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7/30/2019 tasar_Estimation of Uster H
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Estimation of Uster HHairiness Values From Zweigle Hairiness Test Results
Ylmaz ERBL1 and Osman BABAARSLAN2
1,2ukurova University, Textile Engineering Department, Adana/TURKEY
erbily@cu.edu.tr
Abstract: Hairiness is an important physical property for yarns. There are a lot of test
instruments for hairiness test. Uster and Zweigle are two of most using hairiness instruments.
At some cases the yarn producers use Zweigle but the clients are Uster. Thus some problems
occur like reporting yarn hairiness values to customers. Producers tell values at Zweigle
instrument but customer controls these values at Uster and vice versa. This study is focused to
transform Zweigle hairiness test results to Uster H Hairiness values. Two set of data are used
for statistical analysis at SPSS 11.5 software and for creating neural networks at Neuro
Solutions 5.0 software. One set of data has 140 test results for 14 different products. And the
other set of data has 450 test results for 3 different products.
1. Introduction
The hairiness tests are indispensable part of yarn quality control. This physical
property is very effective on goods comfort properties. Also it is important for production
processes. There are a lot of test instruments for hairiness testing. Most using of them are
Uster and Zweigle Hairiness Test instruments. At some cases the producers have problems
with their customers for reporting of production informations because of different test
instruments at them and at their customers. The producer has Zweigle hairiness test
instrument and customer has Uster and vice versa. This causes problems between producers
and customers.
At this study we focused on transformation of Zweigle hairiness test results to Uster Htest results. We used two set of data and tried to create networks for this transformations with
Neuro Solutions 5.0 software.
2. Material and Method
We used two data sets for this study. First data set has 140 test results for 14 different
types of yarns. These yarns are produced at Open-End Rotor Spinning Machine with 4
different raw materials and 4 different types of take-off nozzles. The variables are raw
material, take-off nozzle type, Zweigle - Index values and Uster HHairiness values.The second data set is taken from Ring Spinning Production variables. There are 450
test results for 3 different yarn type. The variables for second data set are color type, blending
rate and Zweigle - S3 and Zweigle - ndex values and Uster HHairiness values..Data sets are analyzed with both statistical regression methods and neural network
models.
2.1. Statistical Approach
For statistical approach the SPSS 11.5 software was used. Curve estimation and linearregression methods were used at statistical approach. At these analyzes only Zweigle Index
variables and Uster H Hairiness variables were used for regression. Each data set wasanalyzed with all curve types and the most suitable type was presented at this paper.
mailto:erbily@cu.edu.trmailto:erbily@cu.edu.trmailto:erbily@cu.edu.tr7/30/2019 tasar_Estimation of Uster H
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2.1.1 Statistical Approach for Data Set 1
Scatter plot graphic of all variables at data set 1 is shown at Figure 1.
ZWEIGLE
1801600140012001000800600400200
7
6
5
4
3
2
1
0
Figure 1 Scatter Plot of Data Set 1
For Data Set 1 which has 140 test results the most suitable model was cubic curve
model. Because the R value of this model was the biggest. It was %63.
Analyze informations and results are shown at below.
MODEL: MOD_19.Dependent variable.. USTER Method.. CUBICListwise Deletion of Missing DataMultiple R .62756R Square .39383Adjusted R Square .38046Standard Error 1.52934
Analysis of Variance:DF Sum of Squares Mean Square
Regression 3 206.66389 68.887964Residuals 136 318.08741 2.338878F = 29.45342 Signif F = .0000
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
ZWEIGLE .002835 .009128 .581056 .311 .7566ZWEIGLE**2 -1.19398410E-05 9.9594E-06 -4.914545 -1.199 .2327ZWEIGLE**3 5.69516470E-09 3.3396E-09 3.919389 . .(Constant) 5.809021 2.523266 2.302 .0228
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The curve for this model is shown at Figure 2.
USTER
ZWEIGLE
18001600140012001000800600400200
7
6
5
4
3
2
1
0
Observed
Cubic
Figure 2 Curve Fit Graphic for Data Set 1
2.1.2 Statistical Approach for Data Set 2
Scatter plot graphic of variables is at Figure 3.
INDEX
220200018001600140012001000800
7
6
5
4
3
2
Figure 3 Scatter Plot of Data Set 2
For Data Set 2 which has 450 test results the most suitable model was quadratic curve
model. Because the R value of this model was the biggest. It was %75.
Analyze informations and results are shown at below.
MODEL: MOD_13.Dependent variable.. USTER_H Method.. QUADRATI
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Listwise Deletion of Missing DataMultiple R .75294R Square .56692Adjusted R Square .56498Standard Error .77754
Analysis of Variance:DF Sum of Squares Mean SquareRegression 2 353.75698 176.87849Residuals 447 270.24167 .60457F = 292.57029 Signif F = .0000
-------------------- Variables in the Equation --------------------Variable B SE B Beta T Sig TINDEX .006778 .001205 1.699637 5.626 .0000INDEX**2 -3.16159540E-06 3.9422E-07 -2.422886 -8.020 .0000(Constant) 1.929809 .897505 2.150 .0321
The curve for this model is shown at Figure 4.
