-Mix Variatio
in Fiber Fineness
Excessive Within-Mix Variationin Fiber Fineness
Excessive Between-Mix Variation
in Color +b or Rd
Excessive Within-Mix Variation
in Color +b or Rd
Yarn
High CountVariation
High Twist
Variation
High Hairiness
Variation
Mixing Fresh
with Stored YarnsHigh Yarn
Irregularity
& Imperfection
Knitting
ImproperStitch Length
Improper
Feed Tension (knitting)
Variation in Fabric
Take-up from loose
to tight
Excessive Lint
Build-Up
Worn*Needles
Double-Feed
End
Weaving
Uneven Warp
Tension
Uneven Let-Off or
Take-up Motion
Uneven Filling
Tension
Different Causes of Fabric Barre
[ * usually produce length direction streaks]
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Shade Variation
Material or machine related
Dyeing & Finishing
Mixing is often a suspect
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Synthetic Fiber Contaminant
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White Specs
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Small Bits of contaminants Spun into the Yarn
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Fill ing Streaks &Slubs of Varying
Lengths
Weak Spots(Over-bleaching)
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8 cm
d~2d
Short Thick Places
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>> 40 cm (16 inch)
d
~40% to 100% of d
Long Thick P laces
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Spun-in or knit-inContaminant?
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Spun-in or knit-inContaminant?
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Spun-in or knit-inContaminant?
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Modeling Fabric Defects: The Problem-Theory
Fabric Defect =f (macroscopic parameters, microscopic parameters, noise parameters)
Fabric Defect = f (MaPs, M iP s, Noise)
MaP =f (visual illusion, physical reflection, gross parameters)
MiP =
f(w ithin-yarn variation, clustering effects, colorbreakdown failure)
Noise =
f (Unknown Parameters, informat ion resolution loss)
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The Textile Process Does not Eliminate Variability.Indeed, it is quite the opposite. As materials flow from one stageof processing to another, components of variability are added and
the final product involves a cumulative variability that is muchhigher than the variability of the input fibers.
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The Textile Product is Positively Deceiving.The main reason, the consumer does not realize the largemagnitude of variability in the final product (fabric or garment)
is that the different components of variability have beensmoothed during processing to produce a product that exhibitsa pattern of Consistent Variability at the naked-eye visual
boundaries.50
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Poor Cotton Mixing is a Sure Defect-CausingFactor & Good Mixing alone Does not AlwaysGuarantee a Defect-Free Fabric
Machine-Related Factors cannot be emphasized enough
99% of Fabric-Defects can be diagnosed withminimum or no testing if every involved personnel
from the fiber to the fabric sector is willing to honestlytells his/her side of the story.Fabric-defect diagnostic work has become more of detective
work because of missing facts
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When business is good, fabric defects arenormally at their lowest rate Coincident!!
In the absence of a well-established problem
theory, in which backward projection of fabricquality is the foundation, fabric defects of thesame type will always re-occur.
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Current yarn testing techniques reveal minimumor no information about potential causes of
Fabric defects.It is truly disturbing that high cost yarn testing equipments available todayreveal minimum or no prediction of potential fabric defects. Indeed, thereis a significant gap between yarn quality as tested in the yarn raw formand corresponding yarn quality as it exists in the fabric. For instance, the50 cm yarn length used to test yarn strength often proves no correlationwith fabric strength or weaving performance. The capacitive mass variationmeasures often prove meaningless with respect to fabric weight variation.
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Micronaire
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BalePopulation
Cotton Mixes
BaleLayd
own
xTime
Upper Control Limit
Center Line
Lower Control Limit
ProcessAverage
x
Out ofcontrol
MicronaireColor +b
Short Fibers
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Micronaire
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Cotton Mixes
BaleLayd
own
x
MicronaireColor +b
Short Fibers
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Between-Mix Pattern
Run
RunTrend
Trend
Trend
Between-Mix Runs or Trends58
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Bale
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Cotton Mixes
R
Tim
e
R1
R2
R3
R4
R5
BalePopulation
Rp
Time
P
rocessRang
e(R) Upper Control Limit
Center Line
Lower Control Limit
Micronaire
Short FibersColor
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Macro-SectionsMicro-Sections
>> FL
A fiber strand that has approx imately zero variabilitybetween consecutive macro-sections and a variabilityof micro-sections that perfectly reflects the naturalvariability in the constituent fibers of the input fiber
mix
Ideally-B lended Fiber Strand: Definition
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The Dimensional Allocationof Different Fiber Segments
w ithin the Structural Boundariesof the Fibrous Assembly
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i j M icro i j
i j M ac ro i j
R
R
P F F F L M ic ro S
P F F F L M a c ro S
=
=
{ / }&
{ / }
where Rij is the representation factor of a certain fineness/length combination in the
micro-section or macro-section of fiber strand.
