20.500.12556/DiRROS-19297 Adaptive visual quality inspection based on defect prediction from production parameters At the end of a production process, the manufactured products must usually be visually inspected to ensure their quality. Often, it is necessary to inspect the final product from several viewpoints. However, the inspection of all possible aspects might take too long and thus create a bottleneck in the production process. In this paper we propose and evaluate a methodology for adaptive, robot-aided visual quality inspection. With the proposed method, the most probable defects are first predicted based on the production process parameters. A suitable classifier for defect prediction is learnt in an unsupervised manner from a database that includes the produced parts and the associated parameters.Arobot then steers the camera only towards viewpoints associated with predicted defects, which implies that the trajectories of robot motion for the inspection might be different for every product. To enable dynamic planning of camera trajectories, we describe a methodology for evaluation and selection of the most appropriate autonomous motion planner. The proposed defect prediction approach was compared to other methods and evaluated on the products from a real-world production line for injection moulding, which was implemented for a producer of parts in the automotive industry. robot learning robotic quality inspection visual quality inspection injection moulding production parameters robot motion planning vizualni nadzor kakovosti true false true IEEE ZDA Angleški jezik Slovenski jezik Neznano 2024-07-15 07:23:04 2024-07-15 07:23:05 2024-07-16 03:45:11 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Soavtorji: Simon Reberšek, Samo Šela, Jure Skvarč, Aleš Ude, Andrej Gams; Opis vira z dne 10. 7. 2024; str. 1-12 Vol. 12 [in press] 2024 0000-00-00 PostprintKoncna VTisku NiDoloceno 0000-00-00 0000-00-00 2024-07-08 007.52 2169-3536 10.1109/ACCESS.2024.3424664 201318659 https://ieeexplore.ieee.org/document/10587254 1 536feeff-426a-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26610 RAZ_Loncarevic_Zvezdan_2024.pdf RAZ_Loncarevic_Zvezdan_2024.pdf 1 B6EFFB21439897CD974B52FF26B6C1CD f4992ec395cb822e750a1c39a86aba4b8f50c5d31a9247a487918794b03a3aec 43e3d5b7-426d-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26611 Institut Jožef Stefan 0 0 0