• Cracking susceptibility is governed by the synergistic effects of hierarchical microstructures, residual stress gradients, and process-induced defects. • Defect morphology and connectivity dominate crack initiation. • The efficacy of post-processing and surface treatments is sensitive to alloy class and service environment. • Predictive qualification requires coupling multiscale/multiphysics models with in-situ experiments. Additive manufacturing (AM) offers unprecedented design freedom for complex metallic components, yet the unique material characteristics inherent to the process, including anisotropic microstructures, high residual stresses, and process-induced defects, pose a critical challenge to their long-term reliability in corrosive environments. Stress Corrosion Cracking (SCC) is a particularly significant threat, as the material features created by AM can profoundly influence material susceptibility to cracking. SCC arises from the intricate interplay between microstructure, stress, and environment. This article provides a comprehensive review of experimental findings and computational models of SCC across different additively manufactured alloy systems. It begins with an overview of commonly recognized mechanistic models to explain SCC, followed by an in-depth discussion of how various AM techniques, processing parameters, AM-induced material features, and post-processing treatments affect SCC susceptibility. Herein, experimental studies are systematically examined to assess how factors affect susceptibility to SCC. The review also summarizes the testing and characterization methods employed in these studies. Finally, the computational modeling landscape is examined, encompassing classical mechanistic frameworks and emerging multiphysics approaches. The article concludes by identifying key challenges and outlining future research directions that aim to advance predictive modeling capabilities and support the qualification of additively manufactured components.
Negi et al. (Wed,) studied this question.