钛合金铣削加工参数多目标优化研究

Multi-objective Optimization of Cutting Parameters during Milling of Titanium Alloys

为解决钛合金铣削加工存在的高成本、低效率问题,以生产效率最大和刀具寿命消耗率最小为目标,建立了铣削参数优化模型。提出了扩展非支配排序遗传算法(ENSGA-Ⅱ),对种群初始化子过程进行规范化处理,保证了种群的多样性和均匀性;根据Pareto最优原理将非支配概念从目标函数空间扩展到约束空间,使得多目标多约束问题的处理更具有适应性和有效性。实例表明,该方法具有良好的寻优能力,能够获得满意的Pareto解集。借助于该方法,工艺人员可根据优化目标灵活地选择铣削参数,更好地协调生产效率、生产成本和表面质量之间的关系。

A multi-objective optimization model of cutting parameters was built to solve the problem of high cost and low efficiency while milling titanium alloys. The maximized productivity and the minimized rate of tool life elapsed were selected as the optimization objectives. An extended non-- dominated sorting genetic algorithm-- ]I （ENSGA-- ]1 ） was presented. A normalized process was add- ed into the subroutine of initialization to get better variety and more uniformly distribution of popula- tion. In order to handle the multi--objective and multi--constraint more adaptively and effectively, a concept of non--dominance was defined extensively from the objectives space to constraints space based on the principle of Pareto optimality. A case was used to investigate the effect of the proposed approach. The results show that the approach gives a good performance in finding satisfying Pareto so- lutions. By means of this approach, the technician is able to flexibly select the cutting parameters ac- cording to the optimization objectives, and to better coordinate the relations between productivity, cost and surface quality.