Automated Ultrasonic Scanning System for In-Situ Defect Detection in Composites During Cure
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Tyler B Hudson↗
SEARCH · Engineering Papers
Results for “Defect detection”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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3D X-ray CT for BGA/CGA Workmanship Defect Detection
Commercial-off-the-shelf (COTS) advanced microelectronic technologies in high-reliability versions are now being considered for use in a number of National Aeronautics and Space Administration (NASA) electronic systems. One of the key drawbacks of advanced electronic packages with hidden solder joint interconnections, such as the column grid array (CGA), is that inspection can be challenging—whether visually or using X-rays. In general, inspection for solder joint integrity is poor, except for identifying shorts. The new, advanced X-ray systems, especially the 3D computer tomography version, may be able to provide the three-dimensional features that are extremely difficult to resolve under the 2D systems.This report presents both 2D and 3D X-ray images along with their representative optical photomicrographs for a number of advanced electronics package assemblies including 3D stack and CGA assemblies before and after thermal cycling.
Ghaffarian, Reza↗
Towards Qualification and Certification of Laser Powder Bed Fusion Ti-6Al-4V with In-Situ Process Monitoring and Automated Defect Detection
Qualification and certification of laser powder bed fusion (LPBF) parts are two challenges that must be answered to ensure suitability for critical applications. In-situ monitoring using high frame rate thermal and conventional optical imaging sensors is applied to the LPBF build process. Currently, the large volume of data from such sensors becomes untenable for manual inspection in production environments. This presentation serves to address this in-situ monitoring deficiency in two ways. First, a framework for managing data streams from LPBF process monitoring sensors is described. Second, two candidate image analysis techniques are presented: one is a set of heuristics that are easily interpretable, and the other is an uninterpretable convolutional neural network. These strategies are compared in terms of performance, computational expense, and speed. These methodologies represent platforms for connecting processing conditions to process modeling efforts aligned with the qualification and certification mission for LPBF Ti-6Al-4V components.
Qualification↗
In Situ Monitoring for Defect Detection and Certification of AM
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in situ monitoring↗
An experiment to assess the cost-benefits of code inspections in large scale software development
This experiment (currently in progress) is designed to measure costs and benefits of different code inspection methods. It is being performed with a real development team writing software for a commercial product. The dependent variables for each code unit's inspection are the elapsed time and the number of defects detected. We manipulate the method of inspection by randomly assigning reviewers, varying the number of reviewers and the number of teams, and, when using more than one team, randomly assigning author repair and non-repair of detected defects between code inspections. After collecting and analyzing the first 17 percent of the data, we have discovered several interesting facts about reviewers, about the defects recorded during reviewer preparation and during the inspection collection meeting, and about the repairs that are eventually made. (1) Only 17 percent of the defects that reviewers record in their preparations are true defects that are later repaired. (2) Defects recorded at the inspection meetings fall into three categories: 18 percent false positives requiring no author repair, 57 percent soft maintenance where the author makes changes only for readability or code standard enforcement, and 25 percent true defects requiring repair. (3) The median elapsed calendar time for code inspections is 10 working days - 8 working days before the collection meeting and 2 after. (4) In the collection meetings, 31 percent of the defects discovered by reviewers during preparation are suppressed. (5) Finally, 33 percent of the true defects recorded are discovered at the collection meetings and not during any reviewer's preparation. The results to date suggest that inspections with two sessions (two different teams) of two reviewers per session (2sX2p) are the most effective. These two-session inspections may be performed with author repair or with no author repair between the two sessions. We are finding that the two-session, two-person with repair (2sX2pR) inspections are the most expensive, taking 15 working days of calendar time from the time the code is ready for review until author repair is complete, whereas two-session, two-person with no repair (2sX2pN) inspections take only 10 working days, but find about 10 percent fewer defects.
