计量学
点云
灰度
计算机视觉
人工智能
计算机科学
特征(语言学)
图像处理
点(几何)
尺寸计量学
曲面(拓扑)
表面计量学
图像(数学)
工程类
光学
数学
表面光洁度
轮廓仪
几何学
机械工程
语言学
哲学
物理
作者
Elnaz Ghanbary Kalajahi,Mehran Mahboubkhah,Ahmad Barari
出处
期刊:Measurement
[Elsevier]
日期:2023-11-01
卷期号:221: 113462-113462
被引量:1
标识
DOI:10.1016/j.measurement.2023.113462
摘要
Online inspection and surface characterization including 3D defect detection of surfaces using 3D scanning and coordinate metrology data has crucial applications in today’s Industry 4.0. Although 3D vision-based metrology methods are superior to 2D in providing spatial information, its processing remains challenge. A novel automated Detailed Deviation Zone Evaluation method is proposed in this paper in which 3D unorganized PC is converted into 2D grayscale image such that the intensity variation of image is proportional to the surface topography. This developed image is addressed as “skin Image” of the scanned surface. The considered point clouds include only XYZ-coordinates. No prior knowledge of defects, and no training set is required. The methodology is fully implemented, and verified by inspecting point clouds of several workpieces with predefined defects. The experimental results show high efficiency of the developed methodology in defect detection and 3D-feature identification irrespective of shape and size.
科研通智能强力驱动
Strongly Powered by AbleSci AI