The surface measures of machined titanium alloys as dental materials can be enhanced by adopting a decision-making algorithm in the machining process. The surface quality is normally characterized by more than one quality parameter. Hence, it is very important to establish multi-criteria decision making to compute the optimal process factors. In the present study, Taguchi–Grey analysis-based criteria decision making has been applied to the input process factors in the wire EDM (electric discharge machining) process. The recast layer thickness, wire wear ratio and micro hardness have been chosen to evaluate the quality measures. It was found that the wire electrode selection was the most influential factor on the quality measures in the WEDM process, due to its significance in creating spark energy. The optimal arrangement of the input process parameters has been found using the proposed approach as gap voltage (70 V), discharge current (15 A) and duty factor (0.6). It was proved that the proposed method can enhance the efficacy of the process. Utilizing the computed combination of optimal process parameters in surface quality analysis has significantly contributed to improving the quality of machining surface.
Inconel 718 alloy has a wide range of applications in the aerospace sector because of its superior mechanical properties and its weldability. The machining of such higher strength materials with complex shapes is possible with wire electrical discharge machining. In the present research, an endeavor was made to enhance the machining process by utilizing zinc-diffused coating brass wire electrode and Taguchi-Data Envelopment Analysis-based Ranking (DEAR) methodology in the process while machining Inconel 718 alloy. Material removal rate, kerf width, and surface roughness were considered as the quality measures. The optimal arrangement of input factors in the Wire Electrical Discharge Machining (WEDM) process were found as 140 µs (Ton), 50 µs (Toff), 60 V (SV), and 5 kg (WT) among the elected factors with the error accuracy of 1.1%. The pulse-off time has the most significance on formulating the quality measures owing to its importance on deionization in the process.
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