Wide Area Monitoring of Aircraft Structures Using Acousto-Ultrasonic Sensor Networks.
Abstract not provided.
Roach, Dennis P.↗
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Safety envelope for load tolerance of structural element design based on multi-stage testing
Structural elements, such as stiffened panels and lap joints, are basic components of aircraft structures. For aircraft structural design, designers select predesigned elements satisfying the design load requirement based on their load-carrying capabilities. Therefore, estimation of safety envelope of structural elements for load tolerances would be a good investment for design purpose. In this article, a method of estimating safety envelope is presented using probabilistic classification, which can estimate a specific level of failure probability under both aleatory and epistemic uncertainties. An important contribution of this article is that the calculation uncertainty is reflected in building a safety envelope using Gaussian process, and the effect of element test data on reducing the calculation uncertainty is incorporated by updating the Gaussian process model with the element test data. It is shown that even one element test can significantly reduce the calculation uncertainty due to lacking knowledge of actual physics, so that conservativeness in a safety envelope is significantly reduced. The proposed approach was demonstrated with a cantilever beam example, which represents a structural element. The example shows that calculation uncertainty provides about 93% conservativeness against the uncertainty due to a few element tests. As a result, it is shown that even a single element test can increase the load tolerance modeled with the safety envelope by 20%.
42 ENGINEERING↗
Application and Certification of Comparative Vacuum Monitoring Sensors for Structural Health Monitoring of 737 Wing Box Fittings
Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft and space vehicles. The use of in-situ sensors for real-time health monitoring of aircraft structures are a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. Other advantages of on-board distributed sensor systems are that they can eliminate costly, and potentially damaging, disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time- consuming manual inspections. This report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
42 ENGINEERING↗
Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection
Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.
composite materials↗
A Weighting Grade-Based Optimization Method for Determining Refill Friction Stir Spot Welding Process Parameters
The welding process used in fabricating thin-walled structures by refill friction stir spot welding (RFSSW) should be characterized by a high strength of welds and high process repeatability which is demonstrated by a small dispersion of the load capacity of the joints. The present work is designed to optimize RFSSW process parameters for 7075-T6 Alclad aluminum alloy sheets used to fabricate aircraft structures. The optimization was performed by scalarization of the objective function using the weighting grades method. The study considers the effect of process parameters, i.e., tool plunge depth, duration of welding, tool rotational speed, on the tensile/shear strength of the joints, and dispersion of the load capacity. It was found that it was possible to choose the optimal welding parameters taking into account maximization of the load capacity and minimization of the dispersion of the joint strength via a best compromise between the tool rotational speed ensuring adequate plasticization of the base material and the duration of welding ensuring that a fine-grained joint microstructure is obtained.
36 MATERIALS SCIENCE↗
Manufacturing and Validation of a Novel Composite Component for Aircraft Main Landing Gear Bay
Composite materials may reduce the final weight of the aircraft structural components, in addition to improve fatigue performance and corrosion resistance. In order to achieve the optimization of air transport systems, making them increasingly sustainable, the structural design must be surely reviewed, starting to follow the “composite thinking” philosophy. The present research provides some relevant outcomes concerning the design of a composite sample for the main landing gear bay of a large commercial airplane (EASA CS25 category), within ITEMB (integrated full composite main landing gear bay concept) project, a program of Clean Sky 2 EU research framework. The most ambitious goal is to develop a new generation of lower center fuselage (LCF) with an innovative integrated landing system in the fuselage, which is considered the next frontier in the development of landing systems for medium-haul aircraft, such as the Airbus A320 aircraft family. The development of a different architecture, with the landing gear integrated within the related fuselage bay, could lead to a simplification of the whole subassembly with potential advantage in terms of construction and assembly times. Final target of the project is the manufacturing of an innovative monolithic composite structure that will replace the actual configuration (a mixed structure of metal and composite subassemblies) reducing or actually removing all the cost of assembly and increasing the production rate. This paper presents the main results of the work, introducing the main processing steps and prototype results; in the last part of the work, also some experimental tests on significant element are introduced as the first assessment of the technology readiness level that has been achieved.
