Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach
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 ele...
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The Serbian Academic Center
2025-03-01
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| Series: | Applied Engineering Letters |
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| Online Access: | https://aeletters.com/wp-content/uploads/2025/03/AEL00415.pdf |
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| author | Sharad Chaudhari Neeraj Sunheriya Jayant Giri Mohammad Kanan Rajkumar Chadge T. Sathish |
| author_facet | Sharad Chaudhari Neeraj Sunheriya Jayant Giri Mohammad Kanan Rajkumar Chadge T. Sathish |
| author_sort | Sharad Chaudhari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df69ca4cd5b6428aab1d89d1f1b73064 |
| institution | OA Journals |
| issn | 2466-4677 2466-4847 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | The Serbian Academic Center |
| record_format | Article |
| series | Applied Engineering Letters |
| spelling | doaj-art-df69ca4cd5b6428aab1d89d1f1b730642025-08-20T01:54:22ZengThe Serbian Academic CenterApplied Engineering Letters2466-46772466-48472025-03-01101486110.46793/aeletters.2025.10.1.5Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approachSharad Chaudhari0Neeraj Sunheriya1Jayant Giri2Mohammad Kanan3Rajkumar Chadge4T. Sathish5Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, 441110, Nagpur, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, 441110, Nagpur, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, 441110, Nagpur, India; Division of Research and Development, Lovely Professional University, Phagwara, India; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, IndiaDepartment of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia; Department of Mechanical Engineering, College of Engineering, Zarqa University, Zarqa, JordanDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, 441110, Nagpur, IndiaDepartment of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Thandalam, Chennai, IndiaThe 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.https://aeletters.com/wp-content/uploads/2025/03/AEL00415.pdfannoptimizationmodellingtoolwedm |
| spellingShingle | Sharad Chaudhari Neeraj Sunheriya Jayant Giri Mohammad Kanan Rajkumar Chadge T. Sathish Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach Applied Engineering Letters ann optimization modelling tool wedm |
| title | Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach |
| title_full | Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach |
| title_fullStr | Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach |
| title_full_unstemmed | Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach |
| title_short | Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach |
| title_sort | qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann taguchi optimization approach |
| topic | ann optimization modelling tool wedm |
| url | https://aeletters.com/wp-content/uploads/2025/03/AEL00415.pdf |
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