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Although a great deal of success in structural integrity management has been achieved, it may be realized by excessively conservative assumptions, high inspection burdens, and aggressive maintenance and repair strategies. Corrosion has been found to reduce fatigue life, but methods to account for it in fatigue modeling are still limited. There is a recognized need to improve structural integrity calculations through the inclusion of the effect of existing corrosion damage on fatigue. The feasibility of a corrosion modified fatigue analysis process that uses finite element analysis and linear elastic fracture mechanics techniques to predict fatigue life of a corroded aircraft component based on the corrosion damage location is demonstrated in this work. A corrosion modified equivalent flaw size was successfully used to predict fatigue crack growth from AA7075-T651 specimens with two different notch geometries and corrosion damage at the notch center.

This work investigates the effects that the confined volume of atmospheric electrolytes has on the galvanic corrosion kinetics of a martensitic stainless steel alloy, UNS S13800, coupled with UNS A97075 in simulated atmospheric environments at relative humidity values that span the range of operational exposures. Restricted volumes found in thin films and droplets have been shown to control reduction reaction kinetics and are an ongoing challenge to characterize and standardize. This, along with the dynamic and high concentration of aggressive ions found in confined electrolytes, creates a unique corrosion system that requires a multifaceted approach to evaluate varied conditions, compare them with traditional measurements, and more accurately predict galvanic atmospheric corrosion. In this work, corrosion currents in galvanic couples were obtained under two environmental conditions: (1) bulk electrolytes, in a standardized test configuration, with chemistries relevant to atmospheric electrolytes; and (2) deliquesced droplets formed and equilibrated at a given temperature and relative humidity value. The corrosion currents for the same galvanic couple specimens were evaluated, using an atmospheric corrosion model, under a thin film electrolyte while statically loaded and unloaded, at two nominally different locations, e.g. Alexandria, VA, and Miami, FL, on the same date, using recorded weather conditions. The modeled corrosion currents were then compared with the currents obtained from the experimental conditions.

This paper presents a new approach to a robust Gaussian process regression, creating a non-parametric Bayesian regression estimate robust to outliers. Most existing approaches replace an outlier-prone Gaussian likelihood with a non-Gaussian likelihood induced from a heavy tail distribution, such as the Laplace distribution and Student-t distribution. However, the use of a non-Gaussian likelihood would incur the need for a computationally expensive Bayesian approximate computation in the posterior inferences. The proposed approach models an outlier as a noisy and biased observation of an unknown regression function, and accordingly, the likelihood contains bias terms to explain the degree of deviations from the regression function. We introduce two bias models that handle the bias terms differently, treating a bias as an unknown and fixed quantity or treating a bias as a random quantity. We entail how the biases can be estimated accurately with other hyperparameters by a regularized maximum likelihood estimation. Conditioned on the bias estimates, the robust GP regression can be reduced to a standard GP regression problem with analytical forms of the predictive mean and variance estimates. Therefore, the proposed approach is simple and very computationally attractive. It also gives a very robust and accurate GP estimate for many tested scenarios. For the numerical evaluation, we perform a comprehensive simulation study to evaluate the proposed approach with the comparison to the existing robust GP approaches under various simulated scenarios of different outlier proportions and different noise levels. The approach is applied to data from two measurement systems, where the predictors are based on robust environmental parameter measurements and the response variables utilize more complex chemical sensing methods that contain a certain percentage of outliers. The utility of the measurement systems and value of the environmental data are improved through the computationally efficient GP regression and bias model.

