Coping with concept drift in a virtual metrology application to predict part quality in micro gear manufacturing
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: Measurement: Sensors, Vol. 38, No. Supplement, 101797, 05.2025.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
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 - Conference code: 14
PY - 2025/5
Y1 - 2025/5
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
VL - 38
JO - Measurement: Sensors
JF - Measurement: Sensors
SN - 2665-9174
IS - Supplement
M1 - 101797
T2 - XXIV IMEKO World Congress 2025
Y2 - 26 August 2024 through 29 August 2024
ER -