Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

Basit Öğe Kaydı
dc.authorid0000-0002-1640-6035en_US
dc.contributor.authorYıldırım, Kenan
dc.contributor.authorÖğüt, Hamdi
dc.contributor.authorUlcay, Yusuf
dc.date.accessioned2021-03-20T20:14:07Z
dc.date.available2021-03-20T20:14:07Z
dc.date.issued2017
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Polimer Malzeme Mühendisliği Bölümüen_US
dc.description.abstractIn the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the non-linear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R-2 = 0.97 vs. R-2 = 0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.en_US
dc.description.sponsorshiptextile company KORTEKSen_US
dc.description.sponsorshipThis work was supported by the textile company KORTEKS.en_US
dc.identifier.endpage16en_US
dc.identifier.issn1558-9250
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage7en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/998
dc.identifier.volume12en_US
dc.identifier.wosWOS:000417360400002en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldırım, Kenan
dc.language.isoenen_US
dc.publisherIndaen_US
dc.relation.ispartofJournal Of Engineered Fibers And Fabricsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleComparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditionsen_US
dc.typeArticleen_US

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WoS İndeksli Yayınlar Koleksiyonu
Polimer Malzeme Mühendisliği Bölümü Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu