This PDF file contains the front matter associated with SPIE Proceedings Volume 10973, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Ensuring adequate quality for additive manufactured (AM) materials presents unique metrology challenges to the on-line process measurement and nondestructive evaluation (NDE) communities. AM parts now have complex forms that are not possible using subtractive manufacturing and there are moves for their use in safety criticality components. This paper briefly reviews the status, challenges and metrology opportunities throughout the AM process from powder to finished parts. The primary focus is on new acoustic signatures that have been demonstrated to correlate process parameters with on-line measurement for monitoring and characterization during the build. In-process, quantitative characterization and monitoring of material state is anticipated to be potentially transformational in advancing adoption of metal AM parts, including offering the potential for early part rejection, part condition guided process control or even potentially in-process repair. This approach will enable more effective deployment of quality assessment metrology into the layer-by-layer material build with designed morphology. In this proof-of-concept study acoustic-based process monitoring signals were collected during the Direct Energy Deposition (DED) AM process with different process conditions to investigate and determine if variations in process conditions can be discriminated. A novel application of signal processing tools is used for the identification and use of metrics based on temporal and spectral features in acoustic signals for the purpose of in-situ monitoring and characterization of conditions in an AM process. Results show that the features identified in signatures are correlated with the process conditions and can be used for classifying different states in the process.

Determining the exact location of people and objects is a big benefit in times of IoT. It can achieve a tailored surrounding for humans for example by controlling lights or air conditioning systems when needed thus saving energy. It can also help with a smoother navigation for example by pointing to available spaces for parking cars or storing goods. In order to conclude an accurate location, large networks of connected sensors are required. These sensors can either be attached to the tracked target or tied to the monitored location. Either way, powering this growing number of sensors poses a challenge. Energy harvesters utilizing the transverse piezoelectric property of PVDF are proposed in order to power such sensors, eliminating the hassle of complex wiring or inconvenient battery replacements. A device that harvests mechanical energy from steps or movement using a multilayered wrap of PVDF-foils has been designed and fabricated. Depending on the application, this energy harvester can be installed in the ground of monitored locations or within the surface of tracked objects. Experimental characterizations of the energy output based on the number of windings and the required force have been conducted. A PVDF-wrap with three windings delivers 3.7 mJ when a force of 640 N, more or less the average human step force, is applied. Wireless sensors nodes, usually requiring less than 1 mJ to detect and transmit an event, can therefore be powered regardless of their location and without the need for inconvenient battery replacements.

This paper presents a genetic-algorithm-based optimization method to design a sensor network for pipeline fracture detection in petrochemical plants. To avoid catastrophic consequences, it is desirable to detect any pipeline fractures as early as possible, while minimizing false alarms. Sensor network design is the first step in structural health monitoring for early detection of pipeline fractures. Sensor network design is conducted in three steps. First, a hydraulic simulation model of the pipeline system is built to calculate pressure drops before and after fracture occurrences. Inherent randomness in model parameters, such as pipeline roughness, is incorporated in the simulation model as physical uncertainty, while uncertainty in pressure measurements is considered as statistical uncertainty. Statistical distributions of pressure heads at different locations are calculated with the simulation model. Second, for a particular set of fracture scenarios, the statistical distributions of pressure heads at individual nodes are computed with the probabilistic simulation model. Then, true positives and false negatives are quantified using a detectability metric. Finally, a mixed integer nonlinear programming (MINLP) optimization problem is formulated to find the optimal sensor locations by maximizing detectability and the optimization problem is solved using a genetic algorithm. The optimal locations computed by three different MNNLP algorithms are compared in terms of accuracy and computational cost. In the future, the optimal design of sensor networks suggested in this work will be compared with experimental results.

