Simulation and Algorithmic Optimization of the Cutting Process for the Green Machining of PM Green Compacts

Abstract

Powder metallurgy (PM) technology is extensively employed in the manufacturing sector, yet its processing presents numerous challenges. To alleviate these difficulties, green machining of PM green compacts has emerged as an effective approach. The aim of this research is to explore the deformation features of green compacts and assess the impact of various machining parameters on the force of cutting. The cutting variables for compacts of PM green were modeled, and the cutting process was analyzed using Abaqus (2022) software. Subsequently, the orthogonal test ANOVA method was utilized to evaluate the significance of each parameter for the cutting force. Optimization of the machining parameters was then achieved through a genetic algorithm for neural network optimization. The investigation revealed that PM green compacts, which are brittle, undergo a plastic deformation stage during cutting and deviate from the traditional model for brittle materials. The findings indicate that cutting thickness exerts the most substantial influence on the cutting force, whereas the speed of cutting, the tool rake angle, and the radius of the rounded edge exert minimal influence. The optimal parameter combination for the cutting of PM green compacts was determined via a genetic algorithm for neural network optimization, yielding a cutting force of 174.998 N at a cutting thickness of 0.15 mm, a cutting speed of 20 m/min, a tool rake angle of 10°, and a radius of the rounded edge of 25 μm, with a discrepancy of 4.05% from the actual measurement.