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An improved case-based reasoning method and its application to predict machining performance

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Abstract

In the machining process, the machining performance which mainly refers to machined surface quality and cutting forces is hard to predict under different tool wear status. In this work, an improved case-based reasoning (ICBR) method is proposed to predict both the cutting force and machined surface roughness. With the emergence of new problem, ICBR method obtains solutions to new problem through case retrieval and reuse. In case retrieval stage, K similar cases to the new problem were retrieved using K nearest neighbor method. By means of the K similar cases, support vector regression machine (SVR) model was established to give the solution of the new problem in case of the reuse stage of ICBR method. Artificial neural network (ANN) was introduced to estimate the influence of machining parameters and tool wear on machining performance. As the ANN and SVR models contain unknown parameters, the novel particle swarm optimization algorithm was proposed to train these models for its capability of fast convergence and global optimum. The proposed ICBR method was used to predict the surface roughness and cutting force. The results showed the proposed ICBR method can give superior prediction accuracy and lower Mean square error than other popular intelligent models. Meantime, the ICBR method possesses good robustness and can be used for the actual machining process.

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Acknowledgements

This work is financially supported by National Natural Science Foundation of China (51675312, 51675313).

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Authors and Affiliations

  1. Center for Advanced Jet Engineering Technologies (CaJET), Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Ministry of Education), National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), School of Mechanical Engineering, Shandong University, 17923 Jing Shi Road, Jinan, 250061, China

    Longhua Xu, Chuanzhen Huang, Jiahui Niu & Hanlian Liu

  2. Jinan Power Co. Ltd. of China National Heavy Duty Truck Group Co., Ltd., Jinan, China

    Chengwu Li

  3. School of Mechanical and Manufacturing Engineering, The University of New South Wales (UNSW), Sydney, NSW, 2052, Australia

    Jun Wang

  4. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Xiaodan Wang

Authors
  1. Longhua Xu
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  2. Chuanzhen Huang
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  3. Jiahui Niu
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  4. Chengwu Li
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  5. Jun Wang
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  6. Hanlian Liu
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  7. Xiaodan Wang
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Correspondence to Chuanzhen Huang.

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Xu, L., Huang, C., Niu, J. et al. An improved case-based reasoning method and its application to predict machining performance. Soft Comput 25, 5683–5697 (2021). https://doi.org/10.1007/s00500-020-05564-6

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