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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Imaging and Segmenting Grains and Subgrains Using Backscattered Electron Techniques

We present two new methods of processing data from backscattered electron signals in a scanning electron microscope to image grains and subgrains. The first combines data from multiple backscattered electron images acquired at different specimen geometries to (1) better reveal grain boundaries in recrystallized microstructures and (2) distinguish between recrystallized and unrecrystallized regions in partially recrystallized microstructures. The second utilizes spherical harmonic transform indexing of electron backscatter diffraction patterns to produce high angular resolution orientation data that enable the characterization of subgrains. Subgrains are produced during high-temperature plastic deformation and have boundary misorientation angles ranging from a few degrees down to a few hundredths of a degree. Here, we also present an algorithm to automatically segment grains from combined backscattered electron image data or grains and subgrains from high angular resolution electron backscatter diffraction data. Together, these new techniques enable rapid measurements of individual grains and subgrains from large populations.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Using EMIS to Identify Top Opportunities for Commercial Building Efficiency

Energy Management and Information Systems (EMIS) comprise a broad family of tools and services to manage commercial building energy use. These technologies offer a mix of capabilities to store, display, and analyze energy use and system data, and in some cases, provide control. EMIS technologies enable 10–20 percent site energy savings in best practice implementations. Energy Information System (EIS) and Fault Detection and Diagnosis (FDD) systems are two key technologies in the EMIS family. Energy Information Systems are broadly defined as the web-based software, data acquisition hardware, and communication systems used to analyze and display building energy performance. At a minimum, an EIS provides daily, hourly or sub-hourly interval meter data at the whole-building level, with graphical and analytical capability. Fault Detection and Diagnosis systems automatically identify heating, ventilation, and air-conditioning (HVAC) system or equipment-level performances issues, and in some cases are able to isolate the root causes of the problem. They use computer algorithms to continuously analyze system-level operational data to detect faults and diagnose their causes. Many FDD tools integrate the trend log data from a Building Automation System (BAS) but otherwise are stand-alone software packages; other types of FDD tools are implemented as “on-board” equipment-embedded diagnostics. (This document focuses on the former.) Analysis approaches adopted in FDD technologies span a variety of techniques from rule-based methods to process history-based approaches. FDD tools automate investigations that can be conducted via manual data inspection by someone with expert knowledge, thereby expanding accessibility and breath of analysis opportunity, and also reducing complexity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Automated metadata, provenance cataloging and navigable interfaces: ensuring the usefulness of extreme-scale data

The MPO (Metadata, Provenance, Ontology) Project successfully addressed the goal of improving the usefulness and traceability of scientific data by building a system that could capture and display all steps in the process of creating, analyzing and disseminating that data. Throughout history, scientists have generated handwritten logbooks to keep track of data, their hypotheses, assumptions, experimental setup, and computational processes as well as reflections on observations and issues encountered. Over the last several decades, with the growth of personal computers, handheld devices, and the World Wide Web, the handwritten logbook has begun to be replaced by electronic logbooks. This transition has brought increased capability such as supporting multi-media, hypertext, and fast searching. However, content creation and metadata (a set of data that describes and gives information about other data) capturing has for the most part remained a manual activity just as it was with handwritten logbooks. This has led to a fragmentation of data, processing, and annotation that has only accelerated as scientific workflows continue to increase in complexity. From a scientific perspective, it is very important to be able to understand the lineage of any piece of data: who, what, when, how, and why. This is typically referred to as data provenance. The fragmentation discussed previously often means that data provenance is lost. As scientific workflows move to powerful computers and become more complex, the ability to track all of the steps involved in creating a piece of data become even more difficult. It was the goal of the MPO (Metadata, Provenance, Ontology) Project to create a system (the MPO System) that allows for automatic provenance and metadata capturing in such a way to allow easy searching and browsing. This goal needed to be accomplished in a general way so that it may be used across a broad range of scientific domains, yet allow the addition of vocabulary (Ontology) that is domain specific as is required for intelligent searching and browsing in the scientific context. Through the creation and deployment of the MPO system, the goals of the project were achieved. An enhanced metadata, provenance, and ontology storage system was created. This was combined with innovative methodologies for navigating and exploring these data using a web browser for both experimental and simulation-based scientific research. In addition, a system to allow scientists to instrument their existing workflows for automatic metadata and provenance is part of the MPO system. In that way, a scientist can continue to use their existing methodology yet easily document their work. Workflows and data provenance can be displayed either graphically or in an electronic notebook format and support advanced search features including via ontology. The MPO system was successfully used in both Climate and Magnetic Fusion Energy Research. The software for the MPO system is located at https://github.com/MPO-Group/MPO and is open source distributed under the Revised BSD License. A demonstration site of the MPO system is open to the public and is available at https://mpo.psfc.mit.edu/. A Docker container release of the command line client is available for public download using the command docker pull jcwright/mpo-cli at https://hub.docker.com/r/jcwright/mpo-cli.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Robust Automatic EXAFS First-Shell Fits

