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CLC number: TQ32

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2011-01-25

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Journal of Zhejiang University SCIENCE A 2011 Vol.12 No.3 P.201-206

http://doi.org/10.1631/jzus.A1000357


Automated process parameters tuning for an injection moulding machine with soft computing


Author(s):  Peng Zhao, Jian-zhong Fu, Hua-min Zhou, Shu-biao Cui

Affiliation(s):  State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China, State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Corresponding email(s):   pengzhao@zju.edu.cn, hmzhou@hust.edu.cn

Key Words:  Injection moulding machine (IMM), Process parameters, Case based reasoning (CBR), Empirical model (EM), Fuzzy logic (FL)


Peng Zhao, Jian-zhong Fu, Hua-min Zhou, Shu-biao Cui. Automated process parameters tuning for an injection moulding machine with soft computing[J]. Journal of Zhejiang University Science A, 2011, 12(3): 201-206.

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T1 - Automated process parameters tuning for an injection moulding machine with soft computing
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DOI - 10.1631/jzus.A1000357


Abstract: 
In injection moulding production, the tuning of the process parameters is a challenging job, which relies heavily on the experience of skilled operators. In this paper, taking into consideration operator assessment during moulding trials, a novel intelligent model for automated tuning of process parameters is proposed. This consists of case based reasoning (CBR), empirical model (EM), and fuzzy logic (FL) methods. CBR and EM are used to imitate recall and intuitive thoughts of skilled operators, respectively, while FL is adopted to simulate the skilled operator optimization thoughts. First, CBR is used to set up the initial process parameters. If CBR fails, EM is employed to calculate the initial parameters. Next, a moulding trial is performed using the initial parameters. Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory. Based on the above methodologies, intelligent software was developed and embedded in the controller of an injection moulding machine. Experimental results show that the intelligent software can be effectively used in practical production, and it greatly reduces the dependence on the experience of the operators.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bozdana, A., Eyercolu, O., 2002. Development of an expert system for the determination of injection moulding parameters of thermoplastic materials: EX-PIMM. Journal of Materials Processing Technology, 128(1-3):113-122.

[2]Chen, M., Tzeng, H., Chen, Y., Chen, S., 2008. The application of fuzzy theory for the control of weld line positions in injection-molded part. ISA transactions, 47(1):119-126.

[3]Chen, W., Tai, P., Wang, M., Deng, W., Chen, C., 2008. A neural network-based approach for dynamic quality prediction in a plastic injection molding process. Expert Systems with Applications, 35(3):843-849.

[4]Chen, Z.B., Turng, L.S., 2005. A review of current developments in process and quality control for injection molding. Advances in Polymer Technology, 24(3):165-182.

[5]He, W., Zhang, Y.F., Lee, K.S., Liu, T.I., 2001. Development of a fuzzy-neuro system for parameter resetting of injection molding. Journal of manufacturing science and engineering, 123(1):110-118.

[6]Kwong, C.K., 2001. A case-based system for process design of injection moulding. International Journal of Computer Applications in Technology, 14(1-3):40-50.

[7]Mok, S.L., Kwong, C.K., Lau, W.S., 1999. Review of research in the determination of process parameters for plastic injection molding. Advances in Polymer Technology, 18(3):225-236.

[8]Shoemaker, J., 2006. Moldflow Design Guide: a Resource for Plastics Engineers. Hanser Gardner Publications, Cincinnati, OH, USA, p.13-14.

[9]Sun, Z., Finnie, G., 2005. A unified logical model for CBR-based e-commerce systems. International Journal of Intelligent Systems, 20(1):29-46.

[10]Zhang, Z.X., Sun, C.T., 1997. Neuro-Fuzzy and Soft Computing. Xi’an Jiaotong University Publishing House, Xi’an, China, p.52-56 (in Chinese).

[11]Zhao, P., Zhou, H.M., Li, Y., Li, D.Q., 2010. Process parameters optimization of injection molding using a fast strip analysis as a surrogate model. The International Journal of Advanced Manufacturing Technology, 49(9-12):949-959.

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