Machine learning for metrology in manufacturing

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.