Surface roughness modeling and milling parameter optimization of 15-5PH stainless steel in milling arc surface

Abstract

In this study, a comprehensive prediction model of surface roughness on arc surface of 15-5PH stainless steel is constructed, which is considered to consist of the certain part and uncertain part. The certain part considered to be dependent on path interval and scallop height is formulated in theory using kinematics analysis and geometric modeling. The uncertain part related to the machining system stability, cooling condition and milling modes is empirically predicted by partial least squares based on experimental data. Then, the prediction model is integrated into particle swarm optimization algorithm to find the optimal parameters for the maximum material removal rate (MRR). Finally, the verification experiments considering validation of prediction model and optimal parameters are conducted. The experimental results show that the prediction accuracy presents well with the mean prediction error 10.49%. The optimization also satisfies the constraint of surface roughness with a 262% increase in MRR.