Jack Beuth

Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF). This study presents an integrated experimental and data-driven framework using airborne acoustic emission (AE) to achieve high-resolution quantification of KH porosity. Experiments conducted on an LPBF system involved in situ acquisition of airborne AE and ex situ porosity imaging via X-ray computed tomography (XCT), synchronized spatiotemporally through photodiode signals with submillisecond precision. We introduce KHLineNum , a spatially resolved porosity metric defined as the number of KH pores per unit scan length, which serves as a physically meaningful indicator of the severity of KH porosity in geometries and scanning strategies. Using AE scalogram data and scan speed, we trained a lightweight convolutional neural network to predict KHLineNum with millisecond-scale temporal resolution, achieving an R 2 value exceeding 0.8. Subsequent analysis identified the 35 – 45 kHz frequency band of AE as particularly informative, consistent with known KH oscillations. Beyond defect quantification, the framework also enables AE-driven direct inference of KH regime boundaries on the power–velocity process map, offering a noninvasive and scalable component to labor-intensive post-process techniques such as XCT. We believe this framework advances AE-based monitoring in LPBF, providing a pathway toward improved quantifiable defect detection and process control.

Laser beam directed energy deposition (DED-LB) is of interest as a manufacturing method for refractory complex concentrated alloys. However, in-situ mixing of metals with large differences in melting temperature and density can lead to inhomogeneities, which in turn can give rise to locally varying physical properties. To gain insight into these issues, we investigate the deposition of refractory metal Nb and Ta powders on like and unlike baseplates. The melting temperatures of Ta and Nb differ by 550 °C, while the density of Ta is twice that of Nb. When Ta powder is deposited on a Nb baseplate, un-melted Ta powder particles are observed. Bands of different compositions are also observed throughout the melt pools, both when Ta powder is deposited on Nb baseplate and when Nb powder is deposited on Ta baseplate. Deposition of the denser Ta atop a Nb baseplate results in a chaotic interface profile, while the interface is smooth in the opposite case. In autogenous melt pools, the grain sizes are about 10 times larger than those of the pure baseplates. However, the average melt pool hardnesses are larger by 6 % and 32 % than those of the Ta and Nb baseplates, respectively. The hardness increase is attributed to work hardening. This work provides a systematic study on process parameter dependence of inhomogeneities in DED-LB melt pools when physical properties of materials to be combined are significantly different.

In laser powder bed fusion (L-PBF), it is still difficult to produce defect-free parts. Spatter particles are one cause of lack-of-fusion (LOF) defects, which develop when spatter particles land on a part and become incorporated into the melt-pool. Preventing spatter-induced defects could thus be possible by planning for spatter contamination in the build planning phase. Several prior works have shown the promise of using computational fluid dynamics coupled with the discrete phase method (CFD-DPM) to predict the landing locations of spatter particles, but these models are too slow and complex for practical use in build planning. The current work thus proposes a machine learning surrogate model of a CFD-DPM model which performs hundreds of times faster than the original model with a root-mean-square error (RMSE) of 6.8 mm. This model is trained on Inconel 718 spatter particles within the EOS M290 L-PBF machine, but the approach is general and could be extended to other materials and L-PBF machines with retraining of the surrogate model. The model’s learned feature importance is evaluated through a SHAP analysis, finding that it follows previous analyses conducted with the original CFD-DPM model. The model’s accuracy and speed open the possibility for interactively and automatically planning builds around spatter contamination, both of which would improve consistency among machine users and could help reduce the amount of spatter contamination during L-PBF builds. • A surrogate model of spatter transport is sucessfully developed and applied. • The surrogate model achieves an average error of 6.8 mm compared to original model. • A prototype of a spatter-aware build planning application is presented and discussed.

Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF). This study presents an integrated experimental and data-driven framework using airborne acoustic emission (AE) to achieve high-resolution quantification of KH porosity. Experiments conducted on an LPBF system involved in situ acquisition of airborne AE and ex situ porosity imaging via X-ray computed tomography (XCT), synchronized spatiotemporally through photodiode signals with submillisecond precision. We introduce KHLineNum, a spatially resolved porosity metric defined as the number of KH pores per unit scan length, which serves as a physically meaningful indicator of the severity of KH porosity in geometries and scanning strategies. Using AE scalogram data and scan speed, we trained a lightweight convolutional neural network to predict KHLineNum with millisecond-scale temporal resolution, achieving an R-squared value exceeding 0.8. Subsequent analysis identified the 35-45 kHz frequency band of AE as particularly informative, consistent with known KH oscillations. Beyond defect quantification, the framework also enables AE-driven direct inference of KH regime boundaries on the power-velocity process map, offering a noninvasive and scalable component to labor-intensive post-process techniques such as XCT. We believe this framework advances AE-based monitoring in LPBF, providing a pathway toward improved quantifiable defect detection and process control.

Theoretical studies have shown that plate-lattice structures exhibit exceptional mechanical properties such as high strength-to-weight ratios. Their fabrication, however, is challenging and has only been realized for metals via the Laser Powder Bed Fusion (LPBF) process. A deeper understanding of the deformation mechanisms of LPBF fabricated plate-lattice structures, including their post-yielding and energy absorption characteristics, is needed to evaluate their applicability in defense, aerospace, and biomedical industries. In this study, AlSi10Mg plate-lattice structures with four unit cell topologies were fabricated using LPBF and tested in quasi-static compression to determine mechanical properties, deformation behaviors, and energy absorption capabilities. Microcomputer tomography revealed surface variations resulting from adhered powder and dross formation were comparable in scale to plate thicknesses. Tested plate-lattices experience primarily stretch-dominant deformation consistent with theoretical Gibson-Ashby models. Stretch-dominant deformation is maintained for large compressive strains post-yielding until brittle fracture occurs in unit cell layers or diagonal bands, leading to high strength localized. For the simple cubic geometry, high yield stresses that were maintained post-yielding resulted in the highest specific energy absorption yet observed in lattice materials, reaching up to 27.2 J/g at a density of 1.23 g/cc. This research highlights AlSi10Mg plate-lattices as excellent candidates for light-weight energy absorption applications.

Abstract Laser powder bed fusion offers a high degree of geometric freedom for manufacturing with novel materials, yet failures during fabrication remain a critical barrier to achieving more complex components. Recoater blade collisions, cracking, and build plate delamination damage parts and performance, especially with hard and high-temperature materials. Cemented carbides are optimal for high-hardness machining and tooling parts, but high thermal gradients and complex composite behaviors exacerbate fabrication issues. Understanding the effects of build process parameters on macroscopic failure modes is critical to mitigate such issues. This study leverages thermomechanical modeling to investigate the effects of process parameter alterations on build stresses and deflection for WC-Ni part fabrication strategies. A comparative analysis revealed that reductions in laser energy density and part sizes reduced part deflection. The simulations reinforced that bed preheating reduced stresses and thermal gradients, but reductions in interlayer timing also benefited builds by adding additional interlayer heating. Sharp geometric features common in cemented carbide machining and tooling parts significantly increased deflection. Exploratory builds achieved high-density (> 97%) WC-17 wt% Ni parts, with delamination occurring only with the geometry with the highest simulated stress. Profilometry revealed over-melting around part edges and high surface roughness (arithmetic mean height up to 61.5 µm), indicating that localized features from scan strategies heavily contribute to recoater collisions. The successful fabrication of a range of parts, including drill bit geometries, demonstrated the effectiveness of parameter refinement as a tool to avoid catastrophic failure events with laser powder bed fusion.

