quality.ppt - waiban.zut.edu.cnwaiban.zut.edu.cn/foreign/files/file/28quality.pdf · irregular...

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14/03/2011 1 Yarn Quality Parameters for Routine Quality Control Mass variation (unevenness) Tensile properties Other Quality Parameters Hairiness Handle Abrasion resistance Pilling resistance Surface friction Surface friction Rigidity Compressibility Lustre ……. Thermal properties Measuring Yarn Evenness Cutting and Weighing W 1 W 2 W 3 ……………….. W n Time consuming Fundamental principle Reference for indirect methods Destructive Requires very sensitive weighing instruments (especially for short term variations) Measuring Yarn Evenness Mechanical Methods Pressure Thickness Length Gap is measured and recorded Fixed Roller Measuring Yarn Evenness Mechanical Methods Non-destructive Faster than cutting and weighing Speed of response limited Irregular shape of the yarn cross section limits correlation between measured yarn thickness and fibre mass

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Page 1: Quality.ppt - waiban.zut.edu.cnwaiban.zut.edu.cn/foreign/Files/File/28Quality.pdf · Irregular shape of the yarn cross section limits correlation between measured yarn thickness and

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Yarn QualityParameters for Routine Quality Control

Mass variation (unevenness)Tensile properties

Other Quality ParametersHairiness

Handle

Abrasion resistancePilling resistance

Surface frictionSurface frictionRigidityCompressibility

Lustre …….Thermal properties

Measuring Yarn Evenness

Cutting and Weighing

W1 W2 W3 ……………….. Wn

Time consuming

Fundamental principleReference for indirect methods

DestructiveRequires very sensitive weighing instruments(especially for short term variations)

Measuring Yarn Evenness

Mechanical Methods

Pressure Thickness

Length

Gap is measured and recorded

Fixed Roller

Measuring Yarn Evenness

Mechanical Methods

Non-destructive

Faster than cutting and weighing

Speed of response limited

Irregular shape of the yarn cross section limits correlation between measured yarn thickness and fibre mass

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Measuring Yarn Evenness

Mechanical Methods

Mainly for thick/soft fibre strands e g slivers (card drawframe)Mainly for thick/soft fibre strands e.g. slivers (card, drawframe)

Optical Method

Measuring Yarn Evenness

Photoelectric

Light Source(Visible orInfra-red)

Yarn

Sensor(Transmitted Signal)(Reflected Signal)

BARCOPROFILE

Measuring Yarn Evenness

Optical Methods

Fast

Non-destructive

Fast

Can detect yarn appearance variation (colour, foreign fibres, twist variation, hairs )

Not affected by climate conditions

Apparent diameter does not always correlate to yarn mass (irregular shape, twist variation)

hairs….)

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Measuring Yarn EvennessCapacitive Methods

Capacitor

Yarn

ElectronicSignal

Capacity depends on:AreaDistanceDielectric constant

Dielectric constant proportional to

yarn mass

Capacity changes indicate yarn mass variation

Measuring Yarn Evenness

F t

Capacitive Methods

Non-destructive

Fast

Cannot detect yarn appearance variation (i t t t f b i )

Sensitive to mass variation

Require standard condition (measurement affected by temperature and moisture)

(important to fabric appearance)

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Measures of Yarn Mass Variation

Coefficient of variation (CV%)

Yarn mass in n samples (same sample length):

x1, x2, x3………………………………….xn

X = mean of all xi = Standard Deviation

2Xxi

CV% = 100/X

1n

Measures of Yarn Mass Variation

Diagram

Length

Mass

Length

Limit Evenness of StapleLimit Evenness of Staple--Fibre YarnFibre Yarn

Perfect fibre arrangement:

Random fibre arrangement

Limit Evenness(cotton): nLimit

CV 100%

n = average number of fibre is yarn cross section

Limit Evenness of Staple-Fibre Yarn

Example

Yarn: 30 tex; Fibre: 2 dtex

n = 30 / 0.2 = 150

%8150

100% L

CV150Limit

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Limit Evenness of Staple-Fibre Yarn

Li itCV 100%

nLimit

Thicker yarn Finer fibre

Greater n

More even yarn

Evenness Index ( I ) =

Measured CV% / CV%-limit

Additions of CV

Process adds CV2

22 CVCVCV

Input with CV1 Output with CVtotal

22

21

CVCVtotal

CV

Roving Frame

Example

22

Sliver CV = 3% Roving CV = 8%

22

21

CVCVtotal

CV

2328 frameroving

CV

= 7.4%

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2007

20%

45%

13%

22%

13%

1997

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Count Variation (CVcb)

Yarn mass variation measured by taking one skein (100 m or 120 yds) of yarn from 20 packages

Usually less than 3%

Fabric Fault Caused By High Count Variation

Count variation is long term variation

Causes are faults in earlier process stages

Opening

Carding

Drawing

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Measured CV is dependent on the sample (cut) length

Longer sampling length normally results in lower CV due to averaging effect

Variance-Length Curve

CV% plotted against Sample Length

CV%

Ideal yarnGood yarn

Yarn with high variation at sample length L

Sample Length

=26.6oIdeal yarn

L

Variance-Length CurveIdentifies non-periodical variations of particular sample length

