A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models. Issue 4 (3rd April 2018)

• Record Type: Journal Article
• Authors: Erbil, Yılmaz; Babaarslan, Osman; Ilhan, İlhami
• Is Part Of: Journal of the Textile Institute. Volume 109:Issue 4(2018)
• Page Start: 560
• Page End: 568
• Publication Date: 2018-04-03
• Subjects: Ternary blends -- open-end yarn -- prediction -- multiple linear regression -- artificial neural networks; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Periodicals; 677.005
• DOI: 10.1080/00405000.2017.1361164
• Languages: English
• ISSNs: 0040-5000

Abstract

Abstract: This study focused on predicting tensile properties of PES/CV/PAN blended Open-End Rotor yarns. The effective factors were fiber blend ratios (six stages from 0 to 100%), linear density (three count levels), mixing method (carding machine and drawframe), and number of passages in drawframe (one and two times) as production parameters. We performed a stepwise multiple linear regression (MLR) analysis and established an artificial neural network (ANN) model that trained with backpropagation rule as Levenberg–Marquardt. Then, we conducted a comparative analysis for both models in terms of prediction performance. As a result, ANN has given a slightly better prediction values than MLR for breaking strength but significantly better prediction values for breaking elongation.