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Injection molding manufacturing process: review of case-based reasoning applications

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  • Mohammad Reza Khosravani

    (University of Siegen)

  • Sara Nasiri

    (University of Siegen)

Abstract

Although manufacturing technology has been developing rapidly, injection molding is still widely used for fabricating plastic parts with complex geometries and precise dimensions. Since the occurrence of faults in injection molding is inevitable, process optimization is desirable. Artificial intelligence (AI) methods are being successfully used for optimization in different branches of science and technology. In this paper, we review the application of one such method, case-based reasoning (CBR), to injection molding. CBR is an AI approach for knowledge representation and manipulation which considers successful solutions of past problems that are likely to serve as candidate solutions for a given problem. This method is being used increasingly in academic and industrial applications. Here, we review CBR systems that are used in injection molding for different purposes, such as process design, processing parameters, fault diagnose, and enhancement of quality control. In addition, we discuss trends for utilization of CBR in different phases of injection molding. The most significant challenges associated with application of CBR to injection molding are also discussed. Finally, the review is concluded by contemplating on some open research areas and future prospects.

Suggested Citation

  • Mohammad Reza Khosravani & Sara Nasiri, 2020. "Injection molding manufacturing process: review of case-based reasoning applications," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 847-864, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01481-0
    DOI: 10.1007/s10845-019-01481-0
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    References listed on IDEAS

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    1. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    2. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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    Cited by:

    1. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Shengrui Yu & Tianfeng Zhang & Yun Zhang & Zhigao Huang & Huang Gao & Wen Han & Lih-Sheng Turng & Huamin Zhou, 2022. "Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 77-89, January.
    3. Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.
    4. Elham Sharifi & Atanu Chaudhuri & Brian Vejrum Waehrens & Lasse Guldborg Staal & Saeed Davoudabadi Farahani, 2021. "Assessing the Suitability of Freeform Injection Molding for Low Volume Injection Molded Parts: A Design Science Approach," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    5. Shengqiang Li & Hua Zhang & Wei Yan & Zhigang Jiang, 2021. "A hybrid method of blockchain and case-based reasoning for remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1389-1399, June.
    6. Guoshen Wu & Zhigang Ren & Jiajun Li & Zongze Wu, 2023. "Optimal Robust Tracking Control of Injection Velocity in an Injection Molding Machine," Mathematics, MDPI, vol. 11(12), pages 1-17, June.

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