Metal additive manufacturing (AM) presents advantages such as increased complexity for a lower part cost and part consolidation compared to traditional manufacturing. The multiscale, multiphase AM processes have been shown to produce parts with non-homogeneous microstructures, leading to variability in the mechanical properties based on complex process–structure–property (p-s-p) relationships. However, the wide range of processing parameters in additive machines presents a challenge in solely experimentally understanding these relationships and calls for the use of digital twins that allow to survey a larger set of parameters using physics-driven methods. Even though physics-driven methods advance the understanding of the p-s-p relationships, they still face challenges of high computing cost and the need for calibration of input parameters. Therefore, data-driven methods have emerged as a new paradigm in the exploration of the p-s-p relationships in metal AM. Data-driven methods are capable of predicting complex phenomena without the need for traditional calibration but also present drawbacks of lack of interpretability and complicated validation. This review article presents a collection of physics- and data-driven methods and examples of their application for understanding the linkages in the p-s-p relationships (in any of the links) in widely used metal AM techniques. The review also contains a discussion of the advantages and disadvantages of the use of each type of model, as well as a vision for the future role of both physics-driven and data-driven models in metal AM.

The microstructure of additively manufactured (AM) metals has been shown to be heterogeneous and spatially non-uniform when compared to conventionally manufactured metals. Consequently, the effective mechanical properties of AM-metal parts are expected to vary both within and among builds. Here, we present a framework for simulating process–(micro)structure–property relationships of AM metals produced via direct laser deposition (DLD). The framework predicts grain nucleation and competitive growth as a function of thermal history for a multi-pass, multi-layer DLD process. The resulting three-dimensional microstructure is automatically sub-sampled to perform virtual mechanical testing throughout the build domain using a parallelized elasto-viscoplastic fast Fourier transform code, accounting for grain-boundary strengthening. The effective stress–strain response of each sub-sampled volume is automatically analyzed to extract effective mechanical properties, which are used to generate property maps showing the spatial variability of effective mechanical properties throughout the simulated build volume. As a demonstration, the framework is applied to different DLD stainless steel 316L build volumes having different process-induced microstructures. The multi-physics framework and property maps could provide a path toward qualification of AM-metal parts.