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Spatially weighted graph theory-based approach for monitoring faults in 3D topographic surfaces
International Journal of Production Research ( IF 9.2 ) Pub Date : 2020-09-07 , DOI: 10.1080/00207543.2020.1812755
Mejdal A. Alqahtani 1 , Myong K. Jeong 1 , Elsayed A. Elsayed 1
Affiliation  

ABSTRACT

Three-dimensional (3D) optical systems have been recently deployed for the assessment of 3D topography of finished products during manufacturing processes. Although the 3D topographic data contain rich information about the product and manufacturing processes, existing monitoring approaches are incapable of capturing the complex characteristics between the topographic values, which makes them ineffective in detecting local and spatial surface faults. We develop a spatially weighted graph theory-based approach for accurate monitoring of 3D topographic surfaces. We imporove the representation of surface characteristicsby proposing the in-control multi-region surface segmentation algorithm, which segments the observed topographic pixels into clusters according to the information learned from in-control surfaces. We propose the maximum local spatial randomness feature for the effective description of local and spatial topographic characteristics. After representing the surface characteristics as a spatially weighted graph network, we monitor its connectivity through the developed spatial graph connectivity statistic. The proposed approach is robust in detecting and locating different forms of local and spatial faults that appear on simulated and real-life topographic surfaces and outperforms the existing monitoring approaches.



中文翻译:

基于空间加权图论的 3D 地形表面断层监测方法

摘要

三维 (3D) 光学系统最近已被部署用于在制造过程中评估成品的 3D 形貌。尽管 3D 地形数据包含有关产品和制造过程的丰富信息,但现有的监测方法无法捕捉地形值之间的复杂特征,这使得它们在检测局部和空间表面故障方面效率低下。我们开发了一种基于空间加权图论的方法,用于准确监测 3D 地形表面。我们通过提出控制内多区域表面分割算法来改进表面特征的表示,该算法根据从控制表面学习到的信息将观察到的地形像素分割成簇。我们提出了最大局部空间随机性特征,以有效描述局部和空间地形特征。在将表面特征表示为空间加权图网络后,我们通过开发的空间图连通性统计监测其连通性。所提出的方法在检测和定位出现在模拟和现实地形表面上的不同形式的局部和空间断层方面是稳健的,并且优于现有的监测方法。

更新日期:2020-09-07
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