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This study presents a framework for mapping the effects of composition and phase fractions on the coefficient of thermal expansion (CTE) and elastic properties in the Al-Fe-Ni ternary, using thermodynamic phase calculations, first-principles property calculations, and experimental validation. The calculation of phase diagrams (CALPHAD) method was used to predict phases within the Al-Fe-Ni ternary, and the CTE and elastic constants of the phases were computed using DFT-based calculations, which were used to determine P-wave modulus and wave speed values. For multi-phase compositions, anticipated bulk CTE and P-wave modulus values were calculated using homogenization equations and individual phase properties. Arc-melted samples of compositions within the ternary were fabricated, and their phases, CTE, and wave speeds were measured, showing agreement with predicted phases and property trends. The approach presented in this work can be used towards property-informed design of alloys, joints, and functionally graded materials. 

Multi-phase alloys may achieve superior performance over single-phase alloys through synergistic combinations of the properties of the individual phases. An understanding of the individual phase properties aids in alloy design and optimization. However, experimental methods for directly characterizing the constitutive behavior of individual phases are limited. In this work, an inverse analysis based on representative volume element (RVE) finite element simulations was used to extract phase-wise constitutive behavior based on micrographs and macroscale constitutive response of two-phase microstructures. Both 2D and 3D RVE simulations were performed and compared to identify the most appropriate boundary conditions for the more time-efficient 2D simulations. The proposed method for extracting phase-specific constitutive behavior was validated by determining phase properties in a range of two-phase materials, including dual phase steel, steel welds, and Al-Ni alloys. This approach provides a means for extracting phase-specific mechanical properties using the microstructure, experimental tensile test data, and phase elastic moduli as input, providing insight into the contributions of individual phases to properties of multi-phase alloys toward new alloy development or microstructure optimization. 

A new metric was developed to quantify the impact of surface-connected defects and internal pores of different morphologies, namely irregular lack of fusion (LoF) pores and spherical keyhole pores, on the mechanical properties and fracture location of AlSi10Mg tensile samples fabricated using laser powder bed fusion additive manufacturing. As defect volume alone has been shown to be insufficient to predict fracture location, the proposed defect impact metric (DIM) incorporates contributions from additional defect features, including proximity to the surface, interaction with neighboring defects, morphology, and reduction in load-bearing cross-sectional area to better assess a defect’s propensity for corresponding to fracture location. The fracture location of keyhole samples was captured by large surface-connected defects with numerous neighboring defects and resulted in increased losses in load-bearing area. In contrast, LoF samples fractured at regions with either large surface-connected defects or large internal pores with many defects in close proximity, high curvatures, and large projected areas. The proposed DIM outperformed existing defect-based frameworks in identifying fracture locations in both LoF and keyhole samples by incorporating surface roughness, defect projected area, and interactions between defects based on distance, volume, and configuration. Additionally, the maximum DIM value within the fracture range was more strongly correlated to strength and ductility than porosity or defect size for LoF samples, demonstrating the potential of the DIM to non-destructively assess the effects of defects on mechanical behavior. The broader applicability of the DIM framework was demonstrated in its ability to capture fracture in both PBF-LB AlSi10Mg and Alloy 718. 

Additively manufactured metals often contain pores, which limit the strength and ductility of resulting components. In this study, a ductile fracture model was developed to describe the effect of pore size, in terms of absolute and relative metrics, on fracture strain under uniaxial tension. The model approximates lack of fusion (LoF) pores as penny-shaped cracks, and damage accumulation was based on the J-integral and secondary Q parameter. The model was calibrated with Ti-6Al-4V samples with intentionally introduced pores fabricated by laser powder bed fusion (PBF-LB) additive manufacturing (AM) in as-built and heat-treated conditions. The model captures the experimentally observed size effect, where for a given pore area fraction, larger samples fracture at smaller strains. By identifying the critical pore size for a single, isolated pore for either load or displacement-controlled applications, the model developed in this study is a crucial step to developing a comprehensive fracture model for establishing confidence in the structural capability of pore-containing additively manufactured components. 

This study demonstrates the simultaneous achievement of high strength and excellent corrosion resistance in a Ni-free, high N austenitic stainless steel fabricated by laser powder bed fusion (PBF-LB). The formation of a single-phase austenitic structure was confirmed through X-ray diffraction analysis, scanning electron microscopy and energy-dispersive X-ray spectroscopy. Cyclic potentiodynamic polarization tests conducted in 0.6 M NaCl solution at room temperature revealed high breakdown potential (1187 ± 31 mVSCE), indicating excellent corrosion resistance for the additively manufactured Ni-free austenitic stainless steel compared to wrought 316L stainless steel. These findings were further supported by immersion tests in FeCl3 solution. The additively fabricated alloy’s yield strength and ultimate tensile strength exceeded 800 MPa and 1 GPa, respectively. The results highlight the potential for developing highly corrosion-resistant, high-strength Ni-free austenitic stainless steel by PBF-LB for possible applications for biomedical implants and structures relating to nuclear energy. 

Additive manufacturing (AM) can be used to fabricate functionally graded materials (FGMs) in which composition, and therefore properties, vary spatially within a component. A practical consideration for FGM fabrication is the effects of dilution. In the gradient region of vertically graded FGMs, dilution from the previous layer with a different composition from that being newly deposited can result in the composition of the newly solidified layer deviating from the feedstock composition from the nozzles. In this study, a dilution model for multi-layer FGM samples is proposed and validated experimentally with an Inconel625 (IN625)-Monel400 FGM sample. Factors that affect the deviation from the designed compositional path are discussed and methods for mitigating dilution effects to produce designed path are provided and experimentally demonstrated in a stainless steel 316 L (SS316L)-50/50 wt% SS316L/Ni-Monel400 FGM sample, aiding in precise production of the designed FGM path. 

Compositionally complex materials (CCMs), such as functionally graded materials (FGMs) made by additive manufacturing (AM) often form undesired phases or cracks, negatively affecting the build. Equilibrium thermodynamic calculations and solidification simulations, such as Scheil–Gulliver, can be used to predict feasible compositions or compositional paths, acting as constraints before empirical or machine learning models are applied to predict properties of interest. In addition, additional analysis of solidification simulations can be used to predict hot-cracking using various criteria to further account for manufacturability. To define and navigate the high order chemical systems of CCMs/FGMs, the open-source tool, AMMap, has been developed utilizing open models and CALPHAD methods for thermodynamic computation. AMMap explores spaces constructed with the nimplex library, using a novel algorithm to represent high-dimensional systems as graphs that can be joined into homogeneous structures and explored with graph traversal algorithms to automate the path-design process. This method allows the use of existing high-performance gradient descent, graph traversal search, and other path optimization algorithms to automate the path-design process with as little prior bias as possible.