Cemented carbides such as cemented tungsten carbide (WC) are known for their use in resilient wear-resistant applications where hardness and thermal stability are imperative. They are composed of carbide particles embedded in a metal binder. Laser Powder Bed Fusion (L-PBF) is a favorable method to form cemented carbides into complex geometries, but composites pose unique challenges relative to metals typically processed by L-PBF. Resolving the melt pool temperature distributions in L-PBF is key to understanding the underlying physics of the fusion process. Using a two-color thermal imaging method, melt pool thermal maps of WC 0.83 -Ni 0.17 were captured with linear energy densities ranging from 500–1750 J/m with and without powder. WC 0.83 -Ni 0.17 melt pools exhibit temperatures above 4000 K, which can lead to the generation of other WC phases. Compared to more common L-PBF materials such as 316L stainless steel (SS), WC 0.83 -Ni 0.17 melt pools reach higher temperatures. Our direct measurements find that the thermal conductivity of WC 0.83 -Ni 0.17 is 30 W/m-K at 300 K, which is higher than the thermal conductivity of 316L SS and suggests that other heat transfer limitations must cause the elevated melt pool temperatures. A FLOW-3D CFD model based on the composite properties was compared to both the melt pool centerline temperatures and width measurements of the samples fabricated by L-PBF. The simulations indicate that specifying the onset of fluidity is key to reproducing the high temperatures observed experimentally. Although Ni has a melting point of 1728 K, the simulations do not match experiments unless the onset of fluidity is set at the melting point of WC (3143 K). Within FLOW-3D, the onset of fluidity is controlled by the critical solid fraction, which is a uniquely important parameter for simulating composite materials.

Purpose Laser hot wire additive manufacturing (LHWAM) is a newer technology within the space of large-scale directed energy deposition (DED) additive manufacturing (AM) processes. This study aims to map known AM flaw types such as lack of fusion and keyholing, as well as a dripping flaw unique to hot wire processes, across process parameter space using a small number of single-track experiments. Design/methodology/approach A semianalytical model was calibrated using a small initial set of experimental data. Lack of fusion and keyholing flaws were mapped across process space using existing models. The dripping flaw was modeled via analytical methods calibrated with experimental data, and then mapped across processing space. Further experimental data beyond the small initial set was used to evaluate the accuracy of the process maps developed. A website and executable were deployed to users of the process for convenient rapid process parameter selection. Findings With the process maps generated during this work, users can easily and rapidly generate desirable parameter sets for a range of conditions, enabling the intelligent utilization of the entire stable processing regime. Practical implications The methodology developed can be applied to other LHWAM machines or DED processes to rapidly and inexpensively generate a systematic understanding of processing space for build planning. Originality/value LHWAM shows advantages over other large-scale DED processes, but a systematic physically informed study of the key flaw regions across process space had not been conducted, limiting more widespread use of the process and creating a gap that this study fills.

Purpose In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning. Design/methodology/approach A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization. Findings Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process. Originality/value With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

Anomalous melt pools during metal additive manufacturing (AM) can lead to deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. We employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. Specifically, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental no-powder melt pool observations. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical models. Additionally, the use of ratiometric temperature estimates improves the accuracy of the model predictions compared to monochromatic imaging. This work establishes a framework extensible towards powder-based AM builds. • We predict 2D melt pool contours from high-speed thermal images using a hybrid CNN-Transformer model. • The model recreates subsurface melt pool structure, evaluated by geometric comparison. • Predictions align with multitrack melting experiments at varied hatch spacings. • Ratiometric temperature estimates yield more accurate melt pool contour predictions than monochromatic input. • Pre-training on simulation data reduces experimental data needed for accurate prediction.