USTER_H
INDEX
2200200018001600140012001000800
7
6
5
4
3
2
1
Observe
Quadratic
Figure 4 Curve Fit Graphic for Data Set 2
2.1.3 Linear Regression Analysis for Data Sets
In addition to curve fit analysis linear regression analysis were studied on both datasets. But no acceptable results were obtained at linear regression. For first data set % 56
success rates was obtained and for the second data set % 75 success rate was obtained. There
is a decrease at first data sets success but the rates around %70-80 are not acceptable fortextile estimations.
Analyze report for first data set at linear regression is shown below.
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Regression
Variables Enter ed/Removedb
INDEX, S3a
. Enter
Model
1
Variables
Entered
Variables
Removed Method
All requested variables entered.a.
Dependent Variable: USTER_Hb.
Model Summ aryb
.560a .314 .304 1.62087
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), INDEX, S3a.
Dependent Variable: USTER_Hb.
ANOVAb
164.821 2 82.411 31.368 .000a
359.930 137 2.627
524.751 139
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), INDEX, S3a.
Dependent Variable: USTER_Hb.
Coefficientsa
5.722 .354 16.154 .000
.002 .001 .839 2.999 .003
-.006 .001 -1.331 -4.755 .000
(Constant)
S3
INDEX
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: USTER_Ha.
Residuals Statisticsa
1.0689 4.9002 3.3751 1.08893 140
-3.0177 2.6070 .0000 1.60917 140
-2.118 1.401 .000 1.000 140
-1.862 1.608 .000 .993 140
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Min imum Maximum Mean Std. Deviation N
Dependent Variable: USTER_Ha.
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Charts
Analyze report for second data set at linear regression is shown below.
Regression
Regression Standardized Residual
1.631.38
1.13.88
.63.38
.13-.13
-.38-.63
-.88-1.13
-1.38
-1.63
-1.88
Histogram
Dependent Variable: USTER_H30
20
10
0
Std. Dev = .9
Mean = 0.00
N = 140.00
Normal P-P Plot of Regression Stan
Dependent Variable: USTER_H
Observed Cum Prob
1.00.75.50.250.00
1.00
.75
.50
.25
0.00
Variables Enter ed/Removedb
INDEX, S3a . Enter
Model
1
Variables
Entered
Variables
Removed Method
All requested variables entered.a.
Dependent Variable: USTER_Hb.
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Model Summ aryb
.758a .575 .573 .77019
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), INDEX, S3a.Dependent Variable: USTER_Hb.
ANOVAb
358.842 2 179.421 302.468 .000a
265.156 447 .593
623.999 449
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), INDEX, S3a.
Dependent Variable: USTER_Hb.
Coefficientsa
8.571 .201 42.688 .000
-.003 .000 -1.317 -8.610 .000
.002 .001 .580 3.790 .000
(Constant)
S3
INDEX
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: USTER_Ha.
Casew ise Diagnosticsa
-3.148 2.95
-3.286 2.08
Case Number
205
305
Std. Residual USTER_H
Dependent Variable: USTER_Ha.
Residuals Statisticsa
2.5150 6.3327 4.5163 .89398 450
-2.5306 2.0807 .0000 .76847 450
-2.239 2.032 .000 1.000 450
-3.286 2.702 .000 .998 450
Predicted Value
Residual
Std. Predicted Value
Std. Residual
Min imum Maximum Mean Std. Deviation N
Dependent Variable: USTER_Ha.
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Charts
2.2. Neural Network Approach
2.2.1 Network 1 (Data Set 1)
For first data set Raw material type, Take-Off Nozzle type and Zweigle index values
were defined as inputs. Uster HHairiness values were defined as desired/output.At the beginning of study the scatter plot of values was drawed for preprocess (Figure
5). This graphic showed us values are 6 different clusters. The study was directed with this
information. At first stage for creating neural network, SOFM (Self Organized Feature Maps)
type of network selected for data set. The network was designed for 6 clusters and at hidden
layer we used softmax axon for transfer function and at hidden layer softmax axon was used
for transfer function and at output layer sigmoid axon was used transfer function.
Before creating network the rows were randomized automatically for best training and
testing. The numbers of training and testing values were selected by percentage as % 65 for
training and % 35 for testing. Thus, the network was created and trained with reserved values
for maximum epoch number as 2000 (1000 for unsupervised learning and 1000 for supervised
learning). The MSE versus Epoch graphic showed at Figure 6 and Training report showed at
Table 1. The minimum and final MSE of network is 0,02.
Regression Standardized Residual
2.752.25
1.751.25
.75.25
-.25-.75
-1.25
-1.75
-2.25
-2.75
-3.25
Histogram
Dependent Variable: USTER_H60
50
40
30
20
10
0
Std. Dev = 1.00
Mean = 0.00
N = 450.00
Normal P-P Plot of Regression
Dependent Variable: USTER_H
Observed Cum Prob
1.00.75.50.250.00
1.00
.75
.50
.25
0.00
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Figure 5 Scatter Plot of Data Set 1
Figure 6 MSE versus Epoch Graphic for Network 1
Table 1 Training Report for Network 1
Best Network Training
Epoch # 999
Minimum MSE 0,020699026
Final MSE 0,020699026
After training of network it was tested. The test results are showed at Figure 7 and
Table 2.