Recent events outlined the relevance of the interactions between industrial and natural hazards (NATECH) particularly for that concern seismic risk. EU regulation, namely Directive 2012/18/EU, among its new elements explicitly requires the analysis of NATECH hazards. The development of a risk analysis methodology for major hazard industrial plants allows the individuation of critical elements of the plants with regard to seismic actions. The following implementation of smart technologies (sensors, actuators, innovative systems for seismic protection) to the critical elements allows a relevant reduction of major hazards and related consequences.

Piezoelectric elements serve as a preferred candidate for measuring dynamic pressure owing to their high sensitivity, signal-to-noise ratio, high natural frequency, and suitability for miniaturization. Polyvinylidene fluoride (PVDF) is a mechanically tough, flexible, low density polymer commercially available as a film. Being mechanically compliant and minimally invasive to the host structure, PVDF can be conformed to a variety of surfaces using adhesive bonding, thus making it a suitable candidate for surface pressure mapping and acoustic pressure measurement applications. However, PVDF sensors in compressive mode are insufficient for the low frequency and high sensitivity requirements of vehicle surface pressure measurements. Under steady flow conditions, cantilever and clamped-clamped unimorphs with segmented electrode coverage configurations serve as alternative candidates for differential pressure measurements. This paper presents an analytical and computational design framework for optimizing the performance of PVDF unimorphs. Electrode coverage, thickness ratio, and elastic modulus ratio are optimized for cantilever and clamped-clamped configurations for a given sensor geometry. The goal of the optimization procedure is to maximize charge sensitivity of the pressure sensor while minimizing deflection. A closed-form solution is derived for deflection and charge sensitivity of cantilever and clamped-clamped configurations based on Euler-Bernoulli beam theory. For a given deflection sensitivity target and sensor geometry, the charge sensitivity of the optimized cantilever sensor is three orders of magnitude greater than compressive (d₃₃ mode) design and 3.15 times higher than the clamped-clamped configuration with segmented electrodes.

Shape memory polymer (SMP) is a class of polymer with properties of non-toxic, environmentally friendly and biocompatibility. It can be activated by an external stimulus to change and subsequently recover its original shape. Duo to its biodegradability, easy forming properties and shape memory effect, shape memory polymer has been widely used in bio-medical applications. This paper details an application of SMP on porous bone tissue scaffold. Compared with traditional bone tissue scaffold, the scaffold based on SMP possesses advantages of low cost, easy assembly and easy adjustment. And most importantly, the bone tissue scaffold based on SMP can adapt to keep the best fixed state. Besides, bone tissue scaffold is designed based on the biological structure, which characteristics of high porosity and high specific surface area will promote the growth of new bone tissue. Combining with 4D printing, it can realize customization. Bone tissue scaffold based on SMP exhibit excellent performance and prove to be a potential application in bone tissue engineering.

Health monitoring of rotating machine elements, such as gears, is challenging because of rotation at high speed in gearboxes, geometric complexity, or space limitation for measurements. The long-term objective of the present research is to develop smart sensor systems for detecting gear failure signs. As the very first step, we proposed a new method to manufacture electrical circuits, such as sensors or antennas, on gears. We had begun to develop a 4-axis laser printing system and showed the laser sintering conditions of the conductive ink splayed on steel plates insulated by polyimide layers. In this paper, a crack detection sensor was designed and printed. The printed sensor can monitor the condition of a plastic gear whose module and number of teeth are 1.0 mm and 48. In addition, an antenna designed for the same size gear was printed on a plastic plate, and the frequency property of the antenna was investigated. As a result, the printed antenna had the 1st natural frequency at 0.3GHz. Finally, monitoring experiments was carried out to check the condition of a smart system consisting of the sensor and antenna from the other antenna having the same dimension. As a result of the experiment, the monitoring of the return loss of the external antenna shows the sensor is healthy or not. The sensor and antenna system will allow for the development of better equipment and detection techniques for health monitoring of gears.