Powder bed fusion-laser beam (PBF-LB) parts experience a significant decline in fatigue performance when process-induced defects are present. In this work, a decline in 4-point bend fatigue life was observed in PBF-LB Ti–6Al–4V coupons fabricated at constant power with increasing scanning velocity and which underwent subsequent stress relief and surface machining. Specifically, the presence of pores that resemble lack-of-fusion (LoF) and a decline in fatigue life were observed at scanning velocities lower than that expected from prior published work. It was hypothesized that this unexpected presence of LoF pores resulted from melt pool geometry variability that was not considered in prior work when the LoF criterion was implemented. Further, these pores can be small in size and infrequent in their occurrence when the melt pool geometry variability is not severe. Such sparse pores are challenging to characterize using conventional 2D characterization methods. This work leverages tall and narrow coupon geometry and high-resolution X-ray micro computed tomography (X- μ CT) to capture LoF porosity. The results show that a modified melt pool overlap-based LoF criterion considering melt pool geometry variability captures the unexpected occurrence of LoF pores observed in X- μ CT. In addition, the LoF percent metric displays a strongly negative correlation with fatigue performance. The insights from this work provide guidance on characterizing melt pool geometry variability across scan lines to systematically evaluate processing parameters that generate LoF pores, which, in turn, could lower fatigue performance.

Abstract Variability in the inherently dynamic nature of additive manufacturing introduces imperfections that hinder the commercialization of new materials. Binder jetting produces ceramic and metallic parts, but low green densities and spreading anomalies reduce the predictability and processability of resulting geometries. In situ feedback presents a method for robust evaluation of spreading anomalies, reducing the number of required builds to refine processing parameters in a multivariate space. In this study, we report layer-wise powder bed semantic segmentation for the first time with a visually light ceramic powder, alumina, or Al 2 O 3 , leveraging an image analysis software to rapidly segment optical images acquired during the additive manufacturing process. Using preexisting image analysis tools allowed for rapid analysis of 316 stainless steel and alumina powders with small data sets by providing an accessible framework for implementing neural networks. Models trained on five build layers for each material to classify base powder, parts, streaking, short spreading, and bumps from recoater friction with testing categorical accuracies greater than 90%. Lower model performance accompanied the more subtle spreading features present in the white alumina compared to the darker steel. Applications of models to new builds demonstrated repeatability with the resulting models, and trends in classified pixels reflected corrections made to processing parameters. Through the development of robust analysis techniques and feedback for new materials, parameters can be corrected as builds progress.

Abstract Binder jet additive manufacturing is well suited for fabricating large (order of cm) and geometrically complex ceramic preforms. However, the main challenge in producing ceramic oxide parts via binder jetting is the high‐temperature postprocess tasked with eliminating internal porosity to achieve full densities. In this work, we demonstrate the ability to produce oxide ceramic parts with desirable densities by sintering binder jetted preforms. We investigate the sintering behavior of binder jetted preforms composed of three oxide powders with distinct morphologies: ball‐milled alumina, gas‐atomized silica, and sintered‐agglomerated zirconia. We fabricate the preform samples using a commercial binder jetting system and a conventional die‐pressing technique to understand the effect of starting densities. Furthermore, we parametrize the heating profiles to understand the effect of sintering temperature, sintering duration, and heating rate on each powder's densification behavior, microstructure, and phase composition. Results show the relatively low starting densities within the binder jetted preforms caused the onset sintering temperature to be higher than what is documented in conventional sintering studies. As expected, we observed sintered densities increase with respect to sintering temperature and duration. These findings were utilized to downselect sintering parameters capable of achieving high densities (>96%). Herein, this study validates the sintering of binder jetted preforms as a suitable way to manufacture ceramic parts, regardless of powder morphologies, thereby increasing the robustness of the supply chain involved in additive manufacturing of ceramic oxides.

Defects in Laser Powder Bed Fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects. Therefore, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by upscaling lightweight coarse mesh simulations. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. We predict the melt pool depth within 3 μm based on low-fidelity input data 4× coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.

