This PDF file contains the front matter associated with SPIE Proceedings Volume 12951, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Optical fibers are one example of waveguides that can transmit multimodal information. This information can be encoded in different optical modes in a multi-mode fiber or in different types of modes. For example, optical fibers have also recently been demonstrated to be excellent waveguide for acoustic modes. This means that sensing does not have to be performed at the location that the optical fiber is bonded to the structure, but instead Lamb waves can be converted into propagating acoustic modes in optical fibers. These modes can be transmitted to different sensor locations within the optical fiber. This presentation discusses the physical characteristics of these optical fiber acoustic modes and their use to increase the signal to noise ratio of the collection of Lamb wave information. Experimental verifications of the physical behavior of these modes using micro-laser Doppler vibrometry is also presented.

Whereas acoustic fields are widely used for nondestructive evaluation and testing, these same fields can be utilized for actuation with appropriate setups and acoustic amplitudes. Here time-averaged acoustic radiation pressures and forces can be leveraged for micromanipulation, with these arising from diffractive acoustic fields, standing pressure fields, travelling waves, acoustic streaming and related nonlinear acoustic phenomenon. Acoustic fields are particularly useful for activities related to patterning, sorting and mixing of microspecimens, where the wavelength of typical ⪆MHz order acoustic approach that of individual cells, affording rapid manipulation activities. Whereas the much of the acoustofluidic literature has examined the use of acoustic standing waves to accomplish fairly simple lines and grids of particles, here we explore the use of novel acoustofluidic effects and method including microresonators, metamaterials and acoustic holography to generate more complex and tailored outcomes. Further we examine our recent work in 3D printing and the potential for future efforts to combine these developments with acoustic micromanipulation.

This research paper addresses the critical issue of identifying structural health conditions of bonded composites when subjected to varying bending modes. In response to heightened demands for eco-efficiency, the aviation sector has embraced lightweight designs, with composite materials supplanting traditional aluminum due to their remarkable tensile stiffness and reduced weight. However, the practical utilization of bonded composites introduces challenges, as non-uniform loads can lead to bending and deformation, potentially hindering failures detection capabilities within the bonded structures. This study explores the technology details for employing guided waves to assess health conditions. The damage extent is analyzed by capturing the reflected guided waves that propagate in the bending deflection of the bonded composite strip. The analytical results are also resolved aiming to compare with experiment and simulation results, which explains in more detail the propagation process of guided waves in bent materials. The Scanning Laser Doppler Vibrometry (SLDV) techniques are used to visualize the full wave fields. The research results show that the bending of the composite structure exerts a discernible impact on the guided wave's ability to accurately quantify cracks. Consequently, the indicators used to gauge faults undergo notable shifts in correspondence with these variations. Notably, employing a prognosis model trained via a sample with zero deflection, to predict the Remaining Useful Life (RUL) of a deflected sample introduces uncertainty error. This underscores the intricate relationship between deflection-induced changes and damage prediction using guided wave analysis. The deviations that occur in the process of predicting RUL serve as a pivotal reminder of the intricate nature of damage assessment in the presence of deflection, warranting comprehensive consideration in the development of predictive models based on bent specimens.

This paper presents a new flow-coating method to fabricate adhesively bonded optical fiber coupler for acoustic signals. The goal is to decrease losses as acoustic signals representing Lamb waves are transferred from one fiber to another. The flow-coating method significantly improves the geometry of the adhesive in the coupler. As a result, the losses to the acoustic signal energy are significantly reduced in the coupler.

For the safety of the lithium-ion batteries widely used for electrical cars and Energy Storage Systems (ESS), maintaining a solid welding connection between a battery cell and a busbar is critical. For example, dozens of battery cells are wire-connected to the busbars of the ESS, and any single failure of the wire welding will result in shutdown of the entire ESS and pose a significant safety risk. Currently, destructive shear force tests are conducted only for a few selected samples, but not for exhaustive real-time inspection. In this study, a laser ultrasonic system is developed for noncontact, nondestructive and real-time inspection of wire welding of lithium-ion batteries. Ultrasonic waves are generated using a pulse laser at a wire, and the corresponding responses are measured before and after the welding point. A weld defect is detected, when the ultrasonic energy substantially attenuates after passing through the welding point. The performance of the proposed inspection technique is examined by inspection over 300 wire welding points of the ESS.

To perform active Structural Health Monitoring (SHM) Guided Waves (GW) have received great interest as they can inspect large areas with a few sensors and are sensitive to barely visible structural damages. Fiber Bragg Grating (FBG) sensors offer several advantages, but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. In addition, the FBG sensors have been shown to be capable of sensing the Shear Horizontal (SH wave) when deployed in perpendicular configuration to the propagating wave. The SH₀ wave is non-dispersive and hence simplifies the signal processing. As a result the SH₀ wave may improve the quality of damage detection and localization. Thus, in this paper the use of SH₀ waves sensed using FBG sensors in edge-filtering configuration has been investigated. The SH wave is generated using d36 based piezo actuators and sensed using FBG sensors. The method is developed for a simple aluminum plate with simulated damage scenarios. The method shows promising results and has potential to be used for reference-free damage detection.

Guided waves can propagate along thin-walled structures, such as pipes and plates, with limited energy loss, thus enabling the efficient inspection and monitoring of large structures from a limited number of sensor locations. This allows Structural Health Monitoring (SHM) using permanently installed monitoring systems with limited access and sensor requirements. However, guided wave propagation is complicated due to multiple propagating wave modes and dispersion, potentially causing the signals to become distorted. Both analytical and numerical analysis and experimental measurements are essential for guided wave research. Specialized laboratory equipment such as non-contact laser vibrometers can be expensive and unaffordable. This contribution presents a preliminary investigation on what can be achieved experimentally using low-cost sensors, suitable for research and teaching in circumstances with limited budgets. For low frequency guided wave propagation in an isotropic plate (A0 Lamb wave mode), the influence of different measurement configurations on the accuracy of group velocity quantification was investigated. Results from experimental setups employing different receive transducers were evaluated and quantified. Accuracy of sensor placement and coupling as well as measurement repeatability and Signal-to-Noise Ratio (SNR) were investigated. Accurate experimental quantification of the group velocity, using the different movable sensors, was demonstrated by comparison to theoretical predictions based on the nominal aluminum material properties.

Bolt looseness can occur subject to long-term structural service. With this concern of structural integrity and safety, there is a huge demand to identify bolt-looseness-caused local debonding in connected structural components such as bolted panels. With the aid of non-contact laser scanning, transverse Operating Deflection Shapes (ODSs) of a bolted panel can be measured with high spatial resolutions. Perturbation to the linear transverse dynamic equilibrium of the panel can be regarded as the Linear Pseudo-Force (LPF), which is applied to the debonding region only and vanishes at undamaged locations. However, nonlinearities caused by contact of debonding interfaces during vibrations are not taken into consideration in the LPF model. As a consequence, only linear damage features can be contained in the LPFs which are established on linear ODSs, leading to incompleteness of damage features. Addressing this problem, this study establishes a Nonlinear Pseudo-Force (NPF) model from the nonlinear transverse motion of equation of a beam-type bi-layer panel model with local debonding. Superior to LPFs, NPFs can extract linear and nonlinear damage features from linear and nonlinear ODSs, respectively. Similar to LPFs, NPFs concentrate in the debonding regions to form local peaks. Therefore, the NPF can be utilized as an ideal nonlinear indicator for the identification of local debonding in bolted panels. The applicability of the NPF is experimentally validated by identifying width-through debonding in a steel panel connected by bolts, whose ODSs at linear and nonlinear (higher) harmonics are acquired through non-contact laser scanning measurement. Experimental results reveal that the NPF can extract complete linear and nonlinear damage features, and hence has a higher-dimensional capacity for identifying local debonding in connected structural components such as bolted panels, whose occurrence, location, and size can be graphically characterized.

By leveraging recent advances in additive manufacturing (AM) and nanotechnology, this research aspires to a new manufacturing framework for functionalizing CFRP with the capacity of in situ, real-time integrity monitoring through life cycle. Made totally via various AM techniques, the CFRP structures consist of the 3D-printed continuous carbon fibers and nylon-based matrix, and Aerosol Jet Printing (AJP)-made implantable piezoresistive sensing units. A nanocomposite ink with Few-Layer Graphene (FLG) and Cellulose Nanocrystals (CNC) is specifically formulated to fabricate the sensing units, and an ultrathin nylon dielectric layer encapsulates each sensing unit for electrical insulation. Multiple such-made sensing units are networked in the CFRP structures. By virtue of the quantum tunneling effect formed among neighboring FLGs, the sensor network sensitively responds to CFRP-guided ultrasonic waves of kilohertz frequencies, as well as static strains applied on the CFRP structures. In conjunction with an enhanced ultrasound tomography algorithm, integrity of CFRP can be monitored in a real-time manner, from manufacturing through service to end-of-life.

To perform active Structural Health Monitoring (SHM) Guided Waves (GW) have received great interest as they can inspect large areas with a few sensors and are sensitive to barely-visible structural damages. Fiber Bragg Grating (FBG) sensors offer several advantages, but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. The sensitivity of the FBG sensors can be further improved through the use of the remote bonding. One of the challenges with the effective and repetitive measurements with the FBG sensors is uncertainty in the bond quality. This is especially important due to the small area of contact of the fiber with the structure. A solution to this is the use of a D-shaped optical fiber which will increase the area in contact. This paper experimentally investigates the coupling of the wave from the structure into the D-shaped fiber. 4 different geometries of optical fiber are studied and the coupling and the propagation amplitudes are investigated using 3D laser Doppler vibrometery measurements.

Ultrasonic Guided Waves (UGWs) are particularly suited for monitoring applications as their high frequency allows them to interact with small defects while traveling long distances. For defect localization in plate structures, Lamb waves are generated and exploited in the UGW sense. While data-driven methods, exclusively driven from the collected time series have proven adept for various damage identification tasks, a more refined characterization calls for additional use of physics-based models. In this work, we demonstrate efficient fusion of UGW data with numerical models of plate structures, which are obtained from high-fidelity spectral element simulations. A major bottleneck associated with such a hybrid modeling scheme lies in the excessive computational cost associated with simulations of high–frequency Lamb waves through plate structures. This is due to their short wavelength and short period, which demands a fine discretization in both space and time. To avoid repeated evaluations of prohibitively expensive computational models, model order reduction methods or surrogates can be adopted. A surrogate model should be based on mechanical information, to reduce the amount of training data required. For practical reasons, surrogate models should further be flexible, allowing for assimilation of multiple defect locations, as well as the simulation of more complex geometrical features, such as rivet holes or boundaries. We show steps toward construction of such a surrogate, which draws its construct from the concept of Frequency Response Functions (FRFs), or in other words, the representation of a system in the frequency domain.

