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DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗

DOE OSTI2026

Thermoreflectance Detection of Point Defects Resulting from Focused Ion Beam Milling

Focused ion beam (FIB) milling is a commonly used tool for nanoscale material processing, such as for transmission electron microscopy (TEM) sample preparation, or the creation of fiducial markers prior to other processes and measurements. During milling, a high energy ion beam is used to remove material via sputtering. The expelled target material may return to the sample surface however, affecting subsequent measurements. Beam spreading or irradiation due to neutral gallium may also irradiate a larger area than intended. Extensive research has explored the effects of FIB milling on the prepared TEM sample, but few have looked at the effects of milling on the properties of the sample surrounding the milled region. We use multiple pump-probe laser-based techniques (time domain thermoreflectance and steady-state thermoreflectance) to measure the spatial extent of FIB-induced surface/subsurface changes on a series of silicon wafers milled at multiple currents and doses. We supplement these measurements with high-resolution scanning transmission electron microscopy, energy dispersive X-ray spectroscopy, stylus profilometry, and time-of-flight secondary ion mass spectroscopy. We find a sample surface affected by the FIB up to 1 mm from where milling occurred, with a notable dependence on the ion beam current. We also note remarkably high sensitivity to surface defects using the thermoreflectance metrologies, including detection where other measurements failed.

defects↗

DOE OSTI2024

e RPCA : Robust Principal Component Analysis for Exponential Family Distributions

Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.

Zheng, Xiaojun↗

DOE OSTI2025

Autonomous Aerosol and Plasma Co‐Jet Printing of Metallic Devices at Ambient Temperature

Abstract Additive manufacturing of metallic materials holds the potential to revolutionize the fabrication of functional devices unattainable via traditional methods. Despite recent advancements, printing metallic materials typically requires thermal processing at elevated temperatures to form dense structures with desired properties, which presents a major challenge for direct printing and integration with temperature‐sensitive materials. Herein, a unique co‐jet printing (CJP) method is reported integrating an aerosol jet and a non‐thermal, atmospheric pressure plasma jet to enable concurrent aerosol deposition of metal nanoparticle inks and in situ sintering at ambient temperature. A machine learning algorithm is integrated with the CJP to perform real‐time defect detection and autonomous correction, enhancing the yield of printed films with high electrical conductivity from 44% to 94%. Concurrent printing and sintering eliminate the need for post‐printing processing, reducing the overall manufacturing time by multiple folds depending on product size. CJP enables direct printing of functional devices on a variety of temperature‐sensitive materials including biological materials. Direct printing of hydration sensors on living plant leaves is demonstrated for long‐duration monitoring of hydration level in the plant. The versatile CJP method opens tremendous opportunities to harmoniously integrate abiotic and biotic materials for emerging applications in wearable/implantable devices and biohybrid systems.

Du, Yipu [Department of Aerospace and Mechanical E↗

DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗

DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗

DOE OSTI2024

Design of robot grippers for binder jet products handling

Dimension accuracy, damage minimization, and defect detection are essential in manufacturing processes, especially additive manufacturing. These types of challenges may arise either during the manufacture of a product or its use. The repeatability of the process is vital in additive manufacturing systems. However, human users may lose concentration and, thus, would be a great alternative as an assistant. Depending on the nature of work, a robot’s fingers might vary, for example, mechanical, electrical, vacuum, two-fingers, and three-fingers. In addition, the end effector plays a vital role in picking up an object in the advanced manufacturing process. However, inbuilt robotic fingers may not be appropriate in different production environments. In this research presented here considering metal binder jet additive manufacturing, the two-finger end- effectors are proposed design, analysis, and experiment to pick up an object after completing the production process from a specific location. The final designs were further printed by using a 3D metal printer and installed in the existing robotic systems. These new designs are used successfully to hold the object from the specific location by reducing the contact force that was not possible with the previously installed end effector's finger. In addition, a numerical study was conducted in order to compare the flowability of the geometric shape of finger's free areas.

42 ENGINEERING↗

DOE OSTI2023

Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions

The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlO$_{x}$/Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces

Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

Engineering↗

DOE OSTI2021

Remote Monitoring and Diagnostics of Pitch-Bearing Defects in an MW-Scale Wind Turbine Using Pitch Symmetrical-Component Analysis

Recently, multiple wind turbine failure databases have reviewed that the pitch system is one of the subassemblies with the highest failure rates and largest contributors to the overall downtime. Therefore, there has been an increasing interest to provide remote health monitoring for wind turbine pitch system. While most of the research articles are discussing pitch actuation system (hydraulic or electric actuator) faults only, there is very limited research on pitch-bearing-defect detection. This article provides a remote and hardware-free solution to monitor multiaxis pitch-bearing health condition called pitch symmetrical-component analysis. It leverages readily available low-resolution (100 Hz) electrical measurements, mechanical measurements, and control signals from the existing pitch control platform, and innovatively applies symmetrical-component analysis in multiphase ac system to multiaxis pitch control system and introduces multiaxis pitch-bearing degradation trending curves. This hardware-free solution can be directly applied to the existing wind turbines and successfully give the wind farm operator an early warning before multiaxis pitch bearing fails. It has been proved to be accurate, low cost, and has minimum impacts on turbine normal operation, and has been validated by field data from several North America MW-scale wind farms. This approach turns out to be the first hardware-free (no additional hardware needed) method to remotely monitor and diagnose multiaxis wind turbine pitch-bearing condition.

