Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Abstract

Temperature stability is critical to the consistency of product quality in the injection molding process, and it is very necessary to improve the temperature control accuracy under dynamic conditions. However, due to the large time delay, strong coupling, and the dynamic characteristics existing in the system, it is not an easy task to achieve precise temperature control in the injection molding process. In this paper, a new intelligent temperature compensation control strategy for the injection molding process under dynamic conditions is proposed in order to solve two key problems in the compensation control strategy: the compensation time and compensation quantity. A data-based feedforward iterative learning control (ILC) algorithm is designed to learn the optimal compensation time. Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. An experimental platform is designed to validate the superiority of the proposed method. Experimental results show that the proposed method can effectively improve temperature control accuracy under dynamic conditions. Meanwhile, the product consistency has also been improved.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Molding

“…These include, material temperature, and the pressure that the material is subject to during the plastication phase. Knowledge of the material composition and control of plastication pressure and speed, can be used to mitigate production problems [13]. This is especially true when considering the entire IMP which is made up of many measurable states such as mold temperature or material composition.…”

Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines

“…Q is the weight matrix of y, and R is the weight matrix of ΔuðkÞ. The precise control of the dynamic model can finally be attained by minimizing the value of the loss function (8), and the ram velocity yðkÞ can also quickly converges to the stated target velocity y ref ðkÞ.…”

“…In recent years, there have been many related researches on control technologies in the field of the injection molding process [3][4][5][6][7][8][9][10][11][12]. For example, Tan et al proposed an adaptive control approach for controlling injection speed changes throughout the injection molding process [4].…”