Teacher-Student Guided Inverse Modeling for Steel Final Hardness Estimation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912632502484992 |
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| author | Alsheikh, Ahmad Fischer, Andreas |
| author_facet | Alsheikh, Ahmad Fischer, Andreas |
| contents | Predicting the final hardness of steel after heat treatment is a challenging regression task due to the many-to-one nature of the process -- different combinations of input parameters (such as temperature, duration, and chemical composition) can result in the same hardness value. This ambiguity makes the inverse problem, estimating input parameters from a desired hardness, particularly difficult. In this work, we propose a novel solution using a Teacher-Student learning framework. First, a forward model (Teacher) is trained to predict final hardness from 13 metallurgical input features. Then, a backward model (Student) is trained to infer plausible input configurations from a target hardness value. The Student is optimized by leveraging feedback from the Teacher in an iterative, supervised loop. We evaluate our method on a publicly available tempered steel dataset and compare it against baseline regression and reinforcement learning models. Results show that our Teacher-Student framework not only achieves higher inverse prediction accuracy but also requires significantly less computational time, demonstrating its effectiveness and efficiency for inverse process modeling in materials science. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_05402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Teacher-Student Guided Inverse Modeling for Steel Final Hardness Estimation Alsheikh, Ahmad Fischer, Andreas Artificial Intelligence I.2.6; I.6.5 Predicting the final hardness of steel after heat treatment is a challenging regression task due to the many-to-one nature of the process -- different combinations of input parameters (such as temperature, duration, and chemical composition) can result in the same hardness value. This ambiguity makes the inverse problem, estimating input parameters from a desired hardness, particularly difficult. In this work, we propose a novel solution using a Teacher-Student learning framework. First, a forward model (Teacher) is trained to predict final hardness from 13 metallurgical input features. Then, a backward model (Student) is trained to infer plausible input configurations from a target hardness value. The Student is optimized by leveraging feedback from the Teacher in an iterative, supervised loop. We evaluate our method on a publicly available tempered steel dataset and compare it against baseline regression and reinforcement learning models. Results show that our Teacher-Student framework not only achieves higher inverse prediction accuracy but also requires significantly less computational time, demonstrating its effectiveness and efficiency for inverse process modeling in materials science. |
| title | Teacher-Student Guided Inverse Modeling for Steel Final Hardness Estimation |
| topic | Artificial Intelligence I.2.6; I.6.5 |
| url | https://arxiv.org/abs/2510.05402 |