Ultrasonic guided waves have the potential to inspect integrated circuit (IC) packages using wave based techniques due to excellent sub surface penetration through metallic as well as dielectric material. Guided waves in a heterogeneous composite assembly such as an IC package have modes with complex dispersion characteristics due to multiple layers of material with intricate geometry. No analytical solution exists for predicting dispersion in highly anisotropic composites. Numerical methods, such as the finite element method, have been used to model dispersion in composites, however these methods are computationally intensive and not feasible for predicting dispersion in IC packages. In this paper, the time-frequency characteristics of guided waves propagating through a complex IC are studied using the synchrosqueezing transform (SST). This is a transform that has been shown to be robust to bounded signal perturbations, to provide highly localized time and frequency information for highly nonlinear modes, and to reconstruct the signal corresponding to each mode. Reference ultrasonic guided wave signals are collected for the IC package in its healthy and damaged states using piezoelectric transducers to characterize the dispersion modes in the excitation region. Initial results demonstrate that the dispersive mode information from the extracted SST ridges provide an effective damage indicator for IC packaging.

Nonlinear ultrasonic techniques have shown the prominent potential for assessing progressive damage occurred in the composite materials in their fatigue life cycles. Stress relaxation in composite material is being measured in two ways, in-situ analysis (on-line technique) using Lamb waves and Off-line technique using pressure wave (Scanning Acoustic Microscope). In this article, the progressive damage was investigated by a set of fatigue loading experiments on woven composite samples followed by a specific duration of stress relaxation in room temperature condition. A quantitative measure of stress relaxation is determined using Scanning Acoustic Microscope for a fatigue cycle of 225000 with the loading frequency of 10 Hz. To prove this claim, a well-established reduced order nonlinear state of Lamb wave due to stress-relaxation was compared with SAM data analysis. A good agreement between these two techniques is reported herein.

In micromechanics, the definition of the optimum Representative Volume Element (RVE) that represents the microstructure of the considered composite material is still an ongoing research subject. Therefore, depending on the required accuracy of the micromechanical model predictions, the size and number of realizations that define the RVE, must be determined through a systematic study. In the present work, an RVE based study is conducted on precipitation hardened NiTi Shape Memory Alloys (SMAs) with Ni₄Ti₃ precipitates. The considered RVEs consist of a homogeneous SMA matrix with periodically arranged precipitates. The precipitates are assumed to have ellipsoid shape and are arranged in random position and orientations, ensuring the periodicity at the boundaries. The non-linear constitutive behavior of the precipitated SMA, is solved by means of a recently developed Fast Fourier Transform variational approach for SMAs. An extensive study is conducted considering RVEs with changing volume fraction and increasing number of precipitates.

Piezoelectric nanocomposites composed of piezoelectric nanowires and flexible polymer have emerged as outstanding applications for flexible energy harvester. Although piezoelectric materials in their bulk form have high electromechanical coupling coefficient and can convert mechanical energy to electrical energy efficiently, they usually have low fracture toughness and are limited in applications due to difficulty in machining and casting it on to curve surfaces. Recently, additive manufacturing process (direct write) have been developed to incorporate piezoelectric nanowires into a polymer matrix with controlled alignment. It is shown that not only direct writing method can solve these issues but also it can improve the performance of the nanocomposite energy harvester significantly. In this paper, an experimentally verified finite element (FE) and micromechanics models are developed for calculation and optimization of g₃₁ voltage coefficient of a piezoelectric energy harvester nanocomposite. It is shown that, by using high aspect ratio nanowires with controlled alignment the g₃₁ coefficient can be enhanced more than five times compared to bulk form. Moreover, it is demonstrated that to achieve highest possible g₃₁ coefficient only a small volume fraction of nanowires is needed and further increase in volume fraction result in the reduction of g₃₁ coefficient.

