In conventional processing, metals go through multiple manufacturing steps including casting, plastic deformation, and heat treatment to achieve the desired property. In additive manufacturing (AM) the same target must be reached in one fabrication process, involving solidification and cyclic remelting. The thermodynamic and kinetic differences between the solid and liquid phases lead to constitutional undercooling, local variations in the solidification interval, and unexpected precipitation of secondary phases. These features may cause many undesired defects, one of which is the so-called hot cracking. The response of the thermodynamic and kinetic nature of these phenomena to high cooling rates provides access to the knowledge-based and tailored design of alloys for AM. Here, we illustrate such an approach by solving the hot cracking problem, using the commercially important IN738LC superalloy as a model material. The same approach could also be applied to adapt other hot-cracking susceptible alloy systems for AM.

In the laser powder bed fusion additive manufacturing of metals, extreme thermal conditions create many highly dynamic physical phenomena, such as vaporization and recoil, Marangoni convection, and protrusion and keyhole instability. Collectively, however, the full set of phenomena is too complicated for practical applications and, in reality, the melting modes are used as a guideline for printing. With an increasing local material temperature beyond the boiling point, the mode can change from conduction to keyhole. These mode designations ignore laser-matter interaction details but in many cases are adequate to determine the approximate microstructures, and hence the properties of the build. To date no consistent, common, and coherent definitions have been agreed upon because of historic limitations in melt pool and vapor depression morphology measurements. In this review, process-based definitions of different melting modes are distinguished from those based on postmortem evidence. The latter are derived mainly from the transverse cross sections of the fusion zone, whereas the former come directly from time-resolved x-ray imaging of melt pool and vapor depression morphologies. These process-based definitions are more strict and physically sound, and they offer new guidelines for laser additive manufacturing practices and create new research directions. The significance of the keyhole, which substantially enhances the laser energy absorption by the melt pool, is highlighted. Recent studies strongly suggest that stable-keyhole laser melting enables efficient, sustainable, and robust additive manufacturing. The realization of this scenario demands the development of multiphysics models, signal translations from morphology to other feasible signals, and in-process metrology across platforms and scales.

To better understand the effects of infiltration on local electrochemistry and transport in solid oxide fuel cell (SOFCs) electrodes, high-throughput, high-performance finite element simulations are presented within dozens of SOFC cathodes containing synthetically generated nanoscale infiltrates. The computational approach retains the complex microstructural morphologies of cathodes, including those of the three backbone phases (gas, ion, and electron conductors) and the infiltrates (an electron conductor), in meshed domains and computes distributions of local electrochemical quantities within the domains. Simulations were implemented on a supercomputer and converged for 48 distinct microstructural subvolumes, with varying backbone heterogeneities and infiltrate loadings. Analyzing both the ensemble (averaged over subvolumes) and the local (evaluated within subvolumes) performance metrics indicate that infiltration of an electron conductor significantly improves the electrochemical performance of each backbone in a linear fashion with the increase of triple phase boundary content, but the essential ionic transport pathways of the backbone are unchanged. These results shed into the and fabrication of electrodes in fuel cells.

The complexity of interphase precipitation in a Fe-0.19C-1.54Mn-0.4Si-0.06Al-0.13Mo-0.06Ti (at\%) high strength low-alloy (HSLA) steel at an early stage of the austenite-to-ferrite transformation was studied by analyzing the solute distribution across ferrite-austenite interfaces with Kurdjumov-Sachs (K-S) and non-K-S orientation relationships (OR). Structural characterization i.e. ferrite/austenite OR and habit plane characteristics was performed by electron backscatter diffraction (EBSD) and the clustering back-calculation approach, while solute distributions i.e. the solute concentration spikes in the interface regions were studied by atom probe tomography (APT) on the specimens specifically prepared across/near the ferrite/austenite interfaces. It was shown for the first time that interphase precipitation is promoted at both types of interface: (i) a K-S OR and habit plane deviated from ideal (110)(alpha)//(111)(gamma) and (ii) a non K-S OR. The key aspect of interphase precipitation is the distribution of solute atoms across the interface, which is pronounced Mn, Ti, Mo and C concentration spikes at the interphase boundary. In contrast, interphase precipitates were not formed at the coherent interface with a K-S OR and habit plane of (110)(alpha)//(111)(gamma). This was correlated with the interfacial condition, where the compositional ratio of substitutional solute and solvent elements remains almost constant across the interface, i.e. Mn and C spikes. Interface compositions in this study did not match with local equilibria (negligible partitioning local equilibrium and paraequilibrium) limits. In addition, it appeared that the interfaces with Mn, Ti, Mo and C concentration spikes form ledges leading to randomly redistributed interphase precipitates. (C) 2021 Elsevier B.V. All rights reserved.

The capability of producing complex, high performance metal parts on demand has established laser powder bed fusion (LPBF) as a promising additive manufacturing technology, yet deeper understanding of the laser-material interaction is crucial to exploit the potential of the process. By simultaneous in-situ synchrotron x-ray and schlieren imaging, we probe directly the interconnected fluid dynamics of the vapour jet formed by the laser and the depression it produces in the melt pool. The combined imaging shows the formation of a stable plume over stable surface depressions, which becomes chaotic following transition to a full keyhole. We quantify process instability across several parameter sets by analysing keyhole and plume morphologies, and identify a previously unreported threshold of the energy input required for stable line scans. The effect of the powder layer and its impact on process stability is explored. These high-speed visualisations of the fluid mechanics governing LPBF enable us to identify unfavourable process dynamics associated with unwanted porosity, aiding the design of process windows at higher power and speed, and providing the potential for in-process monitoring of process stability.

