Integrating Advanced Metrology Technologies for Precision and Reliability in Manufacturing

Abstract

This dissertation presents a comprehensive framework for integrating advanced metrology technologies with digital twin architectures to enhance precision, reliability, and adaptability in modern manufacturing environments. As industrial systems evolve under the influence of Industry 4.0 principles, traditional inspection methods face growing limitations in managing geometric complexity, real-time variability, and predictive quality control demands. This research responds to these challenges by developing a metrology-integrated digital twin (MM-DT) system that unifies portable and fixed measurement tools—including Coordinate Measuring Machines (CMMs), FARO Arms, and Keyence 3D scanners—with adaptive sampling algorithms, predictive modeling, and uncertainty quantification.The proposed framework addresses three primary limitations in current form verification practices: inefficient sampling strategies, limited predictive capabilities, and inadequate representation of measurement uncertainty. By leveraging machine learning and mathematical modeling, the system provides real-time feedback, spatial deviation analysis, and proactive quality insights. A multi-layer digital twin infrastructure facilitates bidirectional data flow between physical assets and virtual representations, enabling dynamic model updates and scenario simulations. The framework introduces novel methods for adaptive sampling based on geometric complexity, functional significance, and historical deviation trends, improving measurement efficiency while preserving verification accuracy. Three industrial case studies—covering aerospace components, medical devices, and automotive assemblies—validate the system’s applicability and effectiveness. Results demonstrate up to 58% reduction in measurement time, improved detection sensitivity for both global and localized deviations, and enhanced predictive accuracy for form deviations and tool wear. The integration of analytical tools such as spectral decomposition, wavelet-based multi-scale analysis, and anomaly detection enables manufacturers to identify root causes of variation and implement targeted corrective actions.This research contributes both theoretically and practically to the fields of metrology, smart manufacturing, and digital twins. It proposes a scalable and modular architecture that bridges measurement science and cyber-physical systems, offering a robust solution for manufacturers seeking to move from reactive inspection toward proactive, intelligence-driven quality assurance. The findings advocate for the redefinition of inspection not merely as a compliance step, but as a continuous and predictive contributor to manufacturing excellence.