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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ZIF-8 Vibrational Spectra: Peak Assignments and Defect Signals

In this study, Zeolitic Imidazolate Framework-8 (ZIF-8) is a promising material for gas separation applications. It also serves as a prototype for numerous ZIFs, including amorphous ones, with a broader range of possible applications, including sensors, catalysis, and lithography. It consists of zinc coordinated with 2-methylimidazolate (2mIm) and has been synthesized with methods ranging from liquid-phase to solvent-free synthesis, which aim to control its crystal size and shape, film thickness and microstructure, and incorporation into nanocomposites. Depending on the synthesis method and post-synthesis treatments, ZIF-8 materials may deviate from the nominal defect-free ZIF-8 crystal structure due to defects like missing 2mIm, missing zinc, and physically adsorbed 2mIm trapped in the ZIF-8 pores, which may alter its performance and stability. Infra-red (IR) spectroscopy has been used to assess the presence of defects in ZIF-8 and related materials. However, conflicting interpretations by various authors persist in the literature. Here, we systematically investigate ZIF-8 vibrational spectra by combining experimental IR spectroscopy and first-principles molecular dynamics simulations, focusing on assigning peaks and elucidating the spectroscopic signals of putative defects present in ZIF-8 material. We attempt to resolve conflicting assignments from the literature and to provide a comprehensive understanding of the vibrational spectra of ZIF-8 and its defect-induced variations, aiming towards more precise quality control and design of ZIF-8-based materials for emerging applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influence of fatigue precracking and specimen size on Master Curve fracture toughness measurements of EUROFER97 and F82H steels

EUROFER97 and F82H steels are two leading reduced-activation ferritic-martensitic (RAFM) steels for fusion first wall and blanket applications. Exposure to the harsh environment of fusion reactors can result in severe degradation of fracture toughness. Thus, the post-irradiation evaluation of fracture toughness is critical for understanding the material behavior. Due to the space constraints of irradiation facilities and challenges in controlling a uniform irradiation condition for large size specimens, the development of small specimen test techniques (SSTT) is indispensable to evaluate the performance of irradiated materials. In this study, we evaluated specimen size effects on the Master Curve fracture toughness of EUROFER97 and F82H steels. A wide variety of specimens, including 0.5 T compact tension (C(T)) specimens, 0.16 T mini-compact tension (miniC(T)) specimens, and 1.65 mm miniature bend bar specimens, were tested. The testing methodology was based on the Master Curve method in the ASTM E1921 standard. No specimen size effect was observed in 0.5 T C(T) and 0.16 T miniC(T) specimens on the Master Curve reference temperature T0, while 1.65 mm miniature bend bar specimens yielded a higher T 0Q . A strong effect of fatigue precracking on T 0 for 0.5 T C(T) and 0.16 T miniC(T) specimens was observed, such that testing on specimens with skewed fatigue precrack fronts resulted in lower T 0 than for specimens with ASTM standard qualified straight fatigue precrack fronts. The results highlight the importance of experimental quality control in developing SSTT for Master Curve fracture toughness testing. Lastly, we also evaluated and provided recommendations on the minimum number of specimens needed for each specimen type for yielding reliable T 0Q values.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of nonlinear ultrasonic waves behavior while interacting with poor interlayer bonds in large-scale additive manufactured materials

Over the past decades, researchers have developed several nonlinear ultrasonic techniques for quality control of materials commonly used in different applications. Owing to the superior sensitivity of nonlinear ultrasound waves to small defects such as micro-cracks, their applicability in different nondestructive testing (NDT) problems has been investigated in numerous studies. These studies utilize frequency domain analysis to detect the generation of higher harmonics because of the formation of defects in the inspected medium. Frequency domain analysis based on the Fourier transform is a significant approach used in linear systems; however, it may not perform adequately on nonlinear systems. Hence, studies on nonlinear dynamics and physics consider analyzing systems' behavior in the phase-space domain. In contrast to the frequency domain analysis, which can result in information loss, analysis in the phase-space domain retains all the information regarding a system's states. Here, we investigate the nonlinearities induced by poor interlayer bonding in polymer-based additive manufactured parts in the phase-space domain. It is convenient to characterize the nonlinearity in the phase-space domain because it provides a geometrical representation of a system's states. Two types of low quality interlayer bond are considered. The first type is simulated artificially while the second type is manufactured by reducing the bond quality during the printing process. The analysis verified that the received ultrasonic signals exhibit classical nonlinear behavior in the phase-space domain while interacting with simulated poor interlayer bonds. In addition, the results showed that the behavior of ultrasonic waves is amplitude-dependent and evolves into models that have not been previously reported. Furthermore, Largest Lyapunov Exponent (LLE) is used to quantify the behavior of nonlinear ultrasonic waves while interacting with poor interlayer bonds. Using LLE, it was observed that the divergence rate of the phase-space trajectories depends on the amplitude of the excitation. This observation quantitatively proves that nonlinear behavior of ultrasound while interacting with poor interlayer bonds can be amplitude-dependent. The results of both simulated and inherent poor interlayer bond cases showed that LLE can be used as a reliable quantitative damage-sensitive feature to detect and potentially characterize weak bonds, which are difficult to detect using conventional approaches. Additionally, the reported results in the phase-space domain provide a basis for proposing a new mathematical model for ultrasonic waves interacting with poor interlayer bonds.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efforts to enhance reproducibility in a human performance research project

Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tri-molybdenum phosphide (Mo 3 P) and multi-walled carbon nanotube junctions for volatile organic compounds (VOCs) detection

Detection and analysis of volatile organic compounds' (VOCs) biomarkers lead to improvement in healthcare diagnosis and other applications such as chemical threat detection and food quality control. Here, we report on tri-molybdenum phosphide (Mo3P) and multiwalled carbon nanotube (MWCNT) junction-based vapor quantum resistive sensors (vQRSs), which exhibit more than one order of magnitude higher sensitivity and superior selectivity for biomarkers in comparison to pristine MWCNT junctions based vQRSs. Transmission electron microscope/scanning tunneling electron microscope with energy dispersive x-ray spectroscopy, x-ray diffraction, and x-ray photoelectron spectroscopy studies reveal the crystallinity and the presence of Mo and P elements in the network. The presence of Mo3P clearly enhanced the performance of vQRS as evidenced in sensitivity and selectivity studies. The vQRSs are stable over extended periods of time and are reproducible, making them a potential candidate for sensing related applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Large–Scale Syntheses of 2D Materials: Flash Joule Heating and Other Methods

In the past 17 years, the larger-scale production of graphene and graphene family materials has proven difficult and costly, thus slowing wider-scale commercial applications. Here, the quality of the graphene that is prepared on larger scales has often been poor, demonstrating a need for improved quality controls. Here, current industrial graphene synthetic and analytical methods, as well as recent academic advancements in larger-scale or sustainable synthesis of graphene, defined here as weights more than 200 mg or films larger than 200 cm 2 , are compiled and reviewed. There is a specific emphasis on recent research in the use of flash Joule heating as a rapid, efficient, and scalable method to produce graphene and other 2D nanomaterials. Reactor design, synthetic strategies, safety considerations, feedstock selection, Raman spectroscopy, and future outlooks for flash Joule heating syntheses are presented. To conclude, the remaining challenges and opportunities in the larger-scale synthesis of graphene and a perspective on the broader use of flash Joule heating for larger-scale 2D materials synthesis are discussed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Positron emission tomography harmonization in the Alzheimer's Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community

Neurosciences & Neurology↗
DOE OSTI2021

Economic risk analysis for the capture of a distributed energy resource using modular chemical process intensification

Recent advances in the chemical process industry have allowed for the intensification of reactors and unit operations, by enhancing heat and mass transfer or combining multiple unit operations. Process intensification can enable chemical plants to be constructed in a more compact, modular fashion, offering improvements over conventional on-site approaches to capital construction, operations and maintenance. This modular chemical process intensification (MCPI) offers several benefits over conventional stick-built (CSB) plant construction in terms of reduced footprint, reduced energy consumption, lower cost, less waste and improved safety and quality control. However, acceptance of MCPI over CSB construction practices within the chemical industry can be impeded by the uncertain risks associated with investing in new technology. Furthermore, the work here documents a case study during the development of a modular chemical plant for capturing distributed energy resources within chemical production. This MCPI approach to plant construction is contrasted with a CSB approach for producing the same chemical product. Data collection tools were developed, based on a literature review, and data was collected from the technology developer to understand the process technology. Sensitivity analysis was then conducted to analyze the business rationale for the application of MCPI over CSB across several market scenarios. It was found that MCPI would be better suited for capacities up to 150 000 metric tons per year, but that improvement in payback period was needed. Additionally, for MCPI approach to achieve acceptable payback periods, efforts are needed to reduce the cost of capital equipment and compress the schedule for ramping up modular production.