The spectral element method is considered as a fast converging and computationally efficient numerical method for modelling propagating waves in composite structures. However, only simplified models for simulating damage either using additional mass, local changes in mechanical properties or separation of element nodes in the damage area are encountered in the literature. In our work, we propose to use contact traction at the damage interface in addition to nodes separation. Contact tractions are represented by Lagrange multipliers and are subject to frictionless sliding Karush-Kuhn-Tucker conditions to prevent the penetration of two composite layers. So far, this type of approach has been implemented in SEM for aluminium cracked structures. While the cracked structure is considered in a two-dimensional domain, clapping delamination in composite must be implemented in a three-dimensional space. The essence in determining the contact forces is to identify the gap between two layers for each time step. The element nodes of one surface, termed” slave”, are projected onto another surface, termed” master”. The gap is equal to the difference of the position vectors of each point, while according to KKT conditions, contact forces appear if the gap is less than zero.

This study focuses on exploiting the non-propagating modes of the ultrasonic guided waves in structural components, such as plates, trusses, beams, pipelines, and rails, which are ubiquitous in today’s civil infrastructural systems. These long-neglected localized modes are not currently used by state-of-the-art engineering practice. A thorough understanding of them and their interactions with various structural defects will lead to a new paradigm of ultrasound technology that can support early warning of structural integrity conditions.

Residual stresses, mainly generated by mechanical processing and strengthening approaches, are known to impose significant influence on the performance of industrial structures. They usually exist as an undesirable source of fractures and failures. Furthermore, the nondestructive evaluation and calibration of residual stresses in the depth direction always stays a significant issue, especially for multilayer viscoelastic composites. Thus, this paper proposes the advancement of residual stress evaluation technology by detecting the variation of guided wave signal and impedance features under different stress conditions. The target structure for nondestructive evaluation task is a typical multilayer, viscoelastic composite shell of a solid rocket motor. By carrying out an in-depth theoretical analysis with Finite Element Modeling (FEM), the sensing sensitivity of the probing ultrasonic wave characteristics are studied systematically. To monitor the residual stress state, a theoretical model is established by considering the stress gradient in each layer, indicating the current stress condition inside the testing object. Consequently, via the comprehensive consideration of transducer dimensions, center frequency, the most effective residual stress monitoring setup is established, and the sensing signals are recorded by Piezoelectric Wafer Active Sensors (PWAS) placing on the object. Finally, different values of prestress are applied to the object, while the nonlinear constitutive law of the layered materials is implemented. The pattern between the sensing signal and the residual stress state is identified. Simulation results show that significant nonlinear features such as higher harmonic generation are fully captured, which are related to the prestress conditions. The amplitude of sensing signals could provide indicative information for evaluating the residual stress state based on the acoustoelastic effects. The amplitude and the peak shift of the impedance spectra are investigated to quantify the residual stress state. The findings of this research possess superb application potential for the evaluation of residual stresses in multilayer composites for enhancing manufacturing quality and avoiding unexpected failures in solid rocket motor industry. This paper finishes with summary, concluding remarks, and suggestions for future work.

The fascinating non-propagating lamb wave modes, Zero-Group-Velocity (ZGV) modes, have ignited profound research curiosity. ZGV modes possess the distinctive attribute of an elapsed group velocity with a finite nonzero wavenumber, indicating a spatially propagating wave package under a motionless envelope. This stationary mode engenders a localized resonance, confining the wave energy in the vicinity. These captivating phenomena have been scrutinized by researchers from the perspective of temporal and spatial domains. Nevertheless, it remains an uncharted frontier that how ZGV modes manifest their peculiarity for steady-state responses in harmonic analysis. Inspired by the unique trembling phenomenon following the appearance of ZGV resonance peaks, this paper aims at revealing the underlying mechanism and fundamental nature of the ZGV trembling phenomena in harmonic analysis, developing a deeper insight into lamb wave modes generation and propagation. The paper commences with the identification and extraction of ZGV modes under the frameworks of analytical analysis, serving as a reference for the subsequent analysis. This is followed by the construction of a finite element model for the implementation of harmonic analysis. Through the meticulous examination of displacement frequency spectra and dispersion curves, the trembling phenomenon following the ZGV resonances is visualized and evaluated. Ultimately, Electro-Mechanical Impedance Spectroscopy (EMIS) is employed to conduct the harmonic tests experimentally to validate the distinct trembling features. The distinct trembling features are attributed to the drastic fluctuations of participation factor for the emerging modes. This paper culminates with summary, concluding remarks, and suggestions for future work.

In this paper authors investigated elastic guided wave-based damage localization in thin panel made out of isotropic material. Elastic waves excitation is based on piezoelectric transducer. Wave sensing is based on Fiber Bragg Gratings (FBG) strain sensors and piezoelectric sensors. Piezoelectric transducers are very popular in applications related to elastic wave generation and sensing. The FBG strain sensors utilized in the past for quasi static or quasi-static strain sensing are more and more utilized in field of elastic wave sensing. In the case of FBG sensors the edge filtering method is utilized. In this purpose tunable laser source is used. Received optical signal is converted to the voltage signal based on photodetector. Attention in this research was focused on dual sensing using FBGs. For this purpose, pair of FBG sensors located on the top and bottom surface is utilized. Damage localization is based on delay-and-sum algorithm. In this paper experimental results are presented.

The goal of this project is to demonstrate an electronically controlled a magnetic latch system driven by shape memory alloy that is capable of lifting and securing a large load. The proposed magnetic latch system does not consume energy when it is latched, and it only requires minimal amount of energy to unlatch. To accomplish that, a dynamically controlled magnetic array is utilized in the latching system. The arrangement of the magnets in the array is controlled using shape memory alloy to change the direction of the array’s magnetic field, allowing the system to switch between latching and unlatching state. When the system is in the latching state, the magnets are arranged such that it will produce a strong uniform magnetic latching force. When unlatching, the magnets are rearranged to shift the magnetic field pattern, allowing the latch to be opened. In this paper, the theory and design of the novel magnetic latch system are described. The operation of the latch system is demonstrated, and the maximum latch force is measured and reported.

Digital Twin (DT) is a modern concept that allows for the creation of the digital model of the manufacturing process, and hence for the product life-cycle monitoring. DT could provide manufacturing details and identify potential shifts and anomalies during printing. It has a significant potential in additive manufacturing as an avenue to address manufacturing uncertainties. In this study, the digital twin for the fused deposition modeling method of the additive manufacturing was developed. DT provides information about the speed of sound within the material. Information about the sound speed is used to evaluate the elastic properties of printed material. The results of sound speed measurements in the PLA samples printed using various combinations of parameters of the printing process are analyzed. Using the measurements results, the response surface linking the elastic properties of the material to the parameters of the printing process is established. Based on the information about speed of sound within the material, further actions could be taken on the adjustment of the printing process in order to obtain desirable properties. An analytical model of the sound speed propagation within the material was developed and evaluated using the experimental data. It is suggested that DT combined with the printing response surface is a valuable approach to control of the printing process. This integrated approach demonstrates considerable promise in steering additive manufacturing towards the attainment of specified material properties.

Remote and complex work sites of wind turbines limit the accessibility of the condition assessment. Wind turbine blades are subject to sustained wind load and harsh natural environmental conditions, which are vulnerable to various faults. Robotic-enabled sensing technology appears to be a promising solution for an efficient wind turbine blade inspection. Together with the recent advances in image processing and deep learning segmentation, automated inspection of wind turbine blades becomes possible. Nevertheless, it remains a challenging task to quantify the damage accurately due to the complex condition of images concerning motion blurs. To address this issue, an integrated framework, i.e., the combination of a Deblur Generative Adversarial Network v2 (DeblurGAN-v2) and You Only Look Once v8 (YOLO-v8) was proposed in this study. Specifically, the mapping between the motion-blurred images and those in high quality was adopted from the open-access pretrained DeblurGAN-v2, based on which the deblurring performance for wind turbine images with various motion blur scales was discussed concerning the image quality. Subsequently, the transfer learning method was implemented to fine-tune YOLO-v8. The well-trained YOLO v8 was then utilized for target defect segmentation on the deblurred images. Finally, various metrics were calculated to evaluate the segmentation accuracy and efficiency. Results prove a good generalization of DeblurGAN-v2 on wind turbine images and clearly illustrate the enhanced performance of the proposed methodology especially when the motion blur scale is within 35. The integrated framework could serve as a reference for dealing with other fuzzy image-related issues.

The lithium-ion batteries used in high-power applications require thousands of cells arranged in arrays where failure of an individual cell may lead to total system failure via thermal runaway. One cause of thermal runaway is high-temperature abuse. This work explores the viability of ultrasonic inspection to detect whether lithium-ion cells have been previously subjected to localized thermal abuse by comparing ultrasonic features recorded during the charge-discharge cycling before and after thermal abuse. We employ 1 MHz Gaussian pulses propagating through mechanically-confined NMC lithium-ion cells as they undergo charge-discharge cycling, localized heating between 50°C and 150°C, and post-abuse charge-discharge cycling. Metrics of the transmitted ultrasonic signal, the Time of Flight Shift (TOFS) and Signal Amplitude (SA), are analyzed to assess their utility in detecting pre-existing damage due to localized thermal abuse. Results indicate that the SA and TOFS trends are strongly affected by previous, localized thermal abuse in the following charge-discharge cycles, while the model provides insight about which cells components are contributing to the observed changes in ultrasonic signals.

An elastic-poroelastic simulation of ultrasound inspection for lithium-metal batteries is presented and compared to empirical reflection spectra measured during battery cycling. Simulated reflection spectra were obtained using a two-dimensional (2D) plane strain model, comprised of dozens of individual microns-thick layers within a Li-metal pouch cell. The simulated reflection spectra were then compared to ultrasonic reflection spectra measurements taken intermittently during cell cycling. A sensitivity analysis and parameter calibration were performed for the pristine pouch cell simulation prior to cycling, providing a baseline to account for difficult to measure poroelastic material parameters. Then, the reduction in solid Li anode thickness and corresponding growth into a mossy lithium layer was modeled to represent aging conditions. Results from both simulations and empirical inspections show similar trends in through-thickness resonance frequencies due to cell aging.