Processing defects remain the primary cause for fatigue failure of laser beam powder bed fusion (PBF-LB) produced components. Accordingly, process mapping methodologies have been extensively developed to identify optimal processing parameters to avoid defects. For structure-critical applications, it is necessary to validate the defect-based process maps through fatigue testing. We quantify the defect structure (porosity) process map for PBF-LB Ti-6Al-4V based on defect populations and fatigue properties. The defect populations were measured in samples fabricated at constant power and small increments in scanning velocity using X-ray micro-computed tomography and 2D metallography and analyzed using a number density approach. Furthermore, 4-point bend fatigue testing was used to establish stress-cycles to failure properties. Our results reveal distinct defect populations in both keyhole and lack-of-fusion defect regimes, with continuous variation in defect density. The number density-based defect size quantity strongly correlates with process parameters, peak stress, and initiating defect size, offering a quantitative approach to establish process-defect-fatigue relationships. We conclude that the process window exists just as clearly for fatigue as it does for defects, although more sensitive to variability in defects. Consequently, within this fatigue-based process window, one can expect to consistently produce dense components with superior fatigue properties.

Abstract Architected materials, exhibiting unique mechanical properties derived from their designs, have seen significant growth due to the design versatility and cost‐effectiveness offered by additive manufacturing. While finite element methods accurately evaluate the mechanical response of these structures, identifying new designs exhibiting specific mechanical properties remains challenging, often requiring computationally expensive simulations and design expertise. This underscores the need for a framework that generates structures based on desired mechanical properties without requiring expert input. In this work, a novel denoising diffusion‐based model is presented that generates complex lattice unit cell structures based on desired mechanical properties, manufacturable via additive techniques. The proposed framework generates unique lattice unit cell structures in the implicit domain which can be easily converted to mesh structures for fabrication and voxel structures for structural analysis. The proposed model accelerates the design process by generating unique structures with both isotropic and anisotropic stiffness, outperforming traditional unit cells like simple cubic and body‐centered‐cubic in energy absorption and compression load at lower densities. Additionally, this work explores a new class of hybrid structures, derived by combining multiple configurations of triply periodic minimal surface structures using non‐linear transition functions, which may offer equivalent or enhanced strength compared to conventional designs.

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.

Abstract Functional grading (FG) is often used to bond dissimilar metals. However, that approach is complicated from a manufacturing perspective, and the associated challenges can outweigh the benefits of FG. Here, we investigate a directly bonded interface by transitioning from stainless steel 304L (SS304L) to Inconel 625 (IN625) using powder-feed directed energy deposition with a laser beam energy source (DED-LB). Both cracking and the presence of carbide phases have been reported in this multi-materials system. Conditions that unambiguously achieve crack-free joints have not yet been established. With DED-LB, we consistently observe solidification cracking in melt pools containing > 50 wt pct SS304L, while no cracking is observed in melt pools with < 40 wt pct SS304L. Variations on the most up-to-date solidification cracking model are applied to gain insight into the cracking dependencies. Parameters that give rise to defect-free single layers also enable defect-free multilayer prints despite the additional thermal cycling. Upon printing and testing full-sized ASTM E8 tensile specimens, the interface is sufficiently strong that failure occurs solely within the SS304L region, indicating a joint strength of > 650 MPa. Thus, a simple method to attain high strength joints for these dissimilar metal alloys is demonstrated.

Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. Specifically, we employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. In this architecture, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical melt pool models. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental melt pool observations.

Metal additive manufacturing is associated with thermal cycles of high rates of heating, melting, cooling, and solidification. Some areas within the build experience thermal cycles that depend on the paths of the energy source. Additionally, geometrical features, such as thin walls and overhangs, can lead to heat accumulation, potentially affecting the microstructure, fatigue life, and induced residual stresses that may lead to dimensional distortion and cracking. The identification of significant heat accumulation can be used for part quality monitoring to inform the design process, enhance the quality of printed parts, and optimize the process parameters. This study aims to efficiently identify heat accumulation with affordable in-situ infrared imaging for further characterization and mitigation to enhance the quality of printed parts. A computational framework employing machine learning is developed to identify zones of local heat accumulation in real time. The effectiveness of this approach is demonstrated by experiments conducted on a build with a wide variety of geometrical features. In addition, characterization and detailed analyses of detected local heat accumulation zones are provided.

