Multiple response optimization of WEDM parameters using grey relational method

Sudharsan, S.; Arul, Rakesh; Ajay, C; Veerakumar, S.

doi:10.1016/j.matpr.2021.11.164

Cited by 2 publications

(1 citation statement)

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“…Furthermore, an optimization study using GRA on the WEDM machining of tungsten carbide material found that the optimal surface roughness (0.3435 µm) can be obtained at current = 2 A, voltage = 5 V and pulse-off time = 8 µs [29]. Chen et al [30] used a combined multi-objective algorithm based on support vector machine and particle swarm optimization (SVR-PSO) to obtain the minimum Ra of 3.6 µm and the maximum MRR of 0.261 mm 2 /s of 65 vol.% SiCp/Al composite.…”

Section: Introductionmentioning

confidence: 99%

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

Aboueleaz,

Naeim,

Abdelgaliel

et al. 2023

JMMP

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This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.

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Section: Introductionmentioning

confidence: 99%