Performance Prediction of Differential Fibers with a Bi-Directional Optimization Approach

Materials

Yi Wang¹  Yongsheng Ding¹  Kuangrong Hao¹  Tong Wang¹ 

[1] College of Information Sciences and Technology, Donghua University, Shanghai 201620, China; E-Mails:

关键词: bi-directional prediction;    neural networks;    multi-objective evolutionary algorithm;    performance prediction;    differential fibers;   

DOI  :  10.3390/ma6125967

来源: mdpi

【 摘 要 】

This paper develops a bi-directional prediction approach to predict the production parameters and performance of differential fibers based on neural networks and a multi-objective evolutionary algorithm. The proposed method does not require accurate description and calculation for the multiple processes, different modes and complex conditions of fiber production. The bi-directional prediction approach includes the forward prediction and backward reasoning. Particle swam optimization algorithms with K-means algorithm are used to minimize the prediction error of the forward prediction results. Based on the forward prediction, backward reasoning uses the multi-objective evolutionary algorithm to find the reasoning results. Experiments with polyester filament parameters of differential production conditions indicate that the proposed approach obtains good prediction results. The results can be used to optimize fiber production and to design differential fibers. This study also has important value and widespread application prospects regarding the spinning of differential fiber optimization.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.