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通过点云完成,提高金属超声波测试中缺陷3D重建的精度

Zhongxin Wang¹, Xue Li², Yanbo Liu³

¹Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China.

|June 20, 2024

概括

一个新的基于注意力的剩余完成网络 (ARC) 解决了超声波缺陷成像中的轮损失. 这种方法提高了金属缺陷特征的3D重建精度.

科学领域:

材料科学材料科学材料科学
非破坏性测试是指非破坏性测试.
计算机视觉计算机视觉

背景情况:

超声波多层聚焦扫描检查遭受了层间轮信息丢失.
这种损失降低了缺陷3D重建的准确性,导致大小和形态特征的错误.
对金属缺陷的准确3D描述对于材料完整性评估至关重要.

研究的目的:

为超声波点云完成提出一种新的方法,以准确地描述金属缺陷.
为了解决在缺陷的3D重建过程中丢失的轮信息的问题.
用超声数据提高缺陷大小和形态特征的准确性.

主要方法:

开发一个基于注意力的剩余完成网络 (ARC),用于超声波点云完成.
ARC网络使用连续的剩余卷积来提取全球轮和局部边缘特征.
一个基于自我注意的解码器通过整合提取的特征来重建丢失的轮信息.

主要成果:

通过超声波显微镜检查的钢板内部缺陷,应用了ARC网络.
完成结果与金属图像进行了比较,证明了改进的3D重建.
缺陷形态和尺寸的特征精度平均提高了10.31%.

结论:

基于注意力的剩余完成网络 (ARC) 有效地重建了超声波数据中丢失的轮信息.
拟议的方法显著提高了金属缺陷的3D重建精度.
ARC为金属材料内部缺陷的高精度3D表征提供了一种新的方法.

关键词:

注意力机制注意力机制金属材料的内部缺陷在金属材料的内部缺陷.剩余完成网络的剩余完成网络.三维重建的三维重建超声波点云是一种超声波点云.

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