基于深度学习和智能优化算法,用于硬度预测和高合金设计的协同框架
Kai Wang1, Xuewen Zhou1, Chuan Liu1
1School of Electromechanical Engineering and Automation, Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528225, China.
ACS omega
|September 22, 2025
概括
本研究引入了一个深度学习框架,用于预测和优化高合金 (HEA) 硬度. 该模型准确地预测硬度,并为所需的性能设计最佳合金组合物.
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
- 合金设计设计 合金设计
背景情况:
- 高合金 (HEAs) 具有独特的特性,但需要复杂的优化.
- 预测和设计HEA属性,就像硬度一样,仍然具有挑战性.
研究的目的:
- 开发一个协同框架,结合深度学习和优化算法,用于HEA硬度预测和组合设计.
- 为了能够准确地预测硬度和逆向设计最佳的HEA组成.
主要方法:
- 一种混合深度学习模型,具有变压器注意力和MLP用于硬度预测.
- 使用相关性分析,固体溶液强化理论和NSGA-III.III的特征选择.
- 用于特征硬度因果关系调查的SHAP分析.
- 埃格雷特队伍优化算法用于反向设计.
主要成果:
- 高精度的硬度预测 (R2 = 0.9813,RMSE = 10.23577) 的使用.
- 通过SHAP分析确定影响HEA硬度的关键特征.
- 为了目标硬度,HEA组合物的成功反向设计.
- 通过激光金属沉积的实验验证显示<10%的偏差.
结论:
- 拟议的框架有效地预测和设计HEA属性.
- 这种方法加速了先进的HEAs的发现和开发.
- 该研究提供了一种系统的方法,用于属性驱动的合金设计.
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