对核结构材料的小尺寸拉伸样本中尺寸效应的实证验证
Longze Li1, John W Merickel2, Yalei Tang2
1Department of Computer Science, University of Idaho, Idaho Falls, ID, 83402, USA.
Scientific reports
|April 22, 2025
概括
机器学习使用大型数据库预测小物质样本的拉伸性能. 这促进了对核结构材料尺寸效应的理解,并验证了测试方法.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 核工程 核工程是指核工程.
背景情况:
- 由于样本可用性有限,了解小规模材料测试中的尺寸效应至关重要.
- 现有的关于样本大小影响的研究通常依赖于有限数量的测试.
- 对于小尺寸样本的准备,测试和分析,已有确定的指导方针.
研究的目的:
- 开发一种机器学习 (ML) 方法来预测小尺寸标本的拉伸性能.
- 量化与ML预测的材料特性相关的不确定性.
- 实验验证小尺寸标本的临界尺寸和分析模型的延长相关性.
主要方法:
- 从经过同行评审的文献中编制了一个大型数据库,包含1050个核结构材料的抗拉试验记录.
- 应用机器学习算法,根据样本大小和材料数据预测拉伸性能.
- 进行了小尺寸样本几何学和分析模型的延长实验验证.
主要成果:
- 成功开发了一种基于ML的方法,用于预测小尺寸标本的拉伸性能.
- 在ML预测中的量化不确定性,提供可靠的属性估计.
- 对于小尺寸标本和分析模型的验证关键维度,以对应延长.
结论:
- 机器学习显示了提高对大小依赖材料行为的理解的巨大潜力.
- 该研究强调了大型开源数据库对于机械测试数据的必要性.
- 需要协调努力来构建全面的数据集,以支持未来的材料科学研究.
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