A COMPARISON OF THE THREE DIFFERENT TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS

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dc.contributor.authorKastaci, Bilge Berkhan
dc.contributor.authorÖzek, Hikmet Ziya
dc.contributor.authorÖzhan, Erkan
dc.date.accessioned2024-10-29T17:43:44Z
dc.date.available2024-10-29T17:43:44Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThe 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.
dc.identifier.doi10.7216/TEKSMUH.1329122
dc.identifier.endpage41
dc.identifier.issn1300-7599
dc.identifier.issue133en_US
dc.identifier.scopus2-s2.0-85193332875
dc.identifier.scopusqualityQ4
dc.identifier.startpage34
dc.identifier.trdizinid1231096
dc.identifier.urihttps://doi.org/10.7216/TEKSMUH.1329122
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1231096
dc.identifier.urihttps://hdl.handle.net/20.500.11776/12589
dc.identifier.volume31
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherChamber of Textile Engineers
dc.relation.ispartofTekstil ve Muhendis
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial neural networks
dc.subjectbreaking strength
dc.subjectdokuma kumaş
dc.subjectkopma mukavemeti
dc.subjectrandom forest algorithm
dc.subjectrastgele orman algoritması
dc.subjectRegression model
dc.subjectRegresyon modeli
dc.subjectwoven fabric
dc.subjectyapay sinir ağları
dc.titleA COMPARISON OF THE THREE DIFFERENT TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS
dc.title.alternativeA COMPARISON OF THE THREE TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS
dc.typeArticle

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