In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.

This paper aims to review the State of the Art in metal self-expanding stents made from nitinol (NiTi), showing shape memory and superelastic behaviors, to identify the challenges and the opportunities for improving patient outcomes. A significant contribution of this paper is its extensive coverage of multidisciplinary aspects, including design, simulation, materials development, manufacturing, bio/hemocompatibility, biomechanics, biomimicry, patency, and testing methodologies. Additionally, the paper offers in-depth insights into the latest practices and emerging trends, with a special emphasis on the transformative potential of additive manufacturing techniques in the development of metal stents. By consolidating existing knowledge and highlighting areas for future innovation, this review provides a valuable roadmap for advancing nitinol stents.