This PDF file contains the front matter associated with SPIE Proceedings Volume 11593, including the Title Page, Copyright information, and Table of Contents.

Predictive health monitoring will require the development of advanced sensing techniques capable of providing quantitative information on the damage state of structural materials. Second harmonic generation techniques can measure absolute, strength-based material parameters which can be coupled with uncertainty models to enable accurate and quantitative life prediction. Starting at the material level, this talk will examine a combination of sensing techniques and physics-based models to characterize damage in metals. These second harmonic techniques are acoustic-wave-based, so component interrogation can be performed with bulk, surface, and guided waves using the same underlying material physics. The talk will consider applications to characterize fatigue damage, thermal embrittlement, irradiation damage, and sensitization.

Additive manufacturing approaches, from spray-coating through drop-on-demand inkjet printing to aerosol jet printing, are used hierarchically to fabricate ultralight, flexible, compatible, nanocomposite sensors with the ability to respond precisely to high-frequency guided ultrasonic waves, yet not at the cost of sacrificing the integrity of host structures. The nanostructure of individual sensing element is morphologically optimized to facilitate triggering of a local quantum tunneling effect when modulated by ultrasonic waves. This study has spotlighted a new breed of functional composites with an endowed capability of self-health monitoring. Not only does it reduce the weight and volume penalties to composites, it also minimizes possible mechanical degradation due to sensor intrusion, blazing a trail in developing “sensor-free” SHM for composites.

Advancements in computer hardware has led to new possibilities for rapid modeling and simulation capabilities across many scientific fields. Nondestructive evaluation (NDE) can benefit from increased use of simulation tools to guide optimization of inspection and health monitoring methods, enhance understanding of data, aid in development of defect characterization methods, and generate data sets for use with machine learning and model-assisted probability of detection. Recent work at NASA has entailed development and benchmarking of both custom simulation codes and commercial simulation tools for ultrasonic wave propagation. This paper describes recent work at NASA in modeling of guided waves in composites and other aerospace materials. Results and computational speeds for a composite benchmark case are reported for a custom finite difference Rotated Staggered Grid code and for the commercial finite element software package, Pogo. Recent progress in linking NDE models to parametric analysis tools is also discussed.

In time domain nonlinear wave propagation analyses, a time-frequency map, which is commonly known as a spectrogram, gives a 2D-image of the evolution of multiple frequency components, also known as harmonics, as time progresses. This is useful in terms of knowing where the specific frequency components are present and how they evolve with time. Drawing a parallel between the time domain and the spatial domain, we have created a wavenumber-distance (𝑘 − 𝑥) spectrogram. Using the 𝑘 − 𝑥 spectrogram, the evolution of the propagating ultrasonic guided wave modes in a waveguide can be observed in terms of wavenumber and propagation distance in the wave-vector direction. The evolution can be driven by a multitude of reasons such as structural discontinuity, material change, and tapered geometry, to name a few. Given the frequency dependent nature of ultrasonic guided waves, frequency tuning can be performed to arrive at the optimal representation of the structural feature using the k-x spectrogram. Moreover, based on the nature of excitation, it is possible to obtain different 𝑘 − 𝑥 spectrogram images for the same set of frequencies. We consider the 𝑘 − 𝑥 spectrogram as a significant development because this type of energy based 2D-imaging in the spatial domain has potential applications in macro-defect localization, analyzing guided waves in inhomogeneous geometries, as well as creating a fingerprint for a given spatial feature in a waveguide or the whole waveguide. In this work, we show some example applications for the k-x spectrogram, deriving waveguide specific images based on the artificially created features in the waveguide. All the simulations are done in the frequency domain using a commercially available finite element package.

Piezoelectric lead zirconate titanate (PZT) sensors are widely used in various structural health monitoring (SHM) applications, where data acquired by the PZT sensors are used for damage detection. Any failure of the PZT sensors will have a detrimental effect in the ability of SHM systems to detect damage. Therefore, detecting faulty PZT sensor is critical to reduce any false-calls associated with malfunctioning sensor to ensure proper functionality of SHM systems. This paper proposes a self-diagnostic method to monitoring the health of PZT sensors using the electro-mechanical impedance (EMI) data in two steps. In the first detection step, the onedimensional convolutional autoencoder (1D-CAE) is employed to obtain the reconstruction error as anomaly scores from the raw EMI data. Hence, the faulty PZT sensors can be detected by comparing the anomaly score with a pre-defined threshold. In the second diagnostic step, the data feature is first extracted with the 1D-CAE. The extracted feature is then fed into a multilayer perceptron (MLP) classifier to classify the fault type of the PZT sensor. The proposed method was validated through experiments, where typical in-service induced damages such as impact, environmental effect, sensor breakage localized high temperature heating, etc. were introduced. The results demonstrate the effectiveness of the proposed method for both detection and diagnosis of various types of PZT sensor damage.

Artificially introduced cracks in necked double shear lugs are monitored using piezoelectric transducers attached to the lug shaft and analyzed by the electro-mechanical impedance (EMI) method with a model-based approach. Numerical simulations with coupled-field finite element (FE) models are used to study the frequency response behavior of necked lugs. Through-cracks with lengths up to 3 mm are located at 90° to the lug axis, which is a critical spot for damage initiation. The shift of a selected resonance frequencies found by analytical and numerical calculations are used to estimate the crack length. Results of numerical FE simulations are validated with multiple experimental measurements .

Fiber Bragg grating (FBG) sensors are popular sensing elements and have a wide application of strain monitoring in the area of structural health monitoring, medical and aerospace due to the features of electromagnetic interference resistance, high sensitivity and simplicity. Here, a simple intensity demodulation configuration based on a semiconductor ring laser is proposed for FBG dynamic strain sensing system. Due to the characteristics of semiconductor optical amplifier, it can act as the gain medium as well as light source. An arrayed waveguide grating module is adapted to be the wavelength demodulator. It is feasible for this configuration to respond when FBG is subjected to dynamic strains at a high frequency. Additionally, a simultaneous dual-channel interrogation system is in detail discussed. This interrogation scheme can be widely utilized in structure health monitoring because of its low insertion loss, high stability and low cost.

In this study, an omnidirectional shear horizontal (SH) wave acoustic transducer (OSH-WAT) is proposed, composed of a circular aluminum structure driven by twelve thickness-mode (d₃₃) piezoelectric wafer active sensors (PWAS). The OSH-WAT contains six units to form an axisymmetric structure, and each unit consists of a cylinder with a cantilever beam and two cubic stubs. Two d₃₃ PWASs acting like a couple, as the actuation sources, are bonded on the opposite sides of the cantilever beam to drive the excitation. The thickness-mode PWASs can produce a forcing pair, which can be converted to a circumferential shear deformation by two adjacent cubic stubs, contributing to the omnidirectional SH₀ wave generation. Multiphysics finite element model (FEM) is constructed based on such a design. Harmonic analysis is conducted to obtain the spectral response of a circular aluminum plate to investigate the omni-directivity of the SH₀ wave excited by the OSH-WAT, so as to identify the “sweet” frequency bands. Thereafter, the coupled field transient dynamic FEM simulations are carried out to acquire the dynamic response of a pitch-catch active sensing procedure. A voltage signal in the form of a 5-count tone burst is applied on each d₃₃-type PWAS to generate SH0 mode waves into the aluminum host plate. The received signals demonstrate the outstanding performance of the successful generation and reception of SH₀ waves. The proposed OSH-WAT may possess great potential in future Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) applications. The paper finishes with summary, concluding remarks, and suggestions for future work.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to their limited sensitivity. FBG sensors in the edge-filtering configuration have overcome the issue with sensitivity and there is a renewed interest in their use. The FBG sensors have directional sensitivity and are passive in nature. This makes it difficult to apply the existing signal processing and damage detection techniques such as tomography, phased array approach etc. As a result, there is a need to develop novel damage detection techniques which take into consideration the specific limitations of the use of FBG sensors. This paper applies the ellipse and hyperbola based techniques for a FBG sensor based network. The paper identifies the specific challenges and applies solutions to overcome those challenges. A comparative assessment of the two methods is presented with the help of experimental data.