0
1
2
3
4
5
6
7
0 200 400 600 800 1000 1200 1400 1600 1800
Uster
Zweigle
Scatter Plot
MSE versus Epoch
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
1 100 199 298 397 496 595 694 793 892 991
Epoch
MSE
Training MSE
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Figure 7 Desired Output and Actual Network Output for Network 1
Table 2 Test Results for Network 1
Performance Uster
MSE 1,697693373
NMSE 0,415308176
MAE 1,127838541
Min Abs Error 0,100209528
Max Abs Error 2,383335686
r 0,810369577
As seen Table 2 the success rate of network is %81. This is a good success rate but as
seen at Figure 7 at some points there are very big differences between network output and real
values. These are not acceptable at textile productions. The properties of textile goods are
very sensitive.
2.2.2 Network 2 (Data Set 1)
At the second stage we decided to try another network type for this data set. The
Radial Basis FunctionRBF type of network was used for the second essay.At creating RBF network 1 hidden layer was used for unknown effects to results. At
hidden layer and output layer linear axon was used as transfer function. Number of clustercenters was setted as 6. Competitive rule was selected as ConscienceFull and metric (the
distance type) was selected as Euclidean. The number of maximum epoch was selected as
1000 at both unsupervised and supervised learnings. Thus maximum epoch of network was
reached to 2000 totally.
After creating network it was trained. The train results are showed at Figure 8 and
Table 3.
0
1
2
3
4
5
6
7
1 5 9 13 17 21 25 29 33 37 41 45 49
Output
Exemplar
Desired Output and Actual Network Output
Uster
Uster Output
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Figure 8 MSE versus Epoch Graphic for Training of Network 2
Table 3 Training Report for Network 2
Best Network Training
Epoch # 999
Minimum MSE 0,006697365
Final MSE 0,006697365
After training the network was tested. The test results are showed at Figure 9 and table 4.
Figure 9 Desired Output and Actual Network Output for Network 2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 100 199 298 397 496 595 694 793 892 991
MSE
Epoch
MSE versus Epoch
Training MSE
0
1
2
3
4
5
6
7
1 5 9 13 17 21 25 29 33 37 41 45 49
Output
Exemplar
Desired Output and Actual Network Output
Uster
Uster Output
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Table 4 Test Results for Network 2
Performance Uster
MSE 0,151083915
NMSE 0,036959787
MAE 0,303178133
Min Abs Error 0,014516129Max Abs Error 0,923944029
r 0,982985944
As seen from Table 4 the success rate of Network 2 is %98. And this shows that RBF type
networks are most suitable for our data set.
2.2.3 Network 3 (Data Set 2)
At third stage data set 2 was used with a network which has same construction with
network 2. But the number of cluster centers was selected as 3 because at this data set we
have 3 different products and as seen Figure 10 the variables can be in 3 different clusters.
With same settings as network 2 the new network was created for 3 cluster centers andtrained for 2000 maximum epochs totally. The train results are showed at Figure 11 and Table
5.
Figure 10 Scatter Plot of Data Set 2 By Different Yarn Types
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
800 1000 1200 1400 1600 1800 2000 2200
Ekru50/50 Melanj50/50 Ekru80/20
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Figure 11 MSE versus Epoch Graphic for Training of Network 3
Table 5 Training Report for Network 3
Best Network Training
Epoch # 455
Minimum MSE 0,008949153
Final MSE 0,008949153
After training the metwork 3 was tested. The test results are showed at Figure 12 and
Table 6.
Figure 12 Desired Output and Actual Network Output for Network 3
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 100 199 298 397 496 595 694 793 892 991
MSE
Epoch
MSE versus Epoch
Training MSE
Desired Output and Actual Network Output
0
1
2
3
4
5
6
7
1 16 31 46 61 76 91 106 121 136 151
Exemplar
Output
USTER H
USTER H Output
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Table 6 Test Results for Network 3
Performance USTER H
MSE 0,070112326
NMSE 0,051184517
MAE 0,199285619
Min Abs Error 0,002170438Max Abs Error 0,949624876
r 0,974117478
As seen Table 6 the success rate of network 3 is % 97.
3. RESULTS and DISCUSSION
For first data set we reached to % 98 success rates and for the second data set we
reached to % 97 success rates at neural network models.
The statistical analyses were deficient for explaining the relationship between Zweigle
Hairiness results and Uster H Hairiness results. But Neural Network models are very adequateand successful for estimation Uster H values from Zweigle Hairiness results and production
variables. From neural network models, the RBF method is the successful model for both two
data sets.
But these estimations which obtained with neural network models are not suitable for
all textile yarns hairiness test results. Because the production variables like raw material,
number of yarn, type of yarn, type of spinning machine and even color of yarn are effective
on hairiness properties.
The obtained models at this study can be used for similar yarn types which are
produced at similar production type. And the other type of yarns and different productions can
be added to model and it can be modified for acquiring model large yarn types ands
productions.
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