Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique
In this investigation, electrical discharge machining (EDM) of hybrid aluminium composite (HAC) comprising AA8081, boron carbide (B4C) (10 wt.%) and graphite (Gr) (5 wt.%) is carried out. The ultrasonic cavitation-aided stir casting procedure is adopted to cast the AMMC. Experiments were designed us...
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2025-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425015212 |
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| author | M. Vivekanandhan N. Senthilkumar B. Deepanraj Nithesh Naik |
| author_facet | M. Vivekanandhan N. Senthilkumar B. Deepanraj Nithesh Naik |
| author_sort | M. Vivekanandhan |
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| description | In this investigation, electrical discharge machining (EDM) of hybrid aluminium composite (HAC) comprising AA8081, boron carbide (B4C) (10 wt.%) and graphite (Gr) (5 wt.%) is carried out. The ultrasonic cavitation-aided stir casting procedure is adopted to cast the AMMC. Experiments were designed using response surface methodology, considering discharge current (DI), pulse off-time (Toff), and pulse on-time (Ton) to optimize the outputs over cut (OC), tool wear rate (TWR), and material removal rate (MRR). Microscopic images reveal the uniform spreading of reinforcements, and the machined layer shows few globules with minimal cracks. Cavitation promotes heterogeneous nucleation, leading to a fine and equiaxed microstructure and reduced porosity. Increased Ton rises MRR and TWR owing to enhanced thermal energy transfer and prolonged spark duration. An intense thermal load and spark energy develop gradual electrode erosion. Increasing the Toff significantly lowers the MRR and TWR. Rising the DI considerably increases the MRR and TWR. Higher Ton, DI, and Toff increases the OC. Desirability analysis finds the optimal machining condition as Ton of 3 μs, Toff of 8.63 μs, and DI of 20 A, providing a higher MRR (0.25 g/min), lower OC (0.278 mm), and TWR (0.058 g/min) with a desirability value of 0.776. A Levenberg-Marquardt Neural Network model (3-7-3 architecture) predicts the outputs more precisely than the regression models, with R2 values of 0.9992, 0.992, 0.9693, and 0.9946 for training, validation, testing, and overall, respectively, with error values lower than those of the experimental datasets. |
| format | Article |
| id | doaj-art-6cd3839f22b34caba90cf1bb56152c69 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-6cd3839f22b34caba90cf1bb56152c692025-08-20T03:26:31ZengElsevierJournal of Materials Research and Technology2238-78542025-07-01371987200410.1016/j.jmrt.2025.06.099Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt techniqueM. Vivekanandhan0N. Senthilkumar1B. Deepanraj2Nithesh Naik3Department of Mechanical Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, 603319, IndiaDepartment of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, Tamil Nadu, 602105, IndiaDepartment of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi ArabiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Corresponding authorIn this investigation, electrical discharge machining (EDM) of hybrid aluminium composite (HAC) comprising AA8081, boron carbide (B4C) (10 wt.%) and graphite (Gr) (5 wt.%) is carried out. The ultrasonic cavitation-aided stir casting procedure is adopted to cast the AMMC. Experiments were designed using response surface methodology, considering discharge current (DI), pulse off-time (Toff), and pulse on-time (Ton) to optimize the outputs over cut (OC), tool wear rate (TWR), and material removal rate (MRR). Microscopic images reveal the uniform spreading of reinforcements, and the machined layer shows few globules with minimal cracks. Cavitation promotes heterogeneous nucleation, leading to a fine and equiaxed microstructure and reduced porosity. Increased Ton rises MRR and TWR owing to enhanced thermal energy transfer and prolonged spark duration. An intense thermal load and spark energy develop gradual electrode erosion. Increasing the Toff significantly lowers the MRR and TWR. Rising the DI considerably increases the MRR and TWR. Higher Ton, DI, and Toff increases the OC. Desirability analysis finds the optimal machining condition as Ton of 3 μs, Toff of 8.63 μs, and DI of 20 A, providing a higher MRR (0.25 g/min), lower OC (0.278 mm), and TWR (0.058 g/min) with a desirability value of 0.776. A Levenberg-Marquardt Neural Network model (3-7-3 architecture) predicts the outputs more precisely than the regression models, with R2 values of 0.9992, 0.992, 0.9693, and 0.9946 for training, validation, testing, and overall, respectively, with error values lower than those of the experimental datasets.http://www.sciencedirect.com/science/article/pii/S2238785425015212AA8081Electrical discharge machiningHybrid aluminium compositeNeural networksResponse surface methodologyUltrasonication |
| spellingShingle | M. Vivekanandhan N. Senthilkumar B. Deepanraj Nithesh Naik Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique Journal of Materials Research and Technology AA8081 Electrical discharge machining Hybrid aluminium composite Neural networks Response surface methodology Ultrasonication |
| title | Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique |
| title_full | Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique |
| title_fullStr | Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique |
| title_full_unstemmed | Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique |
| title_short | Assessment on electrical discharge machining of ultrasonication assisted stir-casted AA8081-B4C-Gr hybrid composites and prediction using Levenberg-Marquardt technique |
| title_sort | assessment on electrical discharge machining of ultrasonication assisted stir casted aa8081 b4c gr hybrid composites and prediction using levenberg marquardt technique |
| topic | AA8081 Electrical discharge machining Hybrid aluminium composite Neural networks Response surface methodology Ultrasonication |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425015212 |
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