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用机器学习发现化学方法,在合金617中爬行破裂方程
Md Abir Hossain1,2, Liangyan Hao3, Wei Xiong3
1Department of Mechanical and Aerospace Engineering, The Ohio State University, Scott Laboratory, 201 W 19th Avenue, Suite E540, Columbus, OH, 43210, USA. hossain.129@osu.edu.
Scientific reports
|February 19, 2025
概括
研究人员使用机器学习 (ML) 发现了超级合金爬行破裂的新方程式,并结合了材料化学. 这个模型预测了性能,并确定了合金617的最佳化学成分,以提高爬行强度.
科学领域:
- 材料科学 材料科学 材料科学
- 金工程 金工程 金工程
- 计算材料科学科学 计算材料科学
背景情况:
- 超级合金中的爬行破裂对于高温应用至关重要.
- 传统模型将压力与温度联系起来,但往往忽略了详细的化学效应.
- 发现涉及材料化学的复杂关系一直是一个重大挑战.
研究的目的:
- 开发一个高维,人类可解释的超级合金爬行破裂的数学模型.
- 将材料化学和化学比率整合到爬行破裂预测中.
- 为了确定合金617的优化化学成分,以提高爬行强度.
主要方法:
- 采用人类监督的机器学习 (ML),特别是具有符号回归的多基因遗传编程 (MGGP).
- 为617合金推导出可解释的爬行破裂方程.
- 与实验和盲目测试数据对比的验证预测.
主要成果:
- 开发了一个最佳的爬行-破裂方程,包括应力,温度,化学和化学比率.
- 衍生式反映了已知的爬行强化和削弱机制.
- 与爬行数据达成统计学上显著的一致性,包括验证集.
- 确定了合金617的最佳化学成分,证明了更好的爬行强度.
结论:
- ML,特别是MGGP,可以成功地推导出复杂的,可解释的材料行为模型.
- 新模型准确地预测了爬行裂变,并揭示了特定化学物质的影响.
- 计算的平衡相图 (CALPHAD) 证实了优化合金中强化相的增强稳定性.
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