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 2.0: improved image segmentation for better rotation angle deviation determination
JF  - Procedia computer science
N2  - The maintenance of special tools is an expensive business. Either manual inspection by an expert costs valuable resources, or the loss of a tool due to irreparable wear is associated with high replacement costs, while reconditioning requires only a fraction. In order to avoid higher costs and drive forward the automation process in production, a German gear manufacturer wants to create an automatic evaluation of skiving gears. As a sub-step of this automated condition detection, it is necessary for wheels to be automatically aligned within a vision-based inspection cell. In extension to a study conducted last year, further image preprocessing steps are implemented in this publication and a new alignment algorithm from the autoencoder family is evaluated. By using an additional synthetic dataset, previous limitations could be clarified. The results show that thorough data preparation is beneficial for all solution approaches and that neural networks can even beat a brute force algorithm.
KW  - image regression
KW  - segmentation
KW  - autoencoder
KW  - gear tooth alignment
KW  - deep learning
Y1  - 2025
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54455
U6  - https://doi.org/10.1016/j.procs.2025.01.187
DO  - https://doi.org/10.1016/j.procs.2025.01.187
VL  - 253
IS  - 6th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2024)
SP  - 1256
EP  - 1265
S1  - 10
PB  - Elsevier
CY  - Amsterdam
ER  -