Fabric defectscottonmix

A Fabric Defect is any abnormality in the Fabricthat hinders its acceptability by the consumer

• A Fabric that exhibits a consistentPerformanceWithin 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 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• 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?

33

Material-Related Factors:

• Fiber contaminants• Excessive neps and seedcoat fragments• Excessive short fiber content • Excessive trash content• High variability between and within-mix • Clusters of unfavorable fiber characteristics• Weight variation• Twist variation• Excessive Hairiness

What are the Factors that could lead to Fabric Defects?

34

At Auburn University Testing Laboratory, we have a very soundsample 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 Microscopic systems,and all advanced physical & chemical testing techniques offibers, yarns, and fabrics.

Since 1989, we have handled over 3000 disputes for over 28companies with a feedback rate down to few hours dependingon the case in hand.

Now, we have a Diagnostic-Expert Software program which assistin 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 Barré

• Material or machine related

• Mixing is often a prime suspect

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Fabric Barrℑℑℑℑ

Raw-Material

Excessive Between-Mix Variation in Fiber Fineness

Excessive Within-Mix Variation in Fiber Fineness

Excessive Between-Mix Variation in Color +b or Rd

Excessive Within-Mix Variation in Color +b or Rd

Yarn

High Count Variation

High Twist Variation

High Hairiness Variation

Mixing Freshwith Stored YarnsHigh Yarn

Irregularity& Imperfection

Knitting

Improper Stitch Length

Improper Feed Tension (knitting)

Variation in FabricTake-up from loose

to tight

Excessive LintBuild-Up

Worn* Needles

Double-FeedEnd

WeavingUneven Warp

Tension

Uneven Let-Off orTake-up Motion

Uneven FillingTension

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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Filling 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 Places

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Spun-in or knit-inContaminant?

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46


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Modeling Fabric Defects: The Problem-Theory

Fabric Defect =f (macroscopic parameters, microscopic parameters, noise parameters)

Fabric Defect = f (MaP’s, MiP’s, Noise)

MaP =f (visual illusion, physical reflection, gross parameters)

MiP =f(within-yarn variation, clustering effects, colorbreakdown failure)

Noise =f (Unknown Parameters, information resolution loss)

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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 the final product involves a cumulative variability that is much higher 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 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

Poor Cotton Mixing is a Sure Defect-CausingFactor & Good Mixing alone Does not AlwaysGuarantee a Defect-Free FabricMachine-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 honestly tells 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 are normally at their lowest rate… Coincident!!

In the absence of a well-established problemtheory, in which backward projection of fabric quality 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 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 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.

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BalePopulation

µµµµ

Cotton Mixes

Bale L

aydown

xTime

Upper Control Limit

Center Line

Lower Control Limit

Pro

cess

Ave

rage

x

Out of control

MicronaireColor +bShort Fibers

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µµµµ

Cotton Mixes

Bale L

aydown

x

MicronaireColor +bShort Fibers

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Between-Mix Pattern

Run

RunTrend

Trend

Trend

Between-Mix Runs or Trends

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Cotton Mixes

R

Tim

e

R1

R2

R3

R4

R5

BalePopulation

Rp

Time

Pro

cess

Ran

ge (

R) Upper Control Limit

Center Line

Lower Control Limit

MicronaireShort FibersColor

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Macro-SectionsMicro-Sections

<<< FL>>>> FL

“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”

Ideally-Blended Fiber Strand: Definition

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

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ij M icro i j

ij M acro i j

R

R

P F F F L M icro S

P F F F L M a cro S

=

=

{ / }

&

{ / }

where Rij is the representation factor of a certain fineness/length combination in themicro-section or macro-section of fiber strand.

The Representation Factor

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σn C n q=

σσσσn = 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

0.9511.051.11.15

FL

0.006

0.0060.007

0.0070.008

0.0080.009

0.0090.01

0.010.011

0.0110.012

0.0120.013

0.0130.014

0.014P

(Mac

ro) P(M

acro

)

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

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0

0.05

0.1

0.15

0.2

0.25

C11

C12

C13

C21

C22

C23

C31

C32

C33

Csh

ort

Fineness/Length Category

P{F

fi/F

LjI

Tuf

t}

120%

Comparison Between Probabilities of Representation in Micro-Sections and Macro-Sections of Fiber Strand [Yarn]

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Appearance (Visual) Blending:

The Homogenization of DifferentFiber Colors in the Fiber Assembly

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ij M icro i

ij M acro i

R P b M icroS

R P b M acro S

= + ≈

= + ≈

{( ) }

&

{( ) }

1

1

The Representation FactorOf Color

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Intimate Blending

Draw Blending

% Black Fibers

Per

cent

age

No.

in

Yar

n C

ross

-Sec

tion

sP

erce

ntag

e N

o.in

Y

arn

Cro

ss-S

ecti

ons

% Black Fibers

The Representation Factor

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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 very cleverly and manage to survive

• They cluster

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Threshold Values of Between-Mix Variability

FS Difference

FE% Difference

FL Diff

eren

ce

Mic Difference

+b Difference

Neps/gDifference

VFM

SFC Difference

1.2 2 3 1 2 3

0.0

4

0.0

5

0.1

200 100 50

3% 2%

1% 0.

1

0.2

0.5

0

.7

1 2 3

UV Range

3.0

2.0

1

.0

2 5 6

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C.V% Mic

2

4

6

8

1

0

12

Threshold Values of Within-Mix Variability

C.V% FS

C.V% FE

C.V%

FL

C.V% +b

Neps/g

VFM

Max. SFC

w

3 5 7 9 11 13

4 5 6 7 8 9

2

3

4

5

6

6.0

4

.0

3.

0

1.0

0

.5

400 200 100

14 12 10 8 6 4

2 4 6 8 10 12

UV Range

10 15 20

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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 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

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Fabric defectscottonmix

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