36 MATERIALS SCIENCE↗
A Review of Metastable Beta Titanium Alloys
In this article, we provide a broad and extensive review of beta titanium alloys. Beta titanium alloys are an important class of alloys that have found use in demanding applications such as aircraft structures and engines, and orthopedic and orthodontic implants. Their high strength, good corrosion resistance, excellent biocompatibility, and ease of fabrication provide significant advantages compared to other high performance alloys. The body-centered cubic (bcc) ß-phase is metastable at temperatures below the beta transus temperature, providing these alloys with a wide range of microstructures and mechanical properties through processing and heat treatment. One attribute important for biomedical applications is the ability to adjust the modulus of elasticity through alloying and altering phase volume fractions. Furthermore, since these alloys are metastable, they experience stress-induced transformations in response to deformation. The attributes of these alloys make them the subject of many recent studies. In addition, researchers are pursuing development of new metastable and near-beta Ti alloys for advanced applications. In this article, we review several important topics of these alloys including phase stability, development history, thermo-mechanical processing and heat treatment, and stress-induced transformations. In addition, we address recent developments in new alloys, phase stability, superelasticity, and additive manufacturing.
beta phase, metastable, titanium, aircraft, biomed↗
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↗
Effect of Localized Corrosion on Fatigue–Crack Growth in 2524-T3 and 2198-T851 Aluminum Alloys Used as Aircraft Materials
Corrosion and fatigue of aluminum alloys are major issues for the in-service life assessment of aircraft structures and for the management of aging air fleets. The aim of this work was to evaluate the effect of localized corrosion on fatigue crack growth (FCG) resistance of the AA2198-T851 Al-Li alloy (Solution Heat Treated, Cold Worked, and Artificially Aged), comparing it with the FCG resistance of AA2524-T3 (Solution Heat Treated and Cold Worked), considering the effect of seawater fog environment. Before fatigue tests, the corrosion behavior of 2198-T851 and 2524-T3 aluminum alloys was verified using open circuit potential and potentiodynamic polarization techniques. Fatigue in air and corrosion fatigue tests were performed applying a stress ratio (R) of 0.1, 15 Hz (air) and 0.1 Hz (seawater fog) frequencies, using a sinusoidal waveform in all cases. The results showed that the localized characteristics of the 2198-T851 and 2524-T3 aluminum alloys are essentially related to the existence of intermetallic compounds, which, due to their different nature, may be cathodic or anodic in relation to the aluminum matrix. The corrosive medium has affected the FCG rate of both aluminum alloys, in a quite similar way. Graphical Abstract: .
36 MATERIALS SCIENCE↗
Measurement-driven, model-based estimation of residual stress and its effects on fatigue crack growth. Part 1: Validation of an eigenstrain model
The objective of this paper is to validate a measurement-driven, model-based approach to estimate residual stress (RS) in samples machined from quenched aluminum stock. Model input is derived from measurement of RS in the parent stock. Validation is performed for prismatic T-sections removed from bars at different locations. We find RS predicted agrees with RS measured, by contour and neutron diffraction methods, with root-mean-square model-measurement difference of 22 MPa. Follow-on work (in Part 2) applies the RS estimation to samples representative of aircraft structures and examines the effects of RS on fatigue crack growth in the RS-bearing samples.
36 MATERIALS SCIENCE↗
DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material
Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.
anomaly detection↗
Static Strain Modelling, Calibration, and Measurements for High-Temperature Wireless SAW Resonator Operation
Static strain measurements are relevant for Integrated System Health Monitoring of civil structures, aircrafts, power plants and advanced manufacturing equipment, with implications for safety, process efficiency, and maintenance costs. Wireless strain sensing is highly desirable where the presence of wires poses significant safety concerns, increases maintenance, and thus overall costs, or where they are not feasible, such as in applications which contain moving parts. This paper presents wireless interrogation of surface acoustic wave resonators (SAWRs) fabricated on langasite (LGS) along Euler angles (0°, 138.5°, 32.9°) and aimed at the detection of static strain at hightemperature (HT, above 100°C). Both commercial HT strain gauges and finite element analysis (FEA Abaqus software) were used and compared for SAWR strain calibration from room temperature (RT) to HT. In addition, this work investigated a compromise between high temperature operation vs. sensitivity based on the thickness of ceramic adhesive used. For instance, a reduction in the adhesive thickness from about 150 μm to 135 μm, resulted in a 39% increase in sensitivity at RT. It was also observed that the sensitivity dropped up to 34% from RT to 200°C. Furthermore, wireless operation, calibration, and increase in sensitivity constitute important advances in the use of LGS SAW devices to monitor static stress in high-temperature harsh environments.
