使用H2O自动ML自动预测裂纹传播
Intisar Omar1, Muhammad Khan1, Andrew Starr1
1School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
我们开发了一个使用H2O的自动机器学习模型,以准确预测ABS等材料的裂传播. 这为结构健康监测提供了可靠的解决方案,并提高了工程安全.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料科学科学 计算材料科学
背景情况:
- 裂传播对结构完整性和部件耐用性产生重大影响.
- 精确预测裂行为对于工程可靠性和安全性至关重要.
- 现有的裂预测方法需要更高效和自动化的解决方案.
研究的目的:
- 开发和评估基于机器学习的自动化模型,用于预测裂传播.
- 为了利用H2O库来分析裂纹模式和预测行为.
- 通过准确的裂预测,增强结构健康监测和工程安全.
主要方法:
- 使用H2O库开发了一个自动化模型.
- 利用了在烯酸丁 styrene (ABS) 标本中裂纹传播实例的数据集.
- 采用严格的评估指标 (MAE,RMSE,R2) 和交叉验证以评估准确性和稳定性.
主要成果:
- 自动化模型在预测裂传播方面表现出了显著的准确性和可靠性.
- 在分析复杂的裂纹模式时,H2O库被证明是有效的.
- 交叉验证证实了该模型在各种数据集中的通用性.
结论:
- 自动机器学习 (AutoML) 为预测裂传播提供了一种强大的方法.
- 该H2O库是结构健康监测应用程序的宝贵工具.
- 这项研究倡导在工程中采用AutoML,以提高安全性和可靠性.
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