Nondestructive characterization of battery structures is important as both a research tool and as a means for developing reliable prognostics for batteries in service. Local Ultrasonic Resonance Spectroscopy (LURS) is a technique that measures spatially localized through-thickness vibrational resonances in layered materials. In battery cells, LURS measurements can reveal layer spacing and changes in mechanical properties. This study examines changes in structure that occur from fabrication to end of life for batteries cycled under different conditions as a demonstration of the capabilities of the LURS approach. Lithium metal pouch cell batteries were studied in both single- and multi-layer form factors. The cells were electrically cycled under constant current conditions at charge rates ranging from 0.2 C to 2 C, where 1 C is the charge rate (C-rate) required to fully charge a battery in one hour. In addition to varying charge rates, cells were also cycled under different temperatures and loading conditions, leading to a wide variety of electrode structures at end of life. LURS scans were conducted at various points in the battery lifetime to examine how damage developed. Multiple processing methods are shown, which help to reveal information about the internal resonance in each case and the ways in which resonance changes due to cell aging and spatial variation in the layered structure. Scan data in some instances showed evidence of manufacturing defects such as foreign object debris (FOD) on the electrode surface. In other cases, scan data showed spatial variation in degradation of the lithium anode surface that was dependent on the charge rate, loading scenario, or cycling temperature.

We developed an in-line quality control scanner that can be used to detect defects in lithium-ion battery electrodes during roll-to-roll manufacturing. Lookin’s scanners employ terahertz radiation which is non-ionizing and non-destructive. The patented hardware used in Lookin’s scanners provide scan speeds that allow real-time detection of defects during manufacturing, for the first time. With early detection of these defects, Lookin’s scanners enable manufacturing of lithium-ion batteries with better shelf life, increased safety, lower cost, and decreased production lead-time.

The surge in demand for high-energy-density lithium-ion batteries has led to the exploration of high-C (high current draw) discharges in various applications. However, these high-C discharges introduce significant challenges related to battery performance and safety. This exploratory study aims to investigate early current interrupt device failure detection mechanisms in 18650 lithium-ion batteries subjected to discharges up to 16C. Our controlled experimental setup induces a 40 amp discharge to a single lithium nickel cobalt aluminum oxide 18650 cell. Employing digital image correlation techniques, the structural changes in the battery are monitored during discharge, pinpointing subtle deformations and strain patterns as potential precursors to failure. This data, coupled with voltage and temperature measurements, offer a more comprehensive understanding of the battery performance under extreme conditions, allowing for future methods to further enhance safety protocols for high-C discharge.

Unlike Topological edge states where bulk-boundary distinction is achieved by insulating the bulk state acoustic, topological phenomena could be explored inside the Bulk exploiting the acoustic spin angular momentum. Here is this presentation we show that despite acoustic phonons are boson like spin zero particles, a topological bulk state is emerged at a specific Dirac state due to intrinsic acoustic spin quantized by +1/2 and -1/2. Time resolved analysis show that the spin vector essentially pass through a manifold causing an accumulation of geometric phase and prevail the topological energy sink.

Precise ultrasound focusing is often limited by the fixed focal points and narrow frequency ranges. In this study, we design and experimentally testify an acoustic meta-lens to facilitate ultrasound focusing in tissues, for potential applications in clinical ultrasound therapy. The proposed meta-lens is capable of focusing broadband ultrasound waves in the megahertz range, by adequately considering acoustic-structure interaction. The manipulation and focusing of ultrasound waves are achieved by inversely optimizing the unit cell parameters of the meta-lens using a genetic algorithm (GA). Both numerical simulation and experiment demonstrate that the inversely designed and GA-optimized meta-lens improves the focusing precision and acoustic energy delivery. In particular, the tunable focal point of the meta-lens enables the scanning of tissue sections without a need to move the meta-lens along soft tissues.

Double-Dirac cones at the center of the Brillouin zone (Г), a very rare phenomena but could be predictively achieved by design. Such design could help use the wave propagation phenomena to tune in such a way that acoustic computing is possible. So far utilizing these phenomena, acoustic logic gates are rare. In most cases deferent configurations are required to create different gates. Here all possible gates AND, NAND, OR and NOR gate design are achieved to perform Boolean algebra through only one structural setup. A simple one degree of freedom and a complex six degrees of freedom systems are proposed and demonstrated.

The field of topological states of matter has garnered considerable attention across electronic, photonic, and phononic systems due to its remarkable abilities in waveguiding and localization, which remain robust against disorders and defects. A fundamental challenge in material physics lies in understanding the interplay between intrinsic properties and those induced by boundaries. In infinite periodic materials, resonant modes are notably absent within band gaps. However, when the material is truncated to form a finite periodic structure, these modes may appear as localized edge modes within the band gap. In this study, we introduce a generalized system by incorporating nonlocal interactions into the well-established Su-Schrieffer-Heeger (SSH) model. This generalized system exhibits a broader range of topological properties, including non-trivial topological phases and associated localized edge states. We conduct a detailed investigation of the zero-energy edge states, exploring their characteristics and behavior. Additionally, we discuss the influence of boundaries on the existence of edge states and consider the impact of Fifth Nearest Neighbors (FNNs) within the system.

The geometric phase is an additional phase factor acquired by oscillating dynamical systems. It has emerged as an insightful parameter to understand the dynamic behavior in a variety of systems, from molecular physics to elastic waveguides. In more recent years, the geometric phase has been widely exploited in connections with the analysis of topological materials. The present article reviews the concept of geometric phase in elastic systems and its connection to the design of elastic topological metamaterials. Examples are presented to explain the theoretical basis of the geometric phase by using arguments of differential geometry and topology. These concepts are then applied to the analysis of a one-dimensional elastic topological metamaterial that possesses localized vibration modes immune to geometric perturbations.

Asymmetric scattering is a phenomenon in which the field scattered from a discontinuity is dependent on the direction of incidence. In waveguide systems such as elastic plates, the existence of multiple propagating modes provides a platform to explore asymmetric scattering through direction-dependent mode coupling. In this talk, we first derive requirements for asymmetric scattering due to reciprocity and passivity in multi-mode systems. These principles are used in the design and modeling of an elastic beam containing a Willis scatterer which produces asymmetric coupling of symmetric and antisymmetric Lamb modes. Discrepancies between Euler-Bernoulli and Timoshenko beam modeling approaches are discussed.

This keynote presentation will cover passive methods for ultrasonic NDT and SHM. These methods apply to cases where the test structure is subjected to uncontrolled excitations (such as from ambient loads) or to a limited number of controlled excitations. In these cases, proper analysis of the reception signals can reveal surprisingly accurate information from array analysis, such as imaging of internal defects. The effort here is to obtain as much information as possible from the receiver signals while exploiting at a minimum the excitation signals in the beamforming process. Examples will be shown for NDT imaging of holes in an aluminum block via plane-wave beamforming and SHM of a composite wind turbine blade via Matched-Field Processing imaging.

Traditional single acoustic source localization techniques often become challenging when multiple acoustic sources are present on spherical structures. Here, a localization technique for multiple acoustic sources is proposed using the time difference of arrival without knowing the acoustic wave speed in the material. The proposed technique does not require solving a system of nonlinear equations; hence, greatly reduces the complexity of calculation. The method to remove the artifacts is given. A finite element model of a spherical surface was created to verify the proposed acoustic source localization technique. The results of numerical simulation prove the reliability of the proposed technique.

The use of cameras has gained popularity in the engineering world due to their ease of use and non-contact nature. The combined use of cameras and Unmanned Aerial Vehicles (UAVs) allows performing complex acquisition in hard-to-reach locations. However, due to the motion of the UAV, measurements can be inaccurate. This study focuses on the mitigation of UAV-induced motion, to enhance the measurement precision for structural dynamic assessment by proposing a combination of sensor-based and algorithm-based camera motion compensation approaches. The sensor-based approach relies on the use of a novel system integrating an Inertial Measurement Unit and two laser distance sensors to account for the low-frequency components of the motion. An Extended Kalman Filter algorithm is then implemented to improve the accuracy of five of the six degrees of freedom of motion. Laboratory experiments were performed to compare the displacement measured with the moving camera post-processed using the proposed method against a reference stationary camera. The results of the experiments showed that the proposed motion-correction method provides displacements that are in good agreement with the stationary camera and show a significant reduction of the induced motion. Further developed, this technique can be used in various applications where motion-corrected data must be obtained for accurate assessment of the dynamic properties of the targeted system.

Many offshore infrastructures have been developed to explore vast marine resources over the past several decades. In addition to the conventional fixed-type offshore infrastructures, a new class of offshore infrastructures, the so-called floating offshore infrastructures, have gained dramatically increasing applications owing to their flexible deployment and enhanced capacity in renewable energy exploitation in deep seawater. As the key functional component of the floating infrastructure, the underwater mooring systems are subject to sustained dynamic loads pertinent to marine waves and currents, which are prone to different types of failures. Identifying those mooring system failures timely and reliably thus plays a vital role in offshore infrastructure health management and maintenance. This study aims to achieve this objective by developing an integrated numerical framework that seamlessly synthesizes the physical mooring system modeling and data-driven analysis. Specifically, a high-fidelity physical model that takes into account the sophisticated fluid-structure interaction is established to mimic the underlying behavior of the mooring system. The mooring line failures are incorporated into the model to generate the respective dynamic responses. With the aid of data-driven modeling, the causative relationship between mooring line failure scenarios and dynamic responses can be characterized. Given the sensor measurement in actual practice, this framework offers a feasible solution for the failure identification of underwater mooring systems. The results clearly demonstrate the feasibility of the proposed methodology.

Additive Manufacturing (AM) or 3D printing has become a popular manufacturing technique that helps to save materials during production. Modern industries have started incorporating printed structural parts into their structures, including those with critical applications, like in aerospace and civil. Similarly for structures made from metals or fibre reinforced polymers there is a need for structural health monitoring of the 3D-printed structural parts. This requires the development of accurate and reliable methods for evaluating and monitoring the structural integrity of such components. The Electromechanical Impedance (EMI) method is frequently used to evaluate the health condition of lightweight structures based on the local structural response in the high-frequency range. This study investigates the usage of EMI that is based both on surface bonded and embedded sensors. As sensors, the piezoelectric discs were used for the measurements. The measurements were made in the 1 kHz to 100 kHz frequency range for the Resistance (R) data. During the study, the simulated damage was introduced, and the sensors' responses were compared to determine the influence of embedding on the damage detection performance.