NDE of ceramic components manufactured by additive technologies Additive Manufacturing (AM) adds in the first instance a new shaping method to the portfolio of established shaping methods of ceramics. New, unseen, shapes can be created directly from the computer model, potentially after numerical optimization (functional design). Sensory or actuator functions can be added by printing functional layers. The additive shaping or process will not result in successful components without considering the technology chain from raw materials and preprocessing to post processing and testing. Non-destructive evaluation (NDE) shall be applied ideally already to prefinished components for optimizing process steps and reducing waste. Additive methods offer new challenges (e.g. internal stresses, imperfect interfaces between layers) and new opportunities for NDE methods. These new opportunities are opened up by investigating thinner layers of materials directly in-operando of the AM shaping process. Ceramics structures, often difficult to investigate, will become better accessible. Fraunhofer IKTS qualifies new optical methods for in-operando use during additive manufacturing. Recent examples of work will be presented.

This paper presents the development of a delamination detection framework for integrated circuit packages aiming at quantitative detection of sealant delamination between integrated heat sink and substrate, which is one of the potential failure mechanisms in integrated circuit packages. This method is expected to overcome the destructive nature of most existing techniques and maintain a relatively low cost of development. Ultrasonic guided waves are used as the interrogation method due to their sensitivity to small-size damage and capability of through-thickness penetration. The complexity of the received ultrasonic signals, caused by the geometric heterogeneity, is resolved and interpreted using a time-frequency signal processing technique. The extracted ultrasonic information, including time-of-arrival and amplitude of wave modes received from different sensing paths under multiple excitation frequencies, is used to construct the feature space for training. An unsupervised learning method, multivariate Gaussian model, is implemented as an information fusion and delamination detection tool. The multivariate Gaussian model efficiently investigates the distribution of feature space including correlations between features and flag the outliers without labeled examples. Results from the developed model are compared with two existing evaluation methods, including pullout test and a metric indicating the extent of delamination, which indicates that the developed method possesses a similar level of accuracy.

Tanks for FRP materials used in the industrial field are composed of composite structures in which a resin and glass fibers are combined. The greatest advantage of FRP materials is its excellent chemical resistance, which makes them to store various chemicals or to keep the purity of the liquid in the container. In case of FRP material, the manufacturing method is different according to the manufacturer, and there are many cases where the FRP materials are manufactured by hand. In this study, non - destructive testing (NDT) of industrial tanks of FRP material was carried out using PAUT equipment. The PAUT equipment based on the UT non-destructive testing is capable of phased array arrangement, enabling comparative inspection in a multifaceted manner. In particular, in the case of the C-Scan mode, since the thickness and the signal are calculated through the 2D mode through designating the inspection area and the entirety of the corresponding section, it is easy to derive the defect and the state result through comparative analysis. In this study, non - destructive testing was carried out on about 150 (10 - 30 year old) FRP tanks in operation in actual industrial field. The thickness, fiber layer, pore, crack, and delamination test method were established through accumulated back data. However, the FRP material tanks differ in shape and internal structure depending on the manufacturing method, product company, and application, and various factors such as fiber porosity should be judged comprehensively. Therefore, data must be carried in advance.

This study attempted to build up an automated post-processing for structural element reconstruction/recognition directly from the scattered point cloud data (PCD). The target structure specimen being scanned was the three-story RC frame constructed in NCREE South Laboratory before shacking table test was performed. Algorithms used for the element reconstruction/recognition including edge signature extraction and data clustering. Edges of the target structure were extracted directly from the raw PCD and the elements were clustered by DBSCAN to ensure the geometric appropriateness. Recognition rate and dimension accuracy were compared with blue print to quantify the recognition quality, accordingly. Study results shows that the automated post-processing can achieve 100% of recognition rate with 95% of dimension accuracy, suggesting that PCD is suitable for computer vision in recognizing structure elements.

