Advancements and applications of digital image correlation to characterize residual stress: A review
Navid Nasajpour-Esfahani, Saba Karimi, S. Ali Nasseri, Homa Borna, Ali Fadavi Boostani, Ruoqi Gao, Wei Huang, Hamid Garmestani, Steven Y. Liang
Abstract
Digital image correlation (DIC) has emerged as a versatile and powerful non-contact optical technique for measuring displacement, strain, and deformation across a wide range of materials and scales. This review explores the evolution, theoretical underpinnings, and cutting-edge advancements of DIC, with a particular focus on its application to characterize residual stresses. Beginning with the foundational principles and historical development of DIC, the review outlines its diverse methodologies, including 2D-DIC, stereo-DIC, and digital volume correlation, and their respective imaging models. Advanced image correlation algorithms, error quantification techniques, and integration with scanning electron microscopy, transmission electron microscopy, and atomic force microscopy are critically examined. Special emphasis is placed on the DIC-hole hybrid drilling technique for residual stress evaluation, highlighting its accuracy, adaptability, and potential in large-scale industrial applications. Additionally, the integration of DIC with finite element modeling and the incorporation of machine learning demonstrate the future direction of fully automated and data-driven DIC systems. This paper not only review state of the art in DIC but also identifies key challenges and future opportunities for expanding its application in materials characterization and structural health monitoring.