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.

这项工作提出了通过遗传算法对商业计算机辅助制造系统可用的三个众所周知的3轴表面加工刀具路径的轨迹优化。刀具路径为Optimized-Z；栅格和3D偏移。提出了一种原始方法，该方法涉及从复杂雕刻表面的实体特征和尖端加工建模工具中获得的数字化信息；强调通过考虑两个优化准则制定的帕累托多目标优化问题；表面质量的偏差和刀具路径时间的生产率。同时检查了拮抗标准，同时研究了由于选择不同的切削刀具而产生的变化以及几个径向通过间隔值，以了解这些刀具路径如何在工艺计划阶段影响加工效率。安L建立了针对上述参数和工具路径检验的_27个全因子设计实验，以研究效果，并质疑了回归模型，以使用四种现代的元启发式方法（即多目标灰狼）来建立评估结果的目标函数（MOGWO）；多目标多宇宙 （MOMVO）；多目标蚁狮 （MOALO）；多目标蜻蜓（MODA）；NSGA-II和evMOGA。结果表明，就生产率和质量的同时受益而言，所有算法都可以有效地解决问题，并通过几种非支配的解决方案来支持决策。