Acoustic source localization techniques for composite laminates are challenging for anisotropic materials. In this paper, an improved signal energy-based acoustic source localization technique without a priori knowledge of the principal axes orientations is proposed. The proposed technique has the advantages of no need to measure arrival time and does not need complex signal processing techniques. It can also be applied in the case of low signal-to-noise ratio. Finite element models of carbon fiber laminate with different stacking sequences are created to verify the proposed technique. The results of the numerical simulation demonstrate the feasibility of the proposed technique.

Valuable information about how a dynamic system’s parameters vary is encoded in changes to the shape and size of its system attractor. Being able to analyze attractor deformation to infer system changes is thus important in many applications, such as damage detection and structural health monitoring. This work presents experimental validation of a geometry-based method for analyzing attractor deformation called Boundary Transformation Vectors (BTVs). Mass added at two locations on a vibrating cantilever beam is identified using BTVs, demonstrating that BTVs can be useful for monitoring small structural changes in physical systems.

Carbon fiber composites have gained widespread popularity as advanced composite materials, finding their widespread applications across industries like aerospace, automobile, transportation, and health care. This popularity stems from their unique mechanical, electrical, and thermal properties. However, it is imperative to acknowledge that the structural integrity of these composites can undergo deterioration over time, mainly due to factors such as fatigue, impact damage, and aging. Hence, there is a burning need for a reliable method to monitor the health of these carbon fiber composite structures to prevent potential failure. The main goal of this research is to investigate the specific measurement issues when implementing a structural health monitoring approach for these composite plates by employing the guided Lamb wave technique. It is well known that the orientation of the fiber within a layered carbon fiber composite plate affects the propagation characteristics of Lamb waves. On the other hand, the sensors on and potentially embedded in the plates also interact with the Lamb wave in a dynamic manner. Using a micro-3D Laser doppler vibrometer, this research explores the microscopic interaction among the sensors, local fiber orientation and texture, and Lamb waves in these carbon fiber composite plates. Such insight can potentially lead us to a more viable means of interpreting the output of sensors and guided Lamb waves to detect defects within these composites.

The process of identifying structural damage can be approached as an optimization challenge, where the goal is to bridge the gap between observed experimental data and theoretical model predictions. This way creates the likelihood to apply the metaheuristic algorithms for the inverse damage identification. Through experiments, we can collect vibration data such as acceleration responses, natural frequencies, mode shapes, and piezoelectric impedance, which serve as indicators of damage. However, such data may be tainted with noise or errors. Furthermore, limitations in model accuracy or a lack of comprehensive understanding of experimental boundary conditions can inject uncertainties into the damage detection process. Traditional probabilistic methods have been employed to counter these uncertainties, but they often rely on predefined statistical distributions, typically Gaussian distribution. In real-world applications, the myriad sources of uncertainty and the paucity of specific experimental data can make it difficult to exactly ascertain these distributions. In this regard, the non-probabilistic interval analysis is introduced. This method leans on the defined bounds of uncertainty in data, rather than their probabilistic nature. It assesses structural damage by measuring factors like the nominal reduction in stiffness, the likelihood of damage, and an index that combines the two, which are quantified through the non-probability reliability method. Besides, the reduced order modeling through component mode synthesis is adopted to speed up the optimization iterations. To validate this approach, vibration-based attributes are used for truss structure, ensuring a robust identification of structural damage when faced with uncertainties in data.

Low frequency guided waves have been used to develop Structural Health Monitoring (SHM) for the early detection of fatigue cracks in metallic aircraft structures. The scattering and mode conversion of guided waves at part-thickness defects was investigated to quantify the sensitivity for defect detection and the potential for the development of a baseline-free SHM methodology employing mode-converted guided waves. Finite Element Analysis (FEA) and experimental validation were conducted to investigate the mode converted scattering from the S₀ to the A₀ Lamb wave mode at part-thickness crack-like defects in an aluminum plate. A piezoelectric (PZT) transducer was experimentally used as a plate edge excitation for the S₀ mode and the out-of-plane displacement was measured using a laser vibrometer. Good agreement between the FEA and experimental results was obtained and the influence of defect depth and length was investigated and quantified.

Lamb wave-based inspection methods show promise in long range Nondestructive Evaluation (NDE) of thin metallic and composite plates. This NDE strategy is typically implemented in a pitch-catch configuration of one actuator and at least one sensor. Despite non-contact methods such as laser ultrasonics and air-coupled transducers, the most common approach relies on contact transducers. Transducer placement is usually performed manually and positional errors and variations in contact conditions are therefore inevitable. Thus, this study investigates the potential improvement in measurement reliability and repeatability through the use of an automated deployment system. The system is comprised of two subsystems: 1) A gel deployment subsystem to deposit the desired amount of couplant at the target location. 2) A transducer deployment subsystem to lower the transducer onto test article. In addition to a detailed description of the developed prototype systems, their combined reliability is demonstrated for experiments on an aluminum panel in a broad frequency range. The results are compared to those obtained via transducers positioned by multiple different human operators. These benchmark experiments are conducted with varying degree of aids, such as placement templates and weights. Furthermore, measurements from manual placement are processed both manually as well as automatically to further illustrate the need for fully automated NDE capabilities. It is shown that the automated prototype transducer deployment system not only reduces manual labor but achieves slightly improved repeatability as compared to an experienced human operator with positioning aids.

As a person moves about in a building, the footsteps induce the propagation of waves in the floors (especially the floor tiles), and researchers have acquired these vibrations to study several things like occupant localization, pedestrian counting, person identification, fall determination, and gait analysis. This work presents similar research but in a different species. Specifically, we show that acquiring the guided ultrasonic waves generated by a mouse’s movement can enhance the analysis of the animal’s gait. In an open-field arena fitted with ultrasonic sensors and a video camera, F os2A−iCreER (TRAP2) mice are allowed to explore and behave (one at a time). The animal’s motion is registered through video and ultrasonic recordings. As the rodent moves around the open field, its voluntary and involuntary movement applies forces to the structure the animal stands on, leading to the generation of acoustic waves. The acoustic waves propagate through the structure as Lamb and Shear Horizontal (SH) waves, which are detected by ultrasonic sensors and acquired through an amplitude threshold-based data acquisition system. With this acquisition system, waves are acquired and stored as discrete Acoustic Emission (AE) hits, each AE hit being a consequence of an animal’s movement or behavior. The time of the AE hit (which indicates the moment of a foot strike/movement) is used to get deeper insights into the animal’s locomotion. The instantaneous speed from the video recordings and the time duration between the subsequent foot strikes (obtained from the AE hits) are combined to propose a procedure for performing gait analysis in an open-field setting. This would lead to a way to not only undertake gait analysis in a free environment but also to undertake an analysis that would decrease variance in the evaluated gait parameters.

Commercial Photoplethysmography (PPG) sensors such as Apple Watch, Fitbit Versa and Polar series rely on the measurement of Continuous-Wave Diffuse Reflection Signal (CW-DRS) from skin to monitor heart rate. However, such techniques have poor depth sensitivity when considering photons from deep dermis where capillary vessels are found, or end-users with darker skin tones; and are prone to Source-Detector Distance (SDD). Using Monte Carlo modeling of light propagation in skin, we closely evaluate the dependence of Continuous-Wave Photoplethysmography (CW-PPG) in commercial wearables on SDD. Ultimately, we introduce the concept of Time-of-Flight PPG (TOF-PPG) which can produce outstanding accuracy over CW-PPG. Moving forward, these results will provide a valuable source for hypothesis generation for TOF-PPG artificial intelligence to correct CW-PPG without extra cost.

Effective NDE inspection systems and methods are highly desired in a broad range of engineering applications including metal structure thickness evaluation. Laser-generated ultrasound has been studied to excite wideband Lamb waves for NDE. By using the ultrasound in conjunction with multidimensional wavefield measurements, obtained by high spatial resolution noncontact laser scanning vibrometer, thickness evaluations for metal components can be achieved. This paper applies a fully noncontact/remote ultrasonic Lamb wave NDE system and explores its application for thickness characterization and evaluation of metal components. This paper first demonstrates the actuation and sensing of Lamb waves in metal components. The non-contact system consists of a Pulsed Laser (PL) working in the thermoelastic regime to excite Lamb waves and Scanning Laser Doppler vibrometer (SLDV) sensing the waves and providing high-resolution multidimensional wavefield signals for evaluation. Enhancements through sensing, actuation parameters, and surface enhancement were attempted to excite very high-frequency Lamb waves. The results show that excited Lamb waves can have a frequency beyond 1 MHz in certain components thinner than 1 mm. The paper continues to show how the acquired Lamb waves can be used for measuring the thickness of the metal components. The method adopted multidimensional Fourier analysis to convert time-space wavefield measurements to frequency-wavenumber representation. The resulting spectrum is then compared to theoretical dispersion curves in frequency-wavenumber for thickness matching to derive the thickness parameter. Proof-of-concept tests were performed with aluminum components of various thicknesses first and then the method was applied to much thinner foil-type material and components made of different materials.

In our research, the techniques and instruments employed for detecting ultrasonic-guided waves are used to enhance rodent behavior analysis. The rodent–either a C57BL/6J or a 129 mouse–was allowed to explore and behave in an open-field arena with an aluminum plate as the floor. As the rodent moved around the open field, its voluntary and involuntary movement applied forces to the aluminum plate, leading to the generation of Lamb and Shear Horizontal (SH) waves in the plate. The generated waves contain information about the rodent’s physiology, behavior, and underlying mental states. First, this paper describes the experimental setup used for this study, emphasizing the methods adopted to facilitate a seamless measurement of reliable data. This would involve measuring the propagating waves with ultrasonic sensors and acquiring them based on the amplitude threshold criterion. Then, the work explains the tests and the techniques used to get the information in the guided waves, which can be used to infer details about the behavior, psychological state, and gait. These three aspects are explored from the tests conducted in the open field with the two strains of mice.

Highly Nonlinear Solitary Waves (HNSWs) are traditionally used in the field of nondestructive evaluation to inspect a material’s property without causing damage. The research in this paper proposes a new application for HNSWs: predicting changes in Intraocular Pressure (IOP) to ensure optimum treatment and prevent the progression of Glaucoma in an eye. The HNSWs used for assessment were collected from a Polydimethylsiloxane (PDMS) eye model and are initiated and stored with a solitary wave transducer. To collect a full range of HNSWs that represent the biological range of IOPs in humans, the PDMS eye model is pressurized from 12mmHg to 26mmHg with 1mmHg increments and waves are collected at each pressure point. Once a HNSW is collected, it is wirelessly transmitted to a server where it is fed into a convolutional neural network to predict the IOP. This is done by extracting relevant features from the HNSW with a Fast Fourier Transform and constructing a spectrogram which can be fed into the algorithm pixel by pixel. This methodology works due to the association of frequency content in the HNSW and changes of the stiffness in the material. In the case of high IOP, the increased pressure pushes against the artificial PDMS cornea and causes it to become stiffer with a higher Young’s modulus. We evaluated the ability of the algorithm to predict IOP based on the spectrogram.

