An Improvised Method of Machinability Evaluation with Predictive Temperature Model for Inconel 625: A Holistic Machinist’s Perspective

Abstract

Machinability of any material is defined as how easily it can be machined (cut) and the factors that govern this machinability comprise machining temperature, tool wear, surface roughness, and the shape of the chip. To enhance the machinability of materials, the improvement of these governing factors is a must. In this regards machinability of Nickel alloys is of great concern as they are associated with problems of high heat generation causing tool wear and poor surface finishing, which adds to the product cost. Therefore, this research aims to improve the machinability of Inconel 625 with the use of MQL assisted with h-BN nano cutting fluid. A comparative study of machining performance of h-BN NMQL with dry and MQL conventional conditions is performed. The outcomes of this study establish the superiority of h-BN over dry machining and MQL conventional machining on various machining parameters by reducing both machining temperature and tool wear. The experimental results revealed that the machining with nano h-BN MQL technique reduces the machining tool tip temperature by 25% and 12%, along with the reduction in tool wear by 67% and 47% in comparison with dry and MQL machining. Additionally, this paper also proposes a numerical model for predicting machining tool temperature using machining parameters (speed, feed and depth of cut) during turning of Inconel 625 under nano h-BN MQL technique.