The document discusses fabric defects, their causes, and analysis methods. It defines fabric defects as abnormalities that hinder consumer acceptability. Defect-free fabrics exhibit consistent performance and appearance. Fabric defects can be caused by machine-related factors like failures in preparation machines or maintenance issues, and material-related factors like fiber contaminants or mixing inconsistencies. Advanced testing systems at Auburn University can conduct defect analysis and diagnostics to identify the root causes of defects through examination of fabric and yarn samples. Understanding the causes of defects can help prevent reoccurrence and improve quality.

1. 31

2. What is a Fabric Defect?
A Fabric Defect is any abnormality in the Fabric
that hinders its acceptability by the consumer
What is a Defect-Free Fabric?
• A Fabric that exhibits a consistent
Performance
Within the boundaries of human use & human
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view
• A Fabric that exhibits a consistent Appearance
Within the human sight boundaries

3. What are the Factors that could lead to
Fabric Defects?
Machine-Related Factors:
• Failure of spinning preparation to eliminate or minimize short
and long-term variation
• Failure of opening and cleaning machines to completely
eliminate contaminants and trash particles
• Failure of the mixing machinery to provide a homogenous blend
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• 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

4. What are the Factors that could lead to
Fabric Defects?
Material-Related Factors:
• Fiber contaminants
• Excessive neps and seedcoat fragments
• Excessive short fiber content
• Excessive trash content
• High variability between and within-mix
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• Clusters of unfavorable fiber characteristics
• Weight variation
• Twist variation
• Excessive Hairiness

5. 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 Diagnostic
systems including optical and scanning Microscopic systems,
and all advanced physical & chemical testing techniques of
fibers, yarns, and fabrics.
Since 1989, we have handled over 3000 disputes for over 28
companies with a feedback rate down to few hours depending
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on the case in hand.
Now, we have a Diagnostic-Expert Software program which assist
in speeding up diagnostic fabric defects analysis using a large
image-base & an image-recognition & comparison system.
Examples from the image-base bank we have are shown below

6. Fabric Barré
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• Material or machine related
• Mixing is often a prime suspect

7. Weaving
Raw-Material
Uneven Warp
Tension
Excessive Between-Mix Variation
in Color +b or Rd Excessive Between-Mix Variation
Uneven Filling in Fiber Fineness
Tension Excessive Within-Mix Variation
in Color +b or Rd
Excessive Within-Mix Variation
in Fiber Fineness
Uneven Let-Off or
Take-up Motion
ℑ
Fabric Barrℑ
High Count
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Improper
Stitch Length Variation
Worn* High Hairiness
Variation
Needles
Improper High Twist
Feed Tension (knitting) Variation
Excessive Lint Mixing Fresh
Build-Up High Yarn with Stored Yarns
Variation in Fabric
Take-up from loose Irregularity
Double-Feed & Imperfection
to tight Yarn
End
Knitting
Different Causes of Fabric Barre
[ * usually produce length direction streaks]

8. Shade Variation
38
• Material or machine related
• Dyeing & Finishing
• Mixing is often a suspect

9. 39
Synthetic Fiber Contaminant

10. 40
White Specs

11. 41
Small Bits of contaminants Spun into the Yarn

12. Filling Streaks &
Slubs of Varying
Lengths
42
Weak Spots
(Over-bleaching)

13. Short Thick Places
8 cm
~2d d
43

14. Long Thick Places
>> 40 cm (16 inch)
d
~40% to 100% of d
44

15. Spun-in or knit-in
Contaminant?
45

16. Spun-in or knit-in
Contaminant?
46

17. Spun-in or knit-in
Contaminant?
47

18. Modeling Fabric Defects: The Problem-Theory
Fabric Defect =
f (macroscopic parameters, microscopic parameters, noise parameters)
Fabric Defect = f (MaP’s, MiP’s, Noise)
MaP =
48
f (visual illusion, physical reflection, gross parameters)
MiP =
f(within-yarn variation, clustering effects, color
breakdown failure)
Noise =
f (Unknown Parameters, information resolution loss)

19. The Textile Process Does not Eliminate Variability….
Indeed, it is quite the opposite. As materials flow from one stage
of processing to another, components of variability are added and
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the final product involves a cumulative variability that is much
higher than the variability of the input fibers.

20. The Textile Product is Positively Deceiving.
The main reason, the consumer does not realize the large
magnitude of variability in the final product (fabric or garment)
is that the different components of variability have been
smoothed during processing to produce a product that exhibits
a pattern of “Consistent Variability” at the naked-eye visual
boundaries.
50

21. Poor Cotton Mixing is a Sure Defect-Causing
Factor & Good Mixing alone Does not Always
Guarantee a Defect-Free Fabric
Machine-Related Factors cannot be emphasized enough
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99% of Fabric-Defects can be diagnosed with
minimum or no testing if every involved personnel
from the fiber to the fabric sector is willing to honestly
tells his/her side of the story.
Fabric-defect diagnostic work has become more of detective
work because of missing facts

22. When business is good, fabric defects are
normally at their lowest rate… Coincident!!
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In the absence of a well-established problem
theory, in which backward projection of fabric
quality is the foundation, fabric defects of the
same type will always re-occur.

