Evaluation of data augmentation and loss functions in semantic image segmentation for drilling tool wear detection

Elke Schlager (), Andreas Windisch (), Lukas Hanna (), Thomas Klünsner (), Elias Jan Hagendorfer () and Tamara Feil ()
Additional contact information
Elke Schlager: Know-Center GmbH
Andreas Windisch: JOANNEUM RESEARCH
Lukas Hanna: Materials Center Leoben Forschung GmbH
Thomas Klünsner: Materials Center Leoben Forschung GmbH
Elias Jan Hagendorfer: Materials Center Leoben Forschung GmbH
Tamara Feil: CERATIZIT Austria GmbH

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 36, 1503 pages

Abstract: Abstract Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. Identification of the wear area in images of cutting inserts is important to building a reliable ground truth for the development of indirect monitoring approaches. Therefore, we present a semantic image segmentation pipeline for wear detection on microscopy images of cutting inserts. A broadly used convolutional neural net, namely a U-Net, is trained with different preprocessing and optimisation task configurations: On the one hand the problem is considered as binary problem, and on the other hand as multiclass problem by differentiating the wear into two different types. By comparing these two problem formulations we investigate whether the separation of the two wear structures improves the performance of the recognition of the wear types. For both problem formulations three loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated.The use of different augmentation intensities during training suggests adequate but not too excessive augmentation, and that with optimal augmentation the choice of loss function gets less important. Furthermore, models are trained on image tiles of different sizes, which has an impact on producing artefacts on the whole image predictions performed by the overlap-tile strategy. In summary, the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function.

Keywords: Semantic image segmentation; Image data augmentation; Drilling tool; Wear segmentation (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02313-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02313-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02313-y

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().