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Objective To estimate the association of different etiologies of cardioembolism (CE), intracranial arterial stenosis (ICAS), or the combination of these conditions with outcomes of mechanical thrombectomy in acute ischemic stroke. Methods Data from the intervention group of the Endovascular therapy for Acute ischemic Stroke Trial (EAST) were analyzed. In 140 patients, the presence of CE, ICAS, neither CE nor ICAS, or both conditions was assessed. The primary outcome was a favorable outcome at 90 days (modified Rankin Scale score 0–2); secondary outcomes included successful reperfusion (modified Thrombolysis In Cerebral Infarction grade 2b–3), symptomatic intracerebral hemorrhage, and 90-day mortality. Results Of 140 patients, 47 had neither CE nor ICAS, 35 had ICAS but not CE, 46 had CE but not ICAS, and 12 had both CE and ICAS. The rate of favorable outcome was 67.1% in the no CE and no ICAS group, 74.3% in the ICAS without CE group, 41.3% in the CE without ICAS group, and 33.3% in the CE and ICAS group. The CE and ICAS group had poor outcomes (odds ratio = 0.20 after adjusting for age, sex, and National Institutes of Health Stroke Scale score; 95% confidence interval, 0.04–0.95; P = 0.043). No significant differences were observed in secondary outcomes. Conclusions The presence of both CE and ICAS was associated with poor outcome in patients with anterior circulation large-vessel occlusion treated with endovascular thrombectomy. Future studies are warranted to further explore this association. Highlights • Our study demonstrated that patients with AIS with both CE and ICAS had poorer outcomes. • Good collaterals are the most important factor for favorable clinical outcome. • Patients with CE and ICAS may experience more difficulty during MT. [ABSTRACT FROM AUTHOR]

Multifunctional tactile sensors that can mimic the sensory capabilities of human skin to perceive various external static and dynamic stimuli are essential to interact with the environment and humans for wearable electronics and soft intelligent robotics. Here, inspired by human skin, we report a textile-based tactile sensor capable of multifunctional sensing for personalized healthcare monitoring and soft robotic control. The tactile sensor consists of a triboelectric nanogenerator sensing layer to mimic the function of fast adapting (FA) mechanoreceptors and a piezoresistive sensing layer to achieve the functionality of slow adapting (SA) mechanoreceptors. The tactile sensor has been demonstrated to be able to recognize voice and monitor physiological signals and human motions in a real-time manner. Combined with a machine learning framework, the tactile sensor is able to percept surface textures and material types with high accuracy. It is also demonstrated as an effective human-machine interface for the control of assistive robotics. Skin-inspired, self-powered, textile tactile sensors are designed for multifunctional sensing in wearables and soft robotics. They use combined triboelectric and piezoresistive sensing layers to mimic functionalities of the fast-adapting (FA) mechanoreceptor and slow-adapting (SA) mechanoreceptor of human skin to detect both the dynamic and static signals under a variety of working conditions for different applications. [Display omitted] • Report a textile-based tactile sensor capable of multifunctional sensing for healthcare monitoring and soft robotics. • The tactile sensor consists of a TENG sensing layer and a piezoresistive sensing layer. • Demonstrate excellent performance in recognizing voice and monitoring physiological signals and human motions. • The tactile sensor can percept surface textures and material types and as a human-machine interface for soft robotics. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymer (CFRP) has become an indispensable key material in the aerospace field due to its advantages of lightweight and high strength. In this study, a new process of selective texturing by femtosecond laser is proposed to improve the surface performance of CFRP without destroying fiber continuity. To explore the process mechanism of selective texturing of CFRP, the concept of the removal threshold is proposed, the influence of laser scanning direction and laser energy on the groove and the HAZ is studied. Furthermore, the optimization strategy of processing parameters during selective texturing is given. It is found that the scanning direction influences the transfer path of the heat significantly and therefore affects the groove geometry. As the scanning angle increases, the ablation threshold of resin rises gradually, while the ablation threshold of carbon fiber experiences a rapid increase before 30° and a gentle increase thereafter. Additionally, the groove shows a steady growth in width and undergoes a stepped decrease in depth, and the width of the HAZ increases. As laser energy increases, the width of the groove and HAZ increases, and the depth of the groove increases in a step pattern. The selective texturing mechanism of surface resin involves the coupling superposition of multiple effects, including photolysis resulting from multi-photon absorption, pyrolysis caused by direct laser energy absorption, thermal effects due to heat transfer of fiber, thermal effects of pyrolysis gas, and mechanical denudation. The laser energy density of 12–13 J/cm2 is the optimal parameter range for achieving selective texturing. • A new process of selective texturing of CFRP is proposed. • The removal threshold is proposed to refine the ablation process of CFRP. • The process mechanism of selective texture is described in detail. • The optimization strategy of processing parameters in selective texturing is given. [ABSTRACT FROM AUTHOR]

Electricity outage during the 2021 Texas power crisis substantively undermines the state's economy. To evaluate the economic impacts, we empirically provide a baseline estimate for such an outage with a GDP elasticity of electricity generation of 0.62 and detect that electricity generation significantly Granger causes Texas' GDP but not vice versa. Our investigation reveals that the 2021 power crisis tends to reduce Texas' GDP by at least 1.44%. Because power system primarily underpins the economy, our findings imply that Texas should utilize various available technological and regulatory options to strengthen its power system to accommodate more resilience. [ABSTRACT FROM AUTHOR]

In this paper, a passive wireless sensor is presented for monitoring structural integrity of wind turbine blades manufactured using reinforced glass fibers. The sensor developed is an Inductor Capacitor (LC) based resonant tank that detects the defects in the near-field region. The sensor is designed such that, strong electric fields exist along the near-field region of the capacitor allowing detection of very small change in effective dielectric constant of the target. The sensitivity of the developed RF sensor is verified by shortwave imaging of the target. The designed sensor has multiple reuse capability and is used in the real-time monitoring of defects along the manufacturing supply chain. A path to two-dimensional simultaneous scanning of target is shown by demonstrating a multiple LC tank array probe. The details of single and array probes for detection of fiber defects are outlined in the paper. [ABSTRACT FROM AUTHOR]

This study introduces a novel framework aimed at addressing the challenge of surface defect characterization in lab-scale tests. It utilizes a high-speed rotational disc setup to simulate the dynamics of rolling contact fatigue found in railway inspections through Motion-Induced Eddy Current Testing (MIECT). A key component of our approach was the integration of experimental data and finite element modeling, aimed at interpreting the relationship between defect dimensions, velocity, and their impact on magnetic sensor outputs. Our research focused on two main objectives: developing a forward model to predict the differential peak-to-peak amplitude ( Δ V p p ) of sensor readings from defect size and velocity, and to perform inverse estimation of defect sizes from Δ V p p across continuous velocity ranges. The key findings reveal that for the forward problem, the Radial Basis Function Multi-Fidelity Scaling (RBF-MFS) method outperforms other multi-fidelity and single-fidelity approaches. Moreover, the proposed Gaussian Process Regression with Multi-Fidelity Scaling and Feature Discretization (GPR-MFS-FD) method outperformed the state-of-the-art multi-fidelity method in the inverse estimation of defect geometries. This innovative method leverages high-fidelity experimental data together with low-fidelity physics simulations via multi-fidelity scaling and feature discretization to effectively manage velocity range inputs, reflecting real-world operational uncertainties in high-speed transport vehicles and infrastructures. Our integrated and novel data-driven approaches advance defect characterization, enhancing MIECT's application in surface defect detection and analysis, with potential extensions to other NDE applications. [ABSTRACT FROM AUTHOR]

