Application of Taguchi Approach on Wire Electrical Discharge Machining of SS304 2021-28-0271
SS304 (Stainless Steel 304) is a nickel- chromium based alloy, that is extensively used for the applications like cryogenic vessels, valves, refrigerator equipment and evaporators because of its high corrosion resistance, ductility and ability to remain as solid up to a temperature of 14000 C. SS304 is one of the tough to machine materials by conventional methods of machining. Wire Electrical Discharge Machining (WEDM) facilitates the ease of machining complicated cuts with hard to machine, conductive materials where high surface finish is required. In this investigation, a study has been done on WEDM of SS304 and mainly to optimize the process parameters during the machining of SS304 by using Taguchi’s analysis. Taguchi’s DoE approach is used to plan the experimental runs and by considering the process parameters such as pulse on time, pulse off time and peak current at three different levels the experiments were conducted. The performance measures considered in present analysis are material removal rate, surface roughness and overcut. Contour plot analysis has been prepared for revealing the overall influence of process variables on desired performance measures. Analysis of Variance (ANOVA) has been used to establish the significance of independent variables on the desired dependent variables. Through this overall investigational analysis, the effect of individual process parameters on the response parameters have been studied.
Stainless steel alloys are primarily applied for high temperature applications. SS304 can maintain its mechanical and chemical characteristics stable in all kinds of working environments. These materials are considered as harder materials due to these extraordinary properties. Stainless Steel 304 is most widely used nickel and chromium based alloy for number of engineering applications such as refrigerator equipments, valves, evaporator coils and cryogenic applications. SS 304 shows high ductility, corrosion resistance and excellent tribological and abrasive behaviour at higher temperatures (Ramesh Raju et al. 2019; Ramesh Raju et al. 2018; Palanisamy et al. 2021; Bagci and Seref, 2006; Kumar et al. 2009). Wire Electrical Discharge Machining (WEDM) is a superior method of modern machining processes which is also familiar as spark erosion machining process. In WEDM, the spark generation is due to the gap voltage developed that is sufficiently higher to develop the required high power spark that rises the temperature to more than 10000 degree Celsius that eventually removes the metal from the work piece as shown in the figure 1. The material removal in this WEDM process is irrespective of the hardness of the material to be machined. (Palanisamy et al. 2020; Davim, 2008; Manikandan et al. 2020; Ugrase et al. 2015; Manikandan et al. 2020; El-Hofy 2005).
Taguchi’s concept is a significant one for planning the experimental trials and also adopted for single response optimization. Optimization of process parameters plays a vital role in reducing the overall cost with an improved quality. An appropriate approach also has been adopted for deriving the multi performance called GRG, which is attained by a Multi Criteria Decision Making Method called Grey Relational Approach. The method of GRA is much helpful for deriving the various combinations for attaining better multiple performance machining
characteristics (Welling, 2014; Leone et al. 2011; Yang et al. 2017; Durairaj et al. 2013; Raj and Radhika, 2017; Palanisamy and Senthil, 2016; Manikandan et al. 2018).
It is surmised from the available research article, that there is a demand of attention on machinability analysis of Wire Electrical Discharge Machining of harder materials. In this present explorative study, an endeavour has been considered for investigating the significance of process variables by Taguchi-Grey approach and to analyze the importance of variables using ANOVA analysis. Contour plots also developed for disclosing the prominence of input variables on desired performance measures.