23. Current yarn testing techniques reveal minimum
or no information about potential causes of
Fabric defects.
It is truly disturbing that high cost yarn testing equipments available today
reveal minimum or no prediction of potential fabric defects. Indeed, there
is a significant gap between yarn quality as tested in the yarn raw form
and corresponding yarn quality as it exists in the fabric. For instance, the
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50 cm yarn length used to test yarn strength often proves no correlation
with fabric strength or weaving performance. The capacitive mass variation
measures often prove meaningless with respect to fabric weight variation.

24. 54

25. 55

26. Micronaire
Bale
Population Color +b
µ Short Fibers
Cotton Mixes
Upper Control Limit
Process Average x
56
Ba
le L
Center Line
ayd
ow
n
Lower Control Limit
Out of
control
Time x

27. Micronaire
Color +b
µ Short Fibers
Cotton Mixes
57
Ba
le L
ayd
ow
n
x

28. Run
Trend
Trend
Trend
Run
Between-Mix Runs or Trends
58
Between-Mix Pattern

29. 59

30. Bale
Population Micronaire
Short Fibers
Rp
Color
R1 Cotton Mixes
Upper Control Limit
Process Range (R)
R2
60
Tim
e
Center Line R3
Lower Control Limit R4
Time R5
R

31. Micro-Sections
Macro-Sections
61
>>>> FL <<< FL
Ideally-Blended Fiber Strand: Definition
“A fiber strand that has approximately zero variability
between consecutive macro-sections and a variability
of micro-sections that perfectly reflects the natural
variability in the constituent fibers of the input fiber
mix”

32. 62

33. The Dimensional Allocation
of Different Fiber Segments
within the Structural Boundaries
of the Fibrous Assembly
63

34. The Representation Factor
R ij M icro
= P { F Fi / F L j M icro S }
&
64
R ij M acro
= P { F Fi / F L j M a cro S }
where Rij is the representation factor of a certain fineness/length combination in the
micro-section or macro-section of fiber strand.

35. The Clustering Effect
σn = C n q
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σn = The standard deviation of the No. of fibers/Cs
C = the average number of fiber ends per cluster
P = 1-q = n/nmax

36. 0.014
0.013
0.014
0.012
0.013
0.011
0.012
P(Macro)
0.01
0.011
0.009
P(Macro)
0.01
0.008
0.009
66
0.007
0.008
0.006
0.007
5
0.006
4.5
5 c
1.1 Mi
1.1 4
5
1.0 1 3.5
FL 5
0.9
Relationship Between the Probability of Representation of Fibers of
Mic/FL Combination in the Macro-Section of Yarn [Ne = 20’s]
P(Macro) = 0.016014+ 0.0665027/Mic+ 0.0113814/FL

37. 0.25
P{Ffi/FLjITuft}
0.2
0.15
120%
0.1
0.05
67
0
C11
C12
C13
C21
C22
C23
C31
C32
C33
Cshort
Fineness/Length Category
Comparison Between Probabilities of Representation in Micro-Sections and
Macro-Sections of Fiber Strand [Yarn]

38. Appearance (Visual) Blending:
The Homogenization of Different
Fiber Colors in the Fiber Assembly
68

39. The Representation Factor
Of Color
R ij M icro = P {( +b ) i M icroS } ≈ 1
69
&
R ij M acro = P {( +b ) i M acro S } ≈ 1

40. The Representation Factor
Yarn Cross-Sections
Percentage No.in
Intimate
Blending
% Black Fibers
70
Yarn Cross-Sections
Percentage No.in
Draw
Blending
% Black Fibers

41. The Clustering Effect
Clusters of Similar Color
Fibers
71

42. • They Undergo Changes During Processing
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• They embed in the fiber bulk very
cleverly and manage to survive
• They cluster

43. Mic Difference
ce
0.7
SF
en
C
er
D
iff
iff
D
er
0.5
3%
FL
en
1
0.
ce
2%
05
0.2
0.
1%
04
0.1
0.
200 100 50
FS Difference
1.2 2 3
Neps/g
2
73
Difference
5
0
1
1.
6
1
0
2
2.
UV Range
2
3
FE
0
FM
3.
%
3
D
V
iff
er
en
+b Difference
ce
Threshold Values of Between-Mix Variability

44. C.V% Mic
12
M
ax
10
.S
FL
14
FC
%
8
6
w
.V
12
C
5
10
6
8
4
4
6
3
2
4
400 200 100
2
C.V% FS
3 5 7 9 11 13
5
Neps/g
0.
10
74
4
2
15
0
1.
20
5
4
0
3.
6
6
7
0
UV Range
4.
8
8
C
.V
FM
9
0
10
%
6.
V
FE
12
C.V% +b
Threshold Values of Within-Mix Variability

45. Closing Remarks
• Every defect should not be treated only as a passing loss, but more importantly
as 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 a
significant 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
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reveal virtually no indication of potential fabric defects. New approaches to
yarn 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