In additively manufactured (AM) metallic materials, the fundamental interrelationships that exist between composition, processing, and microstructure govern these materials’ properties and potential improvements or reductions in performance. For example, by using AM, it is possible to achieve highly desirable microstructural features (e.g., highly refined precipitates) that could not otherwise be achieved by using conventional approaches. Simultaneously, opportunities exist to manage macro-level microstructural characteristics such as residual stress, porosity, and texture, the last of which might be desirable. To predictably realize optimal microstructures, it is necessary to establish a framework that integrates processing variables, alloy composition, and the resulting microstructure. Although such a framework is largely lacking for AM metallic materials, the basic scientific components of the framework exist in literature. This review considers these key components and presents them in a manner that highlights key interdependencies that would form an integrated framework to engineer microstructures using AM.

No abstract

While it is useful to predict properties in metallic materials based upon the composition and microstructure, the complexity of real, multi-component, and multi-phase engineering alloys presents difficulties when attempting to determine constituent-based phenomenological equations. This paper applies an approach based upon the integration of three separate modeling approaches, specifically artificial neural networks, genetic algorithms, and Monte Carlo simulations to determine a mechanism-based equation for the yield strength of a+b processed Ti-6Al-4V (all compositions in weight percent) which consists of a complex multi-phase microstructure with varying spatial and morphological distributions of the key microstructural features. Notably, this is an industrially important alloy yet an alloy for which such an equation does not exist in the published literature. The equation ultimately derived in this work not only can accurately describe the properties of the current dataset but also is consistent with the limited and dissociated information available in the literature regarding certain parameters such as intrinsic yield strength of pure hexagonal close-packed alpha titanium. In addition, this equation suggests new interesting opportunities for controlling yield strength by controlling the relative intrinsic strengths of the two phases through solid solution strengthening.