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Communication Dans Un Congrès Année : 2023

Inspection of mechanical assemblies based on 3D Deep Learning segmentation

Assya Boughrara
Jean-José Orteu
Mathieu Belloc
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Résumé

Our research work is being carried out within the framework of the joint research laboratory ”Inspection 4.0” between IMT Mines Albi/ICA and the company Diota specialized in the development of numerical tools for Industry 4.0. In this work, we are focused on conformity control of complex aeronautical mechanical assemblies, typically an aircraft engine at the end or in the middle of the assembly process. A 3D scanner carried by a robot arm provides acquisitions of 3D point clouds which are further processed by deep segmentation networks. Computer-Aided Design (CAD) model of the mechanical assembly to be inspected is available, which is an important asset of our approach. Our deep learning models are trained on synthetic and simulated data, generated from the CAD models. This research is a continuation of the work presented at the QCAV’2021 conference.
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hal-04168232 , version 1 (21-07-2023)

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Assya Boughrara, Igor Jovančević, Jean-José Orteu, Mathieu Belloc. Inspection of mechanical assemblies based on 3D Deep Learning segmentation. QCAV'2023 - the 16th international conference quality control by artificial vision, Jun 2023, Albi, France. 9 p., ⟨10.1117/12.2692569⟩. ⟨hal-04168232⟩
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