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Top1. Introduction
Among the many super alloys used in manufacturing industries, Inconel 625 alloy materials are extensively used in aerospace and in many other chemical process applications. It is an austenitic nickel-based alloy having excellent resistance to corrosion, oxidation and fatigue. But the machining of this material is very difficult by using traditional machining processes. So among the various non-traditional machining processes, one of the most commonly used processes is PAM. Plasma Arc Machining is a thermal energy based process commonly used for cutting alloys to a stringent design requirements and complex cutting profiles. During the PAM process, a high intensity constricted jet of high temperature plasma arc is produced between workpiece material and electrode nozzle which melts/vaporizes the part profile and expels the molten metal from the cutting region. In spite of its potential advantages, due to the involvement of several process variables in PAM process, improving the cut quality characteristics such as MRR, burr height and kerf ratio are considered to be quite difficult. In order to enhance the cutting quality and performance characteristics of PAM, it is vital to select ideal process variables and their influence in evaluating the part quality. Hence the plasma arc machining is proposed in this research to analyze the machining of Inconel 625 alloy.
Subbarao et al. (2013) used DOE techniques to investigate the influence of PAC variables on Hardox-400 material and they proposed that irregularity in cut surface can be reduced by decreasing the cutting speed and other cut quality characteristics depends on arc voltage. Gariboldi et al., (2005) identified that by using oxygen as cut gas, the geometrical features of better quality where obtained and they also achieved a better HAZ by using nitrogen, during high tolerance PAC of titanium sheet. Ananthakumar et al., (2018) showed that, with increase in cutting speed, the kerf taper was affected and regression models for the output parameters were proposed. Bhowmick et al., (2018) experimentally studied on the PAC of SS 304 grade material and found that increase in speed and thickness has a significant effect on MRR. Maity et al., (2015) investigated on PAC process by studying a hybrid optimization method on AISI 316 steel and they found that torch height along with feed rate is largely contributing in producing of enhanced cutting quality. Ramakrishnan et al., (2018) examined on the surface roughness and kerf width during PAC of SS 321 steel by developing regression models and given as input to the genetic algorithm. Their result shows that surface roughness and HAZ can be minimum if lower values of current are used, stand-off distance, cutting speed, and high gas pressure. Salonitis and Vatousianos (2012) experimentally measured the edge roughness, conicity and the HAZ size for assessing the cut quality. Milan Kumar Das et al., (2014) investigated in PAC of EN 31 steel and showed that gas pressure has the major influence on the MRR characteristics. Abdulkadir Gullu and Umut Atici (2006) have studied the hardness measurements and metallurgical characteristics of AISI 304 and St 52 carbon steels machined by PAM. Rouniyar et al., (2018) conducted a number of experiments and optimized using the grey relational analysis to obtain the optimum parameters on machining the Ti-6Al-4V alloy material using powder mixed EDM process. Veeresh Nayak et al., (2020) conducted a series of experiments and employed taguchi and Pareto ANOVA methods to determine the optimal levels for each output separately. They also performed GRA for converting multiple objective functions with weight fractions using PCA to single objective function. G. C. Manjunath Patel et al., (2020) determined the optimal factor levels of abrasive water jet machining for green composites using MOORA, GRA, TOPSIS and DEAR. G. C. Manjunath Patel et al., (2019) investigated on analysis and optimization of surface quality during turning of high strength aluminium alloy by using the principal component analysis and JAYA algorithm. Ganesh et al., (2018) conducted a series of experiments and used Pareto ANOVA to determine the percent contribution of inputs on output, individually and the optimal factor level is determined for each output separately.