tasarı_Estimation of Uster H

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

[email protected]

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:[email protected]:[email protected]:[email protected]


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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


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.


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.



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



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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.



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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.



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.


Casew ise Diagnosticsa

-3.148 2.95

-3.286 2.08

Case Number

205

305

Std. Residual USTER_H


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



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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 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.


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



Epoch # 999


Final MSE 0,006697365

After training the network was tested. The test results are showed at Figure 9 and table 4.


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


Uster

Uster Output


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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.


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



Epoch # 455


Final MSE 0,008949153

After training the metwork 3 was tested. The test results are showed at Figure 12 and

Table 6.


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


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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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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tasarı_Estimation of Uster H