Inverse Analysis of Inconel 718 Laser-Assisted Milling to Achieve Machined Surface Roughness

Abstract

This manuscript proposes an inverse analysis method for the machined surface roughness in laser-assisted milling on Inconel 718. The method solves the forward problem considering the tool profile and the elastic recovery of machined surface and applies the variance-based recursive method to guide the updating mechanism of process parameters to match the measurements. Subsequently, the inverse analysis identifies four process parameters of feed per tooth, tool tip radius, minimum cutting thickness, and tool tip angle, and finds the optimal solution for target performance, the surface roughness. The measurements are collected under the single beam coaxial laser-assisted milling spindle. The proposed modified Kalman filter algorithm introduces the gain coefficient G when updating the process parameters to improve robustness and accuracy. The inverse analysis is conducted on all measurements, and the average error of target performance is 0.460% when the laser is on and 0.394% when the laser is off. The average difference of process parameters is less than 5%, and the selection process is done in 50 loops within a minute. Therefore, the proposed inverse analysis model is robust, adaptive to different initial guesses and measurements, highly accurate, and saves computation time.