基于BP神经网络的注塑成型工艺优化被引量：6

Process Optimization of Injection Molding Based on BP Neural Network

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摘要以激光器支架为例,运用Moldflow软件进行模流分析,并设置了正交试验,以得到各因素水平的最佳组合,从而减小翘曲变形量,提高塑件质量,使其达到装配要求。然后根据所得数据建立了BP神经网络预测模型,再利用测试样本验证模型的准确性,结果发现仿真值与预测值的误差均在±3%以内。 Taking the laser bracket as an example, the mold flow analysis was carried out by Moldflow soft ware. In order to reduce the warping deformation, improve the quality of plastic parts, and to meet the requirements of assembly, the orthogonal test was set and the optimal combination of factor levels was obtained. According to the data, the BP neural network forecast model was established. Then the accuracy of the model was verified by using the test sample. The results show that the errors of the simulation value and the forecast value are both within ±3%.

作者王卫东宋继顺

机构地区天津理工大学材料科学与工程学院

出处《塑料科技》 CAS 北大核心 2016年第4期76-80,共5页 Plastics Science and Technology

关键词激光器支架模流分析正交试验 BP神经网络 Laser bracket Mold flow analysis Orthogonal test BP neural network

分类号 TQ320.662 [化学工程—合成树脂塑料工业]

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