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Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach
Authors:
Sharad Chaudhari1
, Neeraj Sunheriya1
, Jayant Giri1,2,3
, Mohammad Kanan4,5
,
Rajkumar Chadge1
, T. Sathish6
1Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, 441110, Nagpur, India
2Division of Research and Development, Lovely Professional University, Phagwara, India
3Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara
University, Rajpura, 140401, Punjab, India
4Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah
21448, Saudi Arabia
5Department of Mechanical Engineering, College of Engineering, Zarqa University, Zarqa, Jordan
6Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Saveetha University,
Thandalam, Chennai, India
Received: 12 February 2025
Revised: 20 March 2025
Accepted: 26 March 2025
Published: 31 March 2025
Abstract:
The present study optimizes hard-to-machine materials using hybrid modeling involving artificial neural networks (ANN) and the Taguchi method. The main objective of this work is to reduce tool wear and improve the material removal rate (MRR) along with lowering surface roughness (SR) in the wire electrical discharge machining (WEDM) of SNCM8 alloy steel. The model combines ANN’s predictive capacity with Taguchi’s robustness to forecast machining outcomes as process factors are combined. For this research, an L27 OA is adapted for experimentation; independent variables include current (5 A, 10 A, 15 A), pulse duration (30 µs, 60 µs, 90 µs), and feed rate (FR) (2 mm/min, 4 mm/min, 6 mm/min). The investigated output metrics are MRR, SR, and dimensional accuracy. From the analysis, it is possible to increase the MRR by 20%, from an average of 1.0 g/min to 1.2 g/min, and reduce SR by 15%, from 2.0 µm to 1.7 µm. In addition, the dimensional deviation (DD) was reduced to a minimum of 18%, which reduced from 0.11 mm to 0.09 mm. ANOVA data analysis showed pulse duration and current as the most relevant factors affecting machining performance, accounting for 45 and 35% of the variance. The hybrid model predicted and optimized machining reactions; the ANN predictions were closely aligned with experimental values, with an R-squared value exceeding 0.95. Optimizing parameter settings increased machining efficiency, reduced tool wear by 25%, and improved surface quality, revealing sustainable production techniques.
Keywords:
ANN, Optimization, Modelling, Tool, WEDM
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© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 4
December 2025
Last Edition
Volume 10
Number 4
December 2025
How to Cite
S. Chaudhari, N. Sunheriya, J. Giri, M. Kanan, R. Chadge, T. Sathish, Qualitative Enhancement in Machining Efficiency of SNCM8 Alloy Through Hybrid ANN-Taguchi Optimization Approach. Applied Engineering Letters, 10(1), 2025: 48-61.
https://doi.org/10.46793/aeletters.2025.10.1.5
More Citation Formats
Chaudhari, S., Sunheriya, N., Giri, J., Kanan, M., Chadge, R., & Sathish, T. (2025). Qualitative Enhancement in Machining Efficiency of SNCM8 Alloy Through Hybrid ANN-Taguchi Optimization Approach. Applied Engineering Letters, 10(1), 48-61.
https://doi.org/10.46793/aeletters.2025.10.1.5
Chaudhari, Sharad, et al. “Qualitative Enhancement in Machining Efficiency of SNCM8 Alloy Through Hybrid ANN-Taguchi Optimization Approach.“ Applied Engineering Letters, vol. 10, no. 1, 2025, pp. 48-61.
https://doi.org/10.46793/aeletters.2025.10.1.5
Chaudhari, Sharad, Neeraj Sunheriya, Jayant Giri, Mohammad Kanan, Rajkumar Chadge, and T. Sathish. 2025. “Qualitative Enhancement in Machining Efficiency of SNCM8 Alloy Through Hybrid ANN-Taguchi Optimization Approach.“ Applied Engineering Letters, 10 (1): 48-61.
https://doi.org/10.46793/aeletters.2025.10.1.5
Chaudhari, S., Sunheriya, N., Giri, J., Kanan, M., Chadge, R. and Sathish, T. (2025). Qualitative Enhancement in Machining Efficiency of SNCM8 Alloy Through Hybrid ANN-Taguchi Optimization Approach. Applied Engineering Letters, 10(1), pp. 48-61.
doi: 10.46793/aeletters.2025.10.1.5.