Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control
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...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Polymer Technology |
| Online Access: | http://dx.doi.org/10.1155/2019/1591204 |
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| _version_ | 1832550666864689152 |
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| author | Yufei Ruan Huang Gao Dequn Li |
| author_facet | Yufei Ruan Huang Gao Dequn Li |
| author_sort | Yufei Ruan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f56bf438128947b9b4997f33c9a45310 |
| institution | Kabale University |
| issn | 0730-6679 1098-2329 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Polymer Technology |
| spelling | doaj-art-f56bf438128947b9b4997f33c9a453102025-02-03T06:06:10ZengWileyAdvances in Polymer Technology0730-66791098-23292019-01-01201910.1155/2019/15912041591204Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation ControlYufei Ruan0Huang Gao1Dequn Li2State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, 430074, ChinaState Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, 430074, ChinaState Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, 430074, ChinaTemperature 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.http://dx.doi.org/10.1155/2019/1591204 |
| spellingShingle | Yufei Ruan Huang Gao Dequn Li Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control Advances in Polymer Technology |
| title | Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control |
| title_full | Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control |
| title_fullStr | Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control |
| title_full_unstemmed | Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control |
| title_short | Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control |
| title_sort | improving the consistency of injection molding products by intelligent temperature compensation control |
| url | http://dx.doi.org/10.1155/2019/1591204 |
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