基于改进引力搜索算法的铣削加工参数低碳建模及优化

针对数控机床铣削加工特点，考虑刀具寿命、加工表面质量、切削速率和铣床工艺性能等条件，以铣削加工过程中的单位体积碳排放、单位体积生产成本和加工时间为目标，以切削速率、每齿进给量和切削宽度三参数为优化变量，建立了铣削加工参数多目标优化模型，并提出了一种改进的非支配排序引力搜索算法对该多目标模型进行求解。在所提出算法中采用精英保留策略和位置更新回退操作，引导群体质点向真实Pareto最优解集区域靠近。在遗传算法的交叉操作启发下，提出精英精英交叉策略和精英非精英交叉策略，增加了群体多样性。最后与原始非支配排序引力搜索算法和教学优化算法进行对比，验证了所提改进算法的优越性和可行性。采用灰色关联度法在获得的Pareto最优解集中选择满意解，为面向绿色制造的切削参数优化提供了一种新的思路。

Low Carbon Modeling and Optimization of Milling Parameters Based on Improved Gravity Search Algorithm

Aiming at the milling characteristics of computer numerical control(CNC) machines,a multi-objective optimization model of milling parameters was established considering cutter life, machined surface quality, cutting speed and milling machine process performance, while using carbon emissions per unit volume, cost of production per unit volume and processing time as objectives in milling processes, and using cutting speed, feed per tooth and cutting width as optimization variables.An INSGSA was proposed to solve the problem.In order to find the distributed evenly solution set and guide the group to the region near Pare to optimal solution set, elitist conversation strategy and fallback operations of position update were adopted by the improved algorithm.In addition, inspired by the crossover operation of genetic algorithm, the elite-elite crossover strategy and elite-non-elite crossover strategy were proposed to increase the diversity of the population.Finally, the superiority and feasibility of INSGSA were proved by comparing the non-dominated sorting gravity search algorithm (NSGSA)and the teaching-learning-based optimization (TLBO).Moreover, gray corelation method was adopted to select the satisfactory solution, which gives a new method in cutting parameter optimization for green manufacturing.