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The complex solidification cycles experienced by multi-principal element alloys (MPEAs) during laser-based additive manufacturing (LBAM) often lead to structural defects that affect the build quality. The underlying thermal processes and phase transformations are a function of the process parameters employed. With a moving Gaussian heat source to mimic LBAM and leveraging material thermodynamics guidelines from CALculation of PHAse Diagrams (CALPHAD), we estimate the temperature-dependent thermal properties, phase fractions, and melt pool geometry using an experimentally validated computational fluid dynamics model. The results substantiate that the peak temperatures are inversely correlated to the scan speeds, and the melt pool dimensions can assist in the predictive selection of process parameters such as hatch distance and layer thickness. A relatively low cooling rate recorded during the process is ascribed to the preheating of the substrate to ensure printability of the alloy.

 

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

 

As efforts associated with the exploration of multi-principal element alloys (MPEAs) using computational and data-intensive methods continue to rise, experimental realization and validation of the predicted material properties require high-throughput and combinatorial synthesis of these alloys. While additive manufacturing (AM) has emerged as the leading pathway to address these challenges and for rapid prototyping through part fabrication, extensive research on developing and understanding the process-structure-property correlations is imminent. In particular, directed energy deposition (DED) based AM of MPEAs holds great promise because of the boundless compositional variations possible for functionally graded component manufacturing as well as surface cladding. We analyze the recent efforts in DED of MPEAs, the microstructural evolution during the laser metal deposition of various transition and refractory elements, and assess the effects of various processing parameters on the material phase and properties. Our efforts suggest that the development of robust predictive approaches for process parameter selection and modifying the synthesis mechanisms are essential to enable DED platforms to repeatedly produce defect free, stable and designer MPEAs.