结合多式疲劳骨折表面图像进行分析与CNN的CNN
Katelyn Jones1, Paul Shade2, Reji John2
1Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, 15213, USA. ktj@alumni.cmu.edu.
Scientific reports
|February 20, 2026
概括
这项研究将SEM,BSE和SWLI成像与卷积神经网络 (CNN) 结合起来,分析Ti-6Al-4V疲劳骨折. 整合多种成像模式显著改善了裂生长率和距离预测.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 在Ti-6Al-4V中进行疲劳骨折分析对于材料性能至关重要.
- 传统的成像方法,如SEM提供地形数据.
- 整合多个数据源可以增强分析能力.
研究的目的:
- 调查结合SEM,BSE和SWLI成像模式用于疲劳骨折表面分析的实用性.
- 应用预训练的卷积神经网络 (CNN) 来预测裂纹生长参数.
- 评估多模式数据融合对模型性能的影响.
主要方法:
- 利用扫描电子显微镜 (SEM),反散电子成像 (BSE) 和扫描白光干扰 (SWLI) 来捕获断裂表面数据.
- 雇佣了经过预先培训的CNN,利用彩色频道集成实现多模式数据融合.
- 训练模型来预测距离负载线和裂增长速度的距离.
主要成果:
- 证明不同的成像模式对CNN模型有不同程度的重要性.
- 与SEM单独相比,通过结合模式显示了分类的20%改善和回归结果的60%改善.
- 验证了多模式数据集成的有效性,以加强骨折分析.
结论:
- 结合SEM,BSE和SWLI成像数据,显著提高了CNN用于疲劳骨折分析的预测准确性.
- 多模式成像数据的融合为了解材料疲劳行为提供了一种强大的方法.
- 这种方法为更强大,更准确的材料表征提供了途径.
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