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Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining

Author

Listed:
  • Longhua Xu

    (Shandong University)

  • Chuanzhen Huang

    (Shandong University)

  • Chengwu Li

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

  • Jun Wang

    (The University of New South Wales (UNSW))

  • Hanlian Liu

    (Shandong University)

  • Xiaodan Wang

    (Xi’an Jiaotong-Liverpool University)

Abstract

Compacted graphite iron (CGI) plays an important role in contemporary manufacturing of automobile engine, and coated tool is the best choice for milling of CGI. But studies about the estimation of the wear of coated tool are still rare and incomplete. As tool wear is the main factor that affects the quality of machined surface, in this study, we proposed an intelligent model-adaptive neuro fuzzy inference system (ANFIS) to estimate the tool wear, and ANFIS was learned by the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm is easy to fall into the local minimum, the vibration and communication particle swarm optimization (VCPSO) algorithm was proposed by introducing the self-random vibration and inter-particle communication mechanisms. Besides that, to obtain the optimal combination of milling parameters, the multi-objective optimization based on minimum cutting power, surface roughness and maximum material removal rate (MRR) was studied using VCPSO algorithm. The experimental results showed that the ANFIS learned by VCPSO algorithm (ANFIS-VCPSO) has better performance for the estimation of tool wear compared with other intelligent models. The VCPSO algorithm was tested using Benchmark functions, and the results showed VCPSO algorithm has the global optimization ability. Meantime, the best combinations of milling parameters under different tool wear status were obtained through VCPSO algorithm. The proposed ANFIS-VCPSO model as a new intelligent model can be applied for real-time tool wear monitoring, which can improve the machining efficiency and prolong tool life. In order to meet the requirements of green and intelligent manufacturing, the best combination of milling parameters was also obtained in this work.

Suggested Citation

  • Longhua Xu & Chuanzhen Huang & Chengwu Li & Jun Wang & Hanlian Liu & Xiaodan Wang, 2021. "Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 77-90, January.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01559-0
    DOI: 10.1007/s10845-020-01559-0
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    References listed on IDEAS

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    1. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    2. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
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    Cited by:

    1. Zhang, Jiaqi & Han, Xin & Li, Li & Jia, Shun & Jiang, Zhigang & Duan, Xiangmin & Lai, Kee-hung & Cai, Wei, 2023. "Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality," Energy, Elsevier, vol. 284(C).
    2. Reza Teimouri & Sebastian Skoczypiec, 2024. "Predictive modeling of roughness change in multistep machining," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3577-3598, October.

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