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Influence of yarn count, yarn twist and
yarn technology production on yarn
hairiness
Gabriela Krupincová
Department of Textile Technology,
Faculty of Textile Engineering, Technical University of Liberec
Yarn hairiness is very important parameter of yarn which is related to other
parameters of yarn (influence post-spinning operation) and textile (porosity,
permeability, comfort, aesthetic properties and hand). The concept of hairiness
as a quantitative yarn parameter was firstly mentioned about 1952. Since the
time a lot of instruments and technique for yarn hairiness evaluation was
developed [1].
Hairiness - characterizes the amount of free fibers (fiber loops) protrudes from
the compact yarn body towards the outer yarn surface (fabric, knitting, etc.).
[1] Barella, A.: Yarn Hairiness, Textile Progress, Vo. 13. Nr. 1, The Textile Institute 1983.
Approaches to the yarn hairiness
determination
The first type of testing instruments:
The Uster Tester , The Premier Tester 7000, Kaisokki Laserspot LST,
New methodology
The second type of testing instruments:
The Zweigle hairiness tester, Criter Dum II, Shirley hairiness Meter,
Hairiness Counter and Toray F index tester.
[1] Barella, A.: Yarn Hairiness, Textile Progress, Vo. 13. Nr. 1, The Textile Institute 1983.
Yarn hairiness - Image Analysis
IN-22-108-01/01
2
Yarn hairiness sphere consists of:
free ends of segment 1,
1
the loops of segment 2,
the reversal ends of segment 4 (neglect),
and the reversal loops of segment 5 (neglect).
3
4
r2
5
r1
Double exponential model of fibre distribution in the hairiness sphere:
Thanks to Neckar's model can be obtain the information about two
significantly different components of hairiness. The “dense” component of
hairiness is formed from short fibre ends and the loops near the yarn body.
The “loose” component of hairiness is composed from long flying fibres
(“wild” fibres).
The “dense” component influences the comfort properties of textile materials
whereas the “loose” component creates troubles during post-spinning
operations and the latter reduces the aesthetic property of textile materials.
[3] Neckář, B.: Yarn Hairiness, Part 1: Theoretical model of yarn hairiness, 7.th National conference
Strutex, Technical University of Liberec, Czech Republic 2000.
Experimental technique for yarn hairiness
determination original 1 and innovative 2
OE yarn 72tex am 85ktex2/3m-1
1. yarn bobbin
2. disk tensioning device
3. yarn guide
4. microscope or macro-scope
5. camera
6. PC
4,7mm
1pxl =def.
1,5mm
5,5pxl=def
Resolution of images
548pxl x 704pxl TV camera
960pxl x 1280pxl Digital camera
12mm
0,7pxl=def.
Factors influencing hairiness
• various approach to yarn detection, various characteristics for yarn
hairiness description, differ information about hairiness sphere, differ
precision – connected by using instrument,
• type of fibres (fineness, diameter, shape factor, length, flexural rigidity,
torsion rigidity, tenacity, extension to break, friction, for wool – crimp,
compression resistance),
• yarn twist, yarn count, blending ratio (migration effect),
• technology of production,
• measurement condition (temperature, humidity, test speed).
[3] Neckář, B.: Yarn Hairiness, Part 1: Theoretical model of yarn hairiness, 7.th National conference
Strutex, Technical University of Liberec, Czech Republic 2000.
Experimental results – influence of yarn count and yarn twist
• Resolution of images: 548pxl x 704pxl, calibration: 2,24mm = 1pxl (microscope)
• Experimental material:
Classical ring yarns - five levels of yarn count (14,5tex, 19,5tex, 25tex, 29,5tex,
and 37tex) and three levels of T. M. twist coefficient (3,7Ne1/2in-1, 4Ne1/2in-1 and
4,3Ne1/2in-1) in two variants – combed and carded yarns.
Open-end yarn - five levels of yarn count (14,5tex, 20tex, 35,5tex, 50tex and
72tex) and three levels of Phrix twist coefficient (70ktex2/3m-1, 85ktex2/3m-1 and
100ktex2/3m-1).
Novaspin technology - five levels of yarn count (7,4tex, 10tex, 12tex, 16tex and
20tex) and three levels of Phrix twist coefficient (38 ktex2/3m-1, 56ktex2/3m-1 and
81ktex2/3m-1) combed yarns.
Experimental results – influence of yarn count and yarn twist
10
10
0,06
0,050,06
0,040,05
Ic dense [mm]
4 6
Ic dens [mm]
6 8
H [-]
H [-]
8
0,030,04
2 4
0,02
5
15 0,03
25
35
45
55
65
75
0,01
T [tex]
2
0,02
5% Rca 0
50% Rca -1
95% Rca
500
750
1000
[m ]
5% Rco 0,01
50%ZRco
95% Rco
T
[tex]
20 OE
40
80
5% OE 0
50%
95%60OE
0
Ring combed
Rca Ring carded Rco
N
-1
OE Novaspin
1000
750 Open-end
500
] combed
Z [m
Ring
carded
Ring
Novaspin
Open-end
Ring combed
Ring carded
Open-end
Novaspin
Experimental results – influence of yarn count and yarn twist
0,04 0,05
0,03 0,04
I1 dens [mm]
0,03
0,02 0,03 0,025
I2 dens [mm]
0,01 0,02 0,02
0,03
0,025
0,02
0 0,01 0,015
0 0 0,0120 0,015 40 T [tex] 60
80
0,005
0,01750 Z [m -1] 1000
500
Ring carded
Ring combed
0
0,005
Novaspin
Open-end
Ring carded
Ring combed
0
20
40
80
0
Novaspin
Open-end
T [tex] 60
-1 1000
500
750 Z
[mcombed
]
Ring carded
Ring
Novaspin
Open-end
Ring carded
Ring combed
Novaspin
Open-end
I2 dens [mm]
I1 dens [mm]
0,05
Experimental results – creation of prediction models
The standard or powerful statistical methods allow the prediction model creation.
This approach is limited in case of hairiness parameter prediction because of
factor mutually connection (multicolinearity), factor limited range and proper
selection of technological yarn creation parameters (interdependence yarn count,
yarn twist). Linear regression model is a model which is formed by a linear
combination of explanatory variables or their functions. Parameters can be
estimate by minimization of measure between the vector dependent variable and
the hyper-plane. (finding the minimal length of the residual vector).
Y  X e
2
(
y

