用师生模型反推钢热处理参数，又准又快。
Teacher-Student Guided Inverse Modeling for Steel Final Hardness Estimation
- 用教师模型正向预测硬度，学生模型逆向推参数
- 在公开数据集上逆向预测准确率更高，耗时更少
- 适合材料工艺优化和智能制造场景
预测钢经热处理后的最终硬度是一项具有挑战性的回归任务，因工艺存在多对一特性——不同的输入参数组合（如温度、时间、化学成分）可能产生相同的硬度值。这种模糊性使得从目标硬度反推输入参数的逆问题尤为困难。本文提出一种基于师生学习框架的新方法：首先训练一个前向模型（教师）根据13个冶金特征预测最终硬度；然后训练一个反向模型（学生）从目标硬度值推断合理的输入配置。学生模型通过教师反馈在迭代监督循环中优化。我们在一个公开的调质钢数据集上评估该方法，并与基线回归及强化学习模型对比。结果表明，该师生框架不仅逆向预测精度更高，且计算时间显著减少，验证了其在材料科学逆过程建模中的有效性和高效性。
原文摘要 · Abstract (English)
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.
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