Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool

Abstract

Introduction. In manufacturing, obtaining a specified surface roughness of the machined components is of great importance to fulfill functional requirements. However, this is significantly affected by the heat generated during processing, potentially causing variations in dimensional accuracy. The surface roughness significantly affects the fatigue performance of the component, while the cutting tool's lifespan is dictated by the generation of cutting temperatures. The purpose of the study is to create semi-empirical models for predicting surface roughness and temperature of different work materials. Improved cutting performance is achieved by precisely determining the cutting temperature in the zone being machined. However, calculating the cutting temperature for each specific case is fraught with difficulties in terms of labor resources and financial investments. This paper presents a comprehensive empirical formula designed to predict both theoretical temperature and surface roughness. The methodology. The surface roughness and temperature values were evaluated for EN 8, Al 380, SS 316 and SAE 8620 materials using TiAlN coated carbide tool. The TiAlN coating was formed using Physical Vapor Deposition (PVD) Technique. The response surface methodology was used to prepare predictive models. Cutting speed (140 to 340 m/min), feed (0.08 to 0.24 mm/rev) and depth of cut (0.6 to 1.0 mm) was used as input parameters for measuring the performance of all material in terms of surface roughness and cutting temperature. The tool-work thermocouple principle was used to measure the temperature at the chip-tool interface. A Novel Calibration Setup was developed to establish a connection between the electromotive force (EMF) generated during machining and the cutting temperature. Results and Discussion. It is observed that the power required for machining was largely transformed into heat. The highest cutting temperature is recorded when machining of SS 316 followed by SAE 8620, EN 8. However, low temperature is reported during machining of Al 380 and it is mainly governed by the thermal conductivity of the material. The lowest surface roughness is observed in SAE 8620, EN 8 material followed by SS 316 and Al 380. Semi-empirical method and regression model equations show a good agreement with each other. Statistical analysis of nonlinear estimation reveals that the cutting speed, feed, and density of the material have a greater effect on surface roughness, whereas the depth of cut has a greater effect on temperature generation. The study will be very useful for predicting industrial productivity when machining of EN 8, Al 380, SS 316 and SAE 8620 materials with TiAlN-coated carbide tool.