This article presents a multi-modal hybrid-probe approach to nondestructive inspection of RCF cracks and damage in rails. A combination of electromagnetic (EM) and ultrasonic testing (UT) techniques are presented, which allows for complementary physics to be utilized to enhance detection and characterization of surface and sub-surface cracks in a non-contact manner at high speeds. A novel integrated design which combines the motion-induced eddy current (MIEC) effect and ultrasonic Rayleigh surface waves generated and detected using electromagnetic acoustic transducer (EMAT) is presented. The hybrid probe was tested at low speeds to demonstrate an increased damage localization capability. This was carried out using a data registration and fusion approach between the sensing modalities. Finally, the capability of MIEC effect at high-speeds is demonstrated. The results show that the hybrid probe has a high potential for in-motion, high-speed damage detection and characterization in the future. [ABSTRACT FROM AUTHOR]

Transportation and storage for hydrogen have garnered increasing attention in recent years because of the rapid development of low-carbon hydrogen energy. Pipeline is regarded as one of the most efficient ways for hydrogen transportation. However, many research found that the active hydrogen atoms can penetrate into the material and induce cracking on the pipe. As the initial hydrogen induced cracking is small, detecting these small defects can be challenging. To enhance the detectability of these small defects, we propose a novel capacitive sensor structure featuring two differential excitations. It is noted that the lift-off noise is always a challenging problem in electromagnetic NDE method. Fortunately, this problem can be alleviated by the proposed method. The simulation result shows the output signal will not be significantly influenced by the lift-off changing. Two metallic samples with defects are tested with a PCB-based prototype probe. Experiment result shows that the developed sensor can suppress the lift-off noise by 81.0% and increase the SNR by 592.28%, comparing to the results of a conventional sensor. A defects extraction algorithm is developed based on morphological image processing method to extract the defects from the raw data automatically. Size estimation is conducted and the average quantization error rate is 7.01% for 10 mm defects. Therefore, from the simulation and experimental results, the proposed capacitive sensing method can be a potential approach for hydrogen pipeline inspection. [ABSTRACT FROM AUTHOR]

Abstract Fast reconstruction of three-dimensional (3-D) defect profile from three-axis magnetic flux leakage (MFL) signals is important to the pipeline inline inspection (ILI) in the oil and gas industry. Traditional methods require the processing of a large amount of raw input and output data, which poses significant challenges in balancing the inspection efficiency, i.e. sensing and data processing speed, and the ILI accuracy and robustness. Here, a novel fast reconstruction framework combining key physics-based parameters and data-driven machine learning algorithms is proposed. Geometric parameters based rational Bézier curve (RBC) model is proposed to generate the 3-D defect profile, while local and global feature parameters are determined using a nonlinear least square (NLS) approach from the three-axis MFL signals. These physics-based geometric and feature parameters are then correlated through a least-square support vector machine (LS-SVM). Meanwhile, a pipeline inspection gauge (PIG) is developed to measure the three-axis MFL signals for evaluating the reconstruction performance through field testing. Both simulation and experimental results demonstrate that the proposed method's accuracy, robustness and computation speed have been improved significantly comparing with other existing methods. [ABSTRACT FROM AUTHOR]

The degradation of systems in service, including pipelines, over time highlights the critical need for reliable and accurate defect detection to ensure safe operations. However, single modality-based Nondestructive Evaluation (NDE) data used in practical applications often suffers from noise contamination and errors caused by various factors like lift-off/standoff distances, probe drift, scanning speed, variation in data acquisition rates, and poor sensor sensitivity. The presence of agnostic noise types poses a challenge in extracting defect signals. To address these challenges, this paper presents an automated NDE theory-based data fusion framework aimed at enhancing the detection of surface and near-surface defects in magnetizable and conductive specimens. The Magnetic Flux Leakage (MFL) and Eddy Current (EC) based NDE sensing methods demonstrate the highly heterogeneous nature of noise distributions. Given the heterogeneity of the inspection methods, a screening rule is proposed to determine the conditions under which fusion would be beneficial. An important aspect of the proposed fusion method is registration, which ensures accurate alignment of multi-sensor image data. Two registration methods are proposed in this study as performing blind fusion without registration leads to erroneous results. The first registration method is translational, whereas the second method is registration based on linear optimal transport (OT) which proves to be effective in the boundary conditions. Finally, the registered source images from the EC and MFL modalities are fused using pixel-based fusion algorithms, including transform domain and spatial domain-based methods. Qualitative and quantitative assessments demonstrate that the registered fusion results exhibit higher accuracy and reliability compared to unregistered fused results and source images. Although the fusion method is applied to MFL and EC data in this paper, it is also suitable for other NDE modalities. • Heterogeneous Fusion. • Registration based on Optimal Transport. • Near field NDE sensors. [ABSTRACT FROM AUTHOR]

