相关实验视频
Updated: Jul 1, 2025

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Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
842
通过机器学习预测钢的相变温度
Yupeng Zhang1, Lin Cheng1, Aonan Pan1
1The State Key Laboratory of Refractories and Metallurgy, Hubei Province Key Laboratory of Systems Science on Metallurgical Processing, International Research Institute for Steel Technology, Collaborative Center on Advanced Steels, Wuhan University of Science and Technology, Wuhan 430081, China.
Materials (Basel, Switzerland)
|March 13, 2024
概括
这项研究使用了改进的LightGBM模型来预测钢相转换温度 (Ac1,Ac3,马氏体启动 (MS),贝尼特启动 (BS)). 原子参数显著提高了预测的准确性,揭示了钢铁加工的关键影响因素.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
背景情况:
- 阶段转换温度对于钢的设计,生产和热处理至关重要.
- 了解合金元素对这些温度的影响对于材料性质控制至关重要.
研究的目的:
- 为了研究和预测四个关键的钢阶转化温度:Ac1,Ac3,马氏体转化开始 (MS) 和贝尼特转化开始 (BS).
- 识别和分析合金元素和原子参数对这些转变温度的影响.
主要方法:
- 利用一个改进的梯度增强算法,LightGBM,用于预测建模.
- 包含十八个原子参数,包括化温度,热膨胀系数,瓦伯-克罗默伪电位半径和价值电子数.
- 采用部分依赖图 (PDP) 和沙普利增量解释 (SHAP) 来分析特征的重要性和关系.
主要成果:
- 通过包含原子参数,LightGBM模型实现了显著提高的训练精度.
- 化温度,线性热膨胀系数,原子伪电位半径和价值电子数被确定为四大影响力原子特征.
- 详细的分析显示,合金元素在不同相变换温度下有不同的影响机制.
结论:
- 原子参数对于准确预测钢阶转换温度至关重要.
- 开发的模型和分析为通过组合和热处理控制钢材特性提供了宝贵的见解.
- 这种方法为了解钢的复杂材料行为提供了一个强大的方法.
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