MSTMM-Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm

Abstract

Ultra-precision machining (UPM) has been extensively employed for the production of high-end precision components. The process is highly precise, and the associated cost of production is also high. Optimization of machining parameters in UPM can significantly improve machining efficiency and surface roughness. This study proposes an innovative approach that couples transfer matrix methods for multibody systems (MSTMM) and particle swarm optimization (PSO) to optimize the machining parameters, aiming to simultaneously improve the machining efficiency and surface roughness of UPM machined components. Initially, the dynamic model of an ultra-precision fly-cutting (UPFC) machine tool was developed using MSTMM and validated by machining tests. Subsequently, the PSO algorithm was employed to optimize the machining parameters. Based on the optimized parameters, a 40% reduction in machining time and an 18.6% improvement in surface roughness peak-to-valley (PV) value have been achieved. The proposed method and the optimized parameters were verified through simulations using the MSTMM model, resulting in a minimal error of only 0.9%.