Mechanistic model and probability characteristics of micro-milling force with a new parameter identification method

Abstract

As a critical part of the analysis of the micro-milling process, mechanical modeling directly affects the prediction of the surface finish of machining materials and the cutting stability. In this paper, a flexible force model of micro-milling is established, which accounts for the influence of the actual cycloid tool path, tool runout, elastic recovery, tool deformation, and chip separation state. The Harris Hawks Error Optimization parameter identification method is proposed to efficiently obtain the cutting force coefficient/parameter. Based on the uncertainty of system parameters caused by the manufacturing and assembly process, probability distribution characteristics of the micro-milling force are predicted and analyzed. Additionally, the Adaptive Kriging method is used to reconstruct the complex implicit relationship between the cutting parameters and milling forces, improving the computational efficiency of the probability analysis. Finally, a series of micro-milling experiments and computational results verified the accuracy of the proposed method.