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@PHDTHESIS{Holst:1004409,
author = {Holst, Carsten},
othercontributors = {Bergs, Thomas and Dix, Martin},
title = {{A}utomated flank wear segmentation and measurement with
deep learning image processing; 1. {A}uflage},
volume = {2025,3},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {Apprimus Verlag},
reportid = {RWTH-2025-01423},
isbn = {978-3-98555-261-0},
series = {Innovations in manufacturing technology},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Druckausgabe: 2025. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University; Dissertation,
RWTH Aachen University, 2024},
abstract = {The aim of this thesis was to develop and optimize deep
learning models specifically designed for the identification
of tool wear on microscopic images of cutting tools and
cutting tool edges. Cutting tool wear has an impact on
dimensional accuracy and surface quality of parts,
ultimately affecting the costs associated with meeting part
quality criteria. To accomplish this objective, the creation
of a tool wear model based on empirical tool life trials was
conducted. An outcome of the trials was the generation of a
dataset of images, which were then utilized to develop a
deep learning model capable of segmenting cutting tool flank
wear. To ensure the effectiveness of the deep learning
model, a screening analysis was conducted to investigate
various dataset properties and model hyperparameters that
could influence the quality of predictions. The screening
analysis helped identify the key factors that significantly
impacted the performance of the model. Building upon the
insights gained from the screening analysis, the thesis
proceeded with an in-depth investigation of the most
influential factors. This investigation led to the
development of a decision model that could guide the
selection of dataset-specific hyperparameters for optimal
performance. To validate the effectiveness of the model
optimization strategy, a machine tool integrated measurement
setup was employed, utilizing a microscope as well as a
camera. These use cases provided a practical assessment of
the developed deep learning model and its ability to
identify and assess tool wear in a real-world manufacturing
scenario. By developing and refining deep learning models
for tool wear identification on microscopic images, this
thesis contributes to enhancing the understanding and
management of tool wear in the manufacturing industry. The
optimized models have the potential to facilitate timely
maintenance interventions, minimize production errors, and
reduce costs associated with part quality deviations.
Moreover, the decision model for dataset-specific
hyperparameter selection provides a valuable framework for
researchers and practitioners working on similar image-based
classification problems.},
cin = {417410 / 417400},
ddc = {620},
cid = {$I:(DE-82)417410_20140620$ / $I:(DE-82)417400_20240301$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2025-01423},
url = {https://publications.rwth-aachen.de/record/1004409},
}
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