Non-destructive testing using thermography makes it possible to detect near-surface defects in fiber-reinforced composites as part of quality assurance or maintenance. The quality of the measurement and thus also the detectability of the defects decreases continuously with increasing depth and decreasing defect size. Various post-processing methods, such as pulse-phase thermography (PPT) and higher order statistics (HOS) can be used to improve the contrast or the signal-to-noise ratio of defects, whereby it is important to choose the right parameters depending on the characteristics of the defect. This study investigates the theoretical maximum achievable depths and shows the limits of thermography. For active thermography, impulse thermography is investigated and different post-processing methods are compared. As defect types, delamination in form of air inclusions are considered and their position is varied. Furthermore the influence of the measuring equipment was investigated. The results from the simulation are discussed and compared with results from literature and from experiments.

Damage development in composite structural members under quasi-statically load include distributed matrix cracks, delaminations, and random fiber breaks which start at relatively low loads and at these levels do not pose threat to the load carrying ability of the structure. But at higher load levels the damage growth accelerates and leads to final failure. The existence of undetected impact or other types of damage can severely accelerate this gradual damage growth. Acoustic emission technique can provide a real time assessment of the rate of damage growth. This paper examines the results from test specimens with and without impact damage for which acoustic emission data was collected using wide band sensors capable to recording signals in excess of 2 MHz. Characteristics of signals obtained at different load levels are compared. In these tests unidirectional, cross-ply, and quasi-isotropic carbon-epoxy composite tensile specimens were monitored while they were statically loaded to failure. Huge volume of AE data is obtained during these tests was analyzed in detail to understand the nature of damage growth and identify the source of acoustic emission signals. The difference in the acoustic behavior between undamaged specimens and impact damaged specimens are presented. In addition, the pattern of acoustic emission signals in which a sequence of near identical waveforms appearing in clusters were noted in both undamaged specimens as well as impact damaged specimens. The formation of clusters of such AE signals appears to indicate the approaching failure of the specimen. These clusters of AE signals seem to parallel the formation of clusters of fiber breaks that form near the ultimate failure of composite specimens in CT examinations.

Precision fluid pressure detection in microfluidics is challenging due to the restricted accessibility for integration of pressure sensors. We propose an adapted geometric laser interferometry technique capable of sensing changes in fluid pressure within the microfluidic chips noninvasively. In the past, similar interferometric approaches have been proposed for pressure determination in microfluidic devices; in this study, we experimented a different setup. We allowed a heliumneon laser beam to propagate through the air filled microchannels. We then captured the interference of the reflected waves from the microchip using a high-resolution camera sensor as bright and dark fringes, for applied pressures of 1-10 psi. These fringes shift with changes in air pressure inside the microchannels, and they (fringe shift) are interpreted as related to the changing air index of refraction and density. The use of optical interferometry for microfluidic pressure measurements is limited at this point; however, it is highly promising.

Corrosion has been a great concern in the oil and natural gas industry. A variety of corrosion sensor technologies have been developed based on different sensing principles. Conventional corrosion sensors and emerging sensor technologies are critically reviewed in terms of sensing principles, sensor designs, advantages, and limitations. Conventional corrosion sensors encompass corrosion coupons, electrical resistance probes, electrochemical sensors, ultrasonic testing sensors, magnetic flux leakage sensors, electromagnetic sensors, and inline inspection tools. Emerging sensor technologies include optical fiber sensors (OFS) and passive wireless sensor technology such as surface acoustic wave (SAW) sensors. OFS have advantages of nondestructive monitoring, in-situ distributive measurements, long reach, small size, light weight, inherent immunity to electromagnetic interference, compatibility to optical fiber data communication systems, and improved safety in the presence of flammable gas/oil as compared to electrical based sensors. Passive SAW sensors have advantages of small size, cost efficiency, ease of fabrication, compatibility with wireless telemetry, and adaptability to many applications. Both emerging technologies are promising in corrosion monitoring in the oil and natural gas applications. The ability to monitor corrosion online before the structural integrity is compromised can have a significant impact on preventing catastrophic events resulting from corrosion. Distributed chemical sensing shows promising potential to detect early corrosion onset and monitor corrosive environments for corrosion mitigation management. Additionally, high durability and stability are required for corrosion sensors in extreme service conditions such as high temperature and high pressure during drilling, production, and refining.

