Machine learning for metrology in manufacturing

CIRP Annals, vol. 75, pp. 971–997

Abstract

Machine learning is transforming manufacturing metrology by enabling data-driven modeling, automation, and real-time decision-making across the measurement process. This keynote reviews recent advances and future directions for integrating machine learning (ML) throughout the measurement workflow—from system setup to decision-making—by structuring the analysis of the state of the art using a data flow framework. Key applications include ML-assisted setup and calibration, enhanced measurement, virtual measurements, and classification-based inspection. The remaining key challenge is the integration of metrological traceability, standardized uncertainty quantification, explainability, and reproducibility. Bridging the underlying conceptual gap in understanding and evaluating uncertainty is essential to establish scientifically rigorous and industrially reliable ML-driven metrology for future manufacturing systems.

Authors 7

1. Gisela Lanza corresponding

   Karlsruhe Institute of Technology

   Affiliation as printed

   wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany

2. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT WZL-IQS

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstraße 17, 52074 Aachen, Germany

   WZL-IQS | RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

3. Wim Dewulf

   KU Leuven

   Affiliation as printed

   Department of Mechanical Engineering, KU Leuven, 3001 Leuven, Belgium

4. Hans Nørgaard Hansen

   Technical University of Denmark

   Affiliation as printed

   Department of Civil and Mechanical Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Lundtofte, Denmark

5. Yang Zhang

   Technical University of Denmark

   Affiliation as printed

   Department of Civil and Mechanical Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Lundtofte, Denmark

6. Benjamin Montavon Aachen WZL-IQS

   RWTH Aachen University

   Affiliation as printed

   WZL-IQS | RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

7. Florian Stamer

   Leuphana University of Lüneburg

   Affiliation as printed

   Institute for Production Technology and Systems, Leuphana University, Universitätsallee 1, 21335 Lüneburg, Germany