Optimization modeling and application of machine vision-based robot roller hemming for autobody panels with adhesive

Robot roller hemming is an advanced assembly process used to achieve high-precision manufacturing of autobody panels. The coupling action between metal panels and non-Newtonian adhesive, complex contour curve, and coverage of the autobody and hemming parameters all have significant influence on the forming quality of the autobody. In traditional robot hemming process, roller pose and trajectory are totally set at the beginning and incapable to adjust according to the hemming quality, making it difficult to further improve the appearance and assembly accuracy. Therefore, taking dimension deviation of the autobody as research object, an optimization model and algorithm of the robot roller hemming process were proposed, and built a robot hemming platform to analyze the optimization model and algorithm. Firstly, the stress-geometry interactions of roller hemming process were considered, and the mapping model between the dimension deviation and the hemming parameter (TCP-RTP) and roller pose was established based on FEM-SPH numerical simulation, Frenet frame of differential geometry, and Cartesian coordinate transformation theory, then the optimized roller pose was obtained through the mapping model. Secondly, the detection of hemming defects and measurement of dimensional deviation were realized based on the machine vision. Finally, the experimental platform was constructed and the experiment showed that the dimension deviation of the closure panel was reduced and then overshoots, and finally stabilized within 0.1 mm, after adopting the optimization algorithm.