Contribution à l’évaluation de l’incertitude d’un procédé d’assemblage robotisé assisté par la métrologie 3D utilisant le système iGPS comme encodeurs externes

Abstract

Measurement-Assisted Assembly (MAA) is a promising approach for improving the assembly of large-scale components in aerospace, shipbuilding, and energy industries. However, industrial adoption of these systems is limited by the difficulty in assessing their inherent uncertainty. This thesis presents the development and validation of an experimental methodology to quantify the repeatability and accuracy of a robotic MAA process using the iGPS system as an external encoder. A laboratory-scale testbed was designed, integrating an ABB IRB120™ industrial robot, an iGPS system with six transmitters, and a machined artifact representing a workpiece to assemble. Rigorous experimental protocols were established, including thermal stabilization cycles for the robot and iGPS receivers. A statistical repeatability model based on the X2 distribution with three degrees of freedom was developed to combine deviations along Cartesian axes. Experimental validation on two independent datasets inspired by ISO 9283 standard recommendations demonstrated excellent repeatability, with standard deviations ranging from 4 to 12 µm depending on the axis and tested configuration. The empirical 95% coverage for both datasets (94.0 % and 96.7%) confirms the validity of the proposed model. Accuracy assessment revealed deviations of several millimeters from nominal positions. A detailed analysis of error sources, including receiver permutation tests and comparison with a Leica AT960™ laser tracker, identified that the iGPS system contributes approximately 0.2mm of error, while the majority originates from robot deflection and imperfections in the nominal kinematic model. These results are consistent with published data for the IRB120™ robot with comparable payload.

Date	31 Mar 2026
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Antoine Tahan (Supervisor) & Ahmed Joubair (Co-supervisor)