Inspection of mechanical assemblies based on 3D deep learning approaches

Assya Boughrara; Igor Jovancevic; Hamdi Ben Abdallah; Benoit Dolives; Mathieu Belloc; Jean-José Orteu

doi:10.1117/12.2588986

16 July 2021 Inspection of mechanical assemblies based on 3D deep learning approaches

Assya Boughrara, Igor Jovancevic, Hamdi Ben Abdallah, Benoit Dolives, Mathieu Belloc, Jean-José Orteu

Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision; 1179407 (2021) https://doi.org/10.1117/12.2588986
Event: Fifteenth International Conference on Quality Control by Artificial Vision, 2021, Tokushima, Japan

Abstract

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 classification 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. Several networks are trained and evaluated and results on real clouds are presented.

Citation Download Citation

Assya Boughrara, Igor Jovancevic, Hamdi Ben Abdallah, Benoit Dolives, Mathieu Belloc, and Jean-José Orteu "Inspection of mechanical assemblies based on 3D deep learning approaches", Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 1179407 (16 July 2021); https://doi.org/10.1117/12.2588986

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KEYWORDS

3D modeling

Data modeling

Inspection

3D scanning

Computer aided design

Solid modeling

3D acquisition

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