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@PHDTHESIS{Wiesch:1024444,
author = {Wiesch, Marian},
othercontributors = {Brecher, Christian and Trimpe, Johann Sebastian},
title = {{L}ernende {P}rozesskraftmodellierung zur {P}lanung und
Überwachung in der {E}inzelteilfertigung für
{F}räsanwendungen; 1. {A}uflage},
volume = {2025,27},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {Apprimus Verlag},
reportid = {RWTH-2026-00064},
isbn = {978-3-98555-324-2},
series = {Ergebnisse aus der Produktionstechnik},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Druckausgabe: 2025. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University 2026. -
Weitere Reihe: Werkzeugmaschinen. - Weitere Reihe: Edition
Wissenschaft Apprimus; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {The dissertation develops a novel, learning approach to
predicting process forces in NC milling, thereby addressing
key challenges in modern single-part production. In view of
high component variance, changing boundary conditions, and
increasing quality requirements, classic process force
models are increasingly reaching their limits: They are
often too complex in their parameterization, only
transferable to a limited extent, or do not sufficiently
reflect essential relationships. The aim of this work is
therefore to develop a data-based process force model that
can be used across processes and components and combines the
advantages of artificial neural networks with the
established knowledge of classic modeling approaches. To
this end, the constantly growing database of the CAx process
chain is being systematically developed. Information from
CAD, CAM, NC, and CAQ is contextualized, structured, and
refined from a tool perspective to create a consistent,
comprehensive picture of the manufacturing process. Based on
this, a learning, hybrid overall model is created that
precisely captures complex relationships between process
variables and resulting process forces. This model is
integrated into a milling simulation demonstrator developed
at WZL to validate its practical usefulness in an industrial
environment. The focus is on two use cases: virtual, a
priori verification of component quality in CAM planning and
location-based live process monitoring by comparing
simulated and actual measured process forces. The validation
results clearly show that the learning approach outperforms
conventional models in terms of accuracy, robustness, and
transferability. The dissertation thus contributes to the
further development of digital manufacturing systems and
opens up new potential for more efficient, flexible, and
reliable machining of complex components.},
cin = {417310 / 417200},
ddc = {620},
cid = {$I:(DE-82)417310_20140620$ / $I:(DE-82)417200_20140620$},
pnm = {DFG project G:(GEPRIS)390621612 - EXC 2023: Internet of
Production (IoP) (390621612) / WS-C.I - Processes and
Structures (X080067-WS-C.I) / WS-D.II - External Perspective
(X080067-WS-D.II)},
pid = {G:(GEPRIS)390621612 / G:(DE-82)X080067-WS-C.I /
G:(DE-82)X080067-WS-D.II},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2026-00064},
url = {https://publications.rwth-aachen.de/record/1024444},
}
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