Propagation of iGPS Measurement Uncertainty to GD&T Feature-Level Tolerances in Jigless Assembly

Measurement-Assisted Assembly (MAA) enables flexible and jigless manufacturing by leveraging large-scale metrology systems to guide part positioning and alignment. However, while the measurement uncertainty of such systems can be characterized at the sensor level, its propagation to assembly-level key characteristics (KCs) defined by Geometric Dimensioning and Tolerancing (GD&T) remains insufficiently addressed. This paper proposes and validates a Monte Carlo–based framework to propagate iGPS measurement uncertainties to position-related KCs used in MAA operations. Building on previously established per-axis uncertainty models, the approach evaluates how measurement system configuration, particularly the number and spatial arrangement of detectors, influences KC uncertainty. Experimental results, on a hexagonal plate aluminum artifact with a Ø100 mm bore, demonstrate stable convergence of the Monte Carlo estimates (~10 000 trials) for the bore center position. The resulting uncertainty @ 95% confidence level for the position tolerance is validated with reference values (with laser tracker). This study introduces a generalizable methodology to translate sensor-level uncertainty into GD&T feature-level tolerance estimates for metrology-assisted jigless assembly, enabling tolerance-informed decision-making, improving traceability, and strengthening confidence in large-scale manufacturing operations. The analysis highlights the sensitivity of KC uncertainty to detector configuration and target location within the work volume, providing quantitative insight into measurement-system design choices. Beyond the specific iGPS application for MMA, this work contributes to a broader understanding of how measurement-system configuration influences GD&T requirements through uncertainty propagation.