Environmentally assisted cracking (EAC) of aluminum alloys in corrosive atmospheres is an important maintenance and safety issue for U.S. Department of Defense assets. EAC initiation and propagation of cracks is influenced by the complex interactions of load, environment, and alloy properties. Traditional environmental fracture testing conducted under immersion or constant humidity conditions may produce results that are different than measurements collected under thin electrolyte layers or droplets formed during atmospheric exposure. In addition, most standard methods do not provide instantaneous measures of crack velocity that can be used to identify specific environmental conditions that promote cracking. Improved assessment of EAC susceptibility and the conditions that promote cracking of aluminum alloys has been accomplished with an autonomous, in situ measurement system that can be used in accelerated corrosion test chambers and outdoor exposure sites. Continuous measurements of crack length through...

Widespread damage in aging aircraft is becoming an increasing concern as both civil and military fleet operators are extending the service lifetime of their aircraft. Metallic components undergoing variable cyclic loadings eventually fatigue and form dislocations as precursors to ultimate failure. In order to characterize the progression of fatigue damage precursors (DP), the acoustic nonlinearity parameter is measured as the primary indicator. However, using proven standard ultrasonic technology for nonlinear measurements presents limitations for settings outside of the laboratory environment. This paper presents an approach for ultrasonic inspection through automated immersion scanning of hot section engine components where mature ultrasonic technology is used during periodic inspections. Nonlinear ultrasonic measurements were analyzed using wavelet analysis to extract multiple harmonics from the received signals. Measurements indicated strong correlations of nonlinearity coefficients and levels of fati...

Cyclic loading of mechanical components promotes the formation of dislocation dipoles in metals, which can serve as precursors to crack nucleation and ultimately lead to failure. In the laboratory setting, an acoustic nonlinearity parameter has been assessed as an effective indicator for characterizing the progression of fatigue damage precursors. However, the need to use monochromatic waves of medium-to-high acoustic energy has presented a constraint, making it problematic for use in field applications. This paper presents a potential approach for field measurement of acoustic nonlinearity by using general purpose ultrasonic pulser-receivers. Nonlinear ultrasonic measurements during fatigue testing were analyzed by the using contact and immersion pulse-through method. A novel cross-correlation filtering technique was developed to extract the fundamental and higher harmonic waves from the signals. As in the case of the classic harmonic generation, the nonlinearity parameters of the second and third harmon...

Ocean resources have the potential to provide a large source of renewable energy for communities around the globe. Technologies such as wave energy converters must be designed to operate remotely in harsh environmental conditions. These structures are exposed to widely varying structural loads, and there is interest in developing monitoring systems that can identify the presence of damage, estimate its severity, and provide maintenance or control recommendations that could protect the system from failure. The research presented in this paper focuses on using the electromechanical impedance response of piezoelectric transducers to monitor the health of composite materials similar to those used in the fabrication of several wave energy converters. Techniques have been developed to detect and classify discrete damage events such as holes and slots within composite plates, as well as fatigue damage that evolves due to manufacturing flaws such as delamination and laminate waves. Using data collected over a frequency range of 100 Hz to 100 kHz, a series of genetic algorithms and statistical modeling techniques were used to classify damage type and severity. Plate studies with discrete damage (holes, notches) provided a large dataset of 113 observations comprised of seven distinct classes, one baseline and six damage severities. Random forest techniques were used to classify this population, with accuracies of 93.4% obtained. Fatigue studies of rectangular composite beams containing manufacturing defects (delamination, laminate waves), produced a measurement population of 14 instances comprised of six distinct classes. Framing this problem as the time evolution of damage due to fatigue loads allowed the use of hidden Markov models to differentiate the type of manufacturing flaw present, with results indicating 85.7% accuracy given this limited dataset.

The primary goal of this research effort is to develop nondestructive evaluation techniques capable of detecting material damage precursors mainly for turbine engine materials under low and high-cycle fatigue testing. The experimental results presented in this paper show a significant increase of the relative acoustic nonlinearity, βr, in aluminum and Ni-based superalloy fatigued specimens. While in agreement with the prior research, the main advantage of the current technique over the previous methods is that the ultrasonic beam may be focused to inspect the presence of damage precursors at localized stress concentrator site. For example, when the ultrasonic beam travelled through the root of the round-notched specimens, the acoustic nonlinearity exhibited an increase of approximately 450% as compared to the pristine specimens. This procedure will be further developed to detect damage precursors in propulsion components undergoing thermo-mechanically fatigue to quantify their remaining useful life.