y
)
R2  i
( yi  y ) 2
T
PR2  1 
n
MEP   ( yi  x b(i ) ) / n
i 1
1
b  (X X ) X Y
T
T
i
2
d 
n
2
e
 i
i 1
n * MEP
 ( yi   yi / n)2
 RSC 
AIC  n ln 
  2m
 n 
Experimental results – prediction models of H
Hairiness=bT+cZ+d, Hairiness=aZT+d, Hairiness=aZT+ bT +cZ+d,
Hairiness=bT+ d, Hairiness=cZ+ d
N
R2
Rp
R
MEP
AIC
1 0,8969
0,8044
0,4342
1,1671
1,9754
2 0,8154
0,6649
0,1724
2,0013
8,0544
estimation of a estimation of b estimation of c estimation of d
-0,0005
0,5140
0,0015
3,7124
-0,0041
-0,0071
9,9054
BD
1 0,9921
2 0,9903
3 0,9551
P combed
1 0,9535
2 0,9513
3 0,9160
P carded
1 0,9659
2 0,9635
3 0,9493
0,9842
0,9808
0,9123
0,9249
0,9323
0,7769
0,0129
0,0116
0,0400
-70,5066
-69,5668
-48,7931
0,0000
0,0135
0,0094
-0,0008
0,0261
-0,0009
4,8656
0,9091
0,9050
0,8390
0,6412
0,6635
0,6136
0,0476
0,0443
0,0518
-49,4426
-50,7775
-44,8608
0,9330
0,9283
0,9013
0,7719
0,8041
0,7596
0,0442
0,0376
0,0468
-47,6862
-48,6800
-45,8770
4,8569
7,1306
0,0000
0,0122
0,0569
0,0352
-0,0027
-0,0022
7,3863
4,0943
-0,00005
0,0603
0,0326
0,0713
-0,0017
-0,0022
6,9598
7,1611
4,3432
3,6367
Experimental results – prediction models of Ic dens
Hairiness=bT+cZ+d, Hairiness=aZT+d, Hairiness=aZT+ bT +cZ+d,
Hairiness=bT+ d, Hairiness=cZ+ d
N
R2
R
1
2
Rp
0,8247
0,7290
0,3979
0,6802
0,5315
0,1583
MEP
AIC
estimation of a estimation of b estimation of c estimation of d
0,1626 0,000045 -151,4068
-0,0000024
0,0026
0,000010
0,0090
0,0621 0,000057
-147,6797
0,0001
-0,000016
0,0391
0,0300 0,000089
-140,8929
0,0008
0,0105
0,8661
0,8601
0,8596
0,7501
0,7397
0,7389
0,2474 0,000044
0,2944 0,000040
0,4237 0,000030
-152,9949
-154,3856
-156,3384
0,0000003
0,0002
0,0004
0,0004
-0,000004
-0,000001
0,0228
0,0231
0,0209
0,8984
0,8403
0,8396
0,8071
0,7061
0,7050
0,5085 0,000009
0,3487 0,000013
0,3771 0,000012
-172,5442
-168,2323
-170,1715
-0,0000021
0,0020
0,0007
0,0006
0,000033
0,000004
-0,0051
0,0092
0,0142
0,8098
0,8080
0,8070
0,6558
0,6529
0,6512
0,1131 0,000041
0,1750 0,000036
0,2838 0,000029
-153,4425
-155,3164
-157,2469
0,0000005
0,0006
0,0060
0,0008
0,000001
0,000910
0,0080
0,00001
0,0149
BD
1
2
3
P combed
1
2
3
P carded
1
2
3
Similar results can be found for both component of hairiness I1 dens, I2 dens.
Possibility of prediction
The double exponential model of hair distribution – Professor Neckář
Ci  m hi r 2
r
hi




