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Exploring Artificial Neural Network Techniques for Modeling Surface Roughness in Wire Electrical Discharge Machining of Aluminum/Silicon Carbide Composites

Year: 2025

Type: article

Abstract: Understanding wire-cut electrical discharge machining (WEDM) parameters’ impact on surface roughness (Ra) is crucial for optimizing processes. This study uses artificial neural network (ANN) techniques to estimate the surface roughness of Al/SiC composites during WEDM, examining how process parameters affect the roughness. The experiment used a stir casting aluminum alloy with a 7.5% silicon carbide metal matrix composite (MMC), adjusting parameters like the wire tension (WT), servo voltage (SV), peak current (IP), pulse on time (TON), and pulse off time (TOFF). An ANN model was created to forecast the surface roughness. The study developed an ANN model to forecast surface roughness in Al/SiC composites during WEDM, demonstrating its accuracy in identifying the link between surface finish and input parameters, thereby improving the surface quality. The ANN model accurately predicted the surface roughness based on WEDM parameters, with strong correlations between predictions and actual data, demonstrating its ability to estimate surface quality accurately.

Source: Journal of Composites Science

DOI: 10.3390/jcs9060259

Authors: Yogesh S. Sable, Hanumant M. Dharmadhikari, Sunil A. More, Ioannis E. Sarris

Institutions: Dr. Babasaheb Ambedkar Marathwada University, JSPM's Rajarshi Shahu College of Engineering, University of West Attica

Language: English

Topic: Advanced Machining and Optimization Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

OpenAlex ID: W4410722033

FWCI: 1.803

Cites: 23

Cited by: 8

Related to: 10