摘要
以注射成型机箱壳为例,构建制品CAE分析模型,运用Moldfl ow仿真分析,预测制品缺陷,并选定了优化因素与指标;运用Taguchi试验法和CAE仿真获得数据样本,通过模糊加权综合评分将多目标问题转化为单目标优化;建立了BP神经网络集预测模型,映射了工艺参数与质量指标的非线性关系;采纳遗传算法进行全局寻优,得到试验范围内的最优工艺参数:模具温度为66.3℃,熔体温度为227℃,填充时间为4.6 s,保压压力为填充压力的109%,保压时间为10.2 s,冷却时间为22.7 s。对优化结果进行CAE分析验证,结果表明,神经网络预测结果与CAE模流分析结果相近,实现了制品质量指标的多目标优化。该优化设计方法能有效提高制品质量,缩短生产周期。
Taking the chassis shell as an example,the product CAE analysis model was built,and the Moldflow software was used to analysis the product defect,and the optimized factors and indicators were selected.The data samples was obtained from using Taguchi test analysis and CAE simulation,through fuzzy comprehensive quality weighted evaluation analysis it would be useful to effectively resolve that the multi-objective problem was transformed into the single objective optimization problem.The BP neural network prediction model was established,mapping the nonlinear relationship of the process parameters and the quality index.Adopted the genetic algorithm for global optimization,the optimal process parameters within the test scope are as follow:mold temperature is66.3℃,melt temperature is227℃,filling time is4.6s,holding pressure is109%of the filling pressure,holding time is10.2s,cooling time is22.7s.The optimization results were verified by CAE analysis,the results show that the prediction results of neural network are similar to the analysis of CAE software Moldflow,and the multi-objective optimization for the products quality indicators are achieved.The optimization design method can improve the quality of products and shorten the production cycle.
作者
郝彦琴
龙春光
Hao Yanqin;Long Chunguang(Huaihua Vocational and Technical College, Huaihua 418000, China;Changsha University of Science & Technology , Changsha 410000, China)
出处
《工程塑料应用》
CAS
CSCD
北大核心
2018年第3期69-75,111,共8页
Engineering Plastics Application
基金
湖南省教育厅资助项目(17C1293)
