Optimization of finishing parameters for magnetic compound fluid finishing (MCFF) of copper alloy

Abstract

As an advanced finishing technology, magnetic compound fluid finishing (MCFF) is considered capable of achieving damage-free finishing of low-hardness materials such as copper alloys with appropriate finishing parameters. However, ignoring the influence of the material removal amount on the dimensional accuracy while optimizing finishing parameters may result in excessive material removal and a reduction in the workpiece’s dimensional accuracy. Thus, a novel finishing parameter optimization method considering dimensional accuracy is proposed in this paper. Firstly, the MCFF experiments are planned and carried out for modeling. Secondly, an MCFF model is established based on the integrated learning theory. The established model, with a prediction layer and a fusion layer, is a multi-layer neural network fusion model which can accurately predict the polished surface quality and material removal amount. Thirdly, the finishing parameters are optimized by the multi-objective particle swarm optimization algorithm, taking the effect of material removal on dimensional accuracy into account. Finally, the model’s prediction accuracy and the superiority of the optimized parameters are verified through experiments. The results demonstrate that the developed model can predict the finishing effect correctly, and a workpiece with high-quality polished surfaces and high dimensional accuracy can be obtained with the optimized finishing parameters.