The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 459–466, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-459-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 459–466, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-459-2022
 
30 May 2022
30 May 2022

AUTOMATIC GEOMETRIC INSPECTION IN DIGITAL FABRICATION

K. Mawas, M. Maboudi, and M. Gerke K. Mawas et al.
  • Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig, Germany

Keywords: Laser Scanning, Quality Inspection, Digital Fabrication, Co-registration, As-built vs. As-designed, Digital Twin

Abstract. Additive Manufacturing in construction allows to create complex objects of different materials. Accordingly, an appropriate co-registration and comparison between the printed object and its digital model is needed for several purposes: quality control (QC) to ensure that tolerances are maintained and the realisation of a digital twin which holds the actual geometry. In this paper, we introduce an automated robotic data capturing and direct co-registration method. That is, a terrestrial laser scanner (TLS) is mounted on a robot to be moved freely in the printing room. In addition, various strategies are employed for quality control. The scanner is also mounted on the tripods, to validate the accuracy of our data capturing and co-registration solution. Furthermore, the experiment on real data is conducted for two different objects: a shotcrete printed object and a wax material object. Our experiments revealed that there is almost no influence on the scanner position accuracy compared to classical setup (mounting the TLS on tripod). The scanner position accuracy when mounted on tripods is 0.76 mm and the accuracy achieved by mounting the scanner on the robot is 1.03 mm. From the simulation it is noticed that C2M cannot detect missing extruded parts. To cope this problem, we used C2C and M3C2 algorithms. For real data scenario, without edge trimming and surface finishing it is challenging to interpret the data directly. However, M3C2 provided better results but it requires parameters tuning. In case of the surface finished object C2M and M3C2 algorithms have almost similar results.