Porter, A.↗
Detection of CFRP Composite Manufacturing Defects Using a Guided Wave Approach
NASA Langley Research Center is investigating a guided-wave based defect detection technique for as-fabricated carbon fiber reinforced polymer (CFRP) composites. This technique will be extended to perform in-process cure monitoring, defect detection and size determination, and ultimately a closed-loop process control to maximize composite part quality and consistency. The overall objective of this work is to determine the capability and limitations of the proposed defect detection technique, as well as the number and types of sensors needed to identify the size, type, and location of the predominant types of manufacturing defects associated with laminate layup and cure. This includes, porosity, gaps, overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focuses on detection of the porosity formed from variations in the matrix curing process, and on local overlaps intentionally introduced during layup of the prepreg. By terminating the cycle prematurely, three 24-ply unidirectional composite panels were manufactured such that each subsequent panel had a higher final degree of cure, and lower level of porosity. It was demonstrated that the group velocity, normal to the fiber direction, of a guided wave mode increased by 5.52 percent from the first panel to the second panel and 1.26 percent from the second panel to the third panel. Therefore, group velocity was utilized as a metric for degree of cure and porosity measurements. A fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for the detection and size determination of an overlap By transforming the plate response from the time-space domain to the frequency-wavenumber domain, the total wavefield was then separated into the incident and backscatter waves. The overlap region was accurately imaged by using a zero-lag cross-correlation (ZLCC) imaging condition, implying the incident and backscattered waves are in phase over the overlap boundaries.
Hudson, Tyler B.↗
The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts
Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.
36 MATERIALS SCIENCE↗
Nonlinear ultrasonic scanning to detect material defects
A method and system are provided to detect defects in a material. Waves of known frequency(ies) are mixed at an interaction zone in the material. As a result, at least one of a difference wave and a sum wave are generated in the interaction zone. The difference wave occurs at a difference frequency and the sum wave occurs at a sum frequency. The amplitude of at least one nonlinear signal based on the sum and/or difference waves is then measured. The nonlinear signal is defined as the amplitude of one of the difference wave and sum wave relative to the product of the amplitude of the surface waves. The amplitude of the nonlinear signal is an indication of defects (e.g., dislocation dipole density) in the interaction zone.
Yost, William T.↗
Scanning Ultrasonic Spectroscopy System Developed for the Inspection of Composite Flywheels
Composite flywheels are being considered as replacements for chemical batteries aboard the International Space Station. A flywheel stores energy in a spinning mass that can turn a generator to meet power demands. Because of the high rotational speeds of the spinning mass, extensive testing of the flywheel system must be performed prior to flight certification. With this goal in mind, a new scanning system has been developed at the NASA Glenn Research Center for the nondestructive inspection of composite flywheels and flywheel subcomponents. The system uses ultrasonic waves to excite a material and examines the response to detect and locate flaws and material variations. The ultrasonic spectroscopy system uses a transducer to send swept-frequency ultrasonic waves into a test material and then receives the returning signal with a second transducer. The received signal is then analyzed in the frequency domain using a fast Fourier transform. A second fast Fourier transform is performed to examine the spacing of the peaks in the frequency domain. The spacing of the peaks is related to the standing wave resonances that are present in the material because of the constructive and destructive interferences of the waves in the full material thickness as well as in individual layers within the material. Material variations and flaws are then identified by changes in the amplitudes and positions of the peaks in both the frequency and resonance spacing domains. This work, conducted under a grant through the Cleveland State University, extends the capabilities of an existing point-by-point ultrasonic spectroscopy system, thus allowing full-field automated inspection. Results of an ultrasonic spectroscopy scan of a plastic cylinder with intentionally seeded flaws. The result of an ultrasonic spectroscopy scan of a plastic cylinder used as a proof-of-concept specimen is shown. The cylinder contains a number of flat bottomed holes of various sizes and shapes. The scanning system was able to successfully detect all the defects in the material. Ultrasonic spectroscopy results for a second specimen are shown along with a conventional ultrasonic C-scan. The second specimen is a section of a flywheel subcomponent that has a series of drilled holes and notches. This specimen is employed as a defect detection standard to evaluate the various nondestructive evaluation methods under consideration. Scanning results demonstrate the ability of the system to detect flaws on the order of 10 mils in the radial direction and 5 mils in the circumferential direction. Work conducted to date has shown that scanning ultrasonic spectroscopy is a viable tool for the inspection of composite flywheel systems. Ongoing development work is focused on refining the system and scanning parameters for improved resolution and defect detection.