This paper presents a graphical user interface (GUI) for modeling ultrasonic guided wave propagation in elastic solids. The software exploits the semi-analytical finite element (SAFE) method for the calculation of wave-propagation characteristics. The interface allows for the modeling of piezoelectric effects in plate-like and arbitrary cross-sectional waveguides. The isotropic and anisotropic materials with damping effects are also considered. For anisotropic composite material cases, directivity plots can be extracted, containing the phase-velocities, group velocities, and slowness curves. The frequency-dependent mode shapes can also be obtained, including displacement, strain, stress, and other electric components for piezoelectric materials. The corresponding mode shapes for arbitrary cross-sectional waveguides are presented in the form of vivid animations, demonstrating the cross-sectional harmonic motions. All the computational outcomes are compared with commercial finite element (FE) codes via the Bloch-Floquet boundary conditions. The paper finishes with discussion, concluding remarks, and suggestions for future work.

In this paper, an elastic metamaterial is presented to achieve complete conversion from Lamb modes into the fundamental shear horizontal mode. Modal analysis with Bloch-Floquet boundary condition is performed to obtain the dispersion features of the metamaterial system. By analyzing the resonant modes of the unit cell, a complete SH₀ mode generation band within the A₀ and S₀ modes bandgap can be formed in a wide frequency range. Thereafter, finite element model (FEM) harmonic analyses for an elastic metamaterial plate are carried out to explore the mode conversion efficiency. Finally, a coupled field transient dynamic FEM is constructed to acquire the response of the structure. A 30- count tone burst incident wave containing both A₀ and S₀ modes is excited to propagate into the elastic metamaterial system. The frequency-wavenumber analysis results demonstrate the achievement of the mode conversion behavior, manifested by the strong coupling between guided waves and resonant modes of the composite stubs. The proposed mode conversion behavior may possess great potential in future Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) applications. The paper finishes with summary, concluding remarks, and suggestions for future work.

Transmission tower as a part of the power system, plays a significant role in supporting conductor, ground wire and other accessories in the overhead transmission line. It is very crucial to make sure it runs safely and stably for people's production and life. Due to the high flexibility of the transmission tower structure, the wind load is an important control load and plays a decisive role. Under the cyclic stress caused by wind load for a long time, some minor damages and defects of the tower can easily reach the limit of fatigue and cause local or overall damage. In this research work, one structural health monitoring system using wireless inclinometers is proposed. And then the proposed monitoring system was installed on the transmission tower for remote real-time on-line monitoring. The real inclination response of the structures under environmental load and operational load can be monitored in real time. The inclination response monitored was used to evaluate the structural health conditions of the transmission tower. This kind of monitoring scheme has a good practical application value and can effectively avoid the occurrence of major power accidents. Transmission tower as a part of the power system, plays a significant role in supporting conductor, ground wire and other accessories in the overhead transmission line. It is very crucial to make sure it runs safely and stably for people's production and life. Due to the high flexibility of the transmission tower structure, the wind load is an important control load and plays a decisive role. Under the cyclic stress caused by wind load for a long time, some minor damages and defects of the tower can easily reach the limit of fatigue and cause local or overall damage. In this research work, one structural health monitoring system using wireless inclinometers is proposed. And then the proposed monitoring system was installed on the transmission tower for remote real-time on-line monitoring. The real inclination response of the structures under environmental load and operational load can be monitored in real time. The inclination response monitored was used to evaluate the structural health conditions of the transmission tower. This kind of monitoring scheme has a good practical application value and can effectively avoid the occurrence of major power accidents.

Bridges play a pivotal role in modern society, especially when considering the amount of global automotive transportation; therefore, it is essential to protect these structures from deterioration. Engineers and inspectors are searching for efficient subsurface defect detection methods due to the critical nature of these structures. Ground Penetrating Radar (GPR), a Nondestructive Evaluation (NDE) technique, is a well-established method used to locate subsurface degradation in bridges. The GPR method uses radiofrequency electromagnetic waves to create images of subsurface irregularities by detecting dielectric property differences in bridge decks. Using artificial intelligence (AI) to augment manual GPR data analysis can increase the NDE performance in the field by mitigating data interpretation. One dimensional (1D) Convolutional Neural Networks (CNNs) were employed to evaluate concrete bridge decks to classify the GPR data collected from eight laboratory-created concrete specimens with either defect-free or known artificial subsurface defects. We used 1D-CNNs to classify GPR data for accurate flaw identification. The proposed method’s accuracy was greater than 84%, outperforming existing Machine Learning (ML) based GPR data classifications and demonstrating the proposed method’s effectiveness in detecting subsurface defects.

This research develops lightweight stepped cantilever beams using two auxetic hexagonal and anti-tetrachiral designs to reduce sound radiation from vibrating modes of attached structures. A finite element analysis (FEA) is employed to investigate the equivalent radiated power level (ERPL) of the proposed beams subjected to different excitations. A computational parametric study is performed to minimize the ERPL for two in-plane and out-of-plane configurations of the beams over a frequency range of 0 Hz to 1200 Hz. The vibro-acoustic responses of the beams are validated by two acoustic techniques of the radiated sound pressure level (RSPL) and far-field sound pressure level (FFSPL). Numerical results demonstrate an excellent vibro-acoustic performance in reducing the radiated sound power level for the beams. The proposed beams can be utilized for high-tech devices where the radiated noise control is desired. This paper opens up a great potential of using auxetic stepped cantilever beams for various engineering applications in Acoustics, Civil, Aerospace, Biomedical, and Mechanical Engineering.

Composite structures are widely used in the aviation, automotive industries nowadays. This is because of its corrosion resistance, lightweight, and endurance. Carbon fiber reinforced polymer (CFRP) could be seen even in present-day highend bikes. Even though they are widely used and have a lot of pros, they are prone to various damages and most importantly the impact damage. These impact damages may occur due to accidental drop, low impact drop of tools during testing, etc. This could lead to damage to the material structure, its matrix, fibers, etc. This reduces its entire strength. In our studies, we have focused on the experimental and numerical analysis of the impact damage (ID) detection on multilayered CFRP material using Lamb waves. Lamb waves or guided waves are widely used in various damage detection techniques in the present structural engineering world. The experimental study was performed using a laser Doppler vibrometer (LDV) with piezo zirconate titanate (PZT) based Lamb wave excitation to detect the damage locations. This is followed by signal processing tools to visualize it. The numerical study was conducted using a spectral elemental method (SEM). The results from the experimental, numerical studies revealed the location of damage zones in the CFRP plate.

This paper presents an ultrasonic guided wave tomography for detecting and sizing delaminations in composites using pure shear horizontal (SH) wave. In this method, pure SH0 wave excitation was achieved using the adjustable angle beam transducers (ABT) in quasi-isotropic composite plates. First, a 3-mm quasi-isotropic composite plate with simulated delaminations by Teflon inserts was manufactured. Then, the ABT tuning angle of SH0 mode was calculated from the theoretical phase-velocity dispersion curve based on Snell’s law. Experiments were conducted on the 3-mm quasi-isotropic composite plate for pure SH0 wave excitation and delamination detection. It was found that a significant decrease in the signal amplitude was observed due to the delaminations. Finally, a linear scanning approach in 0° and 90° directions was conducted to cover the whole delamination area. The collected signals were used to visualize the damage through damage imaging methods. SH0 wave tomography based on the reconstruction algorithm for probabilistic inspection of defects (RAPID) was applied for delamination detection and characterization. The experimental results show that the SH0 wave tomography has a good performance for delamination detection, size estimation, and localization in aerospace composites.

Composite structures, consisting of highly anisotropic layers of polymer matrix reinforced with high strength carbon fibers, are widely used for aerospace applications due to their low weight and high strength. However, impact during aircraft operation can lead to barely visible and difficult to detect damage. Depending on impact severity, delaminations can occur that reduce the structural integrity and load carrying capacity. Efficient structural health monitoring (SHM) of composite panels can be achieved using guided ultrasonic waves propagating along the structure. Guided ultrasonic wave propagation and scattering at circular delaminations was modelled using full three-dimensional (3D) Finite Element (FE) simulations in ABAQUS. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. The guided ultrasonic wave propagation and scattered field at an artificial delamination was measured using a noncontact laser interferometer and quantified. Good agreement between experiments and Finite Element predictions was found and the energy trapping on top of a shallow delamination was verified. The influence of delamination shape and depth was investigated from a FE parameter study. The sensitivity of guided waves for the detection of delaminations due to barely visible impact damage (BVID) in composite panels has been verified.

