Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (05): 120-126,136.doi: 10.12052/gdutxb.220063

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Deep Neural Network Based Predictive Control for Injection Molding Process

Li Yao-dong, Ren Zhi-gang, Wu Zong-ze   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-03-29 Published:2022-07-18

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Abstract: The development of injection molding machines has always piqued the industry's interest as an important production and manufacturing equipment for plastic parts in modern industry. How to achieve high-precision, high-efficiency, green, and energy-saving injection molding parts is an important development direction for injection molding machines as the aerospace, power electronics, automobile manufacturing, and other industries grow. A strategy that combines the deep neural network (DNN) is proposed to realize the predictive control of the injection molding process of the injection molding machine, in response to the problem that traditional model predictive control (MPC) finds it difficult to guarantee real-time tracking in the injection molding process. A model predictive controller with constraints is created based on the dynamic model of the injection molding machine injection process, and the controller's operating data is collected and used to train the deep neural network to realize the predictive control of the injection molding process based on the deep neural network control. The simulation results show that this strategy can effectively avoid the complex calculation caused by the model predictive control in the injection molding process and meet the real-time requirements and has a broad application prospect.

Key words: deep neural network (DNN), model predictive control (MPC), injection molding process, optimal control

CLC Number: 

  • TP271
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