Martin, Richard E.↗
Spoof Plasmon Polaritons for NDE Applications an Experimental Demonstration
NASA is exploring the use of microwave spoof plasmon polaritons to detect defects in metallic structures. This work investigates the detection of simulated defects in the form of aluminum wires as small as 1.6 mm diameter and 2mm long that are placed in a grooved aluminum test article. The inclusions simulate manufacturing defects, and the sum of the delta impedance is shown to increase with increasing volume of added metal. A threshold value of 50Ω has been proposed for defect detection. In addition to detecting defects, the technique also estimates the volume of the defect.
Microwave↗
Spoof Plasmon Sensing for NDE Application
NASA is exploring the use of microwave spoof plasmon polaritons to detect defects in metallic structures. This work investigates the detection of simulated defects in the form of aluminum wires as small as 1.6 mm diameter and 2 mm long that are placed in a grooved aluminum test article. The inclusions simulate manufacturing defects, and the sum of the delta impedance is shown to increase with increasing volume of added metal. Based on the results, a threshold value of 50Ω has been proposed for defect detection. In addition to detecting defects, the technique also estimates the volume of the defect.
Microwave↗
2.5D Super-Resolution Approaches for X-Ray Computed Tomography-Based Inspection of Additively Manufactured Parts
X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.
Sullivan, Haley↗
Assurance Equations: A Cost and Criticality Model for Optimizing Quality Assurance Surveillance
The cost of quality vs cost of failure correction has been a long-running topic of discussion within the Aerospace community. It leads directly to concepts of “risk tolerance”, and risk-based decision-making. It would be valuable if there was a way to compute the optimal investment in customer-executed quality assurance activities using defect significance with respect to performance objectives, the activities’ defect detection effectiveness, and the cost-penalty for late discovery of impactful defects. This optimization is particularly of interest to projects whose budget constraints significantly limit their risk management options.The cost to fix defects (i.e., failure correction) escalates as the project matures. There have been studies attempting to determine the relative cost of fixing defects discovered during various phases of a project life cycle with important implications, all of which suggest growth factors are large. The commonly referred to 1:10:100 rule represents a cost multiplier for repair/rework across the Design to Fab to Test hardware development phases. Cost premiums for QA activities also accumulate when they are treated as mandatory (due to schedule drag) or are performed later than their assigned phase.This paper describes the modeling of development phase -dependencies in the conduct of typical customer-executed quality assurance activities. Our initial modeling encompasses:• Distinct phases of the production lifecycle• Multiple kinds of Defects, each with some a-priori likelihood of being present• Each defect’s impact on performance Objectives for a type of hardware• The cost and efficacy of assurance techniques at detecting such Defects• The costs of fixing those Defects detected in a given phase of the production lifecycleThe model captures assurance activities’ abilities to Detect defects. Upon detection it is assumed that the Defect is immediately fixed. Defects that “escape” detection by some activity may thereafter be detected by a later activity, but by then the cost of fixing the Defect may have escalated. Defects are related to the performance Objectives they would detract from, were those Defects to remain present in the operating system.We have constructed and are exploring, a model that relates the importance of hardware system elements to mission objectives, the impact of types of Defects on those hardware types, the cost of customer-executed assurance activities (i.e., supplier controls) and their effectiveness towards reducing an impactful quality escape, and the cost of Defect correction across production phase. We describe the approach taken to select the key model aspects, why they are relevant to our NASA mission, and our efforts to populate it with relevant and contemporary data. We use a notional example to illustrate model design and function.
Plante, Jeannette↗
Probability of detection of defects in coatings with electronic shearography
The goal of this research was to utilize statistical methods to evaluate the probability of detection (POD) of defects in coatings using electronic shearography. The coating system utilized in the POD studies was to be the paint system currently utilized on the external casings of the NASA Space Transportation System (STS) Revised Solid Rocket Motor (RSRM) boosters. The population of samples was to be large enough to determine the minimum defect size for 90 percent probability of detection of 95 percent confidence POD on these coatings. Also, the best methods to excite coatings on aerospace components to induce deformations for measurement by electronic shearography were to be determined.
