DIMENSIONAL METROLOGY WITH FAST AND ACCURATE X-RAY COMPUTED TOMOGRAPHY FOR ADVANCED MANUFACTURING APPLICATIONS

X-ray Computed Tomography (CT) has progressively emerged as a key technology for dimensional metrology in contemporary industry. Nevertheless, despite its significant operational advantages, CT still faces challenges that hinder its wider industrial adoption, particularly related to long acquisition times, its complex measurement-error cause system, and the absence of fully established routes to ensure metrological traceability. These limitations highlight a fundamental trade-off between measurement speed and accuracy, which constitutes one of the most pressing challenges for the industrial deployment of CT technology. In this context, this Ph.D. research addresses the challenge of accelerating CT scanning while preserving dimensional measurement accuracy and metrological traceability. The approach combined experimental investigations, simulation-based analyses, and data-driven workflows to explore strategies that optimize the trade-off between measurement speed and quality. Investigations were performed using calibrated reference artefacts to systematically evaluate the influence of scanning parameters on fast CT measurements of dimensional and geometrical characteristics. Distinct cycle-time budgets were explored, within which CT settings were fine-tuned and adjusted to maintain fixed acquisition times. In particular, the effects of the number of projections, exposure time, scan mode and beam hardening correction were analyzed to assess their impact on measurement accuracy. Complementary industrial case studies, including additively manufactured parts, were used to assess the feasibility of applying fast CT in realistic manufacturing scenarios. In parallel, a digital representation of a commercial CT system was developed using the simulation tool SimCT, enabling sensitivity analyses of individual physical effects. Furthermore, a data-driven pipeline was implemented using open-source platforms, incorporating machine learning models to identify influential parameters and to derive quantitative trade-off metrics between scanning time and measurement accuracy. The results demonstrate that fast CT is feasible in selected industrial and research contexts, provided that acquisition strategies are carefully optimized and feature-specific behaviors are taken into account. Experimental evidence shows that dimensional features exhibit distinct sensitivities to fast scanning, with some feature types remaining robust under reduced projection scenarios, while others are more affected by reconstruction artifacts. Simulation studies confirmed the potential of digital representations to replicate real CT behavior and to isolate the influence of physical effects, while also highlighting the current limitations of simulation tools for complex geometrical features, particularly under fast CT conditions. Moreover, for CT reconstruction, the constrained split Bregman iterative method combined with total variation demonstrated significant improvements over the traditional Feldkamp-Davis-Kress algorithm, achieving up to a 90% enhancement in measurement accuracy under reduced number of projections. Finally, data-driven analyses proved effective in ranking parameter importance, guiding decision-making, and revealing material- and feature-dependent differences in fast CT performance. Building on these findings, this research proposes a conceptual framework for fast CT metrology in advanced manufacturing, integrating experimental insights, simulation capabilities, and data-driven approaches, supported by good metrological practices and enabling technologies of modern industry. Overall, the thesis advances the realization of dimensional metrology with fast and accurate X-ray CT for advanced manufacturing applications, addressing a critical bottleneck in modern quality infrastructure and contributing to its broader adoption in industrial workflows.