Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90\% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

High performance computing simulations of Cr-poisoning are used to develop systematic trends for overpotential driven degradation modes in microstructurally resolved fuel cell cathodes. Oxygen reduction and chromium oxide deposition at triple phase boundaries (tpbs), and species transport, are numerically computed within 19 microstructures (103 µm3 domains) over a range of operating conditions. Three primary input parameters drive degradation: tpb density of the microstructure ρtpb, normalized charge-transfer current density for Cr-poisoning i∗, and galvanostatic current density j. Simulations converged over large fractions of the potential operating time, ranging from thousands to over a hundred thousand hours. For all simulations, normalized overpotential η∗ versus time t curves can be modeled using a progressive, asymptotic function η∗=ma(ta−t) with two fitting parameters: the initial slope m and the asymptote a. A third output parameter tl was defined as the time when a specific fraction of overpotential range was lost. Several methods to determine each output parameter are presented. More importantly, using the large amount of data generated from over 95 time-dependent simulation series (over 29,000 individual simulations), simple correlations are made between the input and output parameters that provide predictive capability of operational lifetime tl (and m and a), without needing further high-performance computing.

Physics-based materials and process modeling has developed to the point where it is supporting the design and qualification of new components produced by many processes, including additive manufacturing (AM). Combinations of computational models that utilize various material and process parameters for AM processes, and which predict the evolution of microstructure and defects, and location-specific mechanical properties are key elements of what is termed model-based material definition (MBMD). These computational tools, when used with integrated, interdisciplinary modeling workflows, provide a holistic engineering framework for additive manufactured materials and processes. Property prediction of printed legacy materials or emerging alloys requires the understanding and control of input powder characteristics and the manufacturing processing path, starting with the feedstock and including, e.g., the local thermal history at scales from individual melt pools to full-scale component geometries. The use of model-based material definitions is critical to capture the spatial variation of processing paths and consequently spatial variations of microstructure and properties throughout the entire volume of printed components. Statistical descriptions of microstructure, whether measured or computed, enable the establishment of statistically equivalent representative volume elements (SERVEs) as discretized sub-volumes of entire component volumes that can be used to subsequently predict local mechanical properties. MBMD enables establishment of component manufacturing and property key characteristics, which can be used as component and process qualification and certification requirements. The alignment of model-guided component testing and associated key characteristics provides for a path for efficient, smart certification and qualification of new AM materials and components. This article is an analysis and demonstration of the application of MBMD to AM materials and components. The MBMD framework described herein is deemed to be an optimal approach for AM component design, manufacture, and qualification.

Composed of individual unit cells strategically arranged to achieve a desired function, lattices are a promising solution for laser powder bed fusion support structure design in additive manufacturing. Despite their many advantages (e.g., multifunctionality and reduced material cost), prior work in lattice support structure design primarily focuses on horizontal support domains that are not translatable to support domains for complex geometries, thereby limiting their application. This work introduces a multi-sized unit cell design optimization (MSO) method to create lattice support structures (LSS) for parts with complex geometries. The proposed method utilizes voxelization to generate LSS using box-like unit cells of different sizes. It also allows for efficient, high-dimensional design optimization for the types and locations of user-specified unit cells through a modified simulated annealing-based optimization algorithm. The effectiveness and efficiency of the MSO method are demonstrated through the case study of an adapter pipe for a high-temperature heat exchanger. For this demonstration, LSS using multi-sized unit cells is designed to increase heat transfer rate while satisfying structural integrity and material cost constraints. The case study results indicate that the design of the LSS derived from the MSO method fulfills all constraints, including the design constraint of 50\% material cost reduction, compared to the solid support structure. In contrast, the lattice support structure designs derived from equal-sized unit cell methods either cannot satisfy all design constraints or have a lower heat transfer rate than the design of the MSO method.

Martensite, α , commonly appears in Ti-6Al-4V upon rapid cooling from above the β-transus temperature. It is known that α decomposes into αand βat high temperatures but well below the β-transus temperature. Here, we study the decomposition of martensitic Ti-6Al-4V under rapid laser heating, employing in situ synchrotron X-ray diffraction. A comparison is made with post-annealed Ti-6Al-4V under heating to elucidate changes without martensite decomposition. The fast acquisition of X-ray diffraction data at 250 Hz temporally resolves the decomposition process initiated by annihilating dislocations in α . The recovery process is accompanied by structural changes in martensite, followed by the phase transformation to β. Thermal profiles estimated from the lattice parameter data reveal the influence of heating rates and dislocation densities on the decomposition process. Throughout the analysis of the diffraction profiles with respect to estimated temperature, we propose a straightforward method for approximating the initiation temperature of martensite decomposition.

