Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications
In this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution trea...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Science and Technology of Advanced Materials |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2025.2449811 |
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| author | Sandeep Jain Ayan Bhowmik Jaichan Lee |
| author_facet | Sandeep Jain Ayan Bhowmik Jaichan Lee |
| author_sort | Sandeep Jain |
| collection | DOAJ |
| description | In this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution treatment, homogenization, extrusion and aging behaviour. The effectiveness of machine learning models is evaluated using performance metrics, including Coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). After modeling and selection of best model, the mechanical behaviour of new alloys was predicted in terms of ultimate tensile strength, yield strength and total elongation. The predicted results highlight the superior predictive accuracy of the K-Nearest Neighbors (KNN) machine learning model, demonstrating its better performance metrics compared with other machine learning approaches. This model has been found to predict the material properties with an effective evaluation matrix (R2 = 0.955, MAE = 3.4% and RMSE = 4.5%). |
| format | Article |
| id | doaj-art-d9bec30f696844cfa5c54d3568fe90d3 |
| institution | Kabale University |
| issn | 1468-6996 1878-5514 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj-art-d9bec30f696844cfa5c54d3568fe90d32025-02-03T10:16:37ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142025-12-0126110.1080/14686996.2025.2449811Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applicationsSandeep Jain0Ayan Bhowmik1Jaichan Lee2School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Materials Science and Engineering, Indian Institute of Technology Delhi, New Delhi, IndiaSchool of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Republic of KoreaIn this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution treatment, homogenization, extrusion and aging behaviour. The effectiveness of machine learning models is evaluated using performance metrics, including Coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). After modeling and selection of best model, the mechanical behaviour of new alloys was predicted in terms of ultimate tensile strength, yield strength and total elongation. The predicted results highlight the superior predictive accuracy of the K-Nearest Neighbors (KNN) machine learning model, demonstrating its better performance metrics compared with other machine learning approaches. This model has been found to predict the material properties with an effective evaluation matrix (R2 = 0.955, MAE = 3.4% and RMSE = 4.5%).https://www.tandfonline.com/doi/10.1080/14686996.2025.2449811Thermomechanical behaviorlight weight Mg alloysrare earth elementsmachine learning |
| spellingShingle | Sandeep Jain Ayan Bhowmik Jaichan Lee Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications Science and Technology of Advanced Materials Thermomechanical behavior light weight Mg alloys rare earth elements machine learning |
| title | Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications |
| title_full | Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications |
| title_fullStr | Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications |
| title_full_unstemmed | Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications |
| title_short | Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications |
| title_sort | machine learning approaches for predicting and validating mechanical properties of mg rare earth alloys for light weight applications |
| topic | Thermomechanical behavior light weight Mg alloys rare earth elements machine learning |
| url | https://www.tandfonline.com/doi/10.1080/14686996.2025.2449811 |
| work_keys_str_mv | AT sandeepjain machinelearningapproachesforpredictingandvalidatingmechanicalpropertiesofmgrareearthalloysforlightweightapplications AT ayanbhowmik machinelearningapproachesforpredictingandvalidatingmechanicalpropertiesofmgrareearthalloysforlightweightapplications AT jaichanlee machinelearningapproachesforpredictingandvalidatingmechanicalpropertiesofmgrareearthalloysforlightweightapplications |