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3D Printing Deformation Estimation Using Artificial Vision Strategies for Smart-Construction

Abstract:

Additive manufacturing is a disruptive technology that enables the efficient construction of lighter and stronger concrete structures. In general, fabrication procedures are produced by industrial 3D printers, which constantly deposit concrete to ensure high building standards and reduce potential deformations in generated components. However, the deposition rate of building materials remains an empirical and heuristic procedure that depends on prior knowledge of the model. This work introduces a methodology to automatically detect deformations in printed layers by analyzing the 3D characterization of concrete structures. To this end, the performance of a monocular camera, LiDAR, and LiDAR-camera is studied according to point cloud density and 3D map reconstruction. In addition, a portable ground-based system for detecting possible deformations is conceived, manufactured, and experimentally tested. Empirical findings show that the proposed system is capable of detecting printed layer variation with a low error of 0.3%, revealing that the low-cost sensors can be an autonomous and highly reliable solution for deformation detection in concrete structures.