Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

Automated summary

This research aims to investigate the feasibility of using the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and optimizing the cutting parameters through the PSO algorithm. The experimental results demonstrate the accurate prediction of surface roughness and determination of the optimal cutting parameter combination.