TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Kiefer, Daniel A1 - Grimm, Florian A1 - Straub, Tim A1 - Bitsch, Günter A1 - Dinther, Clemens Van T1 - Enhancing power skiving tool longevity: the synergy of AI and robotics in manufacturing automation JF - International journal of mechatronics and manufacturing systems N2 - 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. KW - power skiving KW - RUL KW - remaining useful life KW - artificial intelligence KW - robotics KW - anomaly detection KW - deep learning KW - economic efficiency KW - industrial applications Y1 - 2024 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54411 SN - 1753-1039 SS - 1753-1039 U6 - https://doi.org/10.1504/IJMMS.2024.143059 DO - https://doi.org/10.1504/IJMMS.2024.143059 VL - 17 IS - 2 SP - 201 EP - 224 S1 - 24 PB - Inderscience CY - Olney ER -