Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features

Shengrui Yu (), Tianfeng Zhang, Yun Zhang, Zhigao Huang, Huang Gao (), Wen Han, Lih-Sheng Turng and Huamin Zhou
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Shengrui Yu: Jingdezhen Ceramic Institute
Tianfeng Zhang: Jingdezhen Ceramic Institute
Yun Zhang: Huazhong University of Science and Technology
Zhigao Huang: Huazhong University of Science and Technology
Huang Gao: Huazhong University of Science and Technology
Wen Han: Jingdezhen Ceramic Institute
Lih-Sheng Turng: University of Wisconsin-Madison
Huamin Zhou: Huazhong University of Science and Technology

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 1, No 3, 77-89

Abstract: Abstract Process parameters of injection molding are the key factors affecting the final quality and the molding efficiency of products. In the traditional automatic setting of process parameters based on case-based reasoning, only the geometric features of molds are considered, which may not be the representative feature of products and cause the reasoning process to fail. This problem of failure manifests itself in that the molding process parameters inferred by the reasoning system may be very different between molds with similar geometric features or very similar between molds with different geometric features. Therefore, this paper proposes a case-based-reasoning method based on molding features in order to overcome this problem by a method of dimensionality reduction, composed of three stages which (1) obtain the injection pressure profile data through actual injection molding or filling simulation analysis, (2) calculate the similarity of the pressure profiles between target case and each of source cases in case database using the nearest neighbor method, and sort according to the value of similarity, (3) find the case with a maximum of similarity out as the one closest to the target case, and take the process parameters of the most similar case as the solution of the target case according to case modification strategies. This method simplifies the high-dimensional molding features to the pressure profile at the injection location with two-dimensional data features. Experiments show that the new method has a high retrieval accuracy and sensitivity. Moreover, even slight differences in molding can be captured easily.

Keywords: Injection molding; Process parameter; Intelligent setting; Molding feature; Pressure profile (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-020-01658-y

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