Abstract The physics of electromagnetic radiation due to rock facture is complex, and understanding of this phenomena and its relationship with the extent of rock damage is imperative and remains challenging. In this paper, the relationship between the electric dipole moment and the stress change rate at the crack tip and the crack propagation characteristics are established. The stress change process is divided into three stages, the characteristics of electric dipole moment of each stage are analyzed. Simulation studies showed that the dipole frequencies, the angles between the electric dipole and detectors have a significant influence on the detection of those radiations. Self-expanding destructive experiments were designed and carried out for different types of rocks to observe more details of this phenomenon. In the rupture process of the sample, a number of electromagnetic radiation signals were detected. The duration of the signal is about 2 to 3 ms, and the interval between signals varies from 23 to 210 ms. The spectrum of the signal is between 4 kHz and 50 kHz. The complex variation of the signal spectrum and amplitude are due to the different electric dipoles produced by the different stages of crack propagation, and the change in the distance and angle of the radiation source from the detector during the crack propagation. [ABSTRACT FROM AUTHOR]

Abstract A novel deep learning based eddy current inversion algorithm is proposed and investigated in this paper. Eddy current testing (ECT) for defects detection problem is adopted to demonstrated the proposed algorithms. The proposed model based on a Convolutional Neural Network (CNN) is developed to improve the defect detection performance with uncertainty information. The novelty of our work consists in combining characteristics of ECT data with general deep learning model to improve performance of deep learning in ECT field including a region of interest (ROI) method based on robust principle component analysis, a CNN classification model with weighted loss function and measurement of uncertainties. Experimental dataset obtained from eddy current inspection of heat exchanger tubes is utilized to validate the detection performance improvement. As a result, both the classification accuracy and the percentage of defects correctly identified have been increased to almost 100%. [ABSTRACT FROM AUTHOR]

Abstract Subsequent inflammation in stroke plays an important role in the damage of neurons in the perilesional area. Therapeutic intervention targeting inflammation may be a promising complementary strategy to current treatments of stroke. Here, we explored the possible beneficial effects of tyrosol, a derivative of phenethyl alcohol and natural antioxidant, playing an anti-inflammatory role in astrocyte culture and in vitro oxygen glucose deprivation (OGD) model. MTT, western blot, ELISA and EMSA assays were carried out to investigate cell viability, protein expression level, cytokine expression and NF-κB activity. We found tyrosol protected cultured astrocytes against OGD-induced cell viability loss in MTT test. Meanwhile, tyrosol attenuated the released TNF-α and IL-6 level from astrocyte via regulating Janus N-terminal kinase (JNK). The reduction of cytokines from astrocyte might be due to its inhibition of astrocyte activation and regulation of STAT3 signaling pathway since tyrosol attenuated the expression level of GFAP (glial fibrillary acidic protein) and the phosphorylation of STAT3. Additionally, we demonstrated that tyrosol prevented the degradation of IκBα and the increase of IκBα phosphorylation in astrocytes exposed to OGD, which led to the suppression of NF-κB function during ischemia. Collectively, our results showed that tyrosol may be a promising complementary treatment compound for stroke via modulating the inflammatory response in astrocytes during ischemia. Highlights • OGD induces the release of cytokines from astrocytes. • Tyrosol attenuates the neuroinflammation of astrocytes. • Tyrosol suppresses NF-κB function of astrocytes under OGD. • Tyrosol may be a promising supplementary treatment compound for stroke. [ABSTRACT FROM AUTHOR]