Extended X-ray absorption fine structure (EXAFS) is a widely used technique for atomic structure determination. Fourier transformation connects EXAFS in k space and R space. However, determining the appropriate k-range for the transformation can be challenging, but critical for the first-shell fit. In this study, we present an automatic method to determine the k-range using the Larch package and a Python program. The first step is to estimate spectral noise across a series of k-ranges with a fixed minimum value and identify the optimal maximum value in the k-range (k max ). The k max is determined by an empirical noise threshold that marks the point where the noise level in the Fourier transformed spectrum changes dramatically. Using the obtained k max value, the first shell is modeled to determine the minimum k value (k min ) by optimizing the background function through alignment of the spectrum with theory. The optimal k min corresponds to the point of the minimum R-factor, which quantifies the difference between the experimental and fitted spectrum. Our method was tested on various typical datasets and yielded suitable k-ranges for Fourier transformation and accurate first-shell fits. This approach helps avoid unreliable, irreproducible data analysis, especially for noisy data from diluted samples, and enables robust automatic first-shell EXAFS fitting.

EXAFS analysis↗

DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗

DOE OSTI2025

Open Source Intelligence for Cybersecurity Events via Twitter Data

Dale, Dakota↗

DOE OSTI2026

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor

Generation of quantum states of light through off-resonance four-wave mixing in rubidium vapor is a straightforward and well-established technique. However, achieving a sufficiently high and consistent level of intensity difference squeezing (IDS) and intrinsic quantum correlations between photon pairs over an extended timescale, necessary for quantum-light-based nonlinear optical spectroscopy and microscopy, remains challenging and largely unexplored. Here, we report a simple stabilization method combining an active periodic laser frequency retuning with an automatic control algorithm based on quantitatively assessing the factors affecting the IDS level and squeezed light intensity. Validation of our method was performed by acquiring data over a 5-hour period, and the results demonstrate a remarkably stable squeezing level of -7.8 dB in combination with a > 4× reduction of the standard deviation of IDS level from 0.46 dB to 0.10 dB. The achieved stabilization further enables us to quantitatively assess the IDS reduction due to the scattering in a polystyrene bead suspension as a function of sample transmission. Our approach should enable a variety of applications requiring an extended squeezing stability over multiple hours, especially for those following biological processes and chemical reactions in real time.

Allen, Christian Harry [Oak Ridge National Laborat↗

DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Development and operational experience of the web based application to collect, manage, and release the alignment and calibration configurations for data processing at CMS

The alignment and calibration workflows at the Compact Muon Solenoid (CMS) experiment are fundamental to provide a high quality physics data and to maintain the design performance of the experiment. To facilitate the operational efforts required by the experiment, the alignment and calibration team has developed and deployed a set of web-based applications to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics, accessible through the Condition DB Browser. The Condition DB Browser hosts also various data management tools, including a visualization tool that allows to easily inspect alignment an calibration contents, an user-defined notification agent for delivering updates on modification to the database, a logging service for the user and the automatic online-to-offline condition uploads. In this paper we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period.

Md, Hasbuddin↗

DOE OSTI2016

MO-FG-CAMPUS-TeP3-01: A Model of Baseline Shift to Improve Robustness of Proton Therapy Treatments of Moving Tumors

Purpose: The shift in mean position of a moving tumor also known as “baseline shift”, has been modeled, in order to automatically generate uncertainty scenarios for the assessment and robust optimization of proton therapy treatments in lung cancer. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at the planning time are first generated from a 4D-CT data. The mean position of the tumor along the breathing cycle is represented by the GTV contour in the MidPCT. Several studies reported both systematic and random variations of the mean tumor position from fraction to fraction. Our model can simulate this baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall in order to allow the tumor to slip within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce its continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: Based on the first 4D-CT set, the model was able to generate a third 4D-CT that reproduced the 5.8 mm baseline-shift measured in the second 4D-CT. Water equivalent thickness (WET) of the voxels have been computed for the 3 average CTs. The root mean square deviation of the WET in the GTV is 0.34 mm between week 1 and week 2, and 0.08 mm between the simulated data and week 2. Conclusion: Our model can be used to automatically generate uncertainty scenarios for robustness analysis of a proton therapy plan. The generated scenarios can also feed a TPS equipped with a robust optimizer. Kevin Souris, Ana Barragan, and Dario Di Perri are financially supported by Televie Grants from F.R.S.-FNRS.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Developing Ultrahigh-Resolution E3SM Land Model for GPU Systems