42 ENGINEERING↗
Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.
36 MATERIALS SCIENCE↗
Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.
composites↗
Bird-Strike Analysis Using Eulerian Peridynamic Formulation
The purpose of this project between Sandia National Laboratories (SNL) and The Boeing Company (Boeing) was to develop an analysis capability for predicting the loads on aircraft structures due to the inflight impact of birds. The main technical accomplishment was the development and delivery to Boeing of a new software tool that predicts the pressure over a structure's surface area, as a function of time and impact conditions, during the impact. The benefit to Boeing, and to government agencies such as NASA and the FAA, is an accurate tool to help design structures to withstand potentially catastrophic damage due to bird strike.
42 ENGINEERING↗
Tensile Behavior of Inconel X750 Sheets: Effect of Heat Treatment
X-750 is a nickel-chromium based super alloy of usefulness in a wide variety of applications. Its excellent relaxation resistance is useful for high-temperature springs and bolts; it is utilized in gas turbines, rocket engines, nuclear reactors, pressure vessels, tooling, and aircraft structures. Its performance is similar to that of Inconel alloy 600 but it is made precipitation-hardenable by additions of aluminum and titanium. It has good resistance to corrosion and oxidation along with high tensile and creep-rupture properties at temperatures up to 700 deg. C. In this study, the tensile flow behavior of the alloy was studied by performing uniaxial tensile tests under a range of temperatures and at different strain rates. To assess the microstructure-mechanical property relationships in the alloy, microstructure analysis (through the use of electron microscopy and XRD analysis) was utilized post-experiment. This paper will however focus on reporting the tensile properties and temperature and strain rate effects on the as-received alloy vs. the heat-treated alloy. (authors)
36 MATERIALS SCIENCE↗
A non-melting additive approach to structural repair of aluminum aircraft fastener holes
The damage to fastener holes in aerospace aluminum structures presents significant challenges for aircraft durability, and conventional bushing methods for repairing oversized holes often fall short due to the lack of metallurgical bonding and limited edge distance availability. This study investigates additive friction stir deposition, a non-melting additive process, as a viable alternative for the structural repair of aerospace fastener holes. The repair process, demonstrated on AA7050 (Al-Zn-Mg-Cu-Zr) hole structures, involves filling oversized holes with new material and machining to restore the original hole size. The repaired hole coupons are defect-free and exhibit good fatigue performance under fully reversed tension-compression loading (R = -1). At a nominal stress amplitude of 123.5 MPa, the average number of cycles to failure is 12,666 for unrepaired baseline coupons and 17,372 for effectively repaired coupons. Restoring complex geometries without compromising fatigue performance has been difficult in aerospace applications; this study marks the first demonstration of additive repair that consistently outperforms the unrepaired baseline coupons. Notably, the result is achieved through a low-energy, cost-effective solution without the need for post-repair heat treatment. Except for a few outliers, the post-repair fatigue performance generally remains inferior to that of undamaged, pristine coupons, likely due to precipitate evolution in AA7050 caused by the thermomechanical processing nature of additive friction stir deposition. This evolution weakens the repair region and the adjacent base material, leading to faster crack initiation and growth compared to the properly aged base material, AA7050-T7451.
36 MATERIALS SCIENCE↗
A Method For Determining The Radially-Averaged Effective Impact Area For An Aircraft Crash Into A Structure
This report presents a methodology for deriving the equations which can be used for calculating the radially-averaged effective impact area for a theoretical aircraft crash into a structure. Conventionally, a maximum effective impact area has been used in calculating the probability of an aircraft crash into a structure. Whereas the maximum effective impact area is specific to a single direction of flight, the radially-averaged effective impact area takes into consideration the real life random nature of the direction of flight with respect to a structure. Since the radially-averaged effective impact area is less than the maximum effective impact area, the resulting calculated probability of an aircraft crash into a structure is reduced.
97 MATHEMATICS AND COMPUTING↗