In this paper results of analysis of guided wave propagation phenomenon in stiffened composite panel are presented. Panel is made of carbon fibers reinforced polymer. Presented research employs method that is based on full wavefield measurement approach where elastic wave sensing is performed in dense mesh of points covering whole surface of the panel. Elastic wave sensing process is conducted using Scanning Laser Doppler Vibrometer while as waves excitation is performed by piezoelectric transducer. Interactions of elastic waves with stiffener and artificial discontinuities are investigated. Artificial discontinuities in the forms of additional mass and impact damage are investigated. Authors investigated the influence of the reinforcing fibres orientation, problems of material damping and interaction of wave with stiffener on possibility of damage detection. The aim of proposed simple signal processing is damage detection and localization.

Structural Health Monitoring deals mainly with sensorized structures where sensors can be secondary bonded on metallic or composite structural elements. Aerospace structural design must account for Damage Tolerance (DT) of structures. To accomplish the airworthiness, a flawed structure is required to stand the design load without any growth and, eventually, repaired. For metallic materials, the damage tolerance approaches are well-established and rely on the evaluation (theoretically and experimentally) of crack propagation velocity. For composite structures the damage-tolerance design is more challenging as the failures that may occur are of different type, most of the times hidden inside the structure and can grow up to a critical size before the conventional inspection techniques detect them. Within the DT approach one of the showstoppers for the full implementation of adhesive bonds in composites (i.e. stringer-skin connections for stiffened plates) are the airworthiness certification requirements for composite aircraft structures as presented within the FAA Advisory Circular 20-107B. In that document the general methods for substantiating the limit load capacity of any bonded stiffener, the failure of which would result in catastrophic loss of the airplane, are prescribed. Among the suggested methods, the only one really permitting to achieve the optimal bonding efficiency without the addiction of disbond stoppers (i.e. rivets), is a “repeatable and reliable non-destructive inspection techniques ensuring the strength of each joint”. That assumption implies the implementation of a reliable SHM system capable of monitoring the extent of an eventual disbond until it reaches a critical dimension at limit load. This paper will present the preliminary results of a research activity where the authors apply static loads to a stiffened plate made of a skin and a bonded stringer (co-infused) where a disbond “starter” has been included during manufacturing. The plate has been sensorized with a strain gauge system to detect the disbonding evolution during load application, in order to verify the effectiveness within a DT approach.

Evaluation of optimal slope angle of an air-coupled transducer (ACT) is important, to allow for effective generation of Lamb waves in solid structures. This effectiveness relies on both generation of desired wave modes, amplitude of measured signals and large coverage area for damage detection. Phenomenon of non-contact elastic waves generation in plate based on ACT is considered assuming few approaches. Numerical modeling in COMSOL is related to the analysis of different plate thicknesses and ACT excitation frequencies. Experimental research is based on wave excitation using piezoceramic ACT with a base excitation frequency of 40 kHz and SLDV measurements for a 1 mm-thick plate. A comprehensive investigation of optimal ACT slope angle for the generation of A₀ mode is included. Propagation of S₀ mode was not observed in numerical as well as experimental results. Optimal slope angle values were estimated in different ways numerically and experimentally with the use of dispersion curves and based on the amplitude of generated waves. Finally, the optimal angle value was determined and utilized for damage detection and localization problem in an aluminum plate.

In this study, a technique based on guided ultrasonic waves coupled with an uncertainty analysis is developed to quantify the deviation from the assumed nominal value of the material constants of quasi-isotropic fiber-reinforced composites. It is shown that the measured group velocities vary depending on the location within the laminate, opening the possibility of questioning whether the assumed nominal values of the material properties could accurately represent the entire material system at any region. Furthermore, after the identified material parameters are defuzzyfied, a new set of nominal values for the material properties is determined. These preliminary findings might allow for the development of other efficient, nondestructive material characterization techniques in the future.

Additive manufacturing (AM) is a name for techniques applied for constructing three-dimensional (3D) objects in a layer-by-layer process. Such methods can be applied for a variety of materials, like ceramics, polymers, and metals. It results in wide applicability of AM elements in many industrial branches, e.g. energetic, medicine or aerospace. Fiber Bragg grating (FBG) sensors due to their small dimensions and high chemical durability can be embedded into different material types. The goal of the paper is to analyze an influence of FBG sensors embedded into an AM polymeric samples on the polymeric structure durability. The samples will be manufactured using multi-jet printing method. The method was chosen due to its high accuracy of printed polymeric elements and the possibility of manufacturing products with complex shapes. The analyzes will be related to tensile strength determination of the samples with embedded FBG sensors. The comparisons will be performed for the samples without sensors and with sensors after manufacturing and after temperature treatment.

Triboelectric energy harvesters continue to show promising and efficient performance in transferring mechanical energy into electrical energy, making them a prime candidate for biomedical implants. Total Knee Replacement (TKR) is a widely used surgery worldwide and, more so, in the United States. In this paper, triboelectric performance in biomedical applications is evaluated, especially in TKR. Performance of two new configurations of triboelectric energy harvester in TKR is compared as self-powered implanted sensors for loads measurements. The first configuration is a full knee harvester, covering the whole area of the tibial tray. The second configuration consists of two harvesters at the lateral and medial locations. Both configurations to be fit in the knee implant. The two designs’ performance was experimentally evaluated when subjected to an axial cyclic load applied by a dynamic tester at different frequencies. Also, the lateral and medial generators were tested for load imbalance detection producing promising results. Moreover, this study’s findings would contribute to the improvement of TKR by transforming them from passive to smart TKR using these implants, which will lead to better health monitoring.

Total Hip Replacement (THR) involves a conventional medical implant where many interacting factors could cause patient dissatisfaction, sometimes leading to lengthy and risky procedures based on guesses. Energy harvesting from natural human motion is being investigated to create a reliable source that will power smart implants and monitor performance simultaneously without any replacement or exchanges. A novel design of Triboelectric Energy Harvester (TEH) is proposed to retrofit a TEH to the THR implant, making it a smart implant. A custom femoral head was designed to incorporate grooves onto the THR femoral head, maximizing energy production without increasing the overall size. The TEH consists of two Titanium layers separated by a PDMS insulator. The Finite Element Analysis shows that the mechanical spring maintains the contact separation motion of the TEH. A theoretical model of a single-degree-of-freedom system with piecewise functions is proposed based on the FEA results to model the contact and release modes and voltage estimations. This study can open the door and lead to new research in load monitoring for total hip replacement.

In this study, we investigate the feasibility of a self-powered Fowler-Nordheim (FN) sensor-data-logger for postoperative monitoring of spinal fusion progress. The FN sensor-data-logger self-powers itself using the energy harvested by a piezoelectric transducer attached to the spinal fixation device. The same signal is then used by the sensor to infer the fusion progress. We perform experimental studies using corpectomy models to evaluate the performance of the proposed monitoring system. Data measured from the bench-top experiments is used to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.

At an estimated cost of $8 billion annually in the United States, revision total joint replacement surgeries represent a substantial financial burden to the health care system. Fixation failures such as implant loosening and mechanical instability at the cement-to-bone interface are the main causes of long-term implant failure. Early and accurate diagnosis of cement-bone interface failure is critical to develop therapeutic strategies and reduce the risk of delayed revision surgery. Unfortunately, prevailing imaging modalities such as plain radiographs struggle to detect the precursors of implant failure and are often interpreted incorrectly. Our team has recently shown that modification of poly(methyl methacrylate) (PMMA) bone cement with low concentrations of conductive fillers makes it piezoresistive and therefore self-sensing. By electrical impedance tomography (EIT) it is possible to monitor load transfer across the PMMA using real-time electrical measurements, which are physiologically benign and would ultimately be cost-effective in a clinical setting. Herein, we demonstrate the use of machine learning with EIT in order to precisely characterize model failure events. In phantom tests, we introduce three defect types: loosening, vertical cracks, and horizontal cracks. We show that implant defect type can be classified with greater than 95% accuracy by combining principal component analysis (PCA) coupled with the k-nearest neighbor (KNN) algorithm and a two layer feed-forward neural network with three hidden neurons . These preliminary results show that the combination of smart materials, EIT, and machine learning may be a powerful tool for predicting and diagnosing failure in joint replacements.

