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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.02290 |
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| _version_ | 1866908626775441408 |
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| author | Sinha, Samjukta Das, Prabhat |
| author_facet | Sinha, Samjukta Das, Prabhat |
| contents | This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02290 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From data to design: Random forest regression model for predicting mechanical properties of alloy steel Sinha, Samjukta Das, Prabhat Materials Science Artificial Intelligence This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science. |
| title | From data to design: Random forest regression model for predicting mechanical properties of alloy steel |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2511.02290 |