Designing and refactoring complex scientific code, such as the E3SM land model (ELM), for new computing architectures is challenging. This paper presents design strategies and technical approaches to develop a data-oriented, GPU-ready ELM model using compiler directives (OpenACC/OpenMP). We first analyze the datatypes and processes in the original ELM code. Then we present design considerations for ultrahigh-resolution ELM (uELM) development for massive GPU systems. These techniques include the global data-oriented simulation workflow, domain partition, code porting and data copy, memory reduction, parallel loop restructure and flattening, and race condition detection. We implemented the first version of uELM using OpenACC targeting the NVidia GPUs in the Summit supercomputer at Oak Ridge National Laboratory. During the implementation, we developed a software tool (named SPEL) to facilitate code generation, verification, and performance tuning using these techniques. The first uELM implementation for Nvidia GPUs on Summit delivered promising results: 1) over 98% of the ELM code was automatically generated and tuned by scripts. Most ELM modules had better computational performances than the original ELM code for CPUs. The GPU-ready uELM is more scalable than the CPU code on fully-loaded Summit nodes. Example profiling results from several modules are also presented to illustrate the performance improvements and race condition detection. The lessons learned and toolkit developed in the study are also suitable for further uELM deployment using OpenMP on the first US exascale computer, Frontier, equipped with AMD CPUs and GPUs.

Schwartz, Peter↗

DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗

DOE OSTI2014

SU-E-J-117: Verification Method for the Detection Accuracy of Automatic Winston Lutz Test

Purpose: Winston Lutz test (WLT) has been a standard QA procedure performed prior to SRS treatment, to verify the mechanical iso-center setup accuracy upon different Gantry/Couch movements. Several detection algorithms exist,for analyzing the ball-radiation field alignment automatically. However, the accuracy of these algorithms have not been fully addressed. Here, we reveal the possible errors arise from each step in WLT, and verify the software detection accuracy with the Rectilinear Phantom Pointer (RLPP), a tool commonly used for aligning treatment plan coordinate with mechanical iso-center. Methods: WLT was performed with the radio-opaque ball mounted on a MIS and irradiated onto EDR2 films. The films were scanned and processed with an in-house Matlab program for automatic iso-center detection. Tests were also performed to identify the errors arise from setup, film development and scanning process. The radioopaque ball was then mounted onto the RLPP, and offset laterally and longitudinally in 7 known positions ( 0, ±0.2, ±0.5, ±0.8 mm) manually for irradiations. The gantry and couch was set to zero degree for all irradiation. The same scanned images were processed repeatedly to check the repeatability of the software. Results: Miminal discrepancies (mean=0.05mm) were detected with 2 films overlapped and irradiated but developed separately. This reveals the error arise from film processor and scanner alone. Maximum setup errors were found to be around 0.2mm, by analyzing data collected from 10 irradiations over 2 months. For the known shift introduced using the RLPP, the results agree with the manual offset, and fit linearly (R{sup 2}>0.99) when plotted relative to the first ball with zero shift. Conclusion: We systematically reveal the possible errors arise from each step in WLT, and introduce a simple method to verify the detection accuracy of our in-house software using a clinically available tool.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Global centroid moment tensor solutions in a heterogeneous earth: the CMT3D catalogue

SUMMARY For over 40 yr, the global centroid-moment tensor (GCMT) project has determined location and source parameters for globally recorded earthquakes larger than magnitude 5.0. The GCMT database remains a trusted staple for the geophysical community. Its point-source moment-tensor solutions are the result of inversions that model long-period observed seismic waveforms via normal-mode summation for a 1-D reference earth model, augmented by path corrections to capture 3-D variations in surface wave phase speeds, and to account for crustal structure. While this methodology remains essentially unchanged for the ongoing GCMT catalogue, source inversions based on waveform modelling in low-resolution 3-D earth models have revealed small but persistent biases in the standard modelling approach. Keeping pace with the increased capacity and demands of global tomography requires a revised catalogue of centroid-moment tensors (CMT), automatically and reproducibly computed using Green's functions from a state-of-the-art 3-D earth model. In this paper, we modify the current procedure for the full-waveform inversion of seismic traces for the six moment-tensor parameters, centroid latitude, longitude, depth and centroid time of global earthquakes. We take the GCMT solutions as a point of departure but update them to account for the effects of a heterogeneous earth, using the global 3-D wave speed model GLAD-M25. We generate synthetic seismograms from Green's functions computed by the spectral-element method in the 3-D model, select observed seismic data and remove their instrument response, process synthetic and observed data, select segments of observed and synthetic data based on similarity, and invert for new model parameters of the earthquake’s centroid location, time and moment tensor. The events in our new, preliminary database containing 9382 global event solutions, called CMT3D for ‘3-D centroid-moment tensors’, are on average 4 km shallower, about 1 s earlier, about 5 per cent larger in scalar moment, and more double-couple in nature than in the GCMT catalogue. We discuss in detail the geographical and statistical distributions of the updated solutions, and place them in the context of earlier work. We plan to disseminate our CMT3D solutions via the online ShakeMovie platform.