Hydride-dehydride (HDH) Ti-6Al-4V alloy with particle size distribution of 50—120 µm is laser powder bed fusion (L-PBF) processed using optimum processing parameters and a near-fully dense structure with a density of 99.9 \% is achieved. Microstructural observations and phase analyses indicate formation of columnar βgrains with acicular α/α′phases in as-built condition. The roughness of the as-fabricated samples is significant with an average roughness of Ra = 15.71 $\pm$3.96 µm and a root mean square roughness of Rrms = 108.4 $\pm$24.9 µm, however, both values are reduced to Ra = 0.19 $\pm$0.04 µm and Rrms = 4.9 $\pm$0.6 µm after mechanical grinding. Mechanical tests are carried out on as-fabricated specimens followed by stress relief treatment. All samples are tested to failure in fatigue, under fully-reversed tension-compression conditions of R = −1. The as-built samples failed from the surface with crack initiation mainly at micro-notches, whereas after mechanically grinding, crack initiation changed to subsurface defects such as pores. Minimizing surface roughness by mechanically grinding eliminates surface micro-notches which improves fatigue strength in the high cycle fatigue region. Fatigue notch factor calculations showed that the effect of surface roughness was significantly lower when HDH powder is used compared to standard spherical powder. X-ray diffraction analysis revealed an in-plane compressive stress, micro-strain and grain refinement on the surface of the mechanically ground samples. Fractography observations (macroscale) revealed a fully brittle fracture in the first stage of crack growth with a transition to a dominantly ductile fracture in the third stage of crack growth. On the other hand, at the micro scale, even the brittle fracture regions showed evidence of ductile fracture within the α′martensite laths.

Hydride-dehydride (HDH) Ti-6Al-4V powders with non-spherical particle morphology are typically not used in laser-beam powder bed fusion (LB-PBF). Here, HDH powders with two size distributions of 50—120 µm (fine) and 75—175 µm (coarse) are compared for flowability, packing density, and resultant density of the LB-PBF manufactured parts. It is shown that a suitable laser power-velocity-hatch spacing combination can result in part production with a relative density of > 99.5\% in LB-PBF of HDH Ti-6Al-4V powder. Size, morphology, and spatial distribution of pores are analyzed in 2D. The boundaries of the lack-of-fusion and keyhole porosity formation regimes are assessed and results showed parts with a relative density of > 99.5\% could be LPBF processed at a build rate of 1.5—2 times of the nominal production rates in LPBF machines. The synchrotron x-ray high-speed imaging reveals the laser-powder interaction and potential porosity formation mechanism associated with HDH powder. It is found that lower powder packing density of coarse powder and high keyhole fluctuation result in higher fractions of porosity within builds during the LB-PBF process.

Grain boundaries undergo thermally-activated, first-order transitions that result in discontinuous changes of interfacial properties. Importantly, grain boundary transitions lead to changes in bulk material prop-erties (e.g., embrittlement) and/or behavior (e.g., abnormal grain growth). Numerous studies have been completed on the equilibrium states of grain boundaries and their transitions (i.e., complexion transi-tions), but there have been far fewer investigations of complexion transition kinetics; complexion transi-tions occur on the atomic-scale and are therefore challenging to detect experimentally. In this work, a 3D Potts grain growth model with stochastic complexion transitions was employed to investigate complexion transition kinetics. A Johnson-Mehl-Avrami-Kolmogorov (i.e., JMAK) approach was used to extract nucle-ation and growth rates (i.e., transformation rates) , while point process analyses and correlation functions were used to infer complex interrelated nucleation and growth events. Time-temperature-transformation (TTT) diagrams, in particular grain-boundary complexion, transformed grain, and abnormal grain TTT di-agrams, were constructed to summarize the progress of complexion-related transformations. Such dia-grams relate complexion-induced grain growth to the underlying complexion transitions and, in the case of abnormal grain growth (AGG), permit one to assess the role of AGG as a temperature-dependent, time -displaced indicator of complexion transitions. Overall, this work details a theoretical framework that can be used to better understand complexion transition kinetics as well as to develop tools for the design of bulk microstructures.(c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Additive manufacturing (AM) is gaining attention because of its ability to design complex geometries. Direct energy deposition (DED), one of the AM processes, is widely used nowadays for its high deposition rate. When using DED process in manufacturing or repairing, it is important to know the melt pool dimensions as a function of processing parameters to obtain high deposition rate and avoid defects such as lack of fusion. In this study, we used the random forest (RF) algorithm to the predict melt pool dimensions and compared the results against existing physics based lumped model by Doumanidis et al. [1]. The results show that RF model works well to predict the DED melt pool dimensions, where energy density and material volume deposited govern the dimensions. Further, we tested the ubiquitous semi-ellipsoidal shape assumption for DED cross section against the circular shape, and found semi-ellipsoidal shape to be fair when deposition process is stable and free of defects. Overall, this study highlights the applicability of machine learning algorithms for small AM datasets. Keywords: RegressionAnalysis,DirectedEnergyDeposition,Ti—6Al—4V,MeltPoolShape