面向表面粗糙度约束的铣削过程参数优化

Parameter Optimization of Milling Process for Surface Roughness Constraints

在6061铝立铣过程中考虑到控制工件表面粗糙度的要求,人为选择的铣削参数可能比较保守,导致材料去除率降低、制造成本高。以表面粗糙度为约束条件,以最大材料去除率为目标,基于极端提升法(extreme gradient boosting, XGBOOST)建立以主轴转速、进给速度和切深为优化对象的表面粗糙度回归模型,利用遗传算法对主轴转速、进给速度和切深三个铣削参数进行优化。利用遗传算法的多目标优化特点得到较优的铣削参数。通过4组优化结果可以看出,表面粗糙度的最大变化只有0.048μm,而最小的材料去除率提高了2 458.048 mm^(3)/min,在达到表面粗糙度的要求下,提高了加工效率,减小制造成本,具有良好的优化效果,在实际加工中具有一定的指导作用。

In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece,artificially selected milling parameters may be conservative,resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal,the surface roughness regression model is established based on extreme gradient boosting(XGBOOST)with the spindle speed,feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed,feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi-objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048 Rm,while the minimum material removal rate increases by 2458.048 mm^(3)/min.While achieving surface roughness,the processing efficiency is improved,and the manufacturing costs are reduced,resulting in good optimization effects,which has a certain guiding role in the actual processing.