Each spinning stage can be identified by a yarn sample length

L = k lw Dt

k depends on fibre

For cotton:

Lw = average fibre length (weight based)

k = 1.18

For cotton:

Dt = total subsequent draft to yarn

Cotton Fibre Lw = 26 mm

Example

Blowroom CardingD1=150

2nd DrawingD3=8

1st DrawingD2=8

RingframeD5=30

RovingD4=10

Lring=1.18x26=31mm

Lroving=1.18x26x30=0.92m

Ldraw2=0.92x10=9.2m

Ldraw1=9.2x8=74m

Lcard=74x8=600m

Lopen=600x150=90km

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2

1

Roving 2nd Drawing 1st Drawing

Periodic Variation

Due to averaging effect, variance-length curves cannot indentify periodic variations

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Spectrogram

Usually a time-frequency-amplitude plot

For textiles, it is an amplitude-wavelength plot

Wave velocity = frequency X wavelength

In yarn testing, yarn speed is known, so wavelength and frequency can be calculated from each other

The amplitude of any frequency (wavelength) is obtained by Fourier Transform

SpectrogramAmplitude

Natural staple fibres (cotton)

Cut staple (constant l )

Wavelengthl

2.7l2.82lw

Common Periodic Yarn Faultson Spectrogram

Amplitude

Hill (Drafting Wave)Chimney

(Mechanical Defects)

PHarmonics

Wavelength

B

When P/B > 0.5, faults are likely to show in fabric

Example: = 4.3 inches

Detection of Periodic Variation Wrapping Board

7.5 inches 8.6 inches 9.7 inches

7.3 9.8

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Wavelength of mechanical faults =Wavelength of fault X total subsequent draft

e.g. front drafting roller in roving frame D = 25 mmEccentricity of roller causes periodic variation every rotation

Wavelength of fault = D = 0.08 m

Ringframe draft = 30

Wavelength of yarn fault = 0.08 X 30 = 2.4 m

Wavelength of drafting wave =

L = k lw Dtw t

Dt = total subsequent draft to yarn

For yarn: k = 2.75

For roving: k = 3.5

For sliver: k = 4

Usual causes of periodic faults

D f iDrafting wave

Eccentricity of rotating parts (rollers, cylinders, packages, etc)

Surface damage of rotating parts (including dirt accumulation on working(including dirt accumulation on working surfaces) (rollers, aprons, gears, rotors, etc)

40 tex

27mm

23% 12.7 mm

50% 100%

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40 tex

Drafting Wave

26mm

Drafting Wave(Ring)

Drafting Wave(Roving)

38% 12.7 mm

50% 100%

High Short Fibre Content

/2/3/4

Drafting wavein ringframe Damaged

drafting apron

Moire effect in fabric when faulty yarn is used as weft

Tensile Properties

The most important tensile properties

Strength (tenacity)

Variation of strength (CV)

Breaking elongation

Main factors influencing yarn tensile properties

Fibre strengthFibre strength

Fibre elongation

Fibre length

Fibre fineness

Twist level: fundamental for spun yarn

Man-made fibre content

p y

Yarn evenness

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Yarn Tensile Testing

Standard Conditions

Testing length (500 mm)

Testing Methods

CRE (Constant Rate of Elongation) 5m/min

Testing length (500 mm)

Pre-tension 0.5 cN/tex

( g )

CRE 20s to break (ISO, DIN, ASTM)

CRL (Constant Rate of Loading) 20s to break

Load

Elongation

Results dependant on testing method

e.g.:

Combed cotton ring spun yarn

16.0 cN/tex (CRE 5m/min)

14.5 cN/tex (CRE 20s)( )

14.9 cN/tex (CRL 20s)

Carded, Ring, Knitting Yarn

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Cotton, Rotor Yarn

Yarn tensile property is critical to performance of spinning machines and subsequent processes

A small increase in average yarn strength or a small reduction in strength variation can bring very substantial improvement in process efficiency

Further readingR. Furter, "Strength and Elongation Testing of Single and Ply Yarns", T.I.

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Yarn Hairiness

Important to:

Weaving machine performance

Dyeing and finishing performance

F b i

Important to:

Fabric appearance

Pilling performance

Yarn count

Key factors affecting yarn hairiness

Yarn count

Twist levelSpinning and winding speedsFibre finenessFibre typeFibre length

Other spinning conditionsTravellerTwist level

Hairiness Measurement

Z i l H i i M tZweigle Hairiness Meter

Shirley Yarn Hairiness Tester

USTER Tester

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Further reading:1. A Barella, "The Hairiness of Yarns", T.I., 19932. USTER Statistics, Zellweger Uster AG

Yarn FaultsSections of yarn with large deviations

from the mean yarn thickness

Yarn Faults AffectProcess efficiency

Weaving Machine Speed Yarn Breaks/100,000 Picks(Picks/Min) 0 4 8 12

e.g.: Effects of yarn breaks on weaving productivity

(Picks/Min) 0 4 8 12400 105% 102% 100% 98%500 130% 127% 123% 120%600 160% 154% 147% 140%700 180% 174% 167% 160%