Statistics released by the U.S. Department of Energy show that the buildings sector accounts for 40% of primary energy use and associated greenhouse gas emissions, making it essential to reduce their energy consumption to meet national energy, environmental, and cost reduction initiatives. One of the major areas of energy consumption in buildings are heating, ventilation, and air conditioning systems; therefore, reducing heat losses in the structure can help increase the thermal performance of the building. Infrared (IR) imaging and the use of thermal cameras have been very effective for quantifying the thermal efficiency of a system and addressing damages in the structure. At the same time, improvements made in computer-based vision systems made Structure from Motion (SfM), a very promising technique for performing a visual inspection of large-sized systems or structures. SfM is photogrammetric range imaging technique that makes it possible to obtain three-dimensional (3D) renderings from a cloud of two-dimensional images. This research analyzes the feasibility of combining these two techniques to identify the locations of energy loss in buildings and residential homes from multiple perspectives enabling an autonomous inspection. In this study, a proof of concept laboratory experiment is performed. In particular, several tests are executed on a lab-scale structure representing a model home. Different levels of insulation distributed on the structure’s surface are used to determine the combined capability of SfM and IR images in creating a 3D virtual rendering of the structure that illustrates the locations of significant energy loss. Also, the possibility of embedding this system on an unmanned platform (e.g., drone) for expediting the inspection process is considered.

Gas turbine engine makers are always striving to implement newer technologies to help monitor the engine health and performance. Among these technologies is the employment of highly specialized sensors within the engine compartment. The sensors are to screen response of components such as rotor disk blades which are subjects to complex loading conditions and that includes combined thermal and mechanical loads. Detecting unexpected or excessive blade Vibration before failure is critical to ensure safety and to achieve expected apparatus life. Traditional detection methods have relied solely on component inspection during service or on the use of gage telemetry systems as well as other means of non-destructive evaluation. These methods require time and cost and do not provided an accurate feedback of the health when the engine is in operation. At NASA Glenn Research Center, an effort is underway to develop and test validate microwave based blade tip timing sensors in support of the latter concerns and for the purpose of investigating their application for propulsion health monitoring under the Transformational Tools and Technologies Project. This process involves working with prototype sensors to determine their applicability and assess their ability in making blade tip clearance measurements along with further refining a methodology required to extract deflection measurements from the raw data acquired from the sensors. The efforts focus specifically on the use and implementation of microwave based tip-timing sensors that are intended to be used for non-contact stress measurement application. The work includes an experimental task to define the optimum set-up of these sensors, determine their sensitivity in making blade tip deflection measurements and validate their performance against realistic geometries in a spin rig. This also includes analytical calculations to compare to the experimental results. Data pertaining to the findings obtained from the testing as well as the supportive analytical results are presented and discussed.

As plans are made to extend licenses for the aging fleet of commercial nuclear power plants for periods of sixty years and beyond, research into the long-term integrity of their concrete structures has increased. Ultrasound tomography is a useful tool for nondestructive evaluation of these concrete structures. Typically, pulse-echo measurements are made over a large surface and are processed using a reconstruction algorithm, producing a 3D image of the structure that highlights embedded defects. These measurements are processed using a delay-and-sum algorithm such as the synthetic aperture focusing technique (SAFT). Oak Ridge National Laboratory is developing novel ultrasound model-based image reconstruction (U-MBIR) algorithms to improve the imaging capability of pulse-echo ultrasound array imagers. U-MBIR is an inversion technique that reconstructs the sample under test from the ultrasound measurements by formulating and solving a mathematical optimization problem using two sets of terms: one set ensures that the reconstruction matches measured data based on a model for the physics of beam propagation with the noise in the detector, and another set ensures that the reconstruction has certain properties based on a model for the object being scanned. This paper compares three techniques: SAFT, frequency-banded SAFT (FB-SAFT), and U-MBIR. The U-MBIR method produces higher quality images of the underlying concrete structures compared to the images produced using SAFT and FB-SAFT from the same set of measurements. This paper also illustrates the detection of various defects with higher confidence due to the lower noise and artifact-free U-MBIR images.