17 WIND ENERGY↗

DOE OSTI2023

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

This dataset contains layer-wise powder bed images from three different powder bed printing technologies â laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)

36 MATERIALS SCIENCE↗

DOE OSTI2023

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)

This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Synthesis and Single Crystals of Refractory Oxides of Lanthanides and Thorium

At the completion of this program, we can report that we developed a considerable degree of technical improvements in our ability to perform hydrothermal reactions at high temperatures and pressures. We can now routinely perform reactions at 700-750°C and 200 MPa. Currently we are in the process of exploiting this new technology synthesizing a range of exotic new materials investigating relatively poorly understood materials. Our initial efforts focused on the chemistry of rare earth oxides with tetravalent and pentavalent oxides. We recently published a study of the lanthanides with Nb 5+ and Ta 5+ ions, where we grew oxides such as RENdO 4 and RETaO 4 as high quality single crystals. These compounds were targeted as potential hosts for luminescent and scintillation materials, particularly given that they are among the densest oxide hosts and hence have good potential as absorbers for high energy radiation like X-rays and gamma rays. We also isolated a range of unusual new rare earth tantalates with very complex structures. indicating that the chemistry is very sensitive to conditions. We performed some fairly comprehensive examinations of the solid-state chemistry of rare earth ions with various tetravalent metal ions especially Si 4+ , Ge 4+ , Sn 4+ and Ti 4+ . Given the potential role of rare earth silicates in immobilizing radioactive waste elements in long-term storage, and the similarity of our hydrothermal fluids with known geological conditions, this chemistry continues to be relevant. We prepared an extensive series of new lanthanide germanates (e.g. RE 13 Ge 6 O 31 (OH), BaRE 10 (GeO 4 ) 4 O 8 ). and found that there is there is almost no overlap between the chemistry of the rare earth silicates. Stannic oxide (SnO 2 ) is much more refractory and requires higher temperatures and of mineralizer concentrations. One significant result is the growth of RE 2 Sn 2 O 7 pyrochlore single crystals. These are of interest because the rare earth stannate pyrochlores are known to display a wide range of magnetic frustration such as spin ice behavior. We grew high quality single crystals of rare earth germanate and stannate pyrochlores and this led to a collaboration with Professor Kate Ross at Colorado State. Preliminary measurements, indicate that the crystals contain no detectable defects or site disorder. Initial neutron diffraction on single crystals was performed at Oak Ridge, and more detailed experiments involving the Ross group are underway at both NIST and ORNL. This particular chemistry has turned out to be the most potentially significant work on this project and the collaborative effort with Prof. Ross is the topic of a DoE renewal project on quantum materials. Our initial foray into the hydrothermal chemistry of rare earth titanates has also been very promising and a range of cubic and polar ferroic phases of the light rare earths RE 2 Ti 2 O 7 (RE = La - Pr) in the P2 1 phase. We also discovered an interesting new phase Ce 2 Ti 4 O 11 that can have implications in heavy metal immobilization and storage. along with a series of new rare earth titanates (La 5 Ti 4 O 15 (OH) Sm 3 TiO 5 (OH) 3 and Lu 5 Ti 2 O 11 (OH) with exceptionally complex structures. One interesting sidelight has been high temperature hydrothermal chemistry terbium, including the growth of large crystals of TbO(OH). This is not a new compound but it is the first time it has been grown as large single crystals. The Tb atom density is almost as high as that in Tb 2 O 3 and has a very high Verdet constant (ca. 70), making it a very attractive candidate as a Faraday rotator. Unfortunately it is not in a cubic structure but he material is hard, stable, pure and inexpensive, so should still be an attractive Faraday oscillator. We recently received a patent on this material. We also synthesized K 2 Tb(Ge 2 O 7 ) containing stable octahedral Tb 4+ ions, which appears to be the first example of a well-characterized Tb 4+ complex. Given that Tb 4+ has been proposed as a benign surrogate for more treacherous tetravalent ions such as Cf 4+ and Bk 4+ , we think that Tb 4+ silicates can be a particularly useful study for actinide immobilization and related work. We also began reaction studies with rare earths and both ReO 2 and RuO 2 . These resulted in large single crystals of species like RE 5 Ru 2 O 12 , RE 4 Re 2 O 11 , REReO 4 and RE 2 ReO 5 . Several of these samples have already been sent to ORNL for magnetic and neutron diffraction studies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

International Meeting on Fuel Cell and Electrolyzer Quality Control: Summary Report

Quality control (QC) for both polymer electrolyte membrane fuel cell and electrolysis membrane electrode assembly (MEA) materials is a key challenge for scale-up and cost reduction. Developing methods for detecting defects, as well as measuring critical material properties and understanding the impact of as-manufactured variations in these materials on cell performance and lifetime, are critical barriers. To help address these needs, the National Research Council Canada (NRC), Fraunhofer Institute for Solar Energy Systems (ISE), and the National Renewable Energy Laboratory (NREL) have organized and facilitated a series of workshops on the topic, bringing together industry, academia, and research institutions from North America and Europe. Prior workshops in Canada and Germany have focused on the status of quality tool capabilities and identification of needed developments for fuel cells. These meetings have garnered an excellent response and follow-on attendance, with over 100 unique attendees.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

SIS-AOP Cueing/Segmenting Algorithm (FOA_SIS-AOP) Using the Sandia FOA 4.0 Framework

For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Sound Speeds of Solids from Ultrasonic Pulse Receiver Measurements

A common method of determining elastic material properties is utilizing an ultrasonic pulse receiver. This is a non-destructive test (NDT) causing no plastic deformation of the material. It requires a pulse generator, oscilloscope, and two transducers to measure the sound velocity of a material. Both longitudinal and shear sound velocities may be obtained by utilizing the appropriate transducer. Acoustic nondestructive testing methods are often used in manufacturing certification processes and for material defect detection in industry. These methods can also be utilized to directly measure the elastic moduli of a material without damaging the material. Overall, it is a low-cost experiment with minimal preparation, and is easy to use.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts

This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.

99 GENERAL AND MISCELLANEOUS↗