Maddux, Gary A.↗
Defect Recognition for Eddy Current Testing of Spent Nuclear Fuel Canister using Convolutional Neural Network
This paper proposes an accurate and robust defect detection solution for 304L and 306L stainless steel (SS) weld. In the proposed solution, Eddy current testing (ECT) is employed to generate 2-dimensional (2D) data for samples under test with defects. The 2D data can be treated as images for deep learning-based defect detection. Since convolutional neural networks (CNNs) are powerful in processing images, CNN is employed in this study for defect detection. Experiments are conducted on a submerged arc welding (SAW) 304L SS weld sample with an artificial crack generated by waterjet cutting. The ECT data on this seeded fault sample is utilized to verify the proposed solution. For this purpose, the ECT measurement are separated as from Fault area and Normal area, which are used for CNN training. After training, the testing data is used for verification. Experimental results demonstrate the feasibility and effectiveness of the proposed solution.
Niu, Guangxing↗
A Lizard-inspired Tube Inspector (LTI) Robot
This final report summarizes the research findings of the current project. This project is a collaboration between New Mexico State University (NMSU) as a lead (Recipient) and Arizona State University (ASU) as a Co-Recipient. Tubular structures are common components of boilers and heat exchangers in power plants. Over time, these components may suffer corrosion, cracks, and stress-corrosion cracks in either the body or the welded connections. A single tube leakage can cause an outage of several weeks. Regular inspection is a key safety factor when ensuring that power plants are maintained in reliable, operational condition. This inspection, however, is challenging, time-consuming, and in many cases impossible due to accessibility issues and safety concerns. Recent developments in robotic-based inspection can offer a great solution. Hard-to-reach places can be inspected without overhauling the unit, saving time and cost. Although several robots have been designed and implemented successfully for inspecting power plant components, in particular tubular structures, their mobility and flexibility are limited. Most of these robots use wheels for mobility which reduces their maneuverability of these robots. Moreover, they usually use magnets to attach to tubes which will not work on tubes with non-ferromagnetic materials. These robots usually carry measurement tools for a particular non-destructive testing (NDT) method such as ultrasound testing (UT) that requires a couplant to perform a point-by-point (scanning) inspection of the tubular structure. In this project, we developed a versatile lizard-inspired tube inspector (LTI) robot with embedded inspection sensing components for tube inspection which removes the need for point-by-point scanning of tube surface for crack and corrosion detection. Inspired by a “lizard”, the novelty of the current project is the integration of couplant-free ultrasound sensing and transmission, advanced data-driven defect detection and imaging, and friction-based mechanical mobility components in a single robot to eliminate a need for smooth surfaces and simple geometry for mobility and scanning. The LTI robot could replace the wheel-based approach with friction-based mobility to significantly increase the flexibility and maneuverability of the robot. The LTI robot can get into a power plant unit, such as a boiler, from a small area allowing it to access a component of interest for inspection (e.g., move on OD, curved and flat surfaces, non-ferromagnetic or ferromagnetic materials, and tubes with rough surfaces and complex geometries). Additionally, an advanced data-driven method using ultrasound data was pioneered to allow the robot to detect defects in the entire area between the robot’s multi-functional mobility system (grippers). Integrating a couplant-free ultrasound sensing in the robot’s grippers as well as using advanced data-driven methods allowed the LTI to detect defects in the entire cross-section of a tube using its grippers when stationary and when mobile.
20 FOSSIL-FUELED POWER PLANTS↗
Probability of detection of defects in coatings with electronic shearography
The goal of this research was to utilize statistical methods to evaluate the probability of detection (POD) of defects in coatings using electronic shearography. The coating system utilized in the POD studies was to be the paint system currently utilized on the external casings of the NASA space transportation system reusable solid rocket motor boosters. The population of samples was to be large enough to determine the minimum defect size for 90-percent POD of 95-percent confidence POD on these coatings. Also, the best methods to excite coatings on aerospace components to induce deformations for measurement by electronic shearography were to be determined.
Russell, S. S.↗
Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)
RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.
36 MATERIALS SCIENCE↗