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使用机器学习辅助超声波估计合金的全范围应力-张力曲线
Seong-Hyun Park1, Junyeon Chung2, Kiyoon Yi2
1Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.
Ultrasonics
|September 1, 2023
概括
这项研究使用机器学习和超声波信号来非破坏性地估计全范围应力-张力 (SS) 曲线. 这种方法可以在没有破坏性测试的情况下准确预测材料的机械性能,为制造应用提供了潜力.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 非破坏性测试 不破坏性测试
背景情况:
- 全范围应力-延展 (SS) 曲线对于描述材料机械性能至关重要.
- 获得SS曲线的传统方法涉及破坏性拉伸试验,这耗时且资源密集.
- 需要使用非破坏性和高效的方法来估计SS曲线.
研究的目的:
- 开发和验证一种基于机器学习的技术,用于非破坏性地估计全范围应力-张力 (SS) 曲线.
- 探索超声波特性与SS曲线参数之间的关系.
- 为了证明在制造过程中内置SS曲线估计的潜力.
主要方法:
- 应用机器学习算法来分析超声波振幅扫描信号.
- 与SS曲线特征相关的超声波特性 (例如,非线性,衰减).
- 在500个具有不同机械性能的合金样本上验证了该技术.
主要成果:
- 通过使用超声波数据,成功估计了全程SS曲线的非破坏性.
- 超声波信号特征与关键机械性能 (收益强度,最终抗拉强度,延长) 之间建立了相关性.
- 证明了开发的机器学习模型的准确性和可靠性.
结论:
- 超声波测试与机器学习相结合,提供了一种有效的非破坏性方法,用于预测材料应力-张力曲线.
- 该技术消除了传统破坏性拉伸试验的需要.
- 这种方法对材料的表征和制造过程中的质量控制有重大影响.
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