Abstract
The electrical discharge drilling (EDD) process is an effective machining approach to produce various holes in difficult-to-cut materials. However, the energy efficiency of the EDD operation has not thoroughly been considered in published works. The aim of the current work is to optimize varied parameters for enhancing the material removal rate (MRR), saving drilled energy (ED), and decreasing the expansion of the hole (HE) for the EDD process. Three advanced factors, including the gap voltage adjustor (GAP), capacitance parallel connection (CAP), and servo sensitivity selection (SV), are considered. The predictive models of the performances were proposed with the support of the adaptive neuro-based fuzzy inference system (ANFIS). An integrative approach combining the analytic hierarchy process (AHP) and the neighborhood cultivation genetic algorithm (NCGA) was employed to select optimal factors. The findings revealed the optimal values of the CAP, GAP, and SV were 6, 5, and 4, respectively. Moreover, the ED and HE were decreased by 16.78% and 28.68%, while the MRR was enhanced by 89.72%, respectively, as compared to the common setting values. The explored outcome can be employed as a technical solution to enhance the energy efficiency, drilled quality, and productivity of the EDD operation.
Highlights
Electrical discharge machining (EDM) operation is an efficient approach to machine difficult-to-cut materials [1]
The proposed models can be adopted to obtain the optimal inputs for improvements in energy efficiency, proposed models can be adopted to obtain the optimal inputs for improvements in energy efficiency, hole quality, quality, and hole and machining machining rate
The current research addressed the optimization of electrical discharge drilling (EDD) for the material stainless steel 304 (SS304) with the objectives of decreasing the expansion of the hole (HE) as well as the drilled energy (ED) and improving the material removal rate (MRR)
Summary
Electrical discharge machining (EDM) operation is an efficient approach to machine difficult-to-cut materials [1]. EDM processes are extensively applied in industrial applications to produce complex shapes and to achieve high precision. For EDM operations, the material is removed from the surface of a conductive specimen with the support of electrical sparks between the electrode and workpiece. The EDM process is effectively used to machine the components with different dimensional scales. EDM operations can only be employed on the electrically conductive workpiece. These processes have low productivity (e.g., material removal rate), high energy consumed, and low machined quality (e.g., high surface roughness). It is Materials 2020, 13, 2897; doi:10.3390/ma13132897 www.mdpi.com/journal/materials
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