微断裂分析的应用,通过非破坏性测试对材料中的疲劳断裂进行分析
Ulises Sánchez-Santana1, Gerardo Presbítero-Espinosa1, José María Quiroga-Arias2
1Centro de Ingeniería y Desarrollo Industrial, Pie de la Cuesta 702, Desarrollo San Pablo, Querétaro 76130, Mexico.
Materials (Basel, Switzerland)
|February 24, 2024
概括
这项研究引入了一种深度学习模型,用于以超过95%的准确度检测物质疲劳微骨折. 该模型分析微骨折指标,以预测疲劳失败趋势.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 疲劳骨折导致~80%的材料故障,需要可靠的预测方法.
- 预测疲劳失败需要了解体积,周期,应力和韦布尔常数.
- 微裂分析是理解和预测材料疲劳的关键.
研究的目的:
- 开发和验证一个深度学习模型,用于在工业材料中准确检测微碎裂.
- 量化微骨折指标,以改善疲劳失败预测.
- 建立一种分析微骨折生长和疲劳趋势的方法.
主要方法:
- 在小型样本 (<4毫米2) 上进行机械疲劳测试.
- 精确计算机断层扫描用于微骨折识别.
- 深度学习 (卷积神经网络) 用于在图像中检测和分类微碎裂.
- 图像处理用于隔离,测量和分类微碎裂 (面积,周长,长度等). ) 的情况.
主要成果:
- 一个深度学习模型在检测图像中的微碎裂时实现了>95%的准确性.
- 微断裂指标 (面积,周长,特征长度,圆形,方向) 已成功量化.
- 像素测量被转换成米单位 (μm) 用于标准化分析.
结论:
- 开发的深度学习方法使得精确的微碎裂检测和描述成为可能.
- 量化微断裂指标为预测材料疲劳失效提供了有价值的数据.
- 这种方法为确定疲劳骨折发展趋势提供了一条途径.
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