Predict the modelling of electro chemical machining parameters for AA5083/MoS2 composites using Levenberg–Marquardt algorithm
ECM is widely regarded as a highly promising and cost-effective manufacturing technique, especially for processing hard-to-machine materials that are challenging to shape using conventional methods. The machining operations were carried out using an ECM machine with a working voltage range of 0.6 to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/118/e3sconf_sne2-2024_03022.pdf |
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| Summary: | ECM is widely regarded as a highly promising and cost-effective manufacturing technique, especially for processing hard-to-machine materials that are challenging to shape using conventional methods. The machining operations were carried out using an ECM machine with a working voltage range of 0.6 to 1.0 V and a feed rate between 15 and 25 mm/min. A copper electrode was employed alongside an NaCl electrolyte solution for calculating material removal rate on AA5083/MoS2 composites. The Highest MRR is observed when voltage 1.0 V, feed rate 25 mm/min and Electrolyte Concentration 400 g/Lit. To improve the accuracy of the predicted output responses, an artificial neural network (ANN) model was designed using the Levenberg-Marquardt algorithm. The structure with a configuration of 3–10–1, confirmed strong regression fit outcomes, The overall correlation coefficients (R) calculated at 0.96348, confirmed a high level of consistency between the experimental data and the predicted value. |
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| ISSN: | 2267-1242 |