Abstract Angkor temples in Cambodia, an icon of Khmer civilization, display the ancient culture by bas-relief on the sandstone surface of different temples, which are being destroyed by physical, chemical and biological processes for more than a thousand years. To investigate the bio-erosion of temple sandstone at the Royal Palace of Angkor Thom and Beng Mealea in Cambodia, Next Generation Sequencing (NGS) Illumina sequencing technology based on 16S rRNA gene was performed on samples of biofilm and exfoliated sandstone materials to identify the microbial community composition. After quality filtering the raw data, 678,115 quality reads were obtained for bacterial 16S rRNA gene from a total of 13 samples with high Goods coverage and satisfactory rarefaction curves. Higher bacterial diversity was detected in exfoliated sandstone materials than the biofilms, but the lowest in the lower layers of the biofilm than the top layers. At the phylum level, 4 phyla, namely Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi, were the most common and dominant bacterial groups in these samples with each contributing to greater than 3.7% of the total abundance. Both Firmicutes and Gemmatimonadetes were the dominant phyla detected only in exfoliated materials, while Cyanobacteria, Chloroflexi, and unassigned bacteria were more abundant in the biofilms. Hierarchical cluster analysis at the genus level showed that the distribution of bacterial community composition between exfoliated materials and biofilms was significantly different. The microbiota of Beng Mealea and the Royal Palace was different, especially for the biofilm samples. The correlation of environmental factors and bacterial community structure suggested that the nitrification process was more active at Ben Mealea, which might contribute to biodeterioration. This analysis of microbiota in these biofilms and sandstone exfoliation materials provides further information on the responsible microorganisms involved in geobiochemical processes at Angkor monuments and preservation strategies under tropical climate conditions. Graphical abstract Image 1 Highlights • NGS Illumina sequencing of biofilm and exfoliated sandstone materials at Ben Mealea and the Royal Palace. • Microbiota of the Beng Mealea and the Royal Palace was different, especially for the biofilm samples. • Higher bacterial diversity in exfoliated sandstone than biofilms. • Lowest diversity in the bottom layer of the biofilm comparing with the upper layers. • Nitrification process was more active at Ben Mealea than the Royal Palace. [ABSTRACT FROM AUTHOR]

With increasing use of fiber reinforced polymer (FRP) composites in several industrial applications, structural health monitoring and prognosis have become an extremely critical task in recent years. Accurate health prognosis ensures system reliability and aids in estimating the remaining-useful-life (RUL) which in turn reduces repair or replacement costs. In this paper, a framework for the estimation of impact damage propagation in GFRP plates is proposed which utilizes a physical model based on Paris’ law and data obtained from inspection of GFRP specimens by optical transmission scanning (OTS) technique. Advanced signal processing and feature extraction is performed to quantitatively characterize the delamination size from the OTS data. A Bayesian method based on particle filter update has been implemented to estimate the model parameters by taking observed data into account and accurate RUL prediction of the GFRP samples subjected to impact damage has been achieved. Results demonstrate feasibility and potential of the proposed approach as a robust reliability analysis technique of GFRP laminar plates. [ABSTRACT FROM AUTHOR]

