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Journals / Tekstil ve Konfeksiyon / 2026 / Volume: 27 Issue: 1

A RESEARCH ON ESTIMATION OF THE WEAVE FABRIC PROPERTIES WITH THE ARTIFICIAL NEURAL NETWORKS

DOKUMA KUMAŞ ÖZELLİKLERİNİN YAPAY SİNİR AĞLARI İLE TAHMİN EDİLMESİ ÜZERİNE BİR ARAŞTIRMA

Erkan Türker
Pages
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Publication Date
2017-03-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study was supported by the BAP unit of Uşak University.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a feedforward backpropagation artificial neural network (ANN) software was developed and it was tested with two  different fabric group.
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Key Findings & Data:While the former was consisted of 29 cotton samples produced under different conditions, the latter was consisted  of 49 polyester samples produced under the same conditions.
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Conclusion & Impact:This study was supported by the BAP unit of Uşak University.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Artificial neural network · Weaving · regression

Abstract

In this study, a feedforward backpropagation artificial neural network (ANN) software was developed and it was tested with two  different fabric group. While the former was consisted of 29 cotton samples produced under different conditions, the latter was consisted  of 49 polyester samples produced under the same conditions. Each group is divided into training and control groups. The result values of  the control groups produced by the trained neural network were obtained. The results of the linear regression for both groups with the  same method were also obtained. While the fabric weight, thickness, warp and weft tensile strength for the cotton samples are being  examined, the air and water permeability values for the polyester samples were examined. Artificial neural network values for all  samples showed better fit than linear regression values. ANN and linear regression results were produced closer results for the polyester  samples produced under controlled conditions. While ANN results on cotton samples produced under uncontrolled conditions showed  good agreement, the fit of the linear regression results deteriorated. This study was supported by the BAP unit of Uşak University.

Özet

Bu çalışmada ileri yayılımlı geri beslemeli yapay sinir ağı (YSA) yazılımı geliştirilerek iki farklı kumaş gurubu ile test edilmiştir.  Birinci gurup farklı koşullarda üretilmiş 29 pamuklu numuneden, ikinci gurup aynı koşullarda üretilmiş 49 polyester numuneden  oluşturulmuştur. Her gurup kendi içinde eğitim ve kontrol guruplarına ayrılmıştır. Eğitilmiş sinir ağı tarafından üretilen kontrol  gruplarının sonuç değerleri elde edilmiştir. Aynı yöntemle her iki gurup için lineer regresyon sonuçları da elde edilmiştir. Pamuk  numuneler için kumaş ağırlığı, kalınlığı, atkı ve çözgü kopma mukavemeti incelenirken, polyester numuneler için hava ve su  geçirgenliği değerleri incelenmiştir. Tüm numunelerde yapay sinir ağı değerleri lineer regresyon değerlerinden daha iyi uyum  göstermiştir. Kontrollü şartlar altında üretilen polyester numunelerde YSA ve lineer regresyon sonuçları birbirine daha yakın sonuçlar  üretmiştir. Kontrolsüz şartlarda üretilmiş pamuklu numunelerde YSA sonuçları iyi uyum gösterirken regresyon sonuçlarının uyumu  kötüleşmiştir. Bu çalışma Uşak Üniversitesi BAP birimi tarafından desteklenmiştir.

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Cite This Article

Türker, E. (2017). A RESEARCH ON ESTIMATION OF THE WEAVE FABRIC PROPERTIES WITH THE ARTIFICIAL NEURAL NETWORKS. Tekstil ve Konfeksiyon, Volume: 27 Issue: 1. https://ajindex.com/articles/1111416

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References (25)