The use of composite materials of various types and forms has become a significant engineering solution for manufacturing a range of mechanical, aerospace and automotive structures, leading to a significant increase in payload, weight reduction, speed, fire resistance, manoeuvrability and durability in comparison with traditional structural materials [1, 2]. However, the investigation of their mechanical behaviour under high-velocity impact loads is particularly challenging owing to the generation of simultaneous and multiple failure phenomena with an inevitable detriment of residual in-plane properties [3]. In this paper, experimental tests and finite element methods (FEM) were performed on plates and T-stiffened laminates under transient dynamic loading, aiming at better understanding the damage tolerance, failure phenomena and impact damage of these composites. The 3D explicit model, based on continuum damage approach, was built using a DYNA3D suite on a framework of an orthotropic constitutive behaviour with stress-based and energetic failure criteria. Numerical results were validated using visual and ultrasound (C-scan) evaluation from dynamic tests. Furthermore, based on the results of the experimental campaign and the numerical model, a numerical study on the optimisation of the structure of the laminate was carried out, considering the inclusion of a secondary hybrid layer within the layup sequence. The mechanical characteristics and geometry of this secondary phase were studied in order to find the best configuration to optimise damage tolerance and improve dynamic response to out-of-plane loads. Several examples with different initial conditions were carried out on the new hybrid material to demonstrate the excellent predictive capability of the numerical model and to study the influence of new hybrid characteristics on the impact responses and impact-induced damages. Numerical results of analyse and their trend were then presented, showing a decrease of -22%, -33% and -94% in damaged area, a decrease of -33%,-57%, - 58% in maximum indentation and an increase of +62%, +134% and +156% in rebound velocity of projectile due to the presence of TPU layers with 0.25 mm, 0.5 mm and 1 mm of thickness respectively.

The applications of wireless sensor nodes (WSNs) are rapidly growing in civil infrastructures, yet constrained by batteries with the short lifespan. Providing an alternative power source will undoubtedly expand the application for WSNs and other technologies such as energy harvesting. This paper presents a basic field study conducted on a city flyover to explore the possibility of piezoelectric energy harvesting from bridge vibrations. A piezoelectric energy harvester based on Micro Fiber Composite (MFC) has fabricated. The maximum voltage and average power generated by stainless steel PEH with surface bonded MFC patch was found to be 1.34 V and 0.784 μW respectively. Finally, the practical application of energy harvested achieved by lighting an LED with vibration energy.

Renewable energy resources are steadily being important options in the microgrids power systems, energy storage technology become required to manage intermittent of power supply, battery storage can be use in small scale to support the power need, but the cost and lifetime of the battery one of the challenges in utilizing battery storage system in the microgrids. Also, the thermal storage system become desirable due to ability to hold greatly variable quantities of energy at the unchanged temperature. This paper studies a microgrid which supplies solar and wind energy to a single building with both electric load and thermal load. The aim is to investigate the potential benefit of using lower-cost thermal storage to assist in managing renewable power fluctuations, which is appropriate when significant thermal loads are present. Hourly electrical consumption and demand hot water profiles are developed from historical meter data. Model Predictive Control (MPC) has been used to dispatch the power between microgrid component. Renewable energy penetration and renewable energy curtailment are the performance measured. Modeling results indicate that storage balance among battery and thermal storage increasing renewable penetration by 15% and decrease renewable curtailment by 30%.

