IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i4d10.1007_s10845-019-01481-0.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01481-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-019-01481-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Steven O. Otieno & Job M. Wambua & Fredrick M. Mwema & Edwell T. Mharakurwa & Tien-Chien Jen & Esther T. Akinlabi, 2025. "A predictive modelling strategy for warpage and shrinkage defects in plastic injection molding using fuzzy logic and pattern search optimization," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1835-1859, March.
    3. 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.
    4. Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.
    5. 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.
    6. 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.
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    2. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    3. Maximilian Zarte & Agnes Pechmann & Isabel L. Nunes, 2022. "Problems, Needs, and Challenges of a Sustainability-Based Production Planning," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    4. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    5. Wang, Di & He, Bin & Hu, Zhimu, 2024. "Financial technology and firm productivity: Evidence from Chinese listed enterprises," Finance Research Letters, Elsevier, vol. 63(C).
    6. Zhiyuan Fu & Ghulam Rasool Madni, 2024. "Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-23, February.
    7. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    8. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    9. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    10. Seon Han Choi & Byeong Soo Kim, 2025. "Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1547-1561, March.
    11. 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.
    12. Shiguang Li & Yixiang Tian, 2023. "How Does Digital Transformation Affect Total Factor Productivity: Firm-Level Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    13. Muhammad Hassan & Marcus Svadling & Niclas Björsell, 2024. "Experience from implementing digital twins for maintenance in industrial processes," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 875-884, February.
    14. Yanbin Du & Guohua He & Bo Li & Zhijie Zhou & Guoao Wu, 2022. "In-service machine tool remanufacturing: a sustainable resource-saving and high-valued recovery approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1335-1358, January.
    15. Jingbo Liu & Fan Jiang & Shinichi Tashiro & Shujun Chen & Manabu Tanaka, 2025. "A physics-informed and data-driven framework for robotic welding in manufacturing," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
    16. Wei Fang & Lianyu Zheng, 2020. "Shop floor data-driven spatial–temporal verification for manual assembly planning," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1003-1018, April.
    17. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    18. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    19. Chaohong Na & Xue Chen & Xiaojun Li & Yuting Li & Xiaolan Wang, 2022. "Digital Transformation of Value Chains and CSR Performance," Sustainability, MDPI, vol. 14(16), pages 1-24, August.
    20. Mario Vozza & Joseph Polden & Giulio Mattera & Gianfranco Piscopo & Silvestro Vespoli & Luigi Nele, 2024. "Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis," Mathematics, MDPI, vol. 12(21), pages 1-17, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01481-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.