42 ENGINEERING↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Transcriptomics-Based Points of Departure for Daphnia magna Exposed to 18 Per- and Polyfluoroalkyl Substances

Here, per- and polyfluoroalkyl substances (PFAS) represent a large group of contaminants of concern based on their widespread use, environmental persistence, and potential toxicity. Many traditional models for estimating toxicity, bioaccumulation, and other toxicological properties are not well suited for PFAS. Consequently, there is a need to generate hazard information for PFAS in an efficient and cost-effective manner. In the present study, Daphnia magna were exposed to multiple concentrations of 22 different PFAS for 24 h, in 96-well plate format. Following exposure, whole body RNA was extracted and extracts, each representing five exposed individuals, were subjected to RNA sequencing. Following analytical measurements to verify PFAS exposure concentrations, and quality control on processed cDNA libraries for sequencing, concentration-response modeling was applied to the data sets for 18 of the tested compounds, and the concentration at which a concerted molecular response occurred (transcriptomic point of departure; tPOD) was calculated. The tPODs, based on measured concentrations of PFAS, generally ranged from 0.03-0.58 µM (9.9-350 µg/L; interquartile range). In most cases, these concentrations were two orders of magnitude lower than similarly calculated tPODs for human cell lines exposed to PFAS. They were also lower than apical effect concentrations reported for seven PFAS for which some crustacean or invertebrate toxicity data were available, although there were a few exceptions. Despite being lower than most other available hazard benchmarks, Daphnia magna tPODs were, on average, four orders of magnitude greater than the maximum aqueous concentrations of PFAS measured in Great Lakes tributaries. Overall, this high throughput transcriptomics assay with Daphnia magna holds promise as a component of a tiered hazard evaluation strategy employing new approach methodologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Effect of Membrane Casting Irregularities on Initial Fuel Cell Performance

Membrane electrode assemblies (MEA) for polymer electrolyte membrane fuel cells (PEMFC) may possess as-manufactured non-uniformities in any of its constituent components. This article studies casting irregularities located within the PEM to understand their potential impact on MEA initial performance. Membrane material was cast on a lab-scale polymer casting line operating either within or at the boundaries of the process window. The resulting membrane material was either pristine in the former case, or, in the latter case, contained air bubbles, cracks, and other irregularities, ranging in size from about 0.6 to 3 mm. Spatial polarization experiments were conducted using a 121-channel segmented fuel cell system, and thermal imaging was performed subsequently to map hydrogen crossover to spatial performance. While total-cell polarization data was minimally impacted by the irregularities, the spatial diagnostics showed local performance impacts that, in operation over time, could cause degradation in performance or earlier failure of the MEA. Such impacts could lead to these irregularities being classified as defects, i.e., manufacturing variations that should be identified and not included in a fuel cell stack. Classification of irregularities as defects will ultimately assist the industry by contributing to the development of threshold detection limits for in-line quality control diagnostics.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Evaluation of riverbed magnetic susceptibility for mapping biogeochemical hot spots in groundwater‐impacted rivers

Abstract Redox hot spots occurring as metal‐rich anoxic groundwater discharges through oxic wetland and river sediments commonly result in the formation of iron (Fe) oxide precipitates. These redox‐sensitive precipitates influence the release of nutrients and metals to surface water and can act as ‘contaminant sponges’ by absorbing toxic compounds. We explore the feasibility of a non‐invasive, high‐resolution magnetic susceptibility (MS) technique to efficiently map the spatial variations of magnetic Fe oxide precipitates in the shallow bed of three rivers impacted by anoxic groundwater discharge. Laboratory analyses on Mashpee River (MA, USA) sediments demonstrate the sensitivity of MS to sediment Fe concentrations. Field surveys in the Mashpee and Quashnet rivers (MA, USA) reveal several discrete high MS zones, which are associated with likely anoxic groundwater discharge as evaluated by riverbed temperature, vertical head gradient, and groundwater chemistry measurements. In the East River (CO, USA), widespread cobbles/rocks exhibit high background MS from geological ferrimagnetic minerals, thereby obscuring the relatively small enhancement of MS from groundwater induced Fe oxide precipitates. Our study suggests that, in settings with low geological sources of magnetic minerals such as lowland rivers and wetlands, MS may serve as a complementary tool to temperature methods for efficiently mapping Fe oxide accumulation zones due to anoxic groundwater discharges that may function as biogeochemical hot spots and water quality control points in gaining systems.

