A COMPARISON OF THE THREE TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS
Abstract
With the development of technology, artificial intelligence applications in the textile industry are increasing. The uses of these methods present very good results in cases where statistical methods are lacking in the accurate evaluation and analysis of the past data of the enterprises and the estimation of their future situations. In this study, some models are developed, based on this relationship, to estimate the breaking strength of cotton woven fabrics and polyester/viscose blended woven fabrics separately. Breaking strength is considered one of the most important performance characteristics of woven fabrics. It is mostly determined by the structural elements of the fabric. Multiple linear regression, artificial neural networks and random forest algorithms are employed to perform statistical and stochastic analyses on these elements by using industrial data. A total of 147 fabric data sets in warp and weft directions were used for training and test data in cotton fabrics, and 53 fabric data sets in warp and weft directions in blended fabrics. Appropriate models are generated by using Minitab Statistics and Matlab software. Yarn linear densities, yarn production methods, twist amounts, fabric densities, crimp ratios, unit area weights, various weave factors and fabric structure factors were selected as variables of the models estimating the breaking strength of fabrics in both warp and weft directions. These factors were included in the models separately, and the subset that gave the best results was selected and the models were revised. For the three models created, it was seen that the regression models and models based on artificial neural networks performed well in both cotton fabrics and blended fabrics, while random forest algorithms were not very accurate in estimating the breaking strength.
Keywords
regression model artificial neural networks random forest algorithm breaking strength woven fabric
References
- 1. Özdil N (2014). Kumaşlarda Fiziksel Kalite Kontrol Yöntemleri, Ege Üniversitesi Yayınları, 120 sf, İzmir.
- 2. Bozdoğan F (2010). Fiziksel Tekstil Muayeneleri, Ege Üniversitesi Yayınları, 169 sf, İzmir.
- 3. Behera BK, Hari PK (2010). Woven Textile Theory an Aplications. Woodhead Publishing, India, 450 p.
- 4. Öztemel E (2012). Yapay Sinir Ağları , Papatya Yayınları, 232s, İstanbul.
- 5. Shamey R, Hussain T, (2003). Artificial Intelligence in the Colour and Textile Industry, Journal of Color, (33):33-45.
- 6. Demirezen S, Çetin , (2021). Rassal Orman Regresyonu ve Destek Vektör Regresyonu ile Piyasa Takas Fiyatının Tahminlenmesi. Vol.3(1):1-15.
- 7. Yılmaz H, (2014). Random Forest Yönteminde Kayıp Veri Probleminin İncelenmesi Ve Sağlık Alanında Bir Uygulama, Yüksek Lisans Tezi, Eskişehir Osmangazi Üniversitesi.
- 8. Huı C, Lau T, Ng S.F, Chan K.C (2004). Neural Network Prediction of Human Psychological Perceptions of Fabrics Hand. Textile Research Journal 74(5): 375-383.
- 9. Pattanayak A. J, Luximon A, Khandual A (2010). Prediction of drape profile of cotton woven fabrics using artificial neural network and multiple regression method. Textile Research Journal 81(6): 559–566.
- 10. Malik A.Z, Malik M.H, Hussain T, Arain F.A , Phil M (2011). Development of Models to Predict Tensile Strength of Cotton Woven Fabrics. Journal of Engineered Fibers and Fabrics. Vol. 6, Issue 4: 46-53.
- 11. Özek Z (2012). Dokumanın Fiziksel Analizi Lisansüstü Ders notları, Tekirdağ.
- 12. Kumpikaite E (2006). The Influence of Woven Fabric Structure on the Woven Fabric Strength, Materials Science. V0l.12(2).
- 13. Padaki N, Alagırusamy R, Deopura B, Fangueiro R (2010). Studies on Preform Properties of Multilayer Interlocked Woven Structures Using Fabric Geometrical Factors, Journal of Industrial Textiles.
- 14. Hossain M (2016). A Review on Different Factors of Woven Fabrics’ Strength Prediction, Science Research. Vol.4(3):88-97.
