# _Skin Imaging_: A Digital Twin for Geometric Deviations on Manufactured Surfaces
### Motivation and Core Concept  
As has been discussed, geometric deviations are inevitably caused during the manufacturing processes, since every manufacturing process is inherently imprecise and every measuring process involves uncertainties. Therefore, when assessing surface quality in industry, in addition to surface roughness, it is important to consider surface geometrical deviation [37]. Hence, the digital modeling of non-ideal parts (_actual_ manufactured surface) is essential. In this regard, Skin Model as a new paradigm for geometric variations modeling has been introduced based on the Geometrical Product Specification (GPS) standards [38] by [PERSON] et al. [39] and then was further developed and explained by [PERSON] et al. [40,41] and [PERSON] et al. [42,43]. [PERSON] and [PERSON] [8] used finite element method in generating Skin Model as the comprehensive geometric deviation of the measured workpiece. [PERSON] et al. [44] demonstrated accurate tolerance and variation analysis by estimating variation from the skin model and comparing it with the nominal model. Actually, the Skin Model of the measured surface is the detailed continuous 3D digital twin of the _actual_ manufactured surface obtained from discrete finite measurement points, i.e., PCs. Recently, the _\"Skin Imaging\"_ methodology as a new concept of the skin model has been introduced and discussed in [45]. A skin image is an enriched 2D digital twin of the _actual_ geometric deviation of any surface and evaluates the detailed deviation zone of the surface by intensity variations of its pixels. This has been discussed with several planar case studies, and also the concept of inspection of non-planar surfaces with the _skin imaging_ methodology has been demonstrated on NURBS surfaces, which is the most comprehensive method to model parametric surfaces. Generally, any DZE model of the surface shows the deviation of different location of the surface from the ideal one. But hitherto, no specific method has been introduced to analyze the DZE model and use the potential information included. _\"Skin image\"_ provides this opportunity since it represents an unorganized and discrete 3D point cloud of the surface in the organized and continuous 2D image, and hence can be processed by well-developed image processing methods. It also facilitates neighbor searching due to its two-dimensional approach. Moreover, it does not need additional data other than the points' coordinates to be generated. The application of _skin image_ in automatic detection and correction cycles in manufacturing phases has been discussed in depth in [45]. In a nutshell, 3D defect detection from the PC has been a challenge over the years. The existing approaches mostly require pre-known models about the defects, or, in the case of using machine learning methods, it needs a large number of sample defects to train the machines. But more light-weighted methodologies are necessary for the online detection-correction cycle. Concerning the fact that the boundary of an object in the 2D image is clearer and easier to extract, but the image lacks spatial information, recently, researchers have attempted to boost spatial defect detection by using a concurrent 2D image and 3D PC. They perform defect detection in the 2D space and then add the spatial information of the detected defects using the 3D information of the PC [46, 47, 48]. However, they were limited to specific kinds of PC, such as dense density points, points acquired in grid form, or requiring careful and tedious calibration of multiple devices. In fact, no general methodology for 3D defect detection has been proposed yet. The _\"Skin imaging\"_ approach, which represents the spatial information of the point cloud as a 2D image, has shown promising results in precise and efficient 3D defect segmentation and characterization, irrespective of shape or size.