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Enhancing power skiving tool longevity: the synergy of AI and robotics in manufacturing automation

  • 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.

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Metadaten
Author of HS ReutlingenKiefer, Daniel; Grimm, Florian; Straub, Tim; Bitsch, Gütner
URN:urn:nbn:de:bsz:rt2-opus4-54411
DOI:https://doi.org/10.1504/IJMMS.2024.143059
ISSN:1753-1039
eISSN:1753-1047
Erschienen in:International journal of mechatronics and manufacturing systems
Publisher:Inderscience
Place of publication:Olney
Document Type:Journal article
Language:English
Publication year:2024
Tag:RUL; anomaly detection; artificial intelligence; deep learning; economic efficiency; industrial applications; power skiving; remaining useful life; robotics
Volume:17
Issue:2
Page Number:24
First Page:201
Last Page:224
DDC classes:690 Hausbau, Bauhandwerk
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International