Wang, Chen↗
DOE OSTI2020

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

Since the first detailed compositional analysis of dissolved organic matter (DOM) in 1995, high resolution mass spectrometry (HRMS) has become a vital tool for DOM characterization. While the upward trend in HRMS for molecular level analysis of DOM continues, so do the challenges of data comparison and interpretation between laboratories operating instruments of differing performance and user operating conditions. It is therefore essential that the community establishes whether data and trends can be compared robustly between research groups. To this end, 4 identically prepared DOM samples were each studied by 16 laboratories, representing 17 commercially-purchased instruments located across 8 countries, using positive- and negative-mode electrospray ionization (ESI) HRMS analysis. Despite the widely reported sensitivity of ESI experiments to sample matrix, preparation, ion source operation and instrument tuning parameters, the instruments used in this study showed relatively low variability between results for the same samples, particularly H/C and AI metrics. The variation in O/C and m/z metrics was relatively higher, although z-score graphs and %BCD demonstrated that the relative difference in general chemical composition between samples was the same for each instrument included in the study. As such, these metrics can be used for fingerprinting of DOM samples and as a benchmark for quality control in participating laboratories, with future strides in environmental science building upon these efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Organic contamination detection for isotopic analysis of water by laser spectroscopy

Hydrogen and oxygen stable isotope ratios (δ 2 H, δ 17 O, and δ 18 O values) are commonly used tracers of water. These ratios can be measured by isotope ratio infrared spectroscopy (IRIS). However, IRIS approaches are prone to errors induced by organic compounds present in plant, soil, and natural water samples. A novel approach using 17 O-excess values has shown promise for flagging spectrally contaminated plant samples during IRIS analysis. A systematic assessment of this flagging system is needed to prove it useful. Errors induced by methanol and ethanol water mixtures on measured IRIS and isotope ratio mass spectrometry (IRMS) results were evaluated. For IRIS analyses both liquid- and vapour-mode (via direct vapour equilibration) methods are used. The δ 2 H, δ 17 O, and δ 18 O values were measured and compared with known reference values to determine the errors induced by methanol and ethanol contamination. In addition, the 17 O-excess contamination detection approach was tested. This is a post-processing detection tool for both liquid and vapour IRIS triple-isotope analyses, utilizing calculated 17 O-excess values to flag contaminated samples. Organic contamination induced significant errors in IRIS results, not seen in IRMS results. Methanol caused larger errors than ethanol. Results from vapour-IRIS analyses had larger errors than those from liquid-IRIS analyses. The 17 O-excess approach identified methanol driven error in liquid- and vapour-mode IRIS samples at levels where isotope results became unacceptably erroneous. For ethanol contaminated samples, a mix of erroneous and correct flagging occurred with the 17 O-excess method. Here our results indicate that methanol is the more problematic contaminant for data corruption. The 17 O-excess method was therefore useful for data quality control. Organic contamination caused significant errors in IRIS stable isotope results. These errors were larger during vapour analyses than during liquid IRIS analyses, and larger for methanol than ethanol contamination. The 17 O-excess method is highly sensitive for detecting narrowband (methanol) contamination error in vapour and liquid analysis modes in IRIS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Development of an optimized and scalable method for isolation of umbilical cord blood-derived small extracellular vesicles for future clinical use

Abstract Extracellular vesicles (EV) are a promising therapeutic tool in regenerative medicine. These particles were shown to accelerate wound healing, through delivery of regenerative mediators, such as microRNAs. Herein we describe an optimized and upscalable process for the isolation of EV smaller than 200 nm (sEV), secreted by umbilical cord blood mononuclear cells (UCB-MNC) under ischemic conditions and propose quality control thresholds for the isolated vesicles, based on the thorough characterization of their protein, lipid and RNA content. Ultrafiltration and size exclusion chromatography (UF/SEC) optimized methodology proved superior to traditional ultracentrifugation (UC), regarding production time, standardization, scalability, and vesicle yield. Using UF/SEC, we were able to recover approximately 400 times more sEV per mL of media than with UC, and upscaling this process further increases EV yield by about 3-fold. UF/SEC-isolated sEV display many of the sEV/exosomes classical markers and are enriched in molecules with anti-inflammatory and regenerative capacity, such as hemopexin and miR-150. Accordingly, treatment with sEV promotes angiogenesis and extracellular matrix remodeling, in vitro. In vivo, UCB-MNC-sEV significantly accelerate skin regeneration in a mouse model of delayed wound healing. The proposed isolation protocol constitutes a significant improvement compared to UC, the gold-standard in the field. Isolated sEV maintain their regenerative properties, whereas downstream contaminants are minimized. The use of UF/SEC allows for the standardization and upscalability required for mass production of sEV to be used in a clinical setting.

Cardoso, Renato M. S.↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2022

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