Biosensors are a potent tool that are widely used for health care, environmental safety, to all the way for disease diagnostics. Conventional surface acoustic waves (SAW)-grade biosensors are effective, but they often suffer sensitivity challenges leading to false-negatives. Inspired by this, we developed an innovative tone burst interdigitated electrodes (TB-IDT)-based biosensing that utilizes a unique fashion of surface acoustic waves technique for the biosensing phenomena. The TB-IDT consists of varied amplitude and width of the electrodes over the length that covers a wider range of frequency and amplitude access enhancing the sensitivity by many folds. Unlike conventional SAW-grade sensors, the proposed sensor has multidirectional access for better reproducibility and sensitivity in exploring the anisotropic nature of the piezoelectric substrate. The biosensor is highly sensitive and applies to multiple disease biomarkers detection.

This paper proposes a framework of using deep learning-assisted methods for the prediction of interfacial conditions in coated plates using guided wave data. The coating-substrate interface is modeled as a linear spring layer of zero thickness, and the mechanical behavior of this spring layer is characterized by the spring compliance. Both tangential and normal spring compliances are introduced to characterize the bond quality. Numerical simulations are conducted for a wide range of spring compliances to generate the corresponding dispersion curves. A Long Short-Term Memory (LSTM) network is utilized to predict the interfacial conditions. In addition, we consider the delamination cases where the coating layer is completely separated from the substrate over the delaminated region. Finite element simulations are carried out to model guided wave generation, propagation, interaction with delamination, and reception. The time-space images are formed by measuring the time-domain signals by receivers at several locations downstream from the source transducer, which are then fed into the developed Convolutional Neural Network (CNN). Once trained, this Deep-Learning (DL) model enables the accurate prediction of delamination location and size. Results of this paper demonstrate that the proposed methodologies have tremendous potential for characterizing interfacial conditions in practical Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM) applications.

Tire maintenance plays a crucial role in vehicle performance, with the tire being identified as the most important factor. In this study, we introduce an intelligent tire system equipped with composite sensors to enhance driving safety and vehicle management. Analysis of actual traffic accident data reveals that approximately 10% of accidents are attributed to tire-related issues, emphasizing the significance of tire maintenance. However, our investigation suggests that while conventional time and frequency domain techniques are available for fault detection in intelligent tires, they tend to exhibit slightly lower performance compared to those utilizing artificial intelligence. To address this limitation, we propose a deep learning-based diagnosis method. By attaching a 3-axis accelerometer sensor to the tire tread and simulating various failure modes, including Belt/ Bead separation, comprehensive data for analysis were collected. We develop a novel approach using multi-scale feature fusion with adaptive weight calculation using 1-D convolution principles, which significantly improves fault detection accuracy. Experimental results demonstrate the effectiveness of our proposed method, achieving a 100% F1 Score in the classification of Tire Separation faults. Visualization using Uniform Manifold Approximation and Projection (UMAP) further confirms distinct clustering for each fault state. Overall, our study offers valuable insights into tire fault diagnosis and management, contributing to enhanced vehicle safety and performance.

Electrical Impedance Tomography (EIT) is a non-destructive and non-radioactive imaging technique used to detect anomalies in a material of interest. Applications of EIT range from medical imaging and early tumor detection to identifying structural damage. Within the past decade, deep learning (DL)-based EIT reconstruction has been an emerging field of study as it shows promise in addressing many of the challenges associated with the non-linear, ill-conditioned nature of EIT inverse problems. The DL-based approach allows for the conductivity of materials to be reconstructed directly through Neural Networks (NNs) as opposed to iteratively with conventional inverse reconstruction algorithms. So far, the reported DL-based NNs for EIT have mostly been trained by minimizing the Mean Squared Error (MSE) between the predicted and “true” outputs (i.e., conductivity distributions). The performance of these current NNs heavily relies on both the quality and quantity of training data. The NNs trained with simulated data may perform poorly with experimental data. On the other hand, generating sufficient experimental data NN training can be extremely expensive and time-consuming, if feasible at all. To advance the DL-based reconstruction for EIT, this study develops a novel NN architecture, trained with a custom loss function, that serves as a surrogate model for the compressed sensing-based EIT reconstruction algorithm. In other words, the NN is trained to mimic a compressed sensing algorithm that performs the EIT conductivity reconstruction. This approach enables the NN to accurately capture the electrical properties and characteristics of the sensing domain when trained with limited data of varying quality. The performance of the proposed NN was compared to other DL models trained with the traditional MSE loss function by evaluating their reconstruction resolution, accuracy, and other training metrics.

Microcantilevers are widely adopted in various fields for micro-mass sensing due to their simple structure and high sensitivity. The conventional microcantilever-based measurement methods required ultra-precision temperature control. However, such a requirement could hardly be satisfied and the temperature shifting would deteriorate the accuracy of mass sensing. In this research, an impedance-based temperature decoupling method is developed for robust mass sensing over a wide temperature range. The relative relationships of the peaks in the impedance signals are adopted for decoupling the temperature dependent terms in the eigenvalue problem. Besides, a CBAM-CNN network is developed for the modeling and mass sensing. Experimental studies indicate the proposed method yield robust mass sensing with accuracy up to 99.10 % under temperature range from 25 ℃ to 55 ℃. This method enables cantilever-based mass sensing without temperature compensation.

Damage detection plays a pivotal role in structural health monitoring. As indicated in FEMA and ASCE, structural damage relies on story drifts as a fundamental criterion for categorizing damage states and assessing risk levels. In addition, past studies showed that structural stiffness changes were closely linked to the extent of structural damage due to earthquakes. However, both story drifts and stiffness changes are rarely evaluated concurrently to determine structural damage. In this study, three multi-target neural networks are developed using floor accelerations of buildings under seismic excitation to estimate story drifts and remaining stiffness ratios. Notably, all network architectures are identical. The three neural networks in this study differ in applying distinct loss functions and training strategies to assess and compare the performance of the models. All networks are compared through numerical investigation using a three-story finite element model and experimentally verified using a seismically excited full-scale building.

With the development of urbanization, accurately determining the material properties of underwater facilities or structures, such as bridges, under continuous bending load is essential to ensure safe and stable operations. However, the underwater environment is difficult to access, making on-site mechanical property tests challenging due to fluid pressure. Traditional mechanical property measurement methods are often not applicable in underwater environments. This study presents a non-destructive method for flexural modulus measurement underwater using immersion ultrasound transmission and a four-point bending test. The proposed method can be developed as an integrated wireless system for real-time underwater mechanical property measurement.

Anisotropic collagen-based biomaterials have gained significant attention in the fields of tissue engineering and regenerative medicine. They have shown great potential for wound dressing, corneal grafting, and exploring the mechanism of cancer cell invasion. Various external physical field-based methods for the fabrication of anisotropic collagen-based biomaterials have been developed, including electrospinning, microfluidic shearing, mechanical loading, and so on. In this study, we put forward an acoustic streaming-based method that uses acoustic wave-induced fluid streaming to control collagen self-assembly and fiber arrangement. Our acoustic device leverages a piezoelectric transducer to generate traveling acoustic waves in fluids, and the wave-fluid interaction further induces fluid streaming, known as acoustic streaming. If the fluid contains collagen macromolecules, the acoustic streaming is able to affect the collagen self-assembly process to create biomaterials containing directionally arranged collagen fibers along the streaming velocity direction. Therefore, this acoustic streaming-based method allows for manufacturing collagen hydrogel layers that contain acoustically arranged collagen fibers and have controlled anisotropic material properties. We performed a series of proof-of-concept experiments by using a fabricated acoustic device to control the self-assembly process of collagens loaded in a Petri dish. Our results show the effectiveness of arranging collagen fibers that follow the flow direction of acoustic streaming. To better understand the collagen manipulation mechanism, we used particle image velocimetry to characterize the acoustic wave-induced fluid streaming. We expect this study can contribute to the fabrication of collagen-based anisotropic biomaterials for biomedical applications.

Ever growing number of space systems raises a question about their safety and longevity. Over time, the complex space environment influences structural materials and electronics affecting mission objectives and performance. In order to determine the effect of space environment on spacecraft’s structural condition, material properties and structural interfaces need to be assessed in space. Hence, it is suggested that active non-destructive measurements are employed to provide structural health information to the spacecraft’s operator. Electro-mechanical impedance method is a promising approach to SHM of space structures due to its use of small unobtrusive piezoelectric sensors and lightweight hardware. In laboratory settings, this method typically utilizes bulky and heavy instruments, but for mid-frequency bands, a small and lightweight impedance measurement circuit could be built. A miniaturized impedance measurement analyzer was designed and built for applications requiring very small hardware mass. The development was driven by the intention to utilize the miniaturized analyzer for SHM of space structures in orbit. The impedance analyzer was designed and built from off-the-shelf components to enable impedance measurements in lower kHz frequency band. It was used to measure the impedance of a payload structure resembling a fixed plate. The aim of this measurement was to demonstrate data acquisition and storage by the miniaturized impedance logger and to compare its performance to laboratory scale instruments. The paper further discusses the effect of space environment on structures, describes and validates an analytical model for a fixed-fixed rectangular plate and provides analysis of the potential influence of thermal variation and radiation in space on electro-mechanical impedance signature.

This work explores the opportunity for utilizing ultrasonic arrays in a passive manner to increase imaging speed and accuracy, compared to conventional ultrasonic beamforming approaches. While conventional beamforming requires the use of many transmitters as well as receivers, the possibility exists to minimize the number of transmitters and exploit the normalized cross-power spectrum operator to extract "virtual" Impulse Response Functions between pairs of (only) receivers. In principle, this possibility would allow, for example, the use of a single transmitter element and processing the remaining elements as receivers (passively) to obtain similar image quality as using each element as both transmitter and receiver (as done, for example, in a Full Matrix Capture mode). By minimizing the number of transmitters, it would be possible to significantly increase imaging speed (ultrafast imaging) and simplify multiplexer hardware (few high-voltage output channels). The normalized cross-power spectrum could eliminate the distortion in the phase of the Impulse Response Functions which is commonly seen in active transmission modalities. These exciting possibilities are explored in the case of imaging defects in structural components.