High-intensity focused ultrasound (HIFU) is currently being used for the ablation of tissue, such as in the case of prostate cancer. However, targeting tissue deeper inside the body remains challenging due to a variety of complications, including the increased scattering and attenuation of the ultrasonic waves. This work addresses the problem of exciting HIFU waves of a specific, desired wave form. That is, the utilized HIFU transducers are typically driven at their resonance frequency to maximize power output, which leads to significant distortions of the excited wave forms. In turn, these ringing effects can also have an impact on laboratory experiments as the resulting excess oscillations can obscure observations of visualization techniques, and in the clinic may cause unintended energy deposition at the target location. To mitigate this, an iterative learning control (ILC) approach is utilized with the intent of generating precise wave packets. Specifically, a PD-type and an H-infinity Synthesis approach are used to generate the ILC. It is shown that both ILCs lead to significant improvement of the excited pressure waves in simulation, i.e. the waveform more closely represents the desired tone burst. Furthermore, the model-based ILC design is shown to outperform the PD-type ILC, thus providing a systematic methodology. In addition to demonstrating its usefulness for developing new therapies through shadowgraph experiments, the methodology’s feasibility for future clinical use is discussed through an energy deposition analysis of more realistic wave forms for potential HIFU therapies.

Among a variety of existing modalities for noninvasive brain stimulation (NIBS), low-intensity pulsed transcranial focused ultrasound (tFUS) is a promising technique to precisely stimulate deep brain structures due to its high spatial specificity and superior penetration depth. While tFUS is gaining momentum as an emerging NIBS technique, an advisable biosafety-associated combination of sonication parameters including duty cycle and power input remains to be explored. In this study, biosafety of low-intensity pulsed tFUS using various sonication parameters was evaluated by measuring acoustic intensities and temperature variations across a piece of real human skull. The results showed that ISPTA above 480 mW/cm^2 is likely to induce an excessive temperature rise for a sonication duration of 160 seconds. Also, the skull base effect and ultrasound transducer self-heating effect should be noted during the sonication. Based on the findings in this study, an initial biosafety guide was discussed for the future investigation of ultrasound-mediated NIBS.

For certain structure types and damage sizes, guided waves offer some distinct advantages for damage detection, such as distance and sizing potential, greater sensitivity and cost effectiveness. Guided waves exhibit in multiple modes, of which for Lamb waves there are two shapes; symmetric and antisymmetric. In damage detection regimes, information and features of individual modes, which propagate from a single source, are useful for localisation and sizing of damage. This leads to motivation to decompose a single signal into the individual modes that are received in the wave-packet. Decomposition of wave modes is possible in full-field Lamb wave data through a forward-backward, two-dimensional Fourier transform method that involves dispersion curve information; though this method cannot be applied directly to signals at a single location. By using this method, the expected nominal waves can be determined for a given propagation distance; i.e. the individual wave modes expected to be present regardless of damage. In the presence of damage, residual signals will be present which contains information on the damage. In this paper, a Bayesian linear regression technique is used to decompose single multi-mode signals into their individual wave modes, which is then used to determine any residual signals. This decomposition is done by determining the expected shape and size of individual mode signals from the full-field decomposed waves. The information inferred by this method both before and after the wave has propagated through damage is studied.

Modern aerospace structures demand lightweight design procedures and require scheduled maintenance intervals. Supervised deep learning strategies can allow reliable damage detection provided a large amount of data is available to train. These learning algorithms may face problems in the absence of possible damage scenarios in the training dataset. This class imbalance problem in supervised deep learning may curtail the learning process and can possess issues related to generalization on unseen examples. On the other hand, unsupervised deep learning algorithms like autoencoders can handle such situations in the absence of labeled data. In this study, an aerospace composite panel is interrogated with a circular array of piezoelectric transducers using ultrasonic guided waves in a round-robin fashion. The time-series signals are collected for both the healthy and unhealthy state of the structure and transformed into a time-frequency dataset using continuous wavelet transformation. A convolutional autoencoder algorithm trained on healthy signals is used to identify anomalies in the form of delamination in the structure. The proposed methodology can successfully identify delamination in the structure with good accuracy.

Structural Health Monitoring (SHM) deals mainly with structures instrumented by secondary bonded or embedded sensors that, acting as both signal generators and receivers, are able to “interrogate” the structure about its “health status”. Sensorised structures appear promising for reducing the maintenance costs and the weight of aerospace composite structures, without any reduction of the safety level required. Much effort has been spent during last years on signal analysis techniques in order to extract from signals provided by the sensors networks many parameters, metrics, and images correlated to damages existence, location and extensions. As in many other technological fields, like medical image diagnostics, deep learning techniques in general and artificial neural networks in particular can be a very powerful instrument for damage patterns reconstruction and selection provided that a sufficient and consistent amount of data related to healthy and damaged configuration of the item under test are available. Within this work explicit finite element analysis has been employed to simulate waves propagation within composite plates with and without delaminations due to impacts. The numerical results have been previously validated with analytical solutions and experimental signals then have been used to populate the data sets necessary for deep learning. This paper will present the preliminary results achieved by the authors.

The effect of temperature on guided waves is considered one of the crucial aspects of a structural health monitoring procedure. The influence of temperature can cause abrupt variations in the actual signatures and interfere with the existing damage identification strategies. In this paper, we have addressed this issue with a self-supervised deep learning-based temperature compensation methodology. We have used temperature affected time-traces from the dataset and converted them into 2D-representation using continuous wavelet transformation. We have proposed a new philosophy of temperature compensation in which the effect of temperature on the reference signals modifies amplitude and phase of the signal, is considered noise. We have trained a convolutional denoising autoencoder to transform temperature affected signals at any temperature into signals at the reference temperature. The performance of the algorithms is evaluated against unseen examples. It is seen that the proposed methodology can successfully compensate for temperature effects with a low mean squared loss and mean squared error and a high coefficient of determination.

Guided waves (GW) are one of the most promising tools for structural health monitoring (SHM). They allow fast inspection of a large area. Thus GW based SHM is finding applications in several fields like aerospace, automotive, wind energy, etc. Recently, the use of optical fiber sensors and more specifically fiber Bragg grating (FBG) sensors are getting increasing attention from researchers for GW sensing. The FBG sensors are deployed in the edge-filtering configuration for the sensing. The FBG sensors offer several advantages such as light weight and small size. They also offer the chance to be embedded, and multiplexed. Unfortunately, they are passive sensors and show directional sensitivity. Also they are relatively expensive. Thus there is a need to optimize the usage of the sensors to obtain maximum information with minimum cost. Several different optimization strategies have been proposed in the literature including gradient based as well as evolutionary algorithms. For a non-linear, non-differentiable cost function evolutionary algorithms (EA) such as genetic algorithm, particle swarm optimization, etc. have shown a lot of promise. In this paper the Covariance-Matrix Adaptation Evolution Strategy (CMAES) has been employed for the sensor network optimization for GW based SHM. First, the ability of the algorithm to obtain the global optima is investigated and then the results are compared with the genetic algorithm (GA) which is the most commonly used EA. 3 different criteria for the optimization are used namely, the coverage with 3 actuator-sensor (AS) pairs (cov3), coverage with 1 AS pair (cov1), and the scalarized combination of cov3 and cov1. The results indicate that indeed CMAES is a powerful optimization tool for the sensor placement optimization problem.

Significant research over the last decade has focused on elastostatic metamaterials and particularly elastostatic cloaking. A related concept is the "neutral inclusion" (NI): a coated region, where the coating is designed such that fields in the exterior region are unperturbed by the presence of the NI. Until recently it was considered that finite thickness, homogeneous coatings could not be designed for elasticity. Here, two types of neutral inclusion are distinguished, "weak" and "strong" with the former equivalent to low frequency transparency and Christensen and Lo’s generalised self-consistent result from 1979. We show that anisotropic coatings can yield strong neutrality, thus providing new connections between static elastic cloaking, low frequency elastic wave scattering and neutral inclusions.

