Evaluation of the effectiveness of analytical models for cutting temperature in machining medium carbon steels under various cutting parameter combinations

Abstract

During the machining process, the temperature of the workpiece material has a direct effect on both the final product quality and the cutting tool life. The temperature increase can negatively affect the surface finish of the workpiece and safety risks during operation. On the other hand, excessively low temperatures can lead to higher cutting force and heightened friction between the tool and the workpiece. Then, accurate cutting temperature prediction has great importance in the production planning of machined parts. The temperature increase in the machined material is mainly due to the strain rate and the amount of deformed material. Cutting parameters that are directly associated with these factors include cutting speed, depth, feed rate, and tool geometry. In this study, they have proposed five models that consider different combinations of these machining parameters in a selective manner. The modeling process was carried out using a multivariate linear regression technique. These models are straightforward to implement and are applicable to various ranges of machining for medium-carbon steel. The results obtained from this study are satisfactory and align well with the reference experimental observations, demonstrating approximations in the range of 83-91%.