Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions
dc.authorid | 0000-0002-1640-6035 | en_US |
dc.contributor.author | Yıldırım, Kenan | |
dc.contributor.author | Öğüt, Hamdi | |
dc.contributor.author | Ulcay, Yusuf | |
dc.date.accessioned | 2021-03-20T20:14:07Z | |
dc.date.available | 2021-03-20T20:14:07Z | |
dc.date.issued | 2017 | |
dc.department | BTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Polimer Malzeme Mühendisliği Bölümü | en_US |
dc.description.abstract | In 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.sponsorship | textile company KORTEKS | en_US |
dc.description.sponsorship | This work was supported by the textile company KORTEKS. | en_US |
dc.identifier.endpage | 16 | en_US |
dc.identifier.issn | 1558-9250 | |
dc.identifier.issue | 3 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.startpage | 7 | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12885/998 | |
dc.identifier.volume | 12 | en_US |
dc.identifier.wos | WOS:000417360400002 | en_US |
dc.identifier.wosquality | Q3 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.institutionauthor | Yıldırım, Kenan | |
dc.language.iso | en | en_US |
dc.publisher | Inda | en_US |
dc.relation.ispartof | Journal Of Engineered Fibers And Fabrics | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | [No Keywords] | en_US |
dc.title | Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions | en_US |
dc.type | Article | en_US |