This paper presents a comprehensive investigation of solid and hollow polymer-based near-field imaging probes, each coated with a metallic layer on the outer surface, designed to operate in the THz frequency range. These probes are tailored to exploit the near-field properties of THz radiation for achieving sub-wavelength resolution imaging. The proposed probes exhibit a versatile design that has been rigorously examined through advanced electromagnetic simulations. The solid probe focuses on exploiting the dielectric properties of the material to manipulate THz radiation. Conversely, the hollow probe leverages its cavity structure to create resonant modes within the THz frequency range. This resonance phenomenon enhances the probe's ability to guide THz radiation, resulting in superior imaging capabilities. The metallic coating further enhances performance by efficiently coupling with THz waves, leading to improved resolution and signal-to-noise ratios. Overall, this paper presents a thorough investigation of solid and hollow polymer-based THz near-field imaging probes and experimentally demonstrates their effectiveness for high-resolution sub-wavelength imaging applications. These 3D printable probes offer versatile, cost-effective, and disposable imaging solutions for non-destructive material evaluation and sub-cellular scale imaging in various domains.

This paper discusses two improvements to localized tactile feedback on a transparent surface using Time Reversal Mirror (TRM) of ultrasonic guided waves. A mock-up device comprising a glass plate of 150 × 200 × 1 mm3 with a 12-element sparse piezoelectric actuator array bonded at the boundary of the plate is constructed together with driving electronics. The transfer function from each actuator to an arbitrary focus point is first recorded using a laser doppler vibrometer. The re-emission waveforms are then generated by convolving the time-reversed transfer function with a target signal to control the shape of the vibration at the focus point. The re-emitted waveforms are compressed to binary signals to improve the peak displacement of the focused wave field. The mock-up device obtains a peak amplitude of 19 μm on the plate with a peak-to-peak driving voltage of 150 V. Two improvements to the tactile feedback using ultrasonic TRM are described. First, the undesired conversion of ultrasonic vibration to audible spectra resulting from the coupling of the fingertip and the plate surface is investigated. The authors show that despite the inevitable frequency shift, the tone of the audible leakage can be controlled by the repetition period of the TRM. Second, the tactile feeling of a single-point focus of ultrasonic guided wave is acute and hard to detect due to its small resolution cell compared to the size of a fingertip. The authors propose a dual-point focus scheme, the distance of which is less than that of two-points discrimination, to enlarge the perceivable region without changing the spectra of the re-emission signals.

The advent of sensing and edge computing technologies has presented novel prospects for contemporary structural health monitoring. Structural health monitoring systems are collecting vast amounts of data over the time. However, the collected data may be polluted with inaccurate, missing, or irrelevant information, presenting a big challenge for data processing and analysis to retrieve structural conditions. To address this issue, our study introduces a novel edge computing-based data preprocessing strategy. An automated algorithm is developed to detect and discard anomalies for each sensor data type. Crucially, for image data, we perform frequency analysis on local devices to evaluate image clarity, determining the suitability of the gathered images for future analysis. Our methodology ensures that only photos meeting our requirements are uploaded to the central server, significantly reducing network congestion. This localized preprocessing not only diminishes the data transmission volume but also improves source data quality and usability, thereby alleviating the computational burden on the centralized system. Experimental results reveal that our strategy markedly elevates the efficiency and accuracy of structural health monitoring systems, providing a potential technological groundwork for upcoming practical applications.

The study is to establish the non-contact blood pressure measurement model. We propose a novel hybrid blood pressure assessment model. This model employs digital signal processing (DSP) to process the Imaging Photoplethysmography (iPPG) signal, utilizing Support Vector Machine (SVM) classification to determine the optimal signal location through three parameters. It is then compared with a PPG device. Through a CNN-LSTM model, it aims to reconstruct the ideal iPPG signal, transforming signals from the dermal layer into radial artery signals. Based on the Beer-Lambert law, the natural logarithm of iPPG intensity is proportional to blood flow velocity. Thus, a regression model for mean arterial pressure is developed in this work using heart rate and the intensity of iPPG signals. In conclusion, statistical test results confirm the validity of this study, indicating significant potential for the future development of noncontact blood pressure monitoring.

Composite materials play an increasingly important role in renewable energy and aerospace structures. Impact damage can be barely visible and hard to detect, while imposing significant danger to structural safety and reliability. Nonlinear ultrasonic guided wave scattering and mode conversion phenomena in composite panels were investigated. The irregular profiles of the impact damage were implemented in a numerical model to understand the influence from geometric characteristics on the guided wave scattering. Scanning Laser Doppler Vibrometry experiments were performed for comparison with the numerical predictions. Frequency-wavenumber analysis considering the nonlinear information was employed to visualize and quantify the impact damage.

A newly developed Nonlinear Ultrasonic (NLU) technique called sideband peak count-index (or SPC-I) measures the degree of nonlinearity in materials by counting the sideband peaks above a moving threshold line – larger the SPC-I value, higher is the material nonlinearity. In various published papers, the SPC-I technique has shown its effectiveness in Structural Health Monitoring (SHM) applications. However, the effects of different types of nonlinear phenomenon on the sideband peak generation is yet to be investigated in depth. This work addresses this knowledge gap and investigates the effects of different types of nonlinearity on the SPC-I technique. Three types of nonlinearities (material nonlinearity, structural nonlinearity and contact nonlinearity) are investigated separately through numerical modeling. Numerical modeling results show that the sideband peak values do not increase proportional to the input signal strength thus indicating nonlinear response, and different types of nonlinearities affect the SPC-I measurements differently. For the experimental verification a composite plate with impact-induced damage is considered for investigating the material nonlinearity and structural nonlinearity while a linear elastic aluminum plate is used to examine the contact nonlinearity between the transducers and the plate. The trends observed in the experimental observations matched the numerical model predictions. Monitoring damage growth in topographical structures – formed by inserting different materials in a matrix material is also investigated. In addition to the SPC-I technique an emerging acoustic parameter – “geometric phase change” based on the topological acoustics is also adopted for sensing damage growth in the topographical structures. The performance of SPC-I and topological acoustic sensing techniques as well as the Spectral Amplitude Difference (SAD) parameter for sensing the damage growth in topographical structures are compared and discussed.

The damage detection in structures using modulation transfer phenomena is a topic of increasing interest. However, the lack of comprehensive knowledge and established signal processing methods have hindered its widespread application. This paper explores the potential of the modulation transfer phenomenon for damage localization by conducting experiments on test stands with two structures: a damaged and an undamaged beam. A well-defined procedure for processing response signals and damage indicators was established. Before the experiments, modal analysis was conducted to select the appropriate excitation frequency. The presented results include spectra and trends of the damage indicators, demonstrating the viability of using the modulation transfer phenomenon for damage localization. Furthermore, the vibroacoustic modulation phenomenon was observed during the tests. These findings underscore the potential of modulation transfer techniques in structural health monitoring applications.

Structural Health Monitoring appears to be among the few possible strategies in order to reduce maintenance costs and weights of aerospace composite structures by sensorising structures employing secondary bonded or embedded sensors and condition monitoring strategies. Within an SHM global strategy sensors can be employed to detect damaging events (i.e. impacts) or to verify the health status by acquiring signals related to waves traveling into structures. Piezoresistive sensors based on nanotubes or graphene like particles dispersed into a polymer or any other matrix have been developed and characterized during latest years since they present the advantages of easy application and low weight addiction to the primary structure as well as low costs and high integration potentialities. Their sensitivity and gauge factors can vary a lot depending on the percentage of graphene material dispersion, from the matrix type and viscosity, from the dimensions of the sensor and from its shape. This work presents the preliminary results related to new typologies of sensors obtained by melt polystyrene compounding with different amounts of graphene nanoplatelets combined with carbon nanotubes.

The efficacy of using Fiber-Bragg Grating (FBG) sensors for the purpose of sensing and characterizing dynamic deployment of bistable composite tape springs is investigated in this paper. Ultra-thin composite structures such as tape springs have seen increased popularity in spacecraft structures due to enabling the precise deployment of flexible solar arrays, sails, reflectors, and antennas. These composite members can elastically transition from either the coiled or folded state to the deployed extended state while possessing superior stiffness, thermal properties, mass efficiency, and compactness when compared to their metal counterparts. Bistability is leveraged to influence more controllable self-deployment and energy efficient stowage, while reducing or eliminating the need for mechanical restraints or motorized deployment. However, a need exists to monitor both the deployment dynamics and overall structural health of the deployed member. Fiber optic sensors such as FBGs have the capability to sense pressure, temperature, and mechanical strain. Due to their relative thinness, low mass, and flexibility, fiber optics may be integrated into these deployable composite structures without significantly interfering with bistability, packaging, or deployability. This paper experimentally demonstrates dynamic strain sensing of deploying bistable composite tape springs via the integration of fiber optics containing FBG sensors. Free deployment from both coiled and folded stowed configurations are characterized.

Average satellite lifetime in orbit has been gradually increasing since the advent of the space industry. With the increase in satellite lifespan on orbit it is becoming increasingly economically advantageous to refuel and conduct in-orbit servicing rather than launching new satellites. This presents a challenging problem of automatic docking for refueling. Traditionally, docking mechanisms have used either a physical switch, a force sensor, a torque sensor, or a combination of the three. Traditional docking verification techniques using those sensors are suboptimal for use in an orbital servicing satellite as customer’s satellite mass can vary in a wide range while docking velocity also varies. In addition, the refueling system produces a complex pattern of mechanical signals during docking, which is challenging to classify. For this reason, an SHM system with small unobtrusive piezoelectric sensors was proposed to identify and characterize satellite docking. It was decided that the passive monitoring of the docking is preferable overactive methods not to interfere with satellite dynamics and reduce power use. The mechanical waves resulted from the satellite’s docking momentum annulment, thrusters, electrical motors, and mechanical component deployment were passively monitored using an array of piezoelectric wafer sensors. Features in mechanical signals corresponding to the docking were distinguished from other mechanical events normal to satellite’s operation. An algorithm was developed that utilize features specific to docking to classify the quality of the docking engagement including potential false positives and misalignment issues. This algorithm was embedded in a real-time microprocessor which was used to capture passive ultrasonic signals and run the associated data analysis algorithm. Experiments conducted on a laboratory scale docking imitator suggested the applicability of the proposed approach and verified performance on the data acquisition and classification system on exemplary signals.