  1. Halıcı,U.,http://vision1.eee.metu.edu.tr/~halici/543LectureNotes/lecturenotes-pdf/ch1.pdf
  2. http://www.akademiyapayzeka.com/DesktopDefault.aspx?tabindex=4&tabid=4
  3. Ertuğrul, S., Uçar, N., 2000, “Predicting Bursting Strenght of Cotton Plain Knitted Fabrics Using Intelligent Techniques”, Textile Research Journal, Yıl:70,No:10, 845-851.
  4. Uçar, N., Ertuğrul, S., 2007, “Prediction of Fuzz Fibres on Fabric Surface By Using Neural Network and Regression Analysis”, Fibres and Textiles in Eastern Europe, Vol.15, No.2 (61), 58-61.
  5. Feng,C., Kuo,J., Lee, C., J., “A Back-Propagation Neural Network for Recognizing Fabric Defects”, Textile Research Journal, 2003; 73; 147
  6. Boong, S., J., Bae, J., H.,”Automatic Recognition of Woven Fabric Patterns by an Artificial Neural Network”,Textile Research Journal, 2003/ 73, 645-650
  7. Majumdar, P., K., Majumdar, A., “Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models”, Textile Research Journal,2004 /74: 652 -655
  8. Lin,J.,”Prediction of Yarn Shrinkage using Neural Nets”, Textile Research Journal, May 2007,vol. 77, no. 5, 336-342
  9. Üreyen,M., Gürkan,P.,” Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties”, Fibers and Polymers,February 2008, Volume 9, Issue 1, pp 87-91
  10. Hadizadeh,M., Jeddi, Ali A. A., Tehran, M., A., “The Prediction of Initial Load-extension Behavior of Woven Fabrics Using Artificial Neural Network”, Textile Research Journal, November 1, 2009 79: 1599-1609
  11. Türker,E., Şenol, F.,”The Study of Influencing Factors on Water Permeability of Polyester Weave Fabrics”,Tekstil ve Konfeksiyon,Nisan-Haziran 2009,Yıl.19,sayı.2,114-122
  12. Balcı,O., Oğulata, R., T., “Prediction of CIELab Values and Color Changing Occurred After Chemical Finishing Applications By Artificial Neural Networks On Dyed Fabrics “,Tekstil ve Konfeksiyon, 1/2009,61-69
  13. Rocco F., Maurizio, G.,”Yarn Strength Prediction: A PracticalModel Based on Artificial Neural Networks.”, Advances in Mechanical Engineering, Vol 2010, Article ID 640103, 11 pages
  14. Yaman, N., Şenol, M.F., Gurkan, P., “Applying Artificial Neural Networks to Total Hand Evaluation of Disposable Diapers”, Journal of Engineered Fibers and Fabrics,Volume 6, Issue 1 - 2011
  15. Elmas, Ç., 2011, “Yapay Zeka Uygulamaları”,Seçkin Yayıncılık,İstanbul
  16. Bahadir,M., Ç., Bahadir,S., K., Kalaoglu,F., “An Artificial Neural Network Model for Prediction of Bursting Strength of Knitted Fabrics”, IMLCS’2012,Phuket (Thailand)
  17. Öztemel, E., 2012, “Yapay Sinir Ağları”, Papatya Yayıncılık Eğitim, İstanbul.
  18. Yavuz, S., Deveci, M., “İstatistiksel Normalizasyon Tekniklerinin Yapay Sinir Ağının Performansına Etkisi”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 40, Haziran-Aralık 2012 ss. 167-187
  19. Özdemir H., “Artificial Neural Networks and Their Usage in Weaving Technology” Electronic Journal of Textile Technologies”, 2013, 7(1) 51-68
  20. Arıkan Kargı V. S.,” A Comparison of Artificial Neural Networks And Multiple Linear Regression Models as in Predictors of Fabric Weft Defects”, Tekstil ve Konfeksiyon 24(3), 2014
  21. Utkun, E. "Giyim Konforunun Tahminlenmesinde Yapay Sinir Ağları Sistemlerinin Kullanımına Yönelik Bir Literatür Araştırması". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 20 (2015): 272-280
  22. Yamini J., Shelly K., Amandeep M.,2015,”Comfort characteristics of textiles - Objective evaluation and prediction by soft computing techniques”, National Conference on Advances in Engineering, Technology & Management, (AETM’15)
  23. Majumdar, P., K., Majumdar, A., “Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models”, Textile Research Journal,2004 /74: 652 -655
  24. Yaman, N., Şenol, M.F., Gurkan, P., “Applying Artificial Neural Networks to Total Hand Evaluation of Disposable Diapers”, Journal of Engineered Fibers and Fabrics,Volume 6, Issue 1 - 2011
  25. Utkun, E. "Giyim Konforunun Tahminlenmesinde Yapay Sinir Ağları Sistemlerinin Kullanımına Yönelik Bir Literatür Araştırması". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 20 (2015): 272-280