Extracting improved mechanical properties such as high stiffness-high damping and high strength-high toughness are being investigated recently using high symmetry interlocking micro-structures. On the other hand, development of artificially engineered composite metamaterials has significantly widen the usability of such materials in multiple acoustic applications. However, investigation of elastic wave propagation through high symmetry micro-structures is still in trivial stage. In this work, a novel interlocking micro-architecture design which has been reported previously for the extraction of improved mechanical properties has been investigated to explore its acoustic responses. The finite element simulations are performed under dynamic wave propagation load at multiple scales of the geometry and for a range of material properties in frequency domain. The proposed composite structure has shown high symmetry which is uncommon in fiber-reinforced polymer composites and a desirable feature for isotropic behavior. The existence of multiple acoustic features such as band gap and near-isotropic behavior have been established. An exotic wave propagation feature, wave trapping and attenuation, has shown energy encapsulation in a series of repeating structures in a frequency range of 0.5 kHz to 2 kHz.

A high percentage of failures and damage propagation in materials and sensors employed in harsh industrial environments and airborne electronics is due to mechanical failure under tension and compression loads. Therefore, it is of paramount importance to test equipment reliability and ensure its survival in long missions in the presence of physical fluctuations. Mechanical testing systems (MTS) employ mechanical load in laboratories and all the scanning tests are performed after removing the sample from MTS machine. However, more precise tracking of failures and damages is possible only the moment the material is under loads. Hence, to systematically characterize and fully understand damage’s behavior, a system capable of Realtime scanning is required. The primary objective of this study is design, fabrication, and testing of a Realtime ultrasonic scanning using hydraulic arms (RUSH), which provides mechanical loads using hydraulic arms on the specimen and simultaneously scans it with ultrasonic scanning system. RUSH consists of two hydraulic pistons (for mechanical loading) and a main control unit that accurately calculates and sets the actuators’ input signals in order to generate desired load on the materials. In this paper, the system’s architecture, its mechanical structure, and electrical components are described. In addition, to verify RUSH’s performance, various experiments are carried out using unidirectional composites.

This paper shows crack detection systems based on deep neural networks, which analyze meshing vibration of plastic gears. A gear operating test rig has an acceleration sensor attached on a bearing housing and a high-speed camera. The meshing vibration of plastic gears during operation was measured and teeth images that enable us to decide whether cracks exists were captured. After transferring the meshing vibration data in the time domain to the frequency domain by FFT, the amplitude and phase information of the meshing vibration was converted to image data. According to the images from the high-speed camera, the imaged vibration data were separated to two classes, with or without crack, as the training data for deep neural networks. Furthermore, two convolutional neural networks, 4 layers and 16 layers were constructed for classification of crack existence or non-existence, and the systems were learned from the labeled data set. In the training, the random weighting functions of the convolution were prepared, and the number of images were 350 and the number of epoch was 125. The learning of the 4 layers convolutional neural network was finished appropriately, however, the learning of the 16 layers convolutional neural network did not progress at all. Then, the transfer learning method was used for the 16 layers convolutional neural network. The transfer learning of the 16 layers convolutional neural network was finished appropriately, and the accuracy at 125 learning steps reached to 97.2%.

In this paper, we propose a distributed machine learning (DML) algorithm to fulfill the requirements of the smart factory (or Industry 4.0) including self-organization, a distributed control function, communication between the smart components, and real-time decision-making capability. We show the proposed DML algorithm not only enables the smart factory to adjust the components for new demands and circumstances, but also each component of the system acts smart and communicate with each other, either request or offer functions. The DML is an interactive learning mechanism among smart components and a natural way of scaling up learning algorithms. The different machines can have the best learning algorithms of their own data while the communication between different learning processes is an integration of different learning biases that compensate one another for their inefficient characteristics. As such, the size of the smart factory is scalable and the growing amount of data from additional machines has a minor effect on the communication overheat. We will elaborate on the DML model that overcomes the problems of centralized systems and increases the possibility of achieving higher accuracy, especially on a large-size domain.