- 15. Seyam A, El-shiekh A (1994). Mechanics of Woven Fabrics Part4: Critical Rewiwv of Fabric Degree of Tighness and Applications. Vol.64(11):653-662
- 16. Vaidyanathan S (2012). Effect of Weave Structures on the Low Stress Mechanical Properties
A COMPARISON OF THE THREE TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS
Abstract
With the development of technology, artificial intelligence applications in the textile industry are increasing. The uses of these methods present very good results in cases where statistical methods are lacking in the accurate evaluation and analysis of the past data of the enterprises and the estimation of their future situations. In this study, some models are developed, based on this relationship, to estimate the breaking strength of cotton woven fabrics and polyester/viscose blended woven fabrics separately. Breaking strength is considered one of the most important performance characteristics of woven fabrics. It is mostly determined by the structural elements of the fabric. Multiple linear regression, artificial neural networks and random forest algorithms are employed to perform statistical and stochastic analyses on these elements by using industrial data. A total of 147 fabric data sets in warp and weft directions were used for training and test data in cotton fabrics, and 53 fabric data sets in warp and weft directions in blended fabrics. Appropriate models are generated by using Minitab Statistics and Matlab software. Yarn linear densities, yarn production methods, twist amounts, fabric densities, crimp ratios, unit area weights, various weave factors and fabric structure factors were selected as variables of the models estimating the breaking strength of fabrics in both warp and weft directions. These factors were included in the models separately, and the subset that gave the best results was selected and the models were revised. For the three models created, it was seen that the regression models and models based on artificial neural networks performed well in both cotton fabrics and blended fabrics, while random forest algorithms were not very accurate in estimating the breaking strength.
Keywords
regression model artificial neural networks random forest algorithm breaking strength woven fabric
References
- 1. Özdil N (2014). Kumaşlarda Fiziksel Kalite Kontrol Yöntemleri, Ege Üniversitesi Yayınları, 120 sf, İzmir.
- 2. Bozdoğan F (2010). Fiziksel Tekstil Muayeneleri, Ege Üniversitesi Yayınları, 169 sf, İzmir.
- 3. Behera BK, Hari PK (2010). Woven Textile Theory an Aplications. Woodhead Publishing, India, 450 p.
- 4. Öztemel E (2012). Yapay Sinir Ağları , Papatya Yayınları, 232s, İstanbul.
- 5. Shamey R, Hussain T, (2003). Artificial Intelligence in the Colour and Textile Industry, Journal of Color, (33):33-45.
- 6. Demirezen S, Çetin , (2021). Rassal Orman Regresyonu ve Destek Vektör Regresyonu ile Piyasa Takas Fiyatının Tahminlenmesi. Vol.3(1):1-15.
- 7. Yılmaz H, (2014). Random Forest Yönteminde Kayıp Veri Probleminin İncelenmesi Ve Sağlık Alanında Bir Uygulama, Yüksek Lisans Tezi, Eskişehir Osmangazi Üniversitesi.
- 8. Huı C, Lau T, Ng S.F, Chan K.C (2004). Neural Network Prediction of Human Psychological Perceptions of Fabrics Hand. Textile Research Journal 74(5): 375-383.
- 9. Pattanayak A. J, Luximon A, Khandual A (2010). Prediction of drape profile of cotton woven fabrics using artificial neural network and multiple regression method. Textile Research Journal 81(6): 559–566.
- 10. Malik A.Z, Malik M.H, Hussain T, Arain F.A , Phil M (2011). Development of Models to Predict Tensile Strength of Cotton Woven Fabrics. Journal of Engineered Fibers and Fabrics. Vol. 6, Issue 4: 46-53.
- 11. Özek Z (2012). Dokumanın Fiziksel Analizi Lisansüstü Ders notları, Tekirdağ.
- 12. Kumpikaite E (2006). The Influence of Woven Fabric Structure on the Woven Fabric Strength, Materials Science. V0l.12(2).
- 13. Padaki N, Alagırusamy R, Deopura B, Fangueiro R (2010). Studies on Preform Properties of Multilayer Interlocked Woven Structures Using Fabric Geometrical Factors, Journal of Industrial Textiles.
- 14. Hossain M (2016). A Review on Different Factors of Woven Fabrics’ Strength Prediction, Science Research. Vol.4(3):88-97.
- 15. Seyam A, El-shiekh A (1994). Mechanics of Woven Fabrics Part4: Critical Rewiwv of Fabric Degree of Tighness and Applications. Vol.64(11):653-662
- 16. Vaidyanathan S (2012). Effect of Weave Structures on the Low Stress Mechanical Properties
Details
| Primary Language | English |
|---|---|
| Subjects | Textile Technology |
| Journal Section | Articles |
| Authors | |
| Publication Date | March 31, 2024 |
| Published in Issue | Year 2024 |