Predictive modeling of MRR, TWR, and SR in spark-EDM of Al-4.5Cu–SiC using ANN and GEP

In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analys...

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Published inAIP advances Vol. 14; no. 9; pp. 095225 - 095225-14
Main Authors Debnath, Shantanu, Sen, Binayak, Patil, Nagaraj, Kedia, Ankit, Mann, Vikasdeep Singh, Santhosh, A. Johnson, Bhowmik, Abhijit
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 01.09.2024
AIP Publishing LLC
Subjects
Artificial neural networks
Correlation coefficients
Die sinking
Electric discharge machining
Gene expression
Intermetallic compounds
Metal matrix composites
Particulate composites
Performance measurement
Performance prediction
Prediction models
Silicon carbide
Surface roughness
Tool wear
Wear rate
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Summary:In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analysis. This article compares the performance of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models in predicting the Metal Removal Rate (MRR), tool wear rate, and surface roughness in the die-sinking electro-discharge machining (EDM) process of the ex-situ developed Al-4.5%Cu–SiC composites. The study considers three machine parameters—pulse on time (TON), pulse off time (TOFF), and current (I)—along with the weight fraction of SiC particles as input variables for the models. Both ANN and GEP models demonstrated high predictive accuracy for the EDM performance metrics, with correlation coefficients (R) ranging from 0.973 68 to 0.980 65 for the ANN model and 0.980 11 to 0.982 59 for the GEP model. Notably, the GEP model exhibited superior predictive capability, as evidenced by its higher correlation coefficients and lower root mean square error, indicating greater effectiveness in predicting the EDM process outcomes than the ANN model.
ISSN:2158-3226
2158-3226
DOI:10.1063/5.0230832