Inspired by the concept of origami, a design methodology of extremal metamaterial is developed and the effective elastic tensors of the metamaterials are obtained. Different soft modes are realized with special line-plane connections between the cubes and hexahedrons of an origami unit and the transition from nullmode to hexamode is also demonstrated. A new pentamode metamaterial with only one resistant deformation that is not hydrostatic is designed. By further degrading the local connections, a hexamode metamaterial is realized for the first time. It is believed that the design methodology can unlock a new field of extremal metamaterials with novel properties.

This paper presents a tunable ultrasonic lens for the flexible control and modulation of Lamb waves. The lens is comprised of layered slice structures using Shape Memory Alloy (SMA) which could change its material properties under thermal loads. Numerical investigations on the wave dispersion characteristics demonstrate the tuning capability of wave speeds in the slice waveguide. Harmonic and transient dynamic modeling results further present the wave steering and focusing phenomena to a desired direction and focal point, covering a scanning area. Such a capability possesses great application potential to enhance the performance of Lamb wave based SHM and NDE systems.

Corrosion thickness loss due to adverse environmental conditions of pipelines and marine structures can cause degradation of structural health. Monitoring in difficult to access areas can be achieved using high frequency guided waves propagating along the structure, selectively excited using standard ultrasonic angle beam transducers with single sided access. Wave propagation and mode interference depends on the thickness of the structure. At the frequency-thickness range of interest, the two fundamental Lamb wave modes are excited with slightly different wavenumbers, leading to a beating effect with energy transfer through the structure thickness. The beating effect depends on the frequency-thickness product and has been found to be very sensitive to small thickness changes. The guided wave propagation and energy transfer were visualized and predicted using 2D Finite Element simulations. Excellent agreement was found to theoretical beatlength predictions from a fit of the recorded variation of guided wave amplitude along the propagation direction. Laboratory experiments were conducted, with steel specimen wall thickness reduced by consecutive milling and using accelerated corrosion. Signal changes due to the wave mode interference were measured and the wall thickness reduction monitored from the amplitude beatlength. Good agreement with the theoretical predictions was achieved, demonstrating the sensitivity for thickness loss monitoring.

We propose a simple solid-fluid layered structure where narrow Gaussian acoustic beam propagates practically without spreading. It is demonstrated that the coefficient (so-called diffraction coefficient), which defines the rate of linear spreading, vanishes if a certain relation between frequency and direction of propagation is hold. Unlike nonspreading “light bullets” recently proposed in optics, acoustic monochromatic signal can propagate in diffraction-free regime without sophisticated modulation. In the experiment with steel-water layered structure we observe nonspreading propagation of a signal with frequency ~ 100 kHz at a distance ~ 1m. Such long-range collimation is accompanied by negative phase velocity refraction, anomalous dispersion, and very strong anisotropy of the effective mass density. Such long-range collimation is accompanied by negative phase velocity refraction, anomalous dispersion, and very strong anisotropy of the effective mass density.

Composites are widely used in advanced mechanical and aerospace structures due to their outstanding material properties. As a major safety concern for composite structures, impact damage may cause severe mechanical property loss and load-bearing capacity decrease. Impact-induced delamination sites are usually hard to be detected. Thus, it is vital to develop a sensitive impact damage imaging and quantification methodology to facilitate the prompt repairmen and replacement of critical structural parts. This study presents a new nonlinear-ultrasonic-based damage detection technique called the phase mirroring technique. Such a technique utilizes the principles of vibro-acoustic modulation (VAM) and breakage of superposition. The paper starts with a 1D numerical model of the Contact Acoustic Nonlinearity (CAN) based on the Central Difference Method (CDF) to develop a solid understanding of the mechanism behind the ultrasonic nonlinearity. Thereafter, both harmonic and transient analyses are conducted on a 2D coupled-field finite element model with a simulated delamination area to explore the resonance spectrum of the specimen, providing the guidelines for the frequency choice of the pumping wave. Such selected pumping wave can fully vibrate the specimen and engage the nonlinearity to the maximum extend. Subsequently, the flow of the damage detection technique is presented using a 3D coupled-field transient dynamic finite element model. The impact damage is modeled taking a cone shape to better approximate a practical damage, in which the delamination area and stiffness loss vary with layers. This paper finishes with discussion, concluding remarks, and suggestions for future work.

Acoustic metasurface carpet cloak has suffered from narrowband limitation due to highly frequency-dependent phase response. Here, we adopt two entirely different ways to broaden the working frequency range of the carpet cloak with dynamic or static metasurface structure. Numerical simulations and experimental measurements demonstrate that the designed dynamic or static metasurface carpet cloak can both exhibit a broadband stealth performance. The proposed design strategy is expected to break the narrowband limitation of metasurfaces, making a significant difference to the practical applications of the broadband acoustic devices.

Reciprocity is a fundamental property related to T-symmetry of wave equation. Nonreciprocal acoustic transmission becomes possible in a nonlinear or in a moving medium. Viscous losses, which break T-symmetry, are not considered as a nonreciprocal factor. We demonstrate that transmission through a finite-length dissipative phononic crystal is nonreciprocal for asymmetric scatterers. Asymmetric transmission is known even for inviscid background. However, additional nonreciprocal contribution related to the vorticity mode is usually missing. For infinite dissipative phononic crystal we prove that the decay coefficients turn out to be equal for the opposite directions but the velocity remains nonreciprocal due to broken PT-symmetry.

In this research, we study the topological effect in both 1D and 2D metastructures consisting of chiral-shape prismatic tensegrity units. The unique axial-torsional motion introduces coupled local resonance which produces the needed Dirac degeneracy for the topological states at low frequency. A multi-resonantor design is also proposed for additional Dirac cones which can be opened for mutiple non-trivial bandgaps. Further research shows that topological phase transition can be also achieved through the adjustment of chirality. Finally, prestress control on the topological interface states is studied in both 1D and 2D cases.

One of NASA priorities is the in-situ exploration of ocean worlds in the solar system where potentially there might be life under the ice shell. This requires reaching the ocean below extremely cold through significant deep ice. Jupiter’s moon, Europa, is such a challenging body, where it is estimated to have a 40 km thick ice shell. An approach for reaching the ocean has been conceived using a melting probe Cryobot concept that has been studied for a potential future mission. A lander is assumed to be the platform from which the Cryobot would be deployed. The ice penetrating vehicle concept consists of a cylindrical, narrow-body probe that encases a radioisotope heat/power source that would be used to do the penetration by melting through the icy crust. The baseline design of the probe includes a suite of science instruments to analyze the ice during descent and the liquid ocean underneath. For communication, a set of fiber optic wire as well as wireless RF in the very cold porous top layer is assumed, and then acoustic modules would be used for the communication in warmer denser ice over distance of 25 km between the modules. In addition to the acoustic communication modules, a sonar is part of the concept, for obstacle avoidance. The focus of this paper is on the use of elastic waves in the 1kHz range.

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. Current work assumes a single-hit flaw detection methodology i.e. the flaw detection calls are not mapped to form an indication with cluster of pixels in a 2D image. In the LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and 𝑘₁-factor. Similar to traditional POD analysis, a flaw size with chosen POD and confidence is determined. Alternately, POD/Conf. can be determined for a chosen decision threshold for the flaw size used in the analysis. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. If representative flaw sample and noise measurements are used, LS POD results pose no risk in the POD and POF estimation. Risk to meeting NDE flaw size detection requirements can be assessed based on how representative the flaw sample and noise measurements are, and based on positive margin between LS POD results and procedure reliability requirements.

This paper presents a new approach in ultrasonic imaging of solids using ultrasonic Synthetic Aperture Focus Technique (SAFT) with primary applications to the Non-Destructive Evaluation (NDE) of materials and structures. Specifically, the study utilizes Matched-Field Processing (MFP) techniques that exploit the spatial structures of the ultrasonic fields for detection and localization of scatters in the medium. Coherent-broadband MFP is compared to narrowband processing using both conventional time-delay beamformers and the Minimum Variance Distortionless Response (MVDR) beamformers. The decomposition of the time reversal operator (D.O.R.T.) method for active sensing is applied in the eigenmode decomposition of the MFP covariance matrix. The correlation between the scatters and the eigenvectors is studied in both narrowband and broadband MFP beamforming. This study also proposes an improvement in modeling the propagating waves in MFP beamforming. Specifically, the displacement structure of the longitudinal mode is considered in the array reception. Experimental testing demonstrates the ability of the DORT method to successfully separate multiple scatters in the test medium.

