Najm, V., Abbas, T., Aghdeab, S. (2023). Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge. , 41(12), 1594-1610. doi: 10.30684/etj.2023.143347.1581
Vian N. Najm; Tahseen F. Abbas; Shukry H. Aghdeab. "Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge". , 41, 12, 2023, 1594-1610. doi: 10.30684/etj.2023.143347.1581
Najm, V., Abbas, T., Aghdeab, S. (2023). 'Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge', , 41(12), pp. 1594-1610. doi: 10.30684/etj.2023.143347.1581
Najm, V., Abbas, T., Aghdeab, S. Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge. , 2023; 41(12): 1594-1610. doi: 10.30684/etj.2023.143347.1581

Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge

Engineering and Technology Journal
Volume 41, Issue 12, December 2023, Pages 1594-1610    PDF (2.05 M)
Document Type: Research Paper
DOI: 10.30684/etj.2023.143347.1581
Authors
Vian N. Najm* ; Tahseen F. Abbas; Shukry H. Aghdeab
Production Engineering and Metallurgy Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
Hybrid Electrochemical Discharge Machining (ECDM) streamlines the manufacturing process for challenging materials. Tungsten carbide (WC) is widely recognized as a formidable material due to its exceptional hardness and its ability to maintain hardness even at elevated temperatures. This study has conducted a comprehensive investigation of the multi-response optimization of ECDM process parameters to enhance the machining of tungsten carbides, utilizing Grey Relation Analysis (GRA) and Artificial Neural Network (ANN) methods. This optimization's primary objective is to achieve the maximum Material Removal Rate (MRR) and machining depth. The study involved a systematically designed experiment based on the Taguchi design method. Scanning Electron Microscopy (SEM) analysis reveals shallow craters, minimal microcracks, and small melt re-deposits on the machined surfaces, elucidating the smooth surface achieved after ECD machining. The average surface roughness achieved in this study, utilizing Electrochemical Discharge Machining (ECDM) for tungsten, measured at 0.9275 µm. Optimal parameters were determined, including a current of 80 A, a stand-off distance of 0.1 mm, a 30 mm gap, and an electrolyte composed of KCl + KOH for machining tungsten carbide. The results of the Analysis of Variance (ANOVA) indicate that electrolyte concentration has the most significant impact on machining depth and material removal rate (50.55%), followed by the current value (31.32%). Additionally, the ANN results aligned closely with those obtained through GRA. Compositional analysis of the surface using Energy-Dispersive X-ray Spectroscopy (EDS) mapping confirms the presence of oxides and carbon on the machined surface.

Highlights
  • This study implements the machining of tungsten carbide using hybrid electrochemical discharge machining
  • Grey relation analysis (GRA) and artificial neural network (ANN) approaches are utilized for optimization
  • The maximum material removal rate and highest machining depth are attained by applying the optimum parameters
  • The electrolyte concentration affects material removal rate and machining depth most, followed by current.
Keywords
Electrochemical discharge machining (ECDM); material removal rate (MRR); depth of cut (DOC); grey relation analysis (GRA); artificial neural network (ANN)
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