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Application of Taguchi Grey Approach and Sustainable Development of Hybrid Machine Learning Model for Wire Electrical Discharge Machining of Low Carbon Steel
Authors:
Manikandan Natarajan1
, R. Meenakshi Reddy2
, Pasupuleti Thejasree1
,
Neeraj Sunheriya1
, Jayant Giri3,4,5
, Rajkumar Chadge3
, Mohammad Kanan6,7
1Department of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati, 517102, India
3Department of Mechanical Engineering, G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India
3Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
4Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India
5Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
6Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia
7Department of Mechanical Engineering, College of Engineering, Zarqa University, Zarqa, Jordan
Received: 20 February 2025
Revised: 14 May 2025
Accepted: 11 November 2025
Published: 31 March 2026
Abstract:
SAE 1010 steel is widely used in the automotive industry for fasteners and in aerospace and other engineering applications. Traditional machining of complicated forms is difficult. Wire Electrical Discharge Machining (WEDM), a specialized form of EDM, is effective for complex machining operations. This study optimises WEDM of SAE 1010 steel while considering environmental impact. A naturally available dielectric medium promotes sustainability without reducing machining efficiency. Pulse on time, pulse off time, and peak current affect material removal rate (MRR), surface roughness (Ra), and tolerance errors. ANOVA is used to determine the significance of the parameters. A hybrid Grey-ANFIS (Adaptive Neuro-Fuzzy Inference System) model improves performance predictions. The results show that Grey-ANFIS provides accurate forecasts, improving machining parameter optimization. The prediction model was highly accurate, with a MAPE of 0.0432, RMSE of 0.00029, and MAE of 0.000432. The model also correlated well with actual values, with a Correlation Coefficient of 0.9997.
Keywords:
Contemporary machining, WEDM Method, SAE 1010, Taguchi’s approach, GRA, MCDM, Artificial intelligence, ANFIS
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© 2026 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 11
Number 1
March 2026
Last Edition
Volume 11
Number 1
March 2026
How to Cite
M. Natarajan, R.M. Reddy, P. Thejasree, N. Sunheriya, J. Giri, R. Chadge, M. Kanan, Application of Taguchi Grey Approach and Sustainable Development of Hybrid Machine Learning Model for Wire Electrical Discharge Machining of Low Carbon Steel. Applied Engineering Letters, 11(1), 2026: 1-13.
https://doi.org/10.46793/aeletters.2026.11.1.1
More Citation Formats
Natarajan, M., Reddy, R.M., Thejasree, P., Sunheriya, N., Giri, J., Chadge, R., & Kanan, M. (2026). Application of Taguchi Grey Approach and Sustainable Development of Hybrid Machine Learning Model for Wire Electrical Discharge Machining of Low Carbon Steel. Applied Engineering Letters, 11(1), 1-13.
https://doi.org/10.46793/aeletters.2026.11.1.1