Ultrasound computed tomography (USCT) receives increasing attention because of its capability to reconstruct quantitative information about the material property distribution as images with superior resolution. However, one roadblock for the wide adoption of relevant techniques is the high demand for computational resources and the long processing time for solving a large inverse problem in imaging. To alleviate the associated challenges, a two-stage inversion scheme is proposed: 1) the ultrasound scanning signals are first processed using a full waveform inversion (FWI) technique with a single iteration to rapidly create a model (image) with embedded wave speed distribution; 2) the corresponding image will be further improved by feeding into a pre-trained deep neural network. The deep learning models presented in this paper are built upon two architectures to instantaneously solve the associated inverse problems and to produce a high-resolution image in real-time. The first is based on 1D convolutional neural network (1D-CNN) layers with an autoencoder structure. The second implements additional layers and skip connections inspired by a U-Net architecture. The resultant superior reconstructions from both CNNs demonstrate that the proposed framework produces a high-resolution image from a rapidly-generated, low-resolution image in real-time, with dramatically improved results.

Acoustic source localization (ASL) on a thin isotropic spherical shell is more challenging than that for two-dimensional flat plate structures. Here, a localization technique for isotropic spherical shell is proposed based on the triangular time difference using only four sensors without knowing the acoustic wave speed in the material. The proposed technique does not require solving a system of nonlinear equations, thus it greatly reduces the complexity of calculation. A finite element model of a thin isotropic spherical shell was created to verify the proposed acoustic source localization technique. The results of numerical simulation prove the reliability of the proposed technique.

Structural health monitoring (SHM)/nondestructive evaluation (NDE) is an emerging multi-disciplinary field that aims at detecting/characterizing structural damage and providing diagnosis/prognosis of structural health status in a real-time or on-demand manner. It can reduce maintenance costs, shorten the machine service downtime, and improve the safety and reliability of the engineering structures. Acoustic emission (AE) is one of the SHM/NDE methods by means of detecting elastic waves due to dynamic motions at AE sources, such as cracking, delamination, cleavage, and fretting in a material. The acoustic emission inspection technique relies on the AE sensors to collect the AE signals from the structure to monitor the structural health. Conventionally, these AE sensors need to be permanently attached to the structure through the bonding adhesive layer which may introduce contamination to the structure. In this work, the research is focused on investigating non-contact passive sensing of acoustic emission (AE) signals using an air-coupled transducer (ACT). The well-acknowledged pencil-lead-break method has been used to simulate the AE source. A resonant type ACT is used to passively sense the AE signals, which leaves the testing object intact and provides a non-intrusive sensing method. The non-contact AE test on a thin aluminum structure as well as a thick steel structure is first conducted. Next, the investigation is extended to composite structures. Both single-layer composite structure and bonded composite structure are investigated. The results successfully demonstrate the capability of non-contact passive sensing of the AE signals using the ACT method.

Matched Field Processing (MFP) is a generalized beamforming method which matches the received data to a dictionary of replica vectors to localize wave scattering sources (e.g., acoustic sources) in the complex media. The approach has also been used for passive structural monitoring and defect detection. The MFP requires an accurate model of medium, and this is a challenge in some applications. To tackle this issue, data-driven MFP has been recently introduced. Data-driven approaches are considered as model-free methods, which perform with no prior knowledge of the propagation environment to localize a source. This paper introduces a data-driven MFP approach for localizing the primary (i.e., impact) and secondary (i.e., defect) sources in plates. The replica vectors are made using the Fast Fourier Transform of the time history responses of the pristine plate under a controlled external excitation. Then, the MFP is implemented to localize the source. For defect localization, a subtraction approach under Born approximation is employed to remove or weaken the signature of the primary source and extract a set of data which purely contains the acoustic signature of the defect. The performance of the method for primary and secondary source localization is evaluated by studying a small aluminum plate, excited by a controlled broadband noise imposed by an impact hammer. A comparative study is carried out to evaluate the performance of the conventional Bartlett and adaptive White Noise Constraint processors in forming the ambiguity surfaces.

According to the American Road and Transportation Builders Association (ARTBA), 46,052 of America’s 616,087 bridges are rated “structurally deficient” and need urgent repairs. The detection of damages through conventional methods, such as visual inspection and hammer tests are expensive, time-consuming, and challenging to perform without interfering with traffic operations. In the last years, different Non-Destructive Evaluation (NDE) techniques such as computer-vision-based crack detection, impact echo, ultrasonic surface waves, electrical resistivity, ground-penetrating radar, and infrared thermography (IRT) have been developed to inspect aging structures. Among all, IRT has shown the capabilities of detecting defects resulting in different temperature distribution. It can be useful to identify sub-surface damages as delamination and water infiltration, hardly detectable using other traditional methods. In this paper, an algorithm to automatically detect damages in bridges from IR images is proposed. The algorithm exploits the temperature difference between damaged and undamaged parts through machine learning and computer vision techniques to highlight the location of flaws in the structure. Laboratory experiments and real-world analysis on in-service bridges are described in this research to validate the proposed method's accuracy. This study aims to automate the damage detection phases on large-scale structures

Surface crack patterns are one of the earliest damage signs in concrete structures. Existing procedures to visually evaluate the damage rely on experts' judgment to interpret the existing cracks. The initial necessary step to quantify and automate this procedure is crack detection. Precise crack detection provides a reliable basis to update the structural parameters and to predict future behavior. Several methods have been investigated to detect cracks based on image processing methods; but, there are several limitations and inaccuracies in these methods. In a number of cases, recordings during damage occurrence are available. The videos comprise not only spatial information but also temporal information. The videos provide a set of images for a unique damage situation. In this study, using video processing methods, a methodology is developed to track crack formation. In this regard, robust principal component analysis is employed to detect new crack propagation. The experimental test data of RC shear walls are used to assess the implemented methodology. The quasi-static cyclic load is applied to these walls, and several cameras captured the video of walls' behavior. Taking advantage of the phase-based motion processing method, a video stabilization is implemented to enhance the accuracy of the crack detection step. Propagation of cracks is monitored by calculating Gini coefficients for each frame. The results show that monitoring this coefficient can indicate new crack formations.

Within structural health monitoring, the capture and use of acoustic emission is a popular technique for the localisation of damage. In particular, approaches that view localisation as a problem of spatial mapping have performed well when applied to structures containing inhomogeneities such as complex geometrical features or the composition of multiple materials. The maps first require a series of artificial acoustic emission events to be generated across a test specimen, resulting in a mapping that represents difference-in-time-of arrival (dTOA) information. Despite their success, the application of dTOA maps has generally been restricted to applications that can be characterised by Euclidean distance measures. For spherical geometries such as a bearing raceway, this geometrical definition is not representative of the domain. This paper therefore proposes a novel extension to the spatial mapping approach for spherical domains, allowing acoustic emission localisation on a spherical roller bearing. The presented methodology firstly poses the generation of dTOA mappings as a problem of Gaussian process regression. Through a Bayesian framework, the source location likelihood of a real acoustic emission event is then assessed across the surface of the structure. Under the standard Gaussian process convention, the assumption is made that inputs to the kernel can be represented as a function of Euclidean distance. However, as bearings exist in a spherical space, the Euclidean distance will ignore the topological constraints of the bearing and is therefore not an appropriate measure. To bypass this issue, a reduced-rank approach is taken that expresses the covariance function as an approximate eigendecomposition. This approach allows the inputs to be projected onto an eigenbasis, satisfying both the conditions of a valid covariance function, as well as the topological constraints imposed by the geometry of the bearing. The proposed method is then applied to localise AE on a spherical roller bearing that is designed to replicate planetary support bearings that are found in wind turbine gearboxes.

