Tsai Ming-Hong, Fan-Jiang Jia-Chen, Liou Guan-Yan, Cheng Feng-Jung, Hwang Sheng-Jye, Peng Hsin-Shu, Chu Hsiao-Yeh
Department of Mechanical Engineering, National Cheng Kung University, Tainan 700, Taiwan.
Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 400, Taiwan.
Polymers (Basel). 2022 Apr 15;14(8):1607. doi: 10.3390/polym14081607.
This research developed an adaptive control system for injection molding process. The purpose of this control system is to adaptively maintain the consistency of product quality by minimize the mass variation of injection molded parts. The adaptive control system works with the information collected through two sensors installed in the machine only-the injection nozzle pressure sensor and the temperature sensor. In this research, preliminary experiments are purposed to find master pressure curve that relates to product quality. Viscosity index, peak pressure, and timing of the peak pressure are used to characterize the pressure curve. The correlation between product quality and parameters such as switchover position and injection speed were used to produce a training data for back propagation neural network (BPNN) to compute weight and bias which are applied on the adaptive control system. By using this system, the variation of part weight is maintained to be as low as 0.14%.
本研究开发了一种用于注塑成型过程的自适应控制系统。该控制系统的目的是通过最小化注塑零件的质量变化来自适应地保持产品质量的一致性。自适应控制系统利用仅安装在机器中的两个传感器收集的信息——注塑喷嘴压力传感器和温度传感器。在本研究中，初步实验旨在找到与产品质量相关的主压力曲线。粘度指数、峰值压力和峰值压力出现的时间用于表征压力曲线。产品质量与诸如切换位置和注射速度等参数之间的相关性被用于生成反向传播神经网络（BPNN）的训练数据，以计算应用于自适应控制系统的权重和偏差。通过使用该系统，零件重量的变化保持在低至0.14%。
