TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
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  -