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

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Ali Bilen
  • Aaron Grommes
  • Florian Stamer
  • Gisela Lanza

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.

Original languageEnglish
Article number101797
JournalMeasurement: Sensors
DOIs
Publication statusAccepted/In press - 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024

    Research areas

  • Acoustic emission, AI, Micro gears, Quality assurance, Quality prediction, Virtual metrology
  • Engineering