Fibre reinforced polymers are commonly used in many industrial branches. The continuous technical progress in the applied science and technology requires more and more advanced materials. The desire to reduce the weight of structure and reduce their manufacturing costs may affect the safety and reliability of designed structure. The structural damage can occur due to many factors which are difficult to predict in advance. The possible hidden damage can be the source of a further mechanical deterioration of the composite structural element. Damage detection in structures is an active research area since decades. Number of studies is carried out in order to meet this objective, particularly, the identification of the damage size and location. Safety and reliability requirements results in development of a variety of non-destructive testing (NDT) techniques. One of the non-destructive testing techniques that can be applied for evaluation of internal structure of non-conductive materials is THz spectroscopy. The method can be used for identification of material structural disintegrations that results in changes of absorption coefficient, refractive index or scattering of THz waves propagating throughout analysed material. The goal of the paper is to study the sensitivity and applicability limitations of THz spectroscopy method used for evaluation and observation of internal structures of composite element with reference to simultaneous influence of temperature and moisture.

Fibre reinforced polymers are commonly used in many industrial branches. The continuous technical progress in the applied science and technology requires more and more advanced materials. The desire to reduce the weight of structure and reduce their manufacturing costs may affect the safety and reliability of designed structure. The structural damage can occur due to many factors which are difficult to predict in advance. The possible hidden damage can be the source of a further mechanical deterioration of the composite structural element. Damage detection in structures is an active research area since decades. Number of studies is carried out in order to meet this objective, particularly, the identification of the damage size and location. Safety and reliability requirements results in development of a variety of non-destructive testing (NDT) techniques. One of the non-destructive testing techniques that can be applied for evaluation of internal structure of composite materials is infrared thermography (e.g. vibrothermography). The method can be used for identification of material structural disintegrations that results in changes of temperature field distribution. The goal of the paper is to study the sensitivity and applicability limitations of infrared thermography method used for evaluation and observation of internal structures of the composite element with reference to influence of simultaneous temperature and moisture.

The paper provides a model for multi-hit limited sample POD analysis for simulated raster scanning that is used for flaw detection in nondestructive evaluation. Raster scanning of the transducer is used in ultrasonic testing of parts. In multi-hit POD analysis system, resolution is taken into account. The scan data, known as C-scan, is represented as a 2D pixel grid. Typically, the pixel gray value is equal to signal amplitude. The data is taken at every step or index between the pixels. Each pixel represents an area of the part that is sampled by the transducer. The sampled area or the aperture provides the signal amplitude. The step size may be equal to or smaller than the aperture, creating either a non-overlapping or overlapping aperture scan pattern. Optimal scanning uses least number of steps or pixels that can provide reliable flaw detection, accurate flaw sizing and adequate spatial resolution for the target size and larger flaws. Here, the scan patterns are defined in relation to the target flaw size, transducer aperture, and step size. In this work, flaw field is simulated as symmetrical bivariate standard distribution and transducer as a receiver with square aperture. This arrangement is similar to a raster scan with an unfocused transducer. The optimal scan pattern is determined by simulating a number of scan patterns and comparing their signal amplitude, flaw sizing accuracy, probability of detection and probability of false positive in relation to contrast-to-noise ratio. If results of such simulation are corroborated in empirical data, the model can be used in assessing reliability of flaw detection in NDE as well as for choosing optimal scan pattern.

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. LS POD approach for single hit flaw detection is developed first. Current work provides some progress in extending LS POD single hit approach to LS POD multi-hit approach. In multi-hit flaw detection, flaw detection data is mapped to form an indication with cluster of pixels in a 2D image which is absent in single-hit flaw detection. Resolution of the measurement system is additional factor to be accounted for in multi-hit LS POD. Resolution is addressed with a term defined as resolution ratio. These limits can be determined through modeling but should also be corroborated through empirical data. In the LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and 𝑘₁-factor. Similar to traditional POD analysis, goal of the multi-hit LS-POD approach is to determine POD, except emphasis is on relationship of resolution ratio and CNR with POD and POF. The flaw size is determined from the resolution ratio. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. Using simulation, current work indicates that resolution ratios relate to minimum CNR for reliable flaw detection. An idealized case of a round x-ray indication is chosen for the simulation. Empirical approach for multi-hit POD analysis is provided.

In this paper, experimental results of broadband noise reduction in an acoustic flow duct are presented. An active liner composed of an array of electroacoustic absorbers is used. The control law is based on the pressure-based, current driven digital architecture for impedance control with a local control strategy. A wind tunnel test rig named Caïman has been used for the experimental validation. The results confirm the adaptability and the stability of the whole system with the local control strategy. The air flow slightly reduces the efficiency while maintaining the adaptability and the stability.

Owing to their combination of low weight and high strength, carbon fiber reinforced composites are widely used in the aerospace industry, including for primary aircraft structures. Porosity introduced by the manufacturing process can compromise structural performance and integrity, with a maximum porosity content of 2% considered acceptable for many aerospace applications. The main nondestructive evaluation (NDE) techniques used in industry are ultrasonic imaging and X-ray computed tomography, however both techniques have limitations. Edge Illumination X-ray Phase Contrast Imaging (EI XPCi) is a novel technique that exploits the phase effects induced by damage and porosity on the X-ray beam to create improved contrast. EI XPCi is a differential (i.e., sensitive to the first derivative of the phase), multi-modal phase method that uses a set of coded aperture masks to acquire and retrieve the absorption, refraction, and ultra-small-angle scattering signals, the latter arising from sub-pixel sample features. For carbon fiber-reinforced woven composite specimens with varying levels of porosity, porosity quantification obtained through various signals produced by EI XPCi was compared to ultrasonic immersion absorption C-scans and matrix digestion. The standard deviation of the differential phase is introduced as a novel signal for the quantification of porosity in composite plates, with good correlation to ultrasonic attenuation.

Recently, data-driven machine health monitoring has become more popular due to the wide-spread deployment of lowcost sensors and deep learning algorithms’ achievements. The detection of failures of machines can be determined based on failure classification results using deep learning architectures. On this tendency, we constructed a plastic gear failure detection structure using a convolutional neural network. In this study, raw vibration data was converted to frequencydomain data. Amplitudes of frequencies in the monitored frequency band were transferred into images, which then were labeled as crack or non-crack by a high-speed camera. Although deep learning architectures have great potential to automatically learn from complex features of input data, the high-amplitude frequencies reflecting the main vibration causes such as gear meshing frequency and its harmonics or shaft frequency affect the accuracy of learning. Besides, the low-amplitude frequencies in a low-frequency band, which are sensitive to gear failures, show efficiency in early failure signs of the plastic gear. Thus, this paper proposed an image visualization and labeling method by focusing on lowamplitude frequency features in the low-frequency band and lessening high-amplitude frequency features. The results show that the proposed system learning from new visualized images can detect plastic gear’s early failure situation before the initial crack happened.

Effective Damage Identification (DI) plays a critical role in protecting structures against local or global failures caused by hazards. Real-time DI provides instant damage data and increases the safety and serviceability of civil structures. Real-time DI helps to understand the structure's behavior during extreme events that may be unknown at the design stage. This field needs innovative solutions for training supervised machine learning classifiers in the absence of measured damaged data. This research proposes an unconventional deep learning algorithm for vibration-based DI. The proposed real-time data-driven DI methodology does not require any manual feature extraction and uses Artificial Neural Networks (ANNs) to identify the presence and location of damage in discrete structural systems. The input is the response signals measured through sensors (no model-based input information required). A dropout technique regularizes the network and avoids co-adaptation in hidden layers. The neural network is optimized through 10-fold cross-validation. The proposed method's effectiveness in identifying the presence and location of damages is studied using a 4-story 2D structure subjected to artificial accelerograms. The recorded response signals create the feature space in the dataset. The lateral stiffness of columns is reduced randomly by different percentages resembling different damage severities. Considering the validation dataset results, the accuracy of the damage detection task varies from 84 to 99% for different damage severities, and accuracy for the localization task ranges from 78-98%. The results show the promising performance of ANNs for real-time DI and pave the way for training the classifiers using real-life data from undamaged structures and simulate data from damage scenarios.

