塑料科技2018，Vol.46Issue(1)：80-84,5.DOI:10.15925/j.cnki.issn1005-3360.2018.01.013
基于BP神经网络及PSO算法的食品输送齿轮注塑工艺参数优化研究
Research on Optimization of Injection Molding Process Parameters of Food Transportation Gear Based on BP Neural Network and PSO Algorithm
摘要
Abstract
Firstly,the orthogonal test method was used to the warping analysis of the plastic gear for food transportation system,and the minimum warpage of plastic gear was 1.952 mm.Then,the threelayered BP neural network was constructed by using the process parameters and warpage as the input layer and the output layer.After training and testing,a neural network model with better performance was obtained.Finally,the initial fitness value of each particle of particle swarm optimization (PSO) algorithm was calculated by this model,and the process parameters were optimized with the objective of warpage.The optimized minimum warpage of plastic gear was 1.853 mm,which was verified to be 1.830 mm with the error of 1.2％,which is better than that of the orthogonal experiment.关键词
塑料齿轮/正交试验/BP神经网络/粒子群优化算法Key words
Plastic gear/Orthogonal experiment/BP neural network/PSO algorithm分类
化学化工引用本文复制引用
刘月云,刘碧俊..基于BP神经网络及PSO算法的食品输送齿轮注塑工艺参数优化研究[J].塑料科技,2018,46(1):80-84,5.基金项目
淮安市横向课题(JSSP2015143) （JSSP2015143）
校内青年基金项目(3011500185) （3011500185）
