TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Kiefer, Daniel
A1  - Wezel, Stefan
A1  - Böttcher, Alexander
A1  - Grimm, Florian
A1  - Straub, Tim
A1  - Bitsch, Günter
A1  - van Dinther, Clemens
T1  - Anomaly detection in hobbing tool images: using an unsupervised deep learning approach in manufacturing industry
JF  - Procedia computer science
N2  - This study explores the application of the PatchCore algorithm for anomaly classification in hobbing tools, an area of keen interest in industrial artificial intelligence application. Despite utilizing limited training images, the algorithm demonstrates capability in recognizing a variety of anomalies, promising to reduce the time-intensive labeling process traditionally undertaken by domain experts. The algorithm demonstrated an accuracy of 92%, precision of 84%, recall of 100%, and a balanced F1 score of 91%, showcasing its proficiency in identifying anomalies. However, the investigation also highlights that while the algorithm effectively identifies anomalies, it doesn't primarily recognize domain-specific wear issues. Thus, the presented approach is used only for pre-classification, with domain experts subsequently segmenting the images indicating significant wear. The intention is to employ a supervised learning procedure to identify actual wear. This premise will be further investigated in future research studies.
KW  - anomaly detection
KW  - industrial machine learning applications
KW  - tool image analysis
KW  - unsupervised deep learning
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-49315
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2024.02.058
DO  - https://doi.org/10.1016/j.procs.2024.02.058
VL  - 232
IS  - 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)
SP  - 2396
EP  - 2405
S1  - 10
PB  - Elsevier
CY  - Amsterdam
ER  -