Huang Yan-Mao, Jong Wen-Ren, Chen Shia-Chung
Department of Mechanical Engineering, Chung Yuan Cristian University, Taoyuan City 320314, Taiwan.
Polymers (Basel). 2021 Nov 9;13(22):3874. doi: 10.3390/polym13223874.
This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the -axis, shrinkage along the -axis, and shrinkage along the -axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.
本研究探讨了在注塑生产过程中应用CAE时存在的一些问题，其中相当复杂的因素阻碍了其有效利用。在本研究中，利用人工神经网络，即反向传播神经网络（BPNN），对注塑过程进行结果预测。通过输入成型参数中的塑料温度、模具温度、注射速度、保压压力和保压时间，可以更准确地预测以下五个结果：EOF压力、最大冷却时间、沿x轴的翘曲、沿x轴的收缩以及沿y轴的收缩。本研究首先将CAE分析数据用作训练数据，并通过本文介绍的田口方法和随机洗牌方法将误差值降低到5%以下，然后利用迁移学习成功地将CAE数据分析预测的网络转移到实际机器上进行验证。本研究使用反向传播神经网络（BPNN），使用不同的大量数据训练专用预测网络，事实证明该网络速度快，并且使用我们优化的模型可以准确预测结果。
