TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Grimm, Florian A1 - Kiefer, Daniel A1 - Straub, Tim A1 - Bitsch, Günter A1 - van Dinther, Clemens T1 - Automatic gear tooth alignment in vision based preventive maintenance JF - Procedia computer science N2 - Thorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network. KW - preventive maintenance KW - image regression KW - gear tooth alignment KW - quality assurance KW - deep learning Y1 - 2024 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-49334 SN - 1877-0509 SS - 1877-0509 U6 - https://doi.org/10.1016/j.procs.2024.01.154 DO - https://doi.org/10.1016/j.procs.2024.01.154 VL - 232 IS - 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023) SP - 1564 EP - 1572 S1 - 9 PB - Elsevier CY - Amsterdam ER -