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Article Dans Une Revue Additive Manufacturing Année : 2022

Inline monitoring of 3D concrete printing using computer vision

Résumé

The detection of anomalies is at the basis of any 3D printing control. In this paper, we propose a methodology for detection of anomalies based on computer vision. This methodology is composed of three modules: (1) image acquisition, (2) interlayer line and layer segmentation and (3) characterization of the local geometry and texture of the layers and detection of anomalies. The image acquisition is performed with a camera fixed to the printing nozzle. The proposed layer segmentation method recognizes and locates the lines separating the printed layers (F-score = 91%). The third module-taking as input the segmentation and the original image-evaluates the geometry of the layers and the texture of the material. The results are used to detect geometry anomalies when the values are outside the expected range. The material texture is classified into four classes of quality (macro-averaged F-score = 94%). We present the results and show the suitability of our methodology for automatic detection and localization of anomalies on images acquired during a printing session.
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Dates et versions

hal-04078406 , version 1 (23-04-2023)

Identifiants

Citer

Rodrigo Rill-García, Eva Dokladalova, Petr Dokládal, Jean-François Caron, Romain Mesnil, et al.. Inline monitoring of 3D concrete printing using computer vision. Additive Manufacturing, 2022, 60, pp.103175. ⟨10.1016/j.addma.2022.103175⟩. ⟨hal-04078406⟩
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