Artificial Intelligence (AI) is rapidly reshaping research and engineering practices in structural integrity, offering new opportunities for both predictive assessment and design support. Its impact is particularly significant in Additive Manufacturing (AM), where complex process-structure-property relationships, defect formation, and microstructural heterogeneity require advanced analysis and control strategies. Machine Learning (ML) and data-driven approaches enable process optimization, real-time monitoring, property prediction, and performance assessment of additively manufactured components. By identifying non-linear correlations between process parameters, thermal histories, microstructure evolution, and mechanical behavior, AI can support more reliable certification pathways of AM parts.

This Special Issue invites the submission of original research and review papers on AI-driven, physics-informed, and hybrid approaches for design optimization, process monitoring, defect assessment, and performance prediction of AM parts.