Laser profilometry and structured light sensors are being increasingly deployed for pipeline inspection as they provide the operator with a precise 3D map that can enable visual detection and direct insight into the integrity of the pipe. The focus of the presented paper is the design of an integrated robotic structured light sensing system used to improve the performance of 3D defect reconstruction for pipeline inspection while accommodating the uncertainty seen in a real-world environment. Point cloud registration of the consecutive 3D frames is a key factor in building this 3D map; therefore, a comprehensive featureless registration approach is proposed first, which is proven more efficient than conventional feature-based registration algorithms. Wheel odometry from the developed robotic platform and inertial measurements are integrated into the registration algorithm to enhance the 3D reconstruction performance for sensor stabilization. An intensity-based threshold searching method is further applied to retrieve the reconstructed defect size. Lastly, the uncertainties of the structured light sensing are investigated for the total reconstruction uncertainty and estimated measurement uncertainty to be quantified in order to illustrate the measurement precision. The efficacy of the proposed algorithms are supported by experimental results of pipeline inspection. • 3D profiling obtained from structured light sensors is widely applied for pipeline inspection. • Inertial Measurement Unit and Wheel odometry provide orientation and location of the robot as external input. • The proposed sensing system take the global and local positioning information from the sensor to construct a comprehensive 3D point cloud registration approach. • The proposed registration algorithm can reconstruct pipe even with no visual features in inspection, and performs better than other stats of art methods. • Uncertainty sources in this robotic integration sensing system are clarified and the uncertainty from measurement has been investigated to illustrate the reliability of the designed inspection system. [ABSTRACT FROM AUTHOR]

Objectives This study aimed to investigate whether von Willebrand factor (vWF) and high mobility group box 1 (HMGB1) are associated with the severity and clinical outcome of scrub typhus and to seek novel biomarkers for surveillance and prediction of the prognosis of this infection. Methods Serum concentrations of vWF and HMGB1 were measured twice by ELISA for scrub typhus patients ( n = 103), once prior to doxycycline therapy and then on day 7 of doxycycline therapy; concentrations were measured once for healthy controls ( n = 32). Results Among the total 103 patients enrolled, 38 had disease complicated by multiple organ dysfunction syndrome (MODS). Serum concentrations of vWF and HMGB1 were significantly higher in all the patients than in the healthy controls, both prior to doxycycline treatment and on day 7 of doxycycline treatment ( p < 0.01). Furthermore, serum levels of vWF, HMGB1, and creatinine (SCr) in the patients with MODS increased distinctly, while the platelet (PLT) count diminished markedly compared to the levels in patients without MODS ( p < 0.01). The concentration of vWF was positively correlated with that of HMGB1 ( r = 0.764, p < 0.001) and SCr ( r = 0.528, p < 0.001), but negatively correlated with the PLT count ( r = −0.632, p < 0.001). Both HMGB1 and vWF were significantly associated with mortality in scrub typhus (area under the curve (AUC) = 0.864, p = 0.001, and AUC = 0.862, p = 0.001, respectively). Conclusions Elevated levels of vWF and HMGB1 are associated with the severity and clinical outcome of scrub typhus. These represent possible new biomarkers for use in the assessment and prognostic prediction of this infection. [ABSTRACT FROM AUTHOR]

Adhesive bonding is widely used for joining light weight structures and surface preparation methods play a significant role in improving the bonding quality. Bond quality assessment of thermoplastic composites can be challenging due to the highly inhomogeneous structure. This article explores the use of ultrasonic testing to nondestructively characterize the bond state between thermoplastic composites with different surface preparation methods. Classification of bond states was carried out using a physics-based statistical method and machine learning based method. The results suggest that machine learning methods show a higher classification accuracy. The ultrasonics results were validated using destructive lap-shear testing. [ABSTRACT FROM AUTHOR]

