摘要
为了解决无人机固定翼在注塑过程中工艺参数的优化选择问题,在考虑了熔体温度、模具温度、保压压力、保压时间、注射时间因素下,用模流分析软件Moldflow和正交试验相结合的方法对翘曲量、体积收缩率和缩痕指数进行了模拟分析,同时为了提高优化效率,根据正交试验数据建立了BP神经网络预测模型,并用模型对工艺参数进行了优化和实际生产验证。结果表明:优化后的塑件最大翘曲变形量、体积收缩率、缩痕指数分别优化了0.212 5 mm、1.26%、1.223%,提高了塑件质量。而且仿真值与模型的预测值基本吻合,相对误差在3%以内,验证了模型的可行性,为优化工艺参数方面的研究提供了理论依据。
In order to solve the problem about the optimization selection of processing parameters in the injection molding of unmanned aerial vehicle（UAV） fixed-wing, the method of the combination of Moldflow and orthogonal test was adopted. In consideration of the melt temperature, mold temperature, packing pressure, packing time, injection time factors, the warpage, shrinkage and shrinkage index were simulated and analyzed using the method of mold flow analysis software Moldflow and orthogonal test combination. The optimization of process parameters and actual production verification of the moulding part were made by the model. The results show that： after optimization, the maximum warpage, volume shrinkage and shrinkage index of plastic parts are respectively improved by 0.212 5 mm, 1.26% and 1.223%, the quality of the plastic parts is improved. The simulation results are in good agreement with the predicted values of the model, and the relative error is less than 3%, which verifies the feasibility of the model and provides a theoretical basis for optimizing the process parameters.
出处
《塑料科技》
CAS
北大核心
2017年第9期74-78,共5页
Plastics Science and Technology
关键词
注塑成型
参数优化
正交试验
BP神经网络
Injection molding
Parameters optimization
Orthogonal experiment
BP neural network
作者简介
通讯联系人
