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

Introduction. In manufacturing, obtaining a given surface roughness of the machined parts is of great importance to fulfill functional requirements. However, the surface roughness significantly affected by the heat generated during the machining process, which can lead to a decrease in dimensional accuracy. The surface roughness significantly affects the fatigue characteristics of the part, and the service life of the cutting tool is determined by the cutting temperature generation. The purpose of the work. The purpose of this study is to create semi-empirical models for predicting surface roughness and temperature of various work materials. Enhanced cutting performance is achieved by accurately determining the cutting temperature in the machined zone. However, calculating the cutting temperature for each specific case is fraught with diffi culties in terms of labor resources and financial investments. This paper presents a comprehensive empirical formula designed to predict both theoretical temperature and surface roughness. Methodology, The performance of the surface roughness and temperature generation was evaluated for the EN 8, Al 380, SS 316 and SAE 8620 materials when processed with TiAlN-coated carbide tools. The TiAlN coating was obtained by Physical Vapor Deposition (PVD) technique. Response surface methodology was used to prepare predictive models. Cutting speed (from 140 to 340 m/min), feed (from 0.08 to 0.24 mm/rev) and depth of cut (from 0.6 to 1 mm) were used as input parameters to measure the characteristics of all materials in terms of surface roughness and cutting temperature. The tool-work thermocouple principle was used to measure the temperature at the chip-tool interface. Novel Calibration Setup was developed to establish the relationship between the Electromotive Force (EMF) generated during machining and the cutting temperature. Results and Discussion. It is observed that the energy required for mechanical processing was largely converted into heat. The highest cutting temperature is recorded with SS 316, followed by SAE 8620 and EN 8. However, low temperature was reported during machining of Al 380 and it was mainly governed by the thermal conductivity of the material. The lowest surface roughness is observed for SAE 8620, EN 8, followed by SS 316 and Al 380. The semi-empirical method and regression model equations are in good agreement with each other. Statistical analysis of the nonlinear evaluation reveals that cutting speed, feed rate, and material density have a greater influence on the surface roughness, whereas depth of cut has a greater influence on the temperature change. The study will be very useful for predicting industrial performance when machining EN 8, Al 380, SS 316 and SAE 8620 materials with TiAlN-coated carbide tools.