End use performance

Yarn Faults Affect

Process efficiency

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FrequencyFrequent Faults

Distribution of Yarn Faults

Disturbing Faults

Cross-SectionYarn Mass

Mean

Frequent faults (imperfections)

Thin places (-50%)

Thick places (+50%)

Neps (>+200%, but very short, <4 mm)

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Excessive U% (CV%), count variation and imperfections cannot be rectified once the yarn is produced

Disturbing Faults (Seldom-occurring Faults)

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Disturbing faults may be removed during yarn preparation, but will add to cost

A joint is another fault, should be less objectionable than the original fault

After removing a fault, a joint (knot or piecing) must be made

j g

Neps

Two categories

Raw material neps (exist in the fibre)Raw material neps (exist in the fibre)

Process neps (caused by processes)(e.g. dry process condition increases neps)

Factors Influencing Nep Count In The Card Web

Raw material:

Trash content

Process:

Card production speed

Maturity

Ginning

Card settings/conditions

Tandem card

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4.5 4.2 3.0Micronaire

Neps/100 inch2

Effects of Card Production & Cotton Maturity on Nep Count

15202530354045

05

1015

40 60 80 Card Production (lbs/h)

Effects of Flats Setting on Nep Count

14

Neps/100 inch2

6

8

10

12

14

0

2

4

9 12 15 inch (10-3)

16

Effects of Tandem Carding on Nep Count

Neps/100 inch2

6

8

10

12

14

16

0

2

4

Single Card Tandem Card

Trash Particles in Sliver

Effects of Card Clothing on Sliver Trash Content

Trash Particles in Sliver

Limit Value

Card Running Time (Weeks)

Re-grinding

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3000

Neps/1000 yds yarn

Effects of Fibre Neps in Card Web on Neps in Yarn

1000

1500

2000

2500

0

500

0 5 10 15 20

Neps/100 inch2 card web

Nep Content

A significant increase of neps in yarn could be due to:

1 Raw Material1. Raw Material

Change of immature fibre content

Incorrect raw material selection

RemedyRemedy

Fibre testing

Balanced mixing (bale management)

2. Carding faults

P i ditiPoor wire condition

Wrong settings

Incorrect waste extraction

Remedy

Maintenance schedule must be followed

Yarn faults can be classified by their causes

1. Material

Foreign matter(non-textile material) (M1)

Fibre entanglement(bonded man-made fibres) (M2)

Synthetic undrawn fibre (M3)

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Fibre bundles (M4)

2. Preparation

Piecings (P1)

Long slubs (P2)

Short slubs (P3)

3. Spinning

Spun-in fly (S1)

S1

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Loose fly (S2)y ( ) S2

Long collections of fly (S3)

S3

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Fishes or corkscrews (S4)

Drafting apron surface cracks

Static charge

fUnsuitable drafting apron

Bunched fly (S5)y ( )

Chains (collections of short faults) (S6)

Crackers (S7)

Disturbance of drafting by extra long fibre

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Fault Cause Carded Cotton Combed CottonM1 16.2% 11.1%M2 - 4.8%M3 - -P1 10.1% 12.5%P2 - -P3 6.2% 6.9%S1 42.0% 44.0%S2 18.0% 14.3%S3 3.0% 1.0%S4 - 0.8%

USTER

S4 0.8%S5 2.0% 3.1%S6 2.1% 1.3%S7 0.4% 0.2%

Total number of faults 2834 3528Number of factories 137 118

Attention to 3 areas can substantially reduce fault content

Spinning machine cleaning (automatic / manual)

Raw material and carding (better quality, more efficient cleaning, e.g. crushing rollers)

Operator training and supervision (all processes

Quality Control In Spinning - SummeryRaw material

Fibre length, fineness (and the variation of both)Trash contentCotton maturity

Fib tiFibre preparationProcess route (e.g. carded vs. combed)Trash extractionSliver evenness (i.e. autolevelling)

Conditions of machine components(e g card clothing drafting rollers aprons traveller ring)

Machine maintenance

(e.g. card clothing, drafting rollers, aprons, traveller, ring)Machine settings (e.g. card setting,roller setting in drafting, roller pressure)

On-line monitoring and control

Cleaning (all processes)Operator training & supervision

Clearing

The operation of yarn fault removalThe operation of yarn fault removal

Clearers are classified according to method of fault detection.

Two main types

1. Mechanical

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Mechanical Clearer

Yarn

1. Mechanical

Two main types

2. Electrical

Electrical clearers have much higher clearing efficiency, usually above 0.9 while mechanical clearers are in the range of 0.5 to 0.6

Fault clearing requirements are determined by requirements of subsequent processes and end uses

Piecing

Knots

Splices

Air Jet mingles the two strands

When the clamps are opened,

Clamps

twist runs into the joint, strengthening the joint

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Air Splicer

Setting of electrical clearer dependent on yarn count, fibre material, winding speed, and requirement of fault removal