基于支持向量机和粒子群算法的钛合金铣削加工参数优化  

Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm

作　　者：向国齐[1] 殷国富[2] 

机构地区：[1]攀枝花学院资源与环境工程学院,攀枝花617000 [2]四川大学制造科学与工程学院,成都610054

出　　处：《航空制造技术》2016年第23期100-103,107,共5页Aeronautical Manufacturing Technology

摘　　要：钛合金材料在加工过程中受铣削力影响易于产生变形而影响加工效果,为保证加工质量,提高生产效率及降低加工成本,研究切削加工参数的合理选择非常重要。对钛合金材料Ti6Al4V铣削加工进行有限元数值计算,结合试验设计方法构建了基于支持向量机的切削力预测模型,以材料去除率为优化目标,提出了一种基于支持向量机和粒子群算法的优化方法,对钛合金铣削加工参数进行了优化。结果表明,该方法准确、高效、可行,为钛合金加工工艺参数优化提供一种新的方法,具有良好的推广价值。Titanium alloys are widely used in aviation fields, the processing quality of this materials will be affected by the milling force. In order to guarantee the machining quality, improve production efficiency and reduce cost, the cutting parameters of the titanium alloy are reasonable selected, which play an important role. In this paper, the titanium alloy Ti6Al4V milling process is analyzed by finite element method, a milling force prediction model is established based on sup- port vector machine （SVM）. The design methodology based on SVM and particle swarm optimization （PSO） is proposed for titanium alloy milling process cutting parameters. The results show that this methodology is feasible and highly effec- tive. and thus can be used in the machining process parameters optimum and other material processing fields.

关 键 词：钛合金 正交试验 支持向量机 粒子群算法 参数优化 

分 类 号：TG54[金属学及工艺—金属切削加工及机床]