To provide in situ measurements of environmental severity, the LS2A corrosion monitoring sensor suite has been developed and deployed. This system, a wired or wireless sensor node, measures, records, and analyzes environmental and corrosivity parameters. To support maintenance exposure tracking, autonomous data analysis techniques are being developed to track and visualize environmental severity within airframes for clear, intuitive, and informative presentation of long-term environmental exposure.

Navy submarines and surface ships rely on a combination of material coatings and cathodic protection systems to protect steel tanks and enclosures from corrosion. The US Navy spends an estimated $204 million annually in direct costs [1] to locate and repair corrosion related damage on approximately 4000 tanks inspected each year. Presently, a coating health monitoring technology that relies on electrochemical sensors and stochastic models is being developed to quantify the extent and location of coating damage, while also providing users with a real-time assessment of the cathodic protection system performance. Electrochemical impedance spectroscopy (EIS) measurements from a multi-sensor network serve as the basis for assessing the coating’s health, with specific features identified and fed into a neural network model that is trained to identify and track the onset and evolution of coating defects. Previous results have demonstrated the successful performance of this system on oneand two-dimensional test articles in the laboratory. This work extends development into three-dimensional structures and to length-scales that are representative of ballast water tanks commonly found in Navy vessels. doi: 10.12783/SHM2015/149

A method for the generation of hot dip galvanized sheet steel Certified Reference Materials (CRMs) certified for coating mass is described. The method produces high quality coating mass CRMs with combined estimated uncertainties (95% confidence) of about 3% relative standard deviation (rsd). Initially, coating mass x-ray fluorescence (XRF) data are collected on multiple test specimens and used to sort the test specimens by observed coating mass. Several specimens near the mean of the sorted data are retained and the remaining specimens are analyzed for coating mass by a National Institute for Standards and Testing (NIST) traceable weigh-strip-weigh analytical method. The weigh-strip-weigh data are correlated to the initial XRF data using statistical techniques. The retained specimens’ coating masses are calculated using this relationship and are used as the certified coating masses for the retained specimens. This correlation method is a more accurate and efficient technique relative to the traditional “perimeter” certification methods. The present method does not suffer from an increased estimated uncertainty due to the inherent hot dip Zn coating variability. Also, this method provides a simple means to generate NIST traceable high quality CRMs that can be tailored to meet individual users’ requirements.

The main objective of the current work is to develop a practical nondestructive inspection methodology for a highly sound absorbing composite structural system consisting of polymeric and metallic materials. Due to constraints in geometrical shapes and thicknesses of the composite system used in this work, ultrasonic guided wave approach has been chosen. Since the polymer coatings have high damping properties, less energy is dissipated into the adjacent media in the presence of interface delaminations. Experimental measurements performed on a targeted composite system, whether it has an aluminum, carbon-fiber-composite, or steel outer casing, show promising results.

With its extremely negative effects on critical military assets, corrosion continues to be one of the top maintenance cost drivers for the Department of Defense. As of 2010, an estimated $22.9B was required to cover the costs associated with corrosion in the DoD annually. Proper management of corrosion on high value military assets such as aircraft can significantly reduce costs associated with maintenance, component removal, and aircraft availability. This paper will discuss the design, validation, and deployment of a wireless, flight qualified corrosion monitoring system as well as analysis of data collected during field trials.

Dealloying of a binary noble alloy produces a porous layer rich in the more noble element. Application of a tensile load initiates a brittle intergranular (IG) crack in the dealloyed layer that advances into the unattacked material. This study showed that the crack penetration depth (Cd) is proportional to the thickness of the dealloyed layer (t). For a given value of t, the grain-boundary crack penetration distance was shown to decrease as the dealloying potential increased. The dependence of Cd on t and the dealloying potential, as opposed to the applied potential at the time of fracture, supported the film-induced cleavage model.

