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 1.195 μm and 151.9 μm,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.