基于数据驱动的断裂预测的组件的断裂预测.
Hossein Talebi1, Bahador Bahrami1, Mohammad Daneshfar1
1Fatigue and Fracture Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology,Narmak 16846, Tehran, Iran.
概括
数据驱动的方法使用机器学习准确地预测断组件断裂负载. 高斯过程回归实现了92%的准确性,优于其他结构完整性评估模型.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算力学 计算力学 计算力学
- 数据科学数据科学数据科学
背景情况:
- 预测断组件的断裂负荷对于结构完整性至关重要.
- 现有的方法经常与复杂的几何形状和混合模式加载条件作斗争.
- 数据驱动的方法为准确的断裂负载预测提供了一个有希望的替代方案.
研究的目的:
- 开发和评估机器学习模型,用于预测有的易碎元件的断裂负载.
- 为了确定影响混合模式I/II负载下的断裂行为的主要特征.
- 为了比较高斯过程回归,决策树组合和人工神经网络的性能.
主要方法:
- 从文献中收集并预处理了1500多个骨折测试数据点.
- 使用邻近组件分析 (NCA) 选择了六个关键特征.
- 使用贝叶斯优化训练和优化高斯过程回归 (GPR),决策树组合和人工神经网络 (ANN) 模型.
主要成果:
- 在未见的数据上预测断裂负载时,GPR实现了92%的准确性,决策树组合89%和ANN88%.
- 由于其模拟非线性关系和提供不确定性估计的能力,GPR表现出卓越的性能.
- 模型成功地预测了在训练中不包括的VO形口的断裂负载.
结论:
- 数据驱动的机器学习模型显示了高潜力准确的断裂负载预测在有的组件.
- 高斯过程回归对于这个任务特别有效,提供了可靠的预测和不确定性量化.
- 开发的方法可以加强结构完整性评估和设计优化.
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