Cross bore is an intersection of an existing underground pipeline such as a sewer pipeline by a second utility such as a gas pipeline installed using the trenchless technique, which could lead to severe incidents like explosions. There are hundreds of thousands of existing cross bores in the U.S., at an estimated average rate of 0.4 per mile of pipeline reported by Cross Bore Safety Association, which is at high risk and a significant threat to public safety if not well detected, characterized, and mitigated. The research on high-accuracy nondestructive evaluation (NDE) methods that can identify cross bore areas more effectively and efficiently under challenging field environment is of vital importance. This paper proposes a multi-channel capacitive sensing system for cross bores detection and classification, which passes through the gas pipe to inspect the dielectric property changes of surrounding materials and indicates the existence of cross bores. A 3-D simulation modeling tool was developed to optimize the footprint of multi-channel sensor, and the effect of four cross bore types on the received capacitance values was investigated. The lab-scale experiments were performed using the developed multi-channel capacitive sensing system through a soil box setup, and the experimental results indicated that the developed system can identify the four types of cross bores. To achieve an automated decision making for the cross bores detection and classification, machine learning (ML) algorithms were developed through the experimental dataset, and it was found that the subspace k-nearest neighbors (SKNN) performed better with a high classification accuracy. Finally, field test validation was performed at the pipe farm (Gas Technology Institute, IL, USA) and the superior capability of the developed sensing system in identifying cross bores as well as other key parts in gas pipelines including butt fusion and saddle fitting with coil was demonstrated. • A multi-channel capacitive sensing system is proposed for cross bore detection. • Simulation modeling is performed for sensor design and optimization. • Machine learning algorithms are developed to classify cross bore types. • Experimental results show the good detection and classification capabilities. • Field test validation is performed at pipe farm of GTI with multiple gas pipe kits. [ABSTRACT FROM AUTHOR]

As infrastructure decays rapidly due to structural and material aging and failures, the development of new improved, and efficient nondestructive evaluation (NDE) techniques is vital for system health monitoring and preventive maintenance. Composite material, such as carbon fiber reinforced polymer (CFRP) plays a significant role in energy and transportation infrastructure due to the advantages of corrosion resistance, durability, and lightweight, which contributes to minimal maintenance and long service life. However, due to their unique anisotropic dielectric and mechanical characteristics, detecting composite material defects by NDE techniques is still challenging. This paper presents a novel, low-cost miniaturized multi-modality imaging system combining low-frequency capacitive shortwave and high-frequency microwave NDE technologies to detect various types of defects in CFRP materials. Based on the governing electromagnetic theory behind multi-modality electromagnetic NDE methods, the merit of combining low-frequency shortwave and near-field microwave imaging for dielectric characterization of composite materials is thoroughly investigated and demonstrated. Then, a miniaturized imaging system is developed to operate at ultra-wide bands: 10 kHz to 200 MHz for the capacitive shortwave probes and 1 GHz to 9 GHz for the microwave probes. Customized multi-material joining samples of CFRP and steel with different defect types, locations, depths, and sizes are tested by the developed imaging system, and the experimental results of the miniaturized system are compared with the existing table-top systems, which demonstrates comparatively accurate results for the developed multi-modality imaging system. The compact and practical nature of the presented imaging system makes it an optimal tool that can be utilized in field conditions with constrained operational spaces and NDE uncertainties. [ABSTRACT FROM AUTHOR]

Targeted heating of nano- Fe 3 O 4 reinforced thermoplastics using electromagnetic (EM) radiations allows for rapid dis-assembly and re-assembly of bonded structural joints. Alternate EM field causes local heating of the dispersed ferromagnetic nanoparticles (FMNP), thereby melting the surrounding thermoplastic. However, it is essential to accurately measure the temperature of the adhesive since overheating may cause degradation and underheating may introduce inefficient bonding. This paper presents an ultrasonic guided wave (GW) technique for monitoring the adhesive state and provides feedback to control the electromagnetic process. Experiments were performed on single lap-shear joints FMNP reinforced thermoplastics and non-conductive glass fiber reinforced polymer (GFRP) adherends. GW were made to propagate across the bond-line of the joint by actuating and sensing them using miniature piezoelectric wafers. Dispersion relations and dynamic wave propagation were obtained using finite element (FE) modeling. Fundamental longitudinal mode L 0 at 35 kHz was found optimal for bond process monitoring. Mapping between the FE-simulated transmission coefficient of L 0 and actual temperature of the thermoplastic adhesive was established using the dynamic mechanical analysis (DMA) test data. Real-time GW measurements were used as a feedback in the discrete control of the induction heater to provide optimal bonding. The developed ultrasonic technique was successfully validated by a high resolution, optical frequency domain reflectometer (OFDR) based fiber-optic temperature sensor which was embedded inside the adhesive bond-line. Experiments demonstrated good agreement between GW measurements and OFDR system. Overall, the results indicate the potential of GW technique for in-situ monitoring and controlled bonding of reversible lap-joints using electromagnetic heating. [ABSTRACT FROM AUTHOR]

