Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach
In the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate a...
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Taylor & Francis Group
2024-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2024.2366870 |
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| author | Bhavya Lingampalli Sreekanth Dondapati |
| author_facet | Bhavya Lingampalli Sreekanth Dondapati |
| author_sort | Bhavya Lingampalli |
| collection | DOAJ |
| description | In the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate and compare the predictive capacity of both the models. FSP process parameters, namely, tool rotational speed (S), feed rate (F), number of passes (N), and weight percentage of Sn (W) were selected as influential parameters for optimization. The optimum conditions that were predicted by the RSM model to maximize the ultimate tensile strength (UTS) and % elongation (%EL) were a tool rotational speed of 2000 rpm, a feed rate of 0.39 mm/sec, 3 number of passes, and 8 wt.% of Sn which yields the maximum tensile strength of 217 MPa and the maximum %El of 26%. |
| format | Article |
| id | doaj-art-e689a483b565444babfb6c40e26e4b5b |
| institution | OA Journals |
| issn | 2169-3277 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-e689a483b565444babfb6c40e26e4b5b2025-08-20T02:34:04ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772024-12-0112110.1080/21693277.2024.2366870Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approachBhavya Lingampalli0Sreekanth Dondapati1School of Mechanical Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Mechanical Engineering, Vellore Institute of Technology, Chennai, IndiaIn the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate and compare the predictive capacity of both the models. FSP process parameters, namely, tool rotational speed (S), feed rate (F), number of passes (N), and weight percentage of Sn (W) were selected as influential parameters for optimization. The optimum conditions that were predicted by the RSM model to maximize the ultimate tensile strength (UTS) and % elongation (%EL) were a tool rotational speed of 2000 rpm, a feed rate of 0.39 mm/sec, 3 number of passes, and 8 wt.% of Sn which yields the maximum tensile strength of 217 MPa and the maximum %El of 26%.https://www.tandfonline.com/doi/10.1080/21693277.2024.2366870ZK60 Mg alloyfriction stir processingresponse surface methodologyartificial neural networksultimate tensile strength |
| spellingShingle | Bhavya Lingampalli Sreekanth Dondapati Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach Production and Manufacturing Research: An Open Access Journal ZK60 Mg alloy friction stir processing response surface methodology artificial neural networks ultimate tensile strength |
| title | Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach |
| title_full | Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach |
| title_fullStr | Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach |
| title_full_unstemmed | Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach |
| title_short | Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach |
| title_sort | optimization of friction stir process parameters for enhanced mechanical properties in surface alloyed zk60 magnesium with tin sn an rsm ann hybrid approach |
| topic | ZK60 Mg alloy friction stir processing response surface methodology artificial neural networks ultimate tensile strength |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2024.2366870 |
| work_keys_str_mv | AT bhavyalingampalli optimizationoffrictionstirprocessparametersforenhancedmechanicalpropertiesinsurfacealloyedzk60magnesiumwithtinsnanrsmannhybridapproach AT sreekanthdondapati optimizationoffrictionstirprocessparametersforenhancedmechanicalpropertiesinsurfacealloyedzk60magnesiumwithtinsnanrsmannhybridapproach |