The Representation Factor
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Th Cl t i Eff t
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n
C n q=
n = The standard deviation of the No. of fibers/ CsC = the average number of fiber ends per clusterP = 1-q = n/ nmax
The Clustering Effect
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3.5
4
4.5
5
Mic
0.95
11.05
1.11.15
FL
0.006
0.006
0.007
0.0070.008
0.008
0.009
0.009
0.01
0.01
0.011
0.011
0.012
0.012
0.013
0.013
0.014
0.014
P
(Macro)
P(Macro)
Relationship Between the Probability of Representation of Fibers ofMic/FL Combination in the Macro-Section of Yarn [Ne = 20s]
P(Macro) = 0.016014+ 0.0665027/Mic+ 0.0113814/FL
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0
0.05
0.1
0.15
0.2
0.25
C11
C12
C13
C21
C22
C23
C31
C32
C33
Cshort
Fineness/Length Category
P{Ffi/FLjITuft}
120%
Comparison Between Probabilities of Representation in Micro-Sections and
Macro-Sections of Fiber Strand [Yarn]
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A (Vi l) Bl di
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Appearance (Visual) Blending:
The Homogenization of Different
Fiber Colors in the Fiber Assembly
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ij M icro i
ij M ac ro i
R P b M icro S
R P b M acro S
= +
= +
{ ( ) }
&
{( ) }
1
1
The Representation FactorOf Color
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The Representation Factor
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Intimate
Blending
Draw
Blending
% Black Fibers
PercentageNo.in
YarnCross-Sections
PercentageNo.in
YarnCross-Sections
% Black Fibers
p
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The Clustering Effect
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Clusters of Similar Color
Fibers
The Clustering Effect
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They Undergo Changes During Processing
They embed in the fiber bulk verycleverly and manage to survive
They cluster
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ference
Mic DifferenceSFCD
0.7
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Threshold Values of Betw een-Mix Variability
FS Difference
FE%Differen
ce
FLDiffe
r
+b Difference
Neps/g
Difference
VFM
Difference
1.2 2 3
1
2
3
0
.04
0.05
0.1
200 100 50
3
%2%
1%
0.1
0.2
0
.5
1
2
3
UV Range
3.0
2.0
1.0 2 5
6
73
C.V% Mic
10
12
FL
Max.S
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2
4
6
8
1
Threshold Values of Within-M ix Variability
C.V% FS
C.V%FE
C.V%
FL
C.V% +b
Neps/g
VFM
SFCw
3 5 7 9 11 13
4
56
7
89
2
3
4
5
6
6.0
4.0
3.0
1.0
0.5
400 200 100
1412 10
8
6
4
2
4
6
8
10
12
UV Range
10 15
20
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Closing Remarks
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Every defect should not be treated only as a passing loss, but more importantlyas an opportunity to learn more about the root causes of the defect.
As many defects as we see on daily basis we often focus on the effect and
overlook the root causes
The traditional approach of dealing with quality problems passively unless asignificant cost is encountered should give way to more intelligent approaches
in which problem prevention in the first place is the key factor
Current yarn testing techniques are based on traditional thinking and they
reveal virtually no indication of potential fabric defects. New approaches toyarn testing based on fresh innovative thinking should be introduced
When the same type of defects reoccur once, it is perhaps because we failed to
discover the root causes the first time. When the same defect reoccurs
100 times, our intelligence becomes largely in question In the era of SIX SIGMA, you can either lead, follow closely or get out
out of the track Defects are not only about cost or loss, they are more
importantly about customer trust and confidence
Yehia El Mogahzy
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