The application of accelerated bridge construction (ABC) methods is becoming more widespread owing to their many advantages. In this construction method, prefabricated bridge elements are assembled on-site by establishing in-situ joints to minimize on-site construction time. Despite the improved life-cycle performance and cost benefits of ABC bridges, some concerns exist about the degrading environmental effects on the joints and invisible internal damages. In this study, the long-term performance of an ABC bridge that had been in service for more than 50 years was investigated utilizing machine-learning processes. Observation of reflective cracking on the deck surface and leakage through the joints in this bridge indicated some damage to the bridge longitudinal joints. Damages to the joints are not always visible, nor their extent is known. Therefore, a new damage detection approach is proposed that uses the results of a series of load tests as input in machine-learning techniques with the ultimate aim of detecting the location and severity of joint damages with a high level of certainty. The proposed approach uses the bridge responses obtained from a detailed finite element (FE) model under the assumption of various damage scenarios and predicts the potential damages using the training process of machine-learning algorithms and the actual bridge responses. The results show that the supervised learning algorithm successfully estimated the location and amount of damage in the bridge joints.

Many structural systems, such as aircraft, orbital infrastructure, and energy harvesting devices, experience dynamic forces along with changing structural boundary conditions. Collecting and analyzing data on these systems provides useful insight that aids design, evaluation, and function. For real-time decision-making on systems experiencing high-rate changes, completing assessments quickly enough to be relevant poses a unique set of challenges. In systems sufficiently understood and well defined, determining a system's state that experiences high-rate structural boundary condition changes can be accomplished by monitoring its frequency response. In this work, methods of frequency detection applicable to real-time state estimation of structures experiencing high-rate boundary changes were investigated; progress and findings in extracting the frequency response of a structure in real-time are presented here. A novel Delayed Comparison Error Minimization technique is presented and experimentally validated using the DROPBEAR experimental testbed at the Air Force Research Laboratory. This testbench consists of an oscillating beam with one end fixed and roller support that can move along the beam's length. Real-time estimation of pin location through the measurement of beam motion was performed using the novel Delayed Comparison Error Minimization technique. Results are compared against an FFT-based method with a variety of window lengths. The latency and precision of this method are analyzed, and the results from each method are compared, with a discussion on the applicability of each method.

Additive manufacturing is currently one of the most emerging technologies. Such a method can be applied for both polymeric and metal materials. It results in widening of 3D printing application in many industrial branches, e.g. medicine, automotive, aerospace. The power industry is a branch with very high requirements related to additive manufacturing of elements. Therefore, a detailed analysis of proposed technology (particular method, parameters, heat treatment, etc.) is a topic of interest. The material types that are recently applied in additive manufacturing are Ni-based superalloys. The weldability and cracking susceptibility of such materials is still a vast and complex subject for elements made using additive manufacturing, due to manufacturing process characteristic. The paper presents analyses focused on the microstructure of 3D printed Ni-based superalloy structures. The analyses were performed using scanning electron microscope. The method allows to observe the microstructure to detect, localize and determination of the size of microstructure elements and discontinuities.

Guided wave-based methods are promising for efficient, large-scale testing. However, with many nondestructive testing (NDT) techniques, the accuracy and reliability is generally dependent on the operator's conduct. To bridge this gap, a preliminary design is implemented in this work that allows a mobile robot to perform NDT. The design implements an active guided wave-based method centered around air-coupled transducers used in a pitch-catch configuration. The on-board circuitry allows the robot to either output an amplified actuation signal, or capture low-amplitude wave signals. In combination with odometry-type sensors, a map of the (unknown) structure, onto which the robot is deployed, is automatically created. During post-processing, any "hidden" structural features or defects can be identified and localized from the recorded guided wave signals. The unique combination of sensory information is demonstrated in laboratory experiments on unstiffened and stiffened aluminum plates. The feasibility of the robotic NDT, including the detection of a stiffener, is discussed by comparing the results to reference values and existing data from the literature. The results indicate that it is generally feasible to employ a mobile robotic platform to conduct guided wave-based NDT to create a map of an unknown surface.

Photoacoustic imaging is a high-resolution and high-contrast technique, which combines optical contrast with ultrasonic detection to map the distribution of the absorbing pigments in biological tissues. As an important branch of photoacoustic imaging, optical-resolution photoacoustic microscopy (OR-PAM) suffers from narrow depth-of-field (DoF), since the lateral resolution is determined by tight optical focusing. The small DoF will prevent OR-PAM to achieve large volumetric imaging. Here, we developed an ultrafast axial-scanning multifocus photoacoustic microscope with extended depth-of-field based on a tunable acoustic gradient lens (TAG) and fiber delay network. The TAG lens is used to high -speed focus-shift. And a fiber delay network consists of three optical fibers with different lengths is used to split a single laser pulse into three sub-pulses with different delay time. A function generator generates a sinusoidal signal to drive the TAG lens at an eigenmode. The focusing power of the TAG lens will exhibit a sinusoidal oscillation at the frequency of the driving signal. Then, the three sub-pulses synchronizes with three vibration states of the TAG lens, respectively. Finally, we can obtain three focuses with different depth in one A-line data acquisition to improve the DoF. The DoF we measured by a vertically tilted carbon fiber is eatimated to larger than 775 μm, which is ~ three times of that of single-focus PAM. The large DoF of large volumetric PAM was also verified by imaging a tungsten wire network. This system can achieve rapid and large-scale monitoring of physiological activities, which could expand the application of OR-PAM in biomedical researches.

The general cell quantification and identification have technical limitations concerning the fast and accurate detection of complex morphological cells, especially for overlapping cells, irregular cell shapes, bad focal planes, among other factors. We use the deep convolutional neural networks (DCNN) to classify the annotated images of five types of white blood cells. The accuracy and performance of the proposed framework are evaluated for the blood cell classifications. The results demonstrate that the DCNN model performs close to the accuracy of 80% and provides an accurate and fast method for hematological laboratories.

Temperature has significantly negative effect on structural performance, this situation is sure to be even worse for support structure of offshore wind turbine (OWT) in harsh ocean environment. In this study, the daily temperature effects on a support structure of jacket-type OWT are investigated using numerical simulation. The basic theory and method of thermal analysis for structure are briefly introduced. The finite element (FE) models for thermal analysis of the support structure of an OWT are constructed. Then, the time-dependent thermal boundary conditions are determined using the meteorological parameters of a typical sunny day. Accordingly, the thermal boundary conditions are applied on the FE model. Subsequently, the transient heat-transfer analysis is performed for structural temperature calculation. At last, the time-dependent structural temperature variation and distribution of the support structure are discussed. The results show that the temperature effects are considerable and have obvious daily-cycle property. The solar radiation and air temperature are the major influential factors on the structural temperature behavior of the steel tube tower. The temperature effects should be seriously considered in the design and maintenance of the support structure of OWT.

The monitoring value of strain of a concrete bridge is mainly affected by concrete shrinkage and creep, temperature, vehicle loads, etc. Seasonal temperature, daily solar radiation and cold current are the main influencing factors of temperature. Among them, temperature effect of daily solar radiation has a significant impact on the monitoring value of strain. Based on the monitoring data of the concrete bridge in Dongying City, Shandong Province, China, a method to separate the temperature effect of daily solar radiation from the monitoring value of strain of the bridge health monitoring system is obtained. Based on the analysis of the monitoring data of strain and temperature in the concrete girder, a model of the relationship between strain and temperature was established by using the unary linear fitting method. The strain separated from temperature effect of daily solar radiation can provide an effective basis for safety assessment of a concrete bridge.

The lasers used in biomedical engineering, laser nuclear physics and other fields are generally Gaussian beam with nonuniform energy distribution. Compared with point light source, the propagation direction of beam is more concentrated macroscopically. The light energy of a point source diffuses in all directions. It is of great significance to study the propagation of Gaussian light source in biological tissue. In this paper, finite element analysis is used to model and simulate the light transmission and biological heat transfer of Gaussian finite width pulse beam on gastric tumor tissue by COMSOL software. In this study, the object is composed of air, water layer, gastric tissue and gastric tumor. A Gaussian beam with a wavelength of 532nm and a point light source are irradiated on the object respectively. Observed the loss of light energy on various objects and the temperature change caused by the absorption of light energy in biological tissues. The experimental results show that the Gaussian beam has better penetration in the direction perpendicular to the object. Only 1.35% of the light energy is lost in the water layer, while 10% of the light energy generated by the point source is lost in the water layer. Because less light energy arrives at biological tissues in the point light source experiment, the temperature rise of biological tissues is correspondingly reduced. This study has a certain theoretical significance for photoacoustic imaging (PAI) or thermal radiation therapy of gastric tissue.