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Multi-objective modelling and optimal parameter selection of a multi-pass milling process considering uncertain milling stability constraint

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Abstract

Optimizing machining parameters and pass numbers under machining stability constraint is important for manufacture engineers planning a multi-pass milling operation. Considering that milling stability varies with the machine tool dynamics when the tool and workpiece position changes, a generalized regression neural network (GRNN) is adopted to obtain the position-dependent stability constraint. Then a multi-objective optimization model of a multi-pass milling is established, where the objectives are the total cutting time (Tc) and surface roughness (Ra) and the variables are the machining position and cutting parameters for each pass. A non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the multi-objective optimization model for providing an optimal Pareto front, from which one satisfactory solution well balancing the Tc and Ra is selected by combining the entropy-weighted algorithm (EWA) and technique for ordering preferences by similarity to ideal solution (TOPSIS). A case study was carried out to establish a multi-objective optimization model after the milling stability constraint and surface roughness were predicted with the aid of GRNN and back-propagation neural network (BPNN), respectively. An ideal solution containing optimal machining position and cutting parameters was solved using NSGA-II, EWA, and TOPSIS, which was compared with solutions of two mono-objective models for optimizing Tc and Ra, respectively, to validate its feasibility. The observed stability and acceptable Ra value of the milling test under the ideal solution also indicated that the proposed optimization method can realize a trade-off between two conflict objectives and fascinate the process planning of a multi-pass milling.

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Data availability

The data used to support the findings of this work are available from the corresponding author upon request.

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Funding

This research is supported by the National Natural Science Foundation of China under Grant No. 51705058, the China Postdoctoral Science Foundation-funded project under Grant No. 2018M633314, and the Chongqing Special Postdoctoral Science Foundation under Grant No. XmT2018040.

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Authors and Affiliations

  1. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Congying Deng, Jie Shu, Ying Ma, Sheng Lu & Yang Zhao

  2. School of Aeronautics and Astronautics, Sichuan University, Chengdu, 610065, People’s Republic of China

    Jianguo Miao

  3. Institute for Advanced Study, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Congying Deng, Ying Ma, Sheng Lu & Yang Zhao

Authors
  1. Congying Deng
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  2. Jie Shu
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  3. Ying Ma
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  4. Sheng Lu
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  5. Yang Zhao
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  6. Jianguo Miao
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Contributions

Funding acquisition, CY Deng; investigation, J Shu and Y Ma.; methodology, CY Deng, J Shu, and S Lu; software, J Shu, Y Zhao, and JG Miao; experiment, CY Deng and J Shu; writing (original draft preparation), CY Deng, J Shu, and JG Miao; writing (review and editing), CY Deng, Y Ma, and JG Miao; and supervision, CY Deng. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jianguo Miao.

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Deng, C., Shu, J., Ma, Y. et al. Multi-objective modelling and optimal parameter selection of a multi-pass milling process considering uncertain milling stability constraint. Int J Adv Manuf Technol 120, 6225–6240 (2022). https://doi.org/10.1007/s00170-022-09142-y

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  • DOI: https://doi.org/10.1007/s00170-022-09142-y

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