MEASUREMENT OF CHIP-TOOL INTERFACE TEMPERATURE FOR ORTHOGONAL CUTTING

Abstract

Cutting is one of the most important and common manufacturing processes in industry. Machining process is not an easy process to investigate and to model due to the inherent difficulty to know exactly what happens in the region around the tool tip. In metal cutting operations, the importance of knowledge on the temperature distribution in cutting tool is well recognized due to its controlled influence on tool life as well as on the quality of the machined part. The effect of process parameters such as cutting speed, feed rate, depth of cut etc in the metal cutting process is determined by correlating the process parameters with the tool temperature, tool life, wear rate, production cost etc. The main objective of this experiment was to determine the chip-tool interface temperature in orthogonal turning process depending on cutting parameters i.e. cutting speed and depth of cut for different tool and work material combinations using the tool-work thermocouple method. The design matrix was prepared on the basis of two factors, two levels, full factorial design to identify the limits of process parameters. Response surface methodology and regression analysis was used to develop the mathematical model correlating the process parameters with the response variable (chip-tool interface temperature). The calculations were carried out using the software package Minitab 16. The models once developed were checked for adequacy using the ANOVA technique. The significant terms were selected using the p test from the adequate models. Following this the final model was proposed and the main and interaction effects of the process variables on the response variable were plotted and interpreted from the developed graphs. The developed model was used for prediction of response variable by selecting the appropriate process parameter values. The function of the optimization model was to minimize the chip tool interface temperature in orthogonal cutting process using an optimization technique. Genetic algorithm technique was used for modeling the cutting process. Predictive equations previously formulated by RSM method were used in the development of GA architecture for the determination of the temperature for a given set of inputs in the metal cutting problem. A comparative analysis of the performance of RSM model and GA model was done. iv It was concluded that when the cutting speed and the depth of cut were increased the chip-tool interface temperature increased and also observed that the cutting speed has a significant effect on the chip-tool interface temperature in comparison to the effect of depth of cut. These conclusions were verified by the correlation coefficients. The results obtained from the simulation model presented a fast and suitable solution for automatic selection of the machining parameters. The results are further analyzed with the literature available. This experimental work includes discussion on the important input parameters, their effects, conclusions and the several considerations for future work.