Additively manufactured Ti-6Al-4V (Ti64) wall structures used as various structural aerospace components suffer from the spatial variations in mechanical properties, texture-mediated anisotropy, and macrozones that degrade the mechanical performance, especially fatigue, under certain service conditions. The development of texture and macrozone formation in α/β titanium alloys are closely related to the variant selection during the β→α phase transformation in the additive manufacturing process. In this study, electron backscatter diffraction (EBSD) was employed to investigate the variant selection mechanism and macrozone formation criteria in single- and multi-wall specimens manufactured with laser hot wire directed energy deposition (LHW-DED). Variant analysis revealed that strong variant selection with a preferred selection of the type 2, <112¯0>/60$\,^\circ$ variant occurs in both single and multiwalls. The primary factor dictating the selection of the α/α variant boundaries was found to be the cooling rates. The main underlying mechanism governing variant selection to compensate for β→α transformation strain during solid-state phase transformation was determined to be the triple-alpha variants clustering of Category I. Evidence of thermal stress playing a minor role in variant selection was also observed. Furthermore, location-specific variant analysis revealed that the white bands (microstructural bands with distinct etching contrast) on the walls consist of fewer α variants than the bulk. The macrozones formation criteria are proposed and the effect of macrozones on mechanical behavior is discussed in conjunction with the Schmid factor analysis of α variants with respect to different loading directions. The density of macrozones was also quantified.

To investigate the difference between ionic and electronic conductors as infiltrates in solid oxide fuel cells (SOFCs), high -throughput and high-performance finite element simulations were carried out on 51 different cathode microstructures. Five cathode backbones, reconstructed from a commercial SOFC, were infiltrated computationally with varying number densities of nanoscale electronically or ionically conducting particles. Local electrochemical quantities were computed within the volumetric meshes that represent the complex 3D microstructural morphologies that include the infiltrated particles. As infiltrates, ionic conductors improve the performance more than electronic conductors. By differentiating transport and reaction pathways originating from backbone phases and infiltrates, we show that new ionic transport pathways opened by the ionically conducting infiltrates are the origin of this difference. These new transport paths redistribute current throughout the cathode, thereby increasing (decreasing) the available local activation (Ohmic) overpotential at triple phase boundaries and rendering them more active than for the case of electronic conductors as infiltrates. These results give us insight to engineering improved electrodes for SOFCs via infiltration with surface active nano -particles.

This work models the microstructural evolution as a function of position in laser-wire deposited Ti&mdash;6Al&mdash;4V parts using a classical multi-component JMAK nucleation and growth model with additive isothermal time-steps. Model predictions are compared to experimental observations. The model can be used to interpret nucleation and growth kinetics of various characteristic features of the microstructure that are inaccessible by experiments. It explains the presence of layer bands at specific locations unrelated to the weld bead structure. The last re-heating (of multiple thermal cycles) of a solidified layer, and where it peaks, plays a key role in increasing the nucleation density at specific locations, resulting in full \$\$$\backslash$alpha \$\$-colony microstructures constituting the layer bands, while the rest of the build is predominantly basketweave. Additionally, changes in the basketweave \$\$$\backslash$alpha \$\$-lath thickness as a function of the distance from the substrate are studied and compared with an Arrhenius-type function, with results highlighting a more complex relationship than an Arrhenius-type function would suggest.

The three-dimensional grain-averaged response of solid bar samples under non-proportional (NP) elastoplastic axial-torsional loading was investigated using in situ high energy diffraction microscopy (HEDM) and companion crystal plasticity finite element (CPFE) modeling. Important stress metrics including applied shear ( sigma theta Z ) and axial ( sigma ZZ ) stress tensor components, stress and stress deviator tensor invariants (I 1 , J 2 , and J 3 ), von Mises equivalent stress ( sigma grain VM ), maximum resolved shear stress (mRSS), stress triaxiality ( eta), and lode angle parameter ( theta) values were tracked for300 grains under two different loading conditions: (1) Torsion-dominated loading (low NP) and (2) Tension-torsion loading (high NP) in equiatomic NiCoCr, a representative multicomponent face-centered cubic (FCC) superalloy. Overall, significant stress localizations existed within both samples as evidenced by the radial dependence of grain-resolved sigma theta Z , sigma grain VM , and J 2 ; by comparison, I 1 , J 3 , eta, and theta metrics did not show discernible trends within the volume. These stress localizations reveal a complex interplay between axial and shear stress components (e.g., stress coupling) resulting in grain yielding near the sample surface largely driven by shear stress, whereas internal grain yielding was largely accommodated by axial stress. Grain-resolved stress localization trends were described well by the CPFE model, although some discrepancies in magnitude occurred, particularly for volumetric stress metrics (I 1 and eta) due to initial type II residual stress distributions. The superposition of initial residual stress states onto CPFE grain-resolved data significantly improved model accuracy for eta. This suggests that residual stresses more strongly influence the simulation of volumetric rather than deviatoric (yield) stress metrics.