基于改进遗传算法的数控机床加工铣削参数优化方法

Optimization Method for Milling Parameters of CNC Machine Tools Based on Improved Genetic Algorithm

对于数控机床加工铣削参数优化多采用常规的可信度近似模型,但该方法易受到材料失效应变系数的影响,导致优化后的加工效率较低,提出基于改进遗传算法的数控机床加工铣削参数优化方法。根据工件的本构模型,对切削刃进行采样抽取,确定最小铣削力波动位置;引入材料失效准则计算材料失效应变系数,基于此,以加工时间最短、加工成本最低和加工能耗消耗最小为目标建立铣削参数优化模型,并采用改进遗传算法求解模型,通过迭代适应度值,输出最佳铣削参数;最后,采用对比实验的形式对所提方法的优化性能进行测试。测试结果表明:应用所提方法对数控机床加工铣削参数进行优化后,能够有效缩短切削时间,提高加工效率。

For the optimization of milling parameters in CNC machine tools,conventional reliability approximation models are often used,but this method is susceptible to the influence of material failure strain coefficient,resulting in lower processing efficiency after op-timization.Therefore,an optimization method for milling parameters of CNC machine tools based on an improved genetic algorithm was proposed.Based on the constitutive model of the workpiece,the cutting edge was sampled and extracted to determine the minimum mill-ing force fluctuation position.The material failure criterion was introduced to calculate the material failure strain coefficient.Based on this,a milling parameter optimization model was established with the goal of minimizing processing time and processing cost.An im-proved genetic algorithm was used to solve the model,and the optimal milling parameters were output through iterative fitness values.Fi-nally,the optimization performance of the proposed method was tested through comparative experiments.The test results show that apply-ing the proposed method to optimize the milling parameters of CNC machine tools can effectively shorten cutting time and improve ma-chining efficiency.