摘要
以表面粗糙度、刀具耐用度、切削温度、切削区域、切削功率等实际约束条件,建立了以加工精度与金属切除率为双目标的精车切削优化模型。通过实例运用NSGA-II算法与MOPSO算法对精车优化切削模型进行仿真,计算表明NSGA-II算法获得更完整的Pareto解集。在不同的表面粗造度条件下,加工精度和金属切除率Pareto最优解集能由同一线性方程拟合,且拟合相关指数≥0.9997。实践数据表明,精车切削优化计算结果优于经验数据,为精车切削参数优化选择提供了实践指导。
A dual-objective optimization cutting model for finish turning, based on minimum machining precision and maximum metal removal rate, is firstly proposed in this paper. The model is subject to vari- ous practical cutting constraints, including surface roughness, bounds for tool-life, chip-tool interface tem- perature, stable cutting region, cutting power, spindle torque, cutting force, cutting tool strength and fin- ishing parameter relations. The Nondominated sorting genetic algorithmlI （NSGA-II） and multi-objective particle swarm optimization（MOPSO） are applied to the dual-objective nonlinear constrained optimization finish turning cutting model. Simulation results indicate that the Pareto-optimal solutions set by using NS- GA-II algorithm are obtained more complete than the Pareto-optimal solutions set by applying the MOPSO algorithm. Under different surface roughness, the Pareto-optimal solutions set for the machining precision and metal removal rate can be fitted by the same linear equation and these fitting relevant indexes are grea- ter than or equal to 0. 9997. Cutting experiment shows that the data of optimization finish cutting model is better than the experience data, which provides practical guides for optimization of machining parameters.
出处
《组合机床与自动化加工技术》
北大核心
2013年第3期111-114,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
湖北武汉高校科研项目(2010140)
关键词
加工精度
金属切除率
切削优化
machining precision
metal removal rate
optimization of machining parameters
