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An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining

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Abstract

In the high speed milling process, the accurate predictions of surface roughness and residual stress can avoid the deterioration of machined surface quality. But it’s hard to estimate the surface roughness and residual stress under different tool wear status and cutting parameters. In this work, a novel intelligent reasoning method-improved case based reasoning (ICBR) was proposed to predict the surface roughness and residual stress. The inputs of ICBR are cutting parameters and tool wear status. The corresponding outputs of ICBR are surface roughness and residual stress. In the ICBR, K-nearest neighbor method and artificial neural network (ANN) as case retrieval was introduced to retrieve the K similar cases to the inputs. Through retrieving K similar cases, the Gaussian process regression (GPR) model as case reuse was established to output the surface roughness and residual stress. The vibration particle swarm optimization algorithm is proposed to optimize the ANN and GPR models. The high speed milling experiments of Compacted Graphite Iron was performed to validate the performance of ICBR. The experimental results showed that the cutting speed is the most important factor affecting the surface roughness. The feed rate is the most important factor affecting the residual stress. The ICBR gives the accurate estimation of surface roughness with the Mean Absolute Percentage Error of 11.6%. As for residual stress, the prediction accuracy using ICBR is 87.5%. Compared with Back-Propagation neural network, standard CBR and GPR models, the ICBR has better predictive performance and can be used for estimations of surface roughness and residual stress in 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, Jinan, 250061, China

    Longhua Xu, Chuanzhen Huang & 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

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  1. Longhua Xu
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Correspondence to Chuanzhen Huang.

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Xu, L., Huang, C., Li, C. et al. An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining. J Intell Manuf 32, 313–327 (2021). https://doi.org/10.1007/s10845-020-01573-2

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  • DOI: https://doi.org/10.1007/s10845-020-01573-2

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