Optimization method of process parameters based on the constraints of surface quality and geometric feature of elements during copying turning

Abstract

Machining quality and performance are key indicators of CNC machine tool. To enhance the machining of non-circular curves and rotating parts on mid- to low-end CNC machines, this study proposes an optimization method for contour turning parameters based on surface quality and geometric feature constraints. The method integrates trajectory and parameter optimization. First, the Double-Heads Snake (DHS) algorithm generates a smooth G-code tool path comprising straight lines, convex arcs, and concave arcs with varying curvatures. Multi-objective optimization models are then developed for these elements to enhance machining quality and performance stability. Using Back Propagation Neural Networks (BPNN), the models predict surface roughness (Ra) and cutting force (F) based on process parameters and curvature radius, analyzing their influence mechanisms. An improved African Vulture multi-objective optimization algorithm (MOAVOA_improve) is proposed and validated against other algorithms using CEC2009 benchmarks, demonstrating superior performance. Surface quality and geometric constraints guide tool path segmentation, merging adjacent path segments with minimal curvature impact on Ra into single regions. Optimized process parameters are then determined for each region based on curvature and multi-objective solutions. The method is applied to non-circular rotating workpieces and compared with Mastercam and experience-based parameters. Results show significant improvements in machining quality, validating the method's effectiveness and practicality.