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

Autor: Yufei Ruan, Huang Gao, Dequn Li
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Advances in Polymer Technology, Vol 2019 (2019)
Druh dokumentu: article
ISSN: 0730-6679
1098-2329
DOI: 10.1155/2019/1591204
Popis: 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.
Databáze: Directory of Open Access Journals
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