人工神经网络方法用于预测高合金中的硬度
Makachi Nchekwube1, A K Maurya1, Dukhyun Chung1
1Department of Mechanical Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.
Materials (Basel, Switzerland)
|October 29, 2025
概括
预测高合金 (HEA) 硬度对于开发新材料至关重要. 一个人工神经网络 (ANN) 模型根据成分准确预测HEA硬度,识别影响该属性的关键元素.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是金工业的一个方面.
- 计算材料科学科学 计算材料科学
背景情况:
- 高合金 (HEAs) 是具有优越性质的先进材料.
- 由于非线性组成关系,预测HEA硬度是复杂的.
- 了解合金元素效应是HEA设计的关键.
研究的目的:
- 开发一个准确的HEA硬度预测模型.
- 利用实验数据来训练机器学习模型.
- 研究特定合金元素对HEA硬度的影响.
主要方法:
- 使用535个实验数据点开发了一个人工神经网络 (ANN) 模型.
- 输入参数包括组成元素 (Al,Co,Cr,Cu,Mn,Ni,Fe,W,Mo,Ti) 在内.
- 硬度是输出参数;使用相关系数 (Adj R2) 评估模型性能.
主要成果:
- 该ANN模型实现了高精度,Adj R2值为99.84% (训练) 和99.3% (测试).
- Al,Cr和Mn被确定为增强硬度的元素,促进BCC和B2阶段.
- Co,Cu,Fe和Ni通过稳定FCC阶段降低了硬度;W通过晶格扭曲和金属间形成增加了硬度.
结论:
- 开发的ANN模型为预测HEA硬度提供了可靠的工具.
- 特定的合金元素对HEA硬度有可预测的,显著的影响.
- 这些发现指导了具有定制机械性能的新型HEAs的合理设计.
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