Metal additive manufacturing (AM) works on the principle of incremental layer-by-layer material consolidation, facilitating the fabrication of objects of arbitrary complexity through the controlled melting and resolidification of feedstock materials by using high-power energy sources. The focus of metal AM is to produce complex-shaped components made of metals and alloys to meet demands from various industrial sectors such as defense, aerospace, automotive, and biomedicine. Metal AM involves a complex interplay between multiple modes of energy and mass transfer, fluid flow, phase change, and microstructural evolution. Understanding the fundamental physics of these phenomena is a key requirement for metal AM process development and optimization. The effects of material characteristics and processing conditions on the resulting epitaxy and microstructure are of critical interest in metal AM. This article reviews various metal AM processes in the context of fabricating metal and alloy parts through epitaxial solidification, with material systems ranging from pure-metal and prealloyed to multicomponent materials. The aim is to cover the relationships between various AM processes and the resulting microstructures in these material systems.

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Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

Nickel-base superalloy Ren e N5 is deposited on cast Ren e N5 substrates with [100] and [001] crystallographic orientations through scanning laser epitaxy (SLE) applied to gas-atomized prealloyed Ren e N5 powder. Single-pass fabrication of crack-free deposits exceeding 1000 micron is achieved. High-resolution optical microscopy reveals that the deposits have 10 times finer primary dendritic arm spacing compared to the substrate. SEM reveals the presence of finer microstructure in the deposit region compared to the substrate region. XRD and EBSD investigations show that the substrate crystallographic orientation does not affect the unidirectional crystal growth in the deposit region. Vickers microhardness values are higher in the deposits compared to the substrate.

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