2  xd / 2
2  x d / 2
8
hi cos 
hi cos 

I H   ln( Pi ) 
h
C
2
d


2
d 

i i
2


0

 d ln 2 i 1
0


N
Uster statistic - example for 100% cotton yarn
ring yarn - combed - count < 15tex
HU 50%= 16,5993 (590/ T)-0,38018
HU 5%= 5,9177 (590/ T )-0,18277
HU 95%= 35,4762 (590/ T )-0,52115
compact yarn - combed
HU 50%= 19,6786 (590/ T)-0,50769
HU 5%= 13,846 (590/ T )-0,44966
HU 95%= 25,5793 (590/ T )-0,53828
open end yarn - carded
HU 50%= 13,9343 * (590/ T) -0,33833
HU 5%= 7,4797 * (590/ T )-0,21373
HU 95%= 38,4026 * (590/ T )-0,57526
IH [mm] - integral characteristic of hairiness, Pi - probability that the light beam passes
without problems at the distance x, Ci [mm] - parameter of internal yarn structure,
d [mm] - fibre diameter, hi [mm] - half decrease interval of number of protruding fibers,
mhi [-] - packing density of hairs, T [tex] - yarn count, HU [-] - hairiness index (Uster
Statistic).
Summarization – influence of yarn technology production
Classical: Various researches demonstrate that the effect of the number of draw
frame passages influence hairiness significantly. The greater parallelization of
fibres whit a consequent reduction in the number of hooks. The way of sliver
and roving preparation in relation of yarn count and number of drawing
passages for obtaining giving yarn count is very important.
•
Open end: Fibres are better controlled in the rotor as far as possible. For this
reason, in this type of yarn, short ends predominate over long ends. Hairiness is
influenced by the rotor diameter, its surface and its speed.
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Open end yarn has characteristic closed structure with belt fibres. These yarns
have smallest hairiness. Disordered internal structure leads to the smallest
strength. The ring yarn has more arranged structure with higher hairiness and
maximal strength. The experimental yarns have similar internal structure as
ring one. The main differences are in hairiness and looser arrangements in
subsurface layers. Result is slightly lower strength.
Summarization – influence of yarn geometrical parameters
The hairiness sphere consists from different type of fibre segments. The
occurrence of short fibre end is not directly depend to the twist, but the loops
and their arranging is due to higher twist move to the yarn surface. Thanks to
this reason the hairiness decrease when the yarn twist increase.
•
During the twisting of the yarn some fibres are further displaced from their
central position to the yarn surface (fibre migration effect). When the yarn
count increase the diameter increase too because of higher number of fibres in
yarn cross-section. This increase cause the higher probability of occurrence
hairs out of yarn core and the hairiness increase as yarn unevenness.
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Yarns from the same material produced by different technologies have
comparable geometrical characteristics. The main differences are in hairiness,
strength and elongation at break.
Conclusion
• The main factors influence hairiness are: type of fibres, yarn twist, yarn
count, technology of yarn production, approach to yarn hairiness observation
and measurement condition.
• Yarn hairiness influences: porosity, permeability, transport of moisture,
comfort, aesthetic properties and hand of textile.
• The aim of our future work is study of another important factors which can
influence yarn hairiness (steps of production textile materials – weft winding,
warp sizing, yarn dyeing, fabric hairiness, ...).
• Why? The reason is obtaining more deeply information about yarn structure
and its changes during processing. Thanks to detailed knowledge it is possible
predict to yarn behaviour and design fabric and textile products precisely in
the way of customers demand.
• The regression model can be used for estimation hairiness parameter
successfully, but its using is limited. For prediction can be used Uster Statistic
as well as the probability double exponential model of professor Neckář.