A COMPARISON OF THE THREE DIFFERENT TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Chamber of Textile Engineers
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The adaptation and utilization of artificial intelligence techniques for various demands of the textile and apparel industry has been gradually increasing. The use of such methods are particularly very useful when making predictions based on the past company data in the cases where statistical methods are likely to be insufficient. It is obvious that an accurate projection of both structural and performance properties of woven fabrics is extremely important in regard of fabric design. In this study, several models based on multiple linear regression, artificial neural networks and random forest algorithms were developed to predict the breaking strength of woven fabrics which is considered one of the most important performance characteristic. Industrial data comprising variables of 147 sets of pure cotton and 53 sets of polyester/viscose woven fabrics are used. Breaking strength of a fabric is very much effected by basic structural elements of the fabric. For the sake of revealing the best relationship between the breaking strength and variables of fabric, various explanatory variables influencing the fabric properties are taken into consideration and several models were developed by means of Minitab Statistics Program, Weka and R software and the overall results are compared. Among all the models created by the three different techniques, it was found that the regression and artificial neural networks models performed well in both cotton fabrics and blended fabrics, while random forest algorithms were not very accurate in estimating the breaking strength. © (2024), (Chamber of Textile Engineers). All Rights Reserved.
Açıklama
Anahtar Kelimeler
artificial neural networks, breaking strength, dokuma kumaş, kopma mukavemeti, random forest algorithm, rastgele orman algoritması, Regression model, Regresyon modeli, woven fabric, yapay sinir ağları
Kaynak
Tekstil ve Muhendis
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
31
Sayı
133
