A predictive model of milling surface roughness

Abstract

Predicting the surface roughness of machined materials can assure shaping quality and minimize the shaping period. In this study, a milling surface roughness prediction model is proposed that considers the geometric and mechanical factors that influence surface roughness. This model considers not only elasticoplastic deformation but also the influences of cutter parameters, microhardness, cutting force, and material properties. To verify the applicability and accuracy of the model, cutting tests were carried out using orthogonal tests of a Ti alloy (Ti6Al4V). Surface roughness was measured at different feed engagements, feed rates, cutting depths, and cutting widths. The results demonstrate that the proposed model has high applicability and accuracy in predicting the milling surface roughness of Ti6Al4V, the error rate of the best result is only 3.40%, and the average error rate of each group is 10.6%. It can be used in the prediction and analysis of surface roughness after material milling in actual machining.