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt pool images in particular remains computationally demanding in practice due to the high variability of melt pool morphologies and the limitation of data caching and transfer, making it challenging to detect the local lack-of-fusion (LOF) defect occurrences. In this work, we propose a new acoustic and thermal information-based monitoring method that can robustly infer critical LPBF melt pool morphological features in image forms and detect spatially-dependent LOF defects within a short time period. We utilize wavelet scalogram matrices of acoustic and photodiode clip data to identify and predict highly time-resolved (within a 1.0 ms window) visual melt pool characteristics via a well-trained data-driven pipeline. With merely the acoustic and photodiode-collected thermal emission data as the input, the proposed pipeline enables data-driven inference and tracking of highly variable melt pool visual characteristics with R2≥0.8. We subsequently validate our proposed approach to infer local LOF defects between two adjacent scanlines, showing that our proposed approach can outperform our selected baseline theoretical model based on previous literature. Revealing the physical correlation between airborne acoustic emission, thermal emission, and melt pool morphology, our work demonstrates the feasibility of creating an efficient and cost-effective acoustic- and thermal-based approach to facilitate online visual melt pool characterization and LOF defect detection. We believe that our work can further contribute to the advances in quality control for LPBF.

Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the as-fabricated microstructure, this work integrates experiments with a thermodynamic and kinetic modeling framework to probe the limits of the dispersoid sizes and number densities that can be achieved with powder bed fusion-laser beam. Bulk samples of a Ni–20Cr ＋ 1 wt% Y2O3 alloy are fabricated using a range of laser power and scanning velocity combinations. Scanning transmission electron microscopy characterization is performed to quantify the dispersoid size distributions across the processing space. The smallest mean dispersoid diameter (29 nm) is observed at 300 W and 1200 mm/s, with a number density of 1.0 × 1020 m−3. The largest mean diameter (72 nm) is observed at 200 W and 200 mm/s, with a number density of 1.5 × 1019 m−3. Scanning electron microscopy suggests that a considerable fraction of the oxide added to the feedstock is lost during processing, due to oxide agglomeration and the ejection of oxide-rich spatter from the melt pool. After accounting for these losses, the model predictions for the dispersoid diameter and number density align with the experimental trends. The results suggest that the mechanism that limits the final number density is collision coarsening of dispersoids in the melt pool. The modeling framework is leveraged to propose processing strategies to limit dispersoid size and increase number density.

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.

This study introduces the Extended Cluster-based Network Modeling (eCNM), an innovative approach designed to enhance the understanding of coherent structures in turbulent flows.The eCNM focuses on characterizing the dynamics within specific subspaces or subsets of variables, providing valuable insights into complex flow phenomena.In the context of Proper Orthogonal Decomposition, several extended approaches have been proposed to tackle these challenges, such as Extended POD (EPOD) and Extended SPOD (ESPOD).One powerful method for data-driven modeling of complex nonlinear dynamics is the standard Cluster-based Network Modeling (CNM), consisting in an unsupervised machine learning procedure to reduce a dataset of snapshots to a few representative flow states.However, the presence of variable heterogeneity and measurement noise, both in time and space, can complicate interpretations and model training.The Extended Clustering approach offers enhanced control over the clustering process, can lead to significant computational savings, enables the extraction of dynamical features correlated with a specific subdomain or subset of variables, and facilitates the clustering of heterogeneous variables that are challenging to incorporate in a spatial norm.To demonstrate the effectiveness of the eCNM, it has been employed for the analysis of a swirl flame in unforced conditions, characterized by a precessing vortex core (PVC).

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.