Intelligent 3D tool path planning for optimized 3-axis sculptured surface CNC machining through digitized data evaluation and swarm-based evolutionary algorithms

Abstract

This work suggests the trajectory optimization of three well-known 3-axis surface machining tool-paths available to commercial computer-aided manufacturing systems by means of a genetic algorithm. The toolpaths are Optimized-Z; Raster and 3D-Offset. An original approach involving digitized information derived from solid features of complex sculptured surfaces and cutting-edge machining modeling tools is presented; emphasizing to a Pareto multi-objective optimization problem formulated by considering two optimization criteria; surface deviation for quality and tool-path time for productivity. The antagonizing criteria are simultaneously examined whilst the variations owing to different cutting tool selections as well as several radial pass interval values are investigated to understand how these tool-paths influence machining efficiency during process planning stage. An L27 full factorial design of experiments addressing the examination of the aforementioned parameters and tool paths was established to study the effects and regression models were questioned to formulate the objective functions for evaluating the results using four modern meta-heuristics namely, multi-objective grey-wolf (MOGWO); multi-objective multi-universe; (MOMVO); multi-objective ant lion; (MOALO); multi-objective dragonfly (MODA); NSGA-II and evMOGA. Results have shown that all algorithms can efficiently contribute to the problem and support decision making with several non-dominated solutions with regard to the requirements for the simultaneous benefit of productivity and quality.