There is a growing interest in the use of composites in several industries such as aerospace, automotive and civil infrastructure due to their unique properties such as light-weight, corrosion resistance and excellent thermo-mechanical properties. However, it is critical to ensure that no defects, such as disbonds, voids and delaminations are introduced during fabrication or during service. A variety of nondestructive evaluation (NDE) techniques have been proposed and developed to detect such defects that can compromise the integrity of these structures. Microwave NDE techniques are well suited for inspection of dielectric materials such as composites because of the ability of electromagnetic waves to interact with these materials. Far field electromagnetic inspection systems have the capability of rapid, large area inspection at large stand-off distance. However the diffraction limits restrict the resolution of conventional far field imaging. This contribution focuses on enhancing the resolution of microwave far field imaging using metal reflectors to increase the virtual aperture of the receiver array. Model based studies demonstrate the feasibility and robustness of this approach and also determine the limits of this technique. Preliminary experimental results based on a time reversal cavity environment validates the approach for enhancing the resolution of microwave NDE imaging. [ABSTRACT FROM AUTHOR]

• Novel approach to detect and characterize elasto-plastic strain using near-field microwave. • High sensitivity measurement demonstrated through DIC data correlation residual strain. • Non-contact scanning methods developed that provides high resolution and subsurface penetration. • Very large strain (0.3–0.7) exhibited by dielectric materials is successfully measured electromagnetically. Strain distribution is an important indicator of stress concentration, damage initiation and evolution. Many dielectric materials sustain very large strain before failure. In this paper, a near-field microwave high-resolution imaging (NMHI) system is presented to estimate very large deformation. The sensitivity of the microwave imaging system to the dielectric property and geometric changes have been utilized in the present work. A multi-modality data fusion technique is applied to experimentally evaluate the strain in ASTM-D638 standard dog bone structure made of Polyamide 11 (PA-11) material. Plastic strain in the range of 0.3–0.7 has been successfully correlated to the microwave probe response in PA-11 materials. The comparison of strain distribution obtained from NMHI and digital image correlation (DIC) indicate the potential of NMHI as a fast, non-contact method to estimate large mechanical strain. [ABSTRACT FROM AUTHOR]

Prediction of expected life of a composite structure especially at the initial stages of degradation is challenging owing to inherent heterogeneity and lack of robust damage growth models. This paper focuses on prognostic study of matrix stiffness degradation in glass fiber reinforced polymers (GFRP) subjected to fatigue testing using data from multi-modal nondestructive evaluation (NDE) techniques, specifically the optical transmission and guided wave sensing. Combining information from multiple sensors exploits advantages of signal complementary and hence effectively improve damage growth modeling and prediction in composites. However, matrix stiffness inferred from two independent NDE techniques varies owing to differences in their sensitivity, measurement noise or model discrepancy, often leading to inconsistent and inaccurate reliability assessment. A joint likelihood updation technique is therefore proposed in existing particle filtering (PF) framework which enables dynamic optimization of Paris-Paris model parameters at every time step by discarding noisy or biased measurements. Comparison of stiffness prediction using multi-sensor data with prognosis results on single sensor or average measurement demonstrates the benefit of joint likelihood based prediction of residual stiffness. An additional advantage of the proposed approach towards reduction of particle count in existing particle filtering framework is discussed, thereby lowering prediction time and computation resources. Overall, multi-sensor NDE and prognosis methodology is discussed for reliable assessment of fatigue life in GFRP composites structures. [ABSTRACT FROM AUTHOR]