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Prediction of Mechanical Properties of Woven Fabrics by ANN Cover

Prediction of Mechanical Properties of Woven Fabrics by ANN

Open Access
|Dec 2022

Figures & Tables

Table 1

Factors and levels of weft yarns

Levels
Factors123
X1Weft density (picks/m)0.230.250.27
X2Weft yarn count (Nm)40/150/1------
X3Fiber blend ratio of weft yarn Polyester (PE %)0%50%65%
X4Fiber blend ratio of weft yarn Cotton (C%)100%50%35%
Table 2

Experimental model

RunX1X2X3X4
Picks/mWeft yarnPE %C %
count (Nm)
11111
21122
31133
41211
51222
61233
72111
82122
92133
102211
112222
122233
133111
143122
153133
163211
173222
183233
Fig. 1

Neural Network architecture for all mechanical properties

Table 3

ANN Training Algorithm

-Train network using Levenberg-Maquardt back-propagation
- Activation function: (trainlm).-Hidden layer size =10
-Performance: Mean squared error (mse)-Gradient: 1.00e-05
Fig. 2

Overall Training Performance of ANN model

Table 4

Comparison between actual and predicted values of properties tested in the warp direction

RunTensile strength (N)“Stiffness” Bending length * 10−2 (m)Elongation %
ActualPredictedActualPredictedActualPredicted
1357.00357.002.302.3920.5020.90
2334.00334.002.202.1519.4019.30
3377.00377.002.702.6722.4022.50
4368.00368.002.502.5022.3022.00
5358.00358.002.402.4222.0022.00
6360.00360.002.402.3522.6022.60
7368.00368.002.001.9520.7020.70
8369.00369.002.502.5323.1023.40
9360.00360.002.402.3522.6022.60
10351.00351.002.202.1921.6021.70
11350.00350.002.302.3221.3021.40
12331.00331.002.002.0915.7015.70
13353.00353.002.402.4223.3023.00
14350.00350.002.302.3221.3021.40
15353.00353.002.402.4223.3023.00
Table 5

Comparison between actual and predicted values of properties tested in the weft direction

RunTensile strength (N)“Stiffness”Bending length * 10−2 (m)Elongation %
ActualPredictedActualPredictedActualPredicted
1258.00258.002.102.1517.7017.60
2234.00234.002.001.9513.7013.50
3321.00321.002.402.3721.8021.20
4307.00307.002.202.2021.2021.20
5251.00251.002.102.1113.5013.30
6290.00290.002.102.0920.5020.50
7241.00241.002.002.0115.1014.60
8312.00312.002.302.3120.7021.10
9290.00290.002.102.0920.5020.50
10228.00228.002.001.9913.1013.50
11284.00284.002.002.0019.2019.20
12207.00207.001.901.9213.2013.40
13290.00290.002.202.2221.7021.60
14284.00284.002.002.0019.2019.20
15290.00290.002.202.2221.7021.60
Table 6

Comparison between the prediction performance of all properties by ANNs

Tested propertiesTensile strength (N)“Stiffness” Bending length * 10−2 (m)Elongation %
Statistical factorswarpweftwarpweftwarpweft
R-squared :coefficient of determination1.001.000.970.980.990.99
MSE :mean squared error0.000.000.000.000.040.07
RMSE :root mean squared error0.040.030.050.020.200.27
MAE :mean absolute error0.030.020.040.020.150.19
MAPE: mean absolute percentage error0.01%0.01%1.62%0.88%0.69%1.14%
DOI: https://doi.org/10.2478/ftee-2022-0036 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 54 - 59
Published on: Dec 11, 2022
Published by: Łukasiewicz Research Network-Łódź Institute of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2022 Sherien N. Elkateb, published by Łukasiewicz Research Network-Łódź Institute of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.