Active learning for the prediction of shape errors in milling

verfasst von: Berend Denkena, Marcel Wichmanna, Markus Rokickib, Lukas Stürenburg
Abstract: In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.
Organisationseinheit(en): Institut für Fertigungstechnik und Werkzeugmaschinen
Forschungszentrum L3S
Typ: Konferenzaufsatz in Fachzeitschrift
Journal: Procedia CIRP
Band: 126
Seiten: 324-329
Anzahl der Seiten: 6
ISSN: 2212-8271
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Steuerungs- und Systemtechnik, Wirtschaftsingenieurwesen und Fertigungstechnik
Elektronische Version(en): https://doi.org/10.1016/j.procir.2024.08.364 (Zugang: Offen)

BibTeX

@article{b0adf7870fa546079ed737fe53abe9c4,
title = "Active learning for the prediction of shape errors in milling",
abstract = "In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.",
keywords = "Adaptive manufacutring, Machine learning, Machining, Milling, Predictive model",
author = "Berend Denkena and Marcel Wichmanna and Markus Rokickib and Lukas St{\"u}renburg",
note = "Publisher Copyright: {\textcopyright} 2024 Elsevier B.V.. All rights reserved.; 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023 ; Conference date: 12-07-2023 Through 14-07-2023",
year = "2024",
doi = "10.1016/j.procir.2024.08.364",
language = "English",
volume = "126",
pages = "324--329",
journal = "Procedia CIRP",
issn = "2212-8271",
publisher = "Elsevier BV",
}