We demonstrate a novel optically active ring resonator that is 3D printed on the tip of a single-mode optical fiber. The ring resonator is printed using two-photon polymerization of a resin that has been doped with core-shell NaYF₄: Yb³⁺, Er³⁺ nanoparticles. The integration of these optically active nanoparticles into the resonator allows the exploitation of their upconversion luminescence properties, making it sensitive to temperature and strain variations. The sensing performance of the device is based on the change in the luminescence intensity corresponding to the variation in surrounding temperature. As the ambient conditions change, variations in the refractive index and geometry affect the change in the intensity of the ring resonator which can be remotely demodulated from the light emissions collected by the optical fiber itself.

In this work, we report the fabrication and characterization of a fiber-optic temperature sensor based on embedding rare-earth nanoparticle emitters (NaYF4: Yb³⁺, Er³⁺) inside an optical fiber. A micro channel passing through the core of Single Mode Fiber (SMF) is fabricated using a femtosecond laser, and the microcavity is subsequently filled with optically active medium. In this work, the NaYF4: Yb³⁺, Er³⁺ works as an active medium with temperature-sensitive upconversion luminescence properties to provide temperature measurements allowing for real-time temperature monitoring. The obtained results show promise for addressing temperature-related challenges in various fields, reaffirming the crucial role of optical sensors for a wide range of applications including industrial, medical, and environmental monitoring.

Inflatable structures present an efficient solution for deep space habitats, enabling compact storage during launch and providing a large operational volume once deployed in space. However, their lightweight and thin-walled design makes them susceptible to Micro-Meteoroids and Orbital Debris (MMODs), as well as the cumulative effects of creep strain. To ensure the safety of these inflatable habitats, this study used a commercial material extrusion system to prototype a flexible and multifunctional sensor for non-destructive structural health monitoring. This unique sensor consists of a piezoelectric polyvinylidene fluoride-trifluoroethylene (PVDF-trFE) film sandwiched between a pair of electrodes. The piezoelectric layer detects dynamic impact force due to MMODs and the electrodes printed into piezoresistive strain gauges are capable of measuring creep strain. In this study, a comprehensive sensor fabrication technique, including piezoelectric ink synthesis, printer settings, and material post-processing method, was first developed. Subsequent experiments using a mechanical load frame and impact hammer quantified the sensor sensitivity in piezoresistive and piezoelectric mode, respectively. The printed sensor achieved a gauge factor exceeding 8 in piezoresistive mode. An additional machine learning model predicted impact magnitude and impact width with linear correlation coefficients of 0.99 and 0.89, respectively, when compared to the tested values. These promising results underscore the potential for in-situ manufacturing of such multifunctional sensors, paving the way for sustainable deep space missions that not only minimize launch mass but also diminish reliance on exhaustive pre-launch designs.

Optical methods for structural health monitoring have become extremely popular thanks to advancements in camera technology and increased computational capabilities. Among these methods, 3D-digital image correlation (3D-DIC) and 3D-point tracking (3D-PT) have been proposed as a replacement for traditional contact-based techniques given their ability to measure full-field displacements and surface strain maps of structures of interest. 3D-DIC and 3D-PT require the application of well recognizable high-contrast patterns such as stochastic speckle patterns or optical targets on the surface of the targeted structure. However, when large-scale engineering structures (e.g., bridges and wind turbines) must be analyzed, applying the high-contrast patterns is not always feasible, thus limiting the applicability of optical methods. The objective of this research is to develop an approach that allows identifying and tracking already present features on the surface of the structure of interest, such as bolts, letters, stains, rusted patches, and holes. To achieve this goal, a newly proposed Augmented Centroid-Based Detector (A-CBD) is presented to extract and track identifiable features on the surface of the structure of interest. In this paper, the performance of the A-CBD method is compared with a traditional, correlation-based 3D-PT measurement performed with the use of optical targets in a series of laboratory tests. The results show excellent agreement between the two methods, yielding an average Time Response Assurance Criterion (TRAC) value above 99.5 %. If further developed, the proposed A-CBD approach can extend the applicability of optical methods for measurements on real-world structures.

Full-field data provides a comprehensive understanding of the behavior of a system or structure, which is particularly crucial when identifying local damages. This damage may exhibit complex and subtle effects that could be overlooked with sparse measurements. Recent advancements in machine learning, such as Autoencoders (AE), have enabled the reconstruction of full-field data using sparse measurements. However, a study assessing the accuracy of AE in reconstructing full-field data concerning measurement locations, data sparsity, and noise density is still lacking in the context of Nondestructive Evaluation (NDE). To address these gaps, this study adopts a parametric approach to evaluate the effectiveness of an LSTM-based AE model in terms of measurement locations, data sparsity, and noise density. The two sets of data (i.e., configuration #1 and #2) were generated using a finite element method for a 2D metallic plate cooling. The configuration #1 data were then used to train the LSTM-based AE and the model’s full field reconstruction performance was validated on sparse measurements of configuration #2 using the Average Reconstruction Error (ARE) as a testing parameter. The result shows, there was no significant impact of different measurement locations on ARE. Whereas ARE increased with increase in data sparsity and noise density. This research presents a parametric study with potential applications in full-field reconstruction, not limited to thermal data. It can be extended to other applications, such as strain, displacement, and velocity, in scenarios where the targeted system undergoes temporal evolution.

Mass timber is a relatively new and sustainable construction material that can carry large loads. Such loads require new connections that often use very long screws. However, ensuring their mechanical integrity against overtightening is paramount for structural safety. Traditional manual evaluations present limitations. This research employs laser scanning technology for automated evaluation of screw penetration in wood-aluminum interfaces using VGS9180 screws. By applying torque ranging from 10 to 50 Nm in 5 Nm intervals, a critical torque-penetration correlation emerged, highlighting a sensitive range between 35-40 Nm. Within this range, small overtightening can jeopardize the integrity of the connection. The study underscores laser scanning’s efficacy in pinpointing improperly torqued screws, offering a potential leap in quality assurance for timber construction. The results of this study indicate a threshold-based method can detect overtightened screws on the experimental setup when the distance between two surfaces falls beyond 3.2 mm.

Notches (often used to stimulate crack-like defects) in pipes are characterized using Helical-Guided Ultrasonic Waves (HGUW). In thin-walled curved structures with a radius-to-thickness ratio of more than 10/1, Lamb-type guided waves, called the HGUW, propagate. HGUW are plane-strain guided waves propagating circumferentially in helical paths in large-diameter cylindrical structures, with the properties of Lamb waves. They travel in multiple trajectories between two points, and these paths are indexed as orders of the helical path. When the HGUW encounters a notch in its path, it scatters, and the information in the scattering is used to characterize the notch (i.e., determine the notch size). In this work, an approach called the stepped wavelength method is presented to determine the notch size. In this approach, the directivity plots, quantifying from the scattering of the HGUW in all directions around the notch, are evaluated for a set of frequencies (each corresponding to a specific wavelength) from the numerical model of the pristine and damaged pipes. As the wavelength-to-notch size ratio approaches one and increases beyond that, a change in the directivity plot’s profile is witnessed, suggesting a change in the nature of the interaction between the notch and the incident wave. A criterion based on the change in the nature of interaction is developed to estimate the notch size.

In most engineering systems, the acquisition of faulty data is difficult or sometimes not feasible, while normal data are secured. To solve these problems, this paper proposes an fault diagnosis method for electric motor using only normal data with self-labeling based on stacked time-series imaging method. Since only normal data are used for fault diagnosis, a self-labeling method is used to generate a new labeled dataset based on pretext task. To emphasize faulty features from non-stationary faulty data, stacked time-series imaging method is developed. The overall procedure includes the following steps: (1) transformation of a one-dimensional current signal to a two-dimensional image in time-domain, (2) adding sparse features with sparse dictionary learning, (3) stacked images through every window size, and (4) fault classification based on Convolutional Neural Network (CNN) and Mahalanobis distance. Transformation of the time-series signal is based on Recurrence Plots (RP). The proposed RP method develops from sparse dictionary learning that provides the dominant fault feature representations in a robust way. To verify the proposed method, data from real-field manufacturing line is used.

The initialization method in Digital Image Correlation (DIC) is essential for optimizing the correlation criteria and accurately computing the deformations of a material under load. At present, feature-based initialization techniques are widely explored for predicting the deformations of various complex circumstances, such as large deformations for soft materials, non-continuous deformations in heterogeneous materials, etc. However, due to the non-uniform distribution of the detected features, the initialization process goes through biased prediction. This bias occurs due to the sparsity of features in different regions of the sample, which can lead to inaccuracy in identifying the shape of deformation. This study addresses the issue of feature distribution and develops a feature-based template approach for providing initialization points for each subset on a finer scale. The features (interest points) are determined using KAZE feature detector and descriptor algorithm in nonlinear scale space due to its ability to determine consistent, repeatable, distinct features invariant to scale and rotation. The proposed algorithm uses bi-cubic b-spline interpolation to identify the strongest interest point at the subpixel level for each subset (of the input sample images), which works as an initial value for estimating the deformation. Further, a threshold-based incremental reference approach is developed for measuring large deformations and avoiding the cumulative errors associated with the commonly used incremental reference strategy, which is compute-intensive because of the comparison between every previous image and the subsequent images.

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part geometry specimens or in real parts. Typically, surface fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or cylindrical specimens. If a Nondestructive Evaluation (NDE) technique is required to provide a reliably detectable flaw size, denoted as a_90/95, for detection of induced flaws in a part, then a direct method for qualifying the NDE procedure is to use appropriate induced flaw specimens and perform NDE procedure demonstration on the specimens. Probability of Detection (POD) analysis of the empirical data may provide estimation of a_90/95. This approach is described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where embedded tight cracklike induced flaws are to be detected reliably using a signal response based NDE procedure. Here, it is assumed that it is not practical to make surface or embedded induced flaw specimens in part geometry or configuration. Therefore, a direct POD demonstration testing cannot be undertaken. It is also assumed that simulation of signal response is possible for both surface and embedded induced flaws in part geometry specimens using a physics-based model. The proposed approach for NDE procedure qualification uses artificial flaws in simple geometry and part geometry specimens, and induced flaws in the same type of simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws in simple geometry and part geometry specimens. Moreover, simulated signal response data is generated for surface and embedded flaws. Thus, a case of five signal response versus flaw size datasets is considered. Three of the datasets are empirical and two datasets are physics model-based simulation datasets. A method of devising and using transfer function calculation dataset blocks to estimate either the reliably detectable flaw size or the demonstration flaw size is provided.

