基于响应曲面法的MQL辅助切削钛合金的工艺参数优化

Optimization of Process Parameters for MQL-Assisted Cutting of Titanium Alloys Based on Response Surface Method

为了提高切削质量,减少刀具磨损,采用响应曲面法对最小微量润滑辅助切削的主要工艺参数进行优化。以钛合金TC4为切削对象,把工件表面粗糙度和刀具磨损作为评价指标,采用Minitab设计切削速度、进给量、喷射压力三因素的Box-Behnken试验。利用方差和拟合残差概率分布分析三因素的显著性及交互作用,并结合试验检验所建表面粗糙度和刀具磨损二阶响应预测模型的有效性。响应曲面法优化后的最佳工艺参数为切削速度25 m/min、进给量0.103 mm/r、喷射压力0.1 MPa,此时得到的表面粗糙度和刀具磨损量分别为1.195、151.9μm,与预测值的误差分别为5.1%、2.91%。说明基于响应曲面法的微量润滑辅助切削钛合金表面粗糙度和刀具磨损量预测模型准确有效。

In order to improve the cutting quality and reduce the tool wear,the response surface method was used to optimize the main process parameters of the minimum quantity lubrication(MQL)-assisted cutting.Taking titanium alloy TC4 as the cutting object,and taking the workpiece surface roughness and tool wear as the evaluation indicators,the Box-Behnken test with three factors of cutting speed,feed rate and injection pressure was designed by Minitab.The variance and fitting residual probability distribution were used to analyze the significance and interaction of the three factors,and the validity of the second-order response prediction model for surface roughness and tool wear was tested by combining experiments.The optimal process parameters optimized by the response surface method are the cutting speed of 25 m/min,the feed rate of 0.103 mm/r,and the injection pressure of 0.1 MPa.At this time,the obtained surface roughness and tool wear amount are 1.195μm and 151.9μm,which are consistent with the predictions.The errors of the values are 5.1%and 2.91%,respectively.The results show that the MQL-assisted cutting titanium alloy surface roughness and tool wear prediction model based on the response surface method is accurate and effective.