58 GEOSCIENCES↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2017

Convolution Correction Factor Adjustments on Static PRA Models for Event Assessment

Current probabilistic risk assessment (PRA) models will contain cut sets that have time-constrained basic events such as switched components, recovery of failed components, and failures in time. For these cut sets that have multiple time-constrained events that interact (e.g., offsite power recover at the same time as multiple diesel generators fail to run), there is the possibility that the minimal cut set quantified result is too large. Since current PRA models typically assume that the accident scenario starts at “time zero,” the fails-to-run basic events use this starting time as the basis for their respective reliability models. Over time, the standard practice was to recognize this issue and accept it as part of the conservatism built into the event tree/fault tree PRA models. However, now that these same PRA models are being used in a regulatory forum, this conservatism needs to be addressed. Further, this issue exists not only for the baseline PRA results, but for deficiencies that have been observed or those that have the potential of occurrence, whether the deficiency is complete failure or just a degraded condition. We have found that this conservatism becomes very important when evaluating scenarios such as station blackout sequences and other loss of offsite power sequences. This paper will address the theory and implemented process used by the Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) PRA software. Starting with SAPHIRE version 8, the process that was developed is to calculate a “convolution correction factor” based on three distinct calculations. The first calculation is the cut set based frequency calculated via the standard event tree/fault tree PRA model (assuming all run failures start at time equal to zero). The second calculation is to perform the exact time-constrained calculation by integrating the component failure models over the time period of interest (e.g., time to recover, a mission time). For example, this exact calculation for the case of the diesel generators is to convolve their failure models with the offsite power recovery model to obtain the exact frequency. The third calculation is to produce the convolution correction factor by dividing the exact frequency calculation by the standard cut set frequency. This convolution correction factor is then applied to the PRA cut sets in order to remove the time-constrained conservatism. The process used to obtain the convolution correction factor for the baseline PRA is important; however, in this paper we also describe what happens when there is degradation of a component or a potential deficiency. These issues become very important in order to ensure that the convolution correction factor is re-calculated correctly. If the correction factor does not get re-calculated and the same baseline convolution correction factor is used, the final analysis result could be over conservative or under conservative. This paper will discuss how these issues are addressed and provide examples on how SAPHIRE automatically makes the necessary adjustments. MathCad verification results can be computed using the provided equations and input data.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

SU-C-17A-07: The Development of An MR Accelerator-Enabled Planning-To-Delivery Technique for Stereotactic Palliative Radiotherapy Treatment of Spinal Metastases

Purpose: To develop an MR accelerator-enabled online planning-todelivery technique for stereotactic palliative radiotherapy treatment of spinal metastases. The technical challenges include; automated stereotactic treatment planning, online MR-based dose calculation and MR guidance during treatment. Methods: Using the CT data of 20 patients previously treated at our institution, a class solution for automated treatment planning for spinal bone metastases was created. For accurate dose simulation right before treatment, we fused geometrically correct online MR data with pretreatment CT data of the target volume (TV). For target tracking during treatment, a dynamic T2-weighted TSE MR sequence was developed. An in house developed GPU based IMRT optimization and dose calculation algorithm was used for fast treatment planning and simulation. An automatically generated treatment plan developed with this treatment planning system was irradiated on a clinical 6 MV linear accelerator and evaluated using a Delta4 dosimeter. Results: The automated treatment planning method yielded clinically viable plans for all patients. The MR-CT fusion based dose calculation accuracy was within 2% as compared to calculations performed with original CT data. The dynamic T2-weighted TSE MR Sequence was able to provide an update of the anatomical location of the TV every 10 seconds. Dose calculation and optimization of the automatically generated treatment plans using only one GPU took on average 8 minutes. The Delta4 measurement of the irradiated plan agreed with the dose calculation with a 3%/3mm gamma pass rate of 86.4%. Conclusions: The development of an MR accelerator-enabled planning-todelivery technique for stereotactic palliative radiotherapy treatment of spinal metastases was presented. Future work will involve developing an intrafraction motion adaptation strategy, MR-only dose calculation, radiotherapy quality-assurance in a magnetic field, and streamlining the entire treatment process on an MR accelerator.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

36 MATERIALS SCIENCE↗