Optimization of cutting temperature in machining of titanium alloy using Response Surface Method, Genetic Algorithm and Taguchi method

• Published in: Materials today : proceedings Vol. 47; pp. 6285 - 6290
• Main Authors: Jayarjun Kadam, Bhagyashree, Mahajan, K.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2021
• Subjects: Cutting temperature; Genetic Algorithms (GA); Research Surface Method (RSM); Taguchi analysis; Thermal modelling; Titanium alloys; Titanium alloys; Taguchi analysis; Cutting temperature; Genetic Algorithms (GA); Research Surface Method (RSM); Thermal modelling
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2021.05.252

Cutting temperature during machining plays a very important role in the overall performance of machining processes. Since, it was a very difficult task to measure the tool temperature correctly, Finite Element Modeling was used as a modeling tool to predict cutting temperature in the current investigation. Titanium alloys have been generally defined as difficult to cut materials due to their natural properties. The main drawback in machining titanium alloys is high cutting temperature due to high adhesion of tool work interface. This paper presents a finite-element modeling of cutting tool temperature during turning of Titanium alloy Ti-6Al-4 V under dry machining. The ANSYS software was used to determine the cutting temperature at tool nose. The Design of Experiments (DOE) was carried out in Minitab 2018 software. The process parameters considered for design of experiments are cutting speed, feed rate, and depth of cut used for operation. Response Surface Method (RSM) and Taguchi analysis was used to analyse the machining effect on tool material in this study. The purpose of performing an orthogonal array experiment is to determine the optimum level for each of the process parameters and to establish the relative significance of each parameter. An attempt has also been made to optimize the cutting temperature prediction model using Genetic Algorithms (GA) to optimize the objective function. The outcomes acquired through RSM are likewise similar to the outcomes of Genetic Algorithm. The results showed that cutting speed of 120 m/min, feed rate of 0.10 mm/rev and depth of cut of 0.5 mm are desirable for getting optimal conditions.