A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS
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International Journal of Precision Engineering and Manufacturing https://doi.org/10.1007/s12541-020-00402-z
REGULAR PAPER
A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS Zhaopeng He1 · Tielin Shi1 · Jianping Xuan1 · Su Jiang1 · Yinfeng Wang1 Received: 15 March 2020 / Revised: 19 July 2020 / Accepted: 3 August 2020 © Korean Society for Precision Engineering 2020
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. Keywords Optimization · Neural network · Manufacturing · Maximum deviation theory · Tool wear
1 Introduction
* Tielin Shi [email protected] Zhaopeng He [email protected] Jianping Xuan [email protected] Su Jiang [email protected] Yinfeng Wang [email protected] 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
Modern advanced manufacturing technologies are practically developed to optimize the metal cutting process with the reduction of product cost, higher processing efficiency and better product quality, simultaneously. Compared to conventional manufacturing to minimize production costs as a major improvement, optimization is carried out with more comprehensive process considerations in modern manufacturing. For the production of precis
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