LAUSR

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.