Typically, reliably detectable crack size for dye penetrant testing is determined by using test specimens with known size fatigue cracks. Certain surface crack size was qualified in original Standard dye penetrant Probability of Detection (POD) studies. The flaw size was qualified to a minimum 90% POD with 95% confidence (conf.). No corner crack size was qualified by direct POD testing. Typically, the corner and other crack sizes were established based on crack face area equivalency analysis. However, there is no supporting empirical data for crack face area equivalency. The volume of reservoir between crack faces available for dye penetrant to seep in and then bleed back after removal of excess surface dye penetrant is an important factor is forming a dye penetrant indication. The volume available for dye penetrant to seep into a crack would be proportional to the crack face area for cracks. Therefore, the approach has some merit, but other factors such as aspect ratio of crack length-to-depth must be considered. The paper provides an approach to interpret POD demonstrated surface crack size to a corner crack on a corner with any radius based on consideration for crack face area, crack chord equivalency and minimum depth. The reliably detectable flaw sizes for dye penetrant testing are used for fracture mechanics damage tolerance analysis. The paper provides an analysis approach to qualify the reliably detectable dye penetrant corner crack size based on POD demonstrated surface crack size.

This study presents an innovative method for probabilistic modeling of acoustic emission by applying the principles of maximum entropy and utilizing a non-Gaussian probability distribution. The focus is on mathematical modeling of the RA (rise time to amplitude ratio) values versus the Average Frequency (AF) using a fourth-order non-Gaussian probability distribution. The main goal is to introduce this probabilistic model for AE data and showcase some of its benefits. Specifically, the model can 1) identify cracks at early stages and 2) distinguish between shear cracks and tensile (flexural) cracks within concrete and wood. To confirm the effectiveness of the proposed model, experimental data from acoustic emission tests on a concrete slab and a wood specimen are used. The parameters of the proposed probabilistic model capture the transition from one cracking mode to another. The results demonstrate the model’s success in detecting the transition of cracks from tensile to shear at different stages. Capturing the evolution of the cracks is made possible by incorporating the parameter of time alongside the proposed fourth-order probabilistic model. These results indicate that the suggested method is a powerful mathematical approach for probabilistic modeling of RA-AF plots of AE data.

This paper presents a new framework that aims to improve the efficiency of time response analysis for nonlinear dynamical systems by combining conventional time integration methods with Proper Generalized Decomposition (PGD). The PGD approach utilizes low-dimensional subspaces of the time response to approximate the solution as a low-order separated representation of spatial and temporal components, with the Galerkin projection employed to formulate subproblems for each component. The subproblem for spatial basis is viewed as computing a reduced-order criterion, and the temporal problem projected to a subspace spanning this criterion uses time integration to obtain time coefficients. During the time integration, the spatial modes obtained from the calculation of the previous step are used as a reduced basis, and additional spatial modes are added until the residual of equations of motion satisfy the target tolerance. Numerical examples demonstrate that the proposed method allows significant computational savings compared to conventional time integration methods while accurately reflecting the nonlinear behavior.

This research aims to develop a status imaging system for a Li-Ion battery by utilizing guided ultrasonic waves with an embedded sensor network. Li-Ion Battery (LIB) has emerged as an essential powering element in the future mobility industry including electric vehicles, unmanned aerial vehicles, and urban air mobility. Conventional safety monitoring of LIB mostly depends on the electric signals of each LIB unit, yet the electric signal-based monitoring has shown its technical limitations in detecting local mechanical/chemical status in LIBs. Therefore, this study investigated a status imaging system to detect local changes in an LIB using an active sensing system. The research scope of this study is to detect and localize the simulated mechanical degradation within a LiFePo₄ (LFP) battery having a relatively large dimension (300×210×12 mm³ ). Nine piezoelectric wafers were embedded on the LIB surface. The excitation frequency was determined by observing the signal-to-noise ratio in the frequency range from 60 to 280kHz. As for the status imaging algorithm, we employed a probabilistic reconstruction algorithm, where the index was developed based on the Continuous Wavelet Transform (CWT). The local mechanical change in the LIB was realized by placing a heavy (~ 0.5 kg) weight on a certain spot. The experiment results showed that the proposed imaging method (i.e., CWT-based imaging) could detect the localized mechanical degradation of the LFP battery in a more significant imaging contrast (⪆+20%) compared to other existing methods. This research will provide a new methodology to monitor the localized state-of-health of a large LIB.

This study aims to detect micro defects in Multilayer Ceramic Capacitors (MLCC) using a new signal processing method for Electromechanical (EM) responses. Microscopic defects within MLCC structures, not screened during the manufacturing production stage, can cause damage to electronic products and further lead to overall system failure. Therefore, it is very important to secure reliable defect detection technology. However, conventional methods, such as x-ray and ultrasound tests, are destructive, cost-ineffective, and inaccurate. In this study, we applied a new signal processing method based on the Hilbert-Huang transform (HHT) of the EM response for the nondestructive screening of MLCCs with hidden defects. We first observed that infinitesimal, irregular oscillation in the instantaneous frequency in the transient response when the MLCC had defects. Therefore, we introduced a new signal processing method that takes the Fourier Transform (FT) of the Weighted-Instantaneous Frequency (WIF) concerning the frequency-oscillatory rate, to capture the infinitesimal change in the HHT signal. Specifically, we confirmed that subharmonic terms, possibly caused by the nonlinear effect, arose in the WIF when the target MLCC had a micro defect. The method was first validated through signals obtained from transient finite element analysis, followed by a comparison with the test data from actual MLCCs. The increased subharmonic terms in the WIF could be an effective indication of the defect in the MLCC. This study will provide a new methodology for detecting microdefects in MLCC.

This paper explores the intersection between forensic science and Structural Health Monitoring (SHM), focusing on the pivotal role of visual indicators. These indicators are crucial in both contexts - from discerning injuries on humans to identifying structural defects. We present a novel approach utilizing computer vision-based diagnostics to aid victims of violence through advanced bruise detection, thus enhancing post-trauma care. Leveraging a specialized dataset, our study confronts the challenges inherent in data preparation and organization, as well as achieving expert consensus. We modify lightweight deep learning algorithms originally developed for engineered system diagnostics for application in the medical forensics domain. This adaptation aims to detect bruise areas under varying conditions, such as differences in skin color and lighting. A key question we address is the generalizability of these methods in diverse medical bruising scenarios, a fundamental challenge shared with SHM. Our research highlights the importance of domain knowledge transfer, drawing parallels between SHM and forensic science, and underscores the potential of this interdisciplinary approach.

Composite honeycomb sandwich samples are commonly used because they offer a high strength-to-weight ratio, making them ideal for lightweight and durable structures. However, ensuring their structural integrity is essential for safety and performance. Multimodal NDT (Non-Destructive Testing) inspection and characterization of composite honeycomb sandwich samples is a critical process in various industries, including aerospace, automotive, and civil engineering. The use of NDT techniques makes it possible to verify the quality of the composite material and identify any defects. In this context, we provide a comparison of several techniques as nondestructive methods on a sample of interest to the aerospace industry and evaluates the parameters of their use: shearography, infrared thermography and laser ultrasonic. Using non-destructive testing techniques, it is possible to check the quality of composite materials and identify any programmed flaws. These techniques allow for frequent inspections without compromising the integrity of the material. This helps ensure the safety and reliability of products using composite materials. From the preliminary results it is evident that the combined use of the described non-destructive testing (NDT) techniques can significantly improve the reliability and accuracy of the quality control process for a wide variety of materials and defects.

Muscle monitoring is considered important and biomedical applications such as health diagnosis and motion sensing based on muscle biosignals are becoming a reality and has potential. Electromyography (EMG) signals and muscle strain signals are the most reliable biosignals that is observed in muscles. Recently, many dry-type surface EMG (sEMG) electrodes have been developed to overcome the shortcomings of wet-type electrode such as durability and reusability, so the EMG electrode is demonstrated using Edge Functionalized Graphene (EFG) which has high conductivity that does not need to go through graphene reduction process and has good performance of EMG sensing with long durability and reusability. In case of strain sensor, yarn type electrode is demonstrated based on energy harvesting by electrochemical capacitance change of Carbon Nanotube (CNT) coil, which generates signal without any bias voltage. By integrating sEMG electrode and strain sensing electrode, patch type biceps muscle monitoring sensor is demonstrated which monitors and distinguishes moving directions (muscle relaxation, muscle contraction), angles and type of the muscle movement.

The Total Hip Replacement (THR) is a procedure involved removing the damaged bone and cartilage and replacing with prosthetic components, which is one of the most common and successful surgeries. However, clinic examination and medical imaging methods are the main and only efforts to evaluate the status of THR, which is time-consuming and costly. Therefore, to develop a rapid and real-time evaluation of THR is meaningful and promising. Acoustic Emission (AE) is a non-invasive and Non-Destructive Evaluation (NDE) method, which is also accurate, reliable and real-time. The use of AE to assess damage after THR can be feasible. Firstly, using silicone material to simulate in the hip in the axial and radial force, ultrasonic wave propagation characteristics in silicone to do exploratory tests. Secondly, this study proposed to introduce the AE to evaluate the status of prosthetic components. The feasibility of adapting AE has been discussed based on numerical model. As the first step, the behavior of elastic wave propagation is the main concern. A model of the bio-layer between the in-vitro sensor and the hip was built through COMSOL Multiphysics. The parametric study was conducted with consideration of the influence of frequencies of elastic wave, human action and bio-layer thickness on AE signal propagation. The results show the attenuation of the AE signal with the increase of frequency, the AE source position and the change of bio-layer thickness. The study can provide the basic understanding of elastic wave propagation due to different human action and status of the prosthetic components, which is beneficial for further design of in-vitro device in addition to the clinic examination.

Temperature is an important environmental load on offshore wind power systems. The harsh marine environment characterized by high temperatures and radiation, along with the slender and towering metal structure system, makes the temperature effects on offshore wind turbine support structures highly substantial and complex. However, the temperature load has not received sufficient attention and, in some cases, has been overlooked in the design and operation of wind turbine support structures. This paper focuses on the support tower structure of a wind power system, conducting finite element analysis and research on the temperature load of the support tower structure. First, the marine thermal environment of the wind turbine tower in service is analyzed to clarify the factors and mechanisms that affect the tower temperature. Secondly, based on the heat exchange and transfer process of the actual structure, a finite element calculation model for the temperature field of the wind turbine tower structure is established. Then, the finite element transient thermal analysis method is used to numerically calculate the temperature field of the wind turbine tower in service. Finally, the optimization of the finite element calculation model is carried out. The accurate finite element analysis of the temperature field of the wind turbine support tower structure is achieved, which provides the important theoretical foundation and technical support for the design of the wind turbine support structure considering the influence of the temperature effect, identification of the service state, and safety management of operation and maintenance.