Wear and corrosion involve many mechanical and chemical mechanisms, and the combined action of these mechanisms often result in significant mutual reinforcement. Other researchers have studied the interactions of wear and corrosion in a qualitative way for specific wear-corrosion environments. The US Bureau of Mines is interested in presenting the results of wear, corrosion and wear-corrosion tests in a standard format, so that these data can be utilized to develop practical engineering criteria for any tribosystem. To reach this goal, penetration rate equations have been developed to quantify the wear and corrosion processes and wear-corrosion synergism.

The military spends billions of dollars annually on inspection, identification, and repair of damage resulting from aircraft corrosion. The currently available methods for identifying aircraft corrosion damage involve expensive, labor intensive scheduled inspections, resulting in longer periods in depot, and reduction in aircraft availability. In order to increase aircraft safety, availability, and operational efficiency, an on-platform monitoring system capable of fusing data streams from an array of environmental and corrosivity sensors is needed to provide inspection-free indicators of the existence of corrosion as well as the level of corrosive severity in difficult to access aircraft locations. This paper will discuss the design, test, and validation of such a system utilizing a wireless, ultra-low power network of sensors.1 2

A critical examination of the surface mobility mechanism of stress-corrosion cracking of Galvele is presented. Using arguments based upon the thermodynamics of stressed solids serious errors in the development are elucidated and an accurate analysis of his ideas is presented. It is shown that the original treatment of surface mobility overestimates stress-corrosion crack velocities in ductile fee metals by about 14 orders of magnitude.

Corrosion of aircraft and rotorcraft is one of the primary maintenance cost drivers for the US Military, costing billions of dollars annually, and is by far the largest single maintenance cost for Navy and Marine Corps airframes. Various forms of localized corrosion, such as pitting corrosion, crevice corrosion, exfoliation, and environment assisted cracking, are particularly destructive and can lead to significant structural integrity problems for aircraft. Widespread current practice employs costly schedule based inspection and maintenance to retain acceptable risk levels. To enable condition based maintenance techniques, Luna is developing a diagnostic and prognostic system based on an intelligent wireless corrosion monitoring network.

Corrosion of aircraft and rotorcraft costs the US military billions of dollars annually, and is by far the largest single maintenance cost driver for Navy and Marine Corps airframes. The various forms of localized corrosion, such as pitting corrosion, crevice corrosion, exfoliation, and environment assisted cracking, are particularly destructive and frequently occur without any outward signs of damage. To maintain acceptable risk levels, costly schedule based inspection and maintenance practices are used. In order to move away from schedule based maintenance and enable condition based maintenance techniques, a miniature corrosion monitoring smart sensor network to support diagnostics and prognostics for aircraft health management is being developed. The development of an ultra-low power, wireless, embedded corrosion monitoring system based on the IEEE 1451.X open architecture for smart transducers will be discussed in this paper. This system, funded through a NAVAIR Phase II SBIR, is capable of monitoring, recording, and analyzing data from environmental and corrosivity sensors for the purpose of aircraft health management. This paper will present the use of a standard network architecture consisting of transducer interface modules (TIMs) and network capable application processors (NCAPs), allowing for ease of system integration and plug-and-play simplicity. The hardware and software designs, relying on ultra-low power components and embedded energy conservation algorithms, will be presented. This low-power approach to aircraft corrosion and health monitoring is ideal for integration with energy harvesting techniques, giving rise to a self-contained, self-sustaining sensor network. Finally, corrosion modeling and embedded algorithm development based on data fusion from both commercial off the shelf (COTS) and novel, developmental sensors will be discussed and shown to be powerful diagnostic and prognostic tools. 1 2