@article{KieferGrimmStraubetal.2024, author = {Kiefer, Daniel and Grimm, Florian and Straub, Tim and Bitsch, G{\"u}nter and Dinther, Clemens Van}, title = {Enhancing power skiving tool longevity: the synergy of AI and robotics in manufacturing automation}, journal = {International journal of mechatronics and manufacturing systems}, volume = {17}, number = {2}, issn = {1753-1039}, doi = {10.1504/IJMMS.2024.143059}, institution = {ESB Business School}, pages = {201 -- 224}, year = {2024}, abstract = {In gear manufacturing, the longevity and cost-effectiveness of power skiving tools are essential. This study presents an innovative approach that combines artificial intelligence and robotics in manufacturing automation to prevent tool breakage to improve the remaining useful life (RUL). Using a robotic cell, the system captures six images per tooth from different angles. An unsupervised generative deep learning model approach is used because it is more suitable for industrial application as it can be trained with a small number of defect-free images. It is used in a first step as a classifier and, in a second step, to segment tool wear. This approach promises economic benefits by reducing manual inspection and, through automated tool inspection, detecting wear earlier to prevent tool breakage.}, language = {en} }