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A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS

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Abstract

7075 aluminum alloy has been widely applied in the field of aerospace and marine sheet metal because of its protruding mechanical and corrosion resistance. In this paper, the problem of selecting optimal process parameters to optimize multiple processing variables had been studied in precision manufacturing. Multi-objective particle swarm optimized neural networks system was put forward to determine the optimal cutting conditions with multi-objective particle swarm algorithm and multiple neural networks as prediction models of machining variables. Precision parts manufacturing of 7075 aluminum alloy would go through two operations of material removal and surface forming. Firstly, optimal cutting conditions were determined to minimize tool wear while maximizing metal removal rate in material removal stage. Secondly, it was significant and meaningful to select optimal cutting conditions corresponding to the best surface quality and minimum root mean square of tool vibration in surface forming stage. Orthogonal experiments had been carried out to observe the relationship between machining-related variables and cutting parameters in detail. Multiple neural networks were trained to establish predictive models of cutting process from orthogonal experimental and statistical data. Maximum deviation theory sorted the Pareto solutions searched by optimization process of neural networks driven by multi-objective particle swarm algorithm. The top ranking Pareto solutions had been determined as the optimal cutting parameters combination for material removal and surface forming stages, respectively. Finally, the proposed optimization system can also be used to optimize the processing of other difficult-to-machine materials.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51575202, 51675204) and Science Challenge Project (Grant No. TZ2018006-0102-01). The authors sincerely thank the equipment and measuring devices provided by the Precision Measurement Laboratory of the School of Mechanical Science and Engineering for current research work.

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China

    Zhaopeng He, Tielin Shi, Jian** Xuan, Su Jiang & Yinfeng Wang

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  1. Zhaopeng He
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  2. Tielin Shi
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  3. Jian** Xuan
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Correspondence to Tielin Shi.

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He, Z., Shi, T., Xuan, J. et al. A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS. Int. J. Precis. Eng. Manuf. 21, 2011–2026 (2020). https://doi.org/10.1007/s12541-020-00402-z

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  • DOI: https://doi.org/10.1007/s12541-020-00402-z

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