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Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Standard

Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing. / Bilen, Ali; Grommes, Aaron; Stamer, Florian et al.
in: Measurement: Sensors, 2025.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Bilen, A, Grommes, A, Stamer, F & Lanza, G 2025, 'Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing', Measurement: Sensors. https://doi.org/10.1016/j.measen.2024.101797

APA

Bilen, A., Grommes, A., Stamer, F., & Lanza, G. (im Druck). Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing. Measurement: Sensors, Artikel 101797. https://doi.org/10.1016/j.measen.2024.101797

Vancouver

Bilen A, Grommes A, Stamer F, Lanza G. Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing. Measurement: Sensors. 2025;101797. doi: 10.1016/j.measen.2024.101797

Bibtex

@article{1c2ff8a916bd48c7a50639913e895b86,
title = "Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing",
abstract = "This study is focused on advancing the application of structure-borne sound sensors in the manufacturing of micro gears. A robust approach is developed through the incorporation of machine learning techniques to address concept drift, especially resulting from tool wear during the hobbing process. A detailed long-term study is undertaken to refine the predictive models, ensuring their accuracy in the face of a dynamic manufacturing environment. By tackling the challenges associated with concept drift, the capabilities of acoustic emission sensing are sought to be fully exploited. The aim is to enhance the reliability and precision of quality predictions, establishing a new benchmark in the field and significantly contributing to the optimization of micro gear production processes.",
keywords = "Acoustic emission, AI, Micro gears, Quality assurance, Quality prediction, Virtual metrology, Engineering",
author = "Ali Bilen and Aaron Grommes and Florian Stamer and Gisela Lanza",
note = "Publisher Copyright: {\textcopyright} 2024",
year = "2025",
doi = "10.1016/j.measen.2024.101797",
language = "English",
journal = "Measurement: Sensors",
issn = "2665-9174",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing

AU - Bilen, Ali

AU - Grommes, Aaron

AU - Stamer, Florian

AU - Lanza, Gisela

N1 - Publisher Copyright: © 2024

PY - 2025

Y1 - 2025

N2 - This study is focused on advancing the application of structure-borne sound sensors in the manufacturing of micro gears. A robust approach is developed through the incorporation of machine learning techniques to address concept drift, especially resulting from tool wear during the hobbing process. A detailed long-term study is undertaken to refine the predictive models, ensuring their accuracy in the face of a dynamic manufacturing environment. By tackling the challenges associated with concept drift, the capabilities of acoustic emission sensing are sought to be fully exploited. The aim is to enhance the reliability and precision of quality predictions, establishing a new benchmark in the field and significantly contributing to the optimization of micro gear production processes.

AB - This study is focused on advancing the application of structure-borne sound sensors in the manufacturing of micro gears. A robust approach is developed through the incorporation of machine learning techniques to address concept drift, especially resulting from tool wear during the hobbing process. A detailed long-term study is undertaken to refine the predictive models, ensuring their accuracy in the face of a dynamic manufacturing environment. By tackling the challenges associated with concept drift, the capabilities of acoustic emission sensing are sought to be fully exploited. The aim is to enhance the reliability and precision of quality predictions, establishing a new benchmark in the field and significantly contributing to the optimization of micro gear production processes.

KW - Acoustic emission

KW - AI

KW - Micro gears

KW - Quality assurance

KW - Quality prediction

KW - Virtual metrology

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85213942278&partnerID=8YFLogxK

U2 - 10.1016/j.measen.2024.101797

DO - 10.1016/j.measen.2024.101797

M3 - Journal articles

AN - SCOPUS:85213942278

JO - Measurement: Sensors

JF - Measurement: Sensors

SN - 2665-9174

M1 - 101797

ER -

DOI