Deep Neural Network Based Predictive Control for Injection Molding Process

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

doi:10.12052/gdutxb.220063

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Deep Neural Network Based Predictive Control for Injection Molding Process

更新时间：2026-05-27

- Deep Neural Network Based Predictive Control for Injection Molding Process
- Vol. 39, Issue 5, Pages: 120-126, 136(2022)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220063
  CLC： TP271
- Published：2022
- 稿件说明：
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黎耀东, 任志刚, 吴宗泽. 基于深度神经网络的注塑过程预测控制[J]. 广东工业大学学报, 2022, 39(5): 120-126, 136.

Li Yao-dong, Ren Zhi-gang, Wu Zong-ze. Deep Neural Network Based Predictive Control for Injection Molding Process[J]. 2022, 39(5): 120-126, 136.
黎耀东, 任志刚, 吴宗泽. 基于深度神经网络的注塑过程预测控制[J]. 广东工业大学学报, 2022, 39(5): 120-126, 136. DOI： 10.12052/gdutxb.220063.

Li Yao-dong, Ren Zhi-gang, Wu Zong-ze. Deep Neural Network Based Predictive Control for Injection Molding Process[J]. 2022, 39(5): 120-126, 136. DOI： 10.12052/gdutxb.220063.

摘要

注塑机作为现代工业中重要的塑料件生产与制造设备，其智能化发展一直备受业界关注。伴随着航空航天、电力电子、汽车制造等行业的发展，如何实现对注塑件的高精度、高效率、绿色节能化生产，是目前注塑机的重要研制方向。本文针对注塑成型过程中采用传统模型预测控制(Model Predictive Control

MPC)难以保证跟踪控制实时性的问题，提出一种结合深度神经网络(Deep Neural Network

DNN)学习的注塑过程预测控制方法。在注塑机注射过程动力学模型基础上，创建带约束条件的模型预测控制器，对控制器运行数据进行采集并用以训练深度神经网络，实现了基于深度神经网络控制的注射速度的跟踪预测控制。仿真实验结果表明，采用本文所提出的学习预测控制策略能够有效避免注塑过程中因模型预测控制所产生的复杂计算，并满足工业实时性要求，具有应用前景。

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.

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references

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