Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics最新文献

{"title":"Meso-scale energy harvester: a comparison between MEMS and micromachined designs","authors":"Guilherme C. Miron, D. Braga, J. Cordioli","doi":"10.1117/12.2657520","DOIUrl":"https://doi.org/10.1117/12.2657520","url":null,"abstract":"With smaller, cheaper, and more energy-efficient electrical components, energy harvesting systems have been a more attractive source of energy supply for wireless sensors, transducers, and other devices. One great example of mostly unused energy is the vibration of industrial machines. Along with the rise of predictive maintenance, more wireless sensors have been used to monitor those machines. Where the vibration energy present in those machines can be used to extend the sensor’s life constrained by the battery. This work presents two fabrication approaches to design these devices using the piezoelectric principle: MEMS fabrication and micro-machined devices. MEMS are widely investigated for harvesting purposes for their capability of building complex microscale structures (< 0.1 cm3). However, it can be difficult to designing MEMS energy harvesting systems for the low frequency range (40 Hz to 200 Hz), which is the operating range for standard industrial machines. The adapted micro-machined harvesters from off-the-shelf piezoelectric components mostly used in macro-scale applications (> 10 cm3), can be an alternative in this situation. Numerical models were developed to simulate the dynamic behavior of the piezoelectric device and used as input for design optimization. The models were improved using a differential evolution algorithm optimizing in terms of the Normalized Power Density (NPD) and Mechanical stress. In order to validate these models, prototypes were built ns tested, with the results compared considering the NPD and frequency bandwidth. The optimization process raised key design aspects of meso-scale low-frequency piezoelectric devices, including stress limits of thin-film piezoelectric and fabrication complexity, Overall, these aspects suggest that there is an advantage of micro-machined designs over MEMS devices for these applications.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"7 1","pages":"124831O - 124831O-12"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82685712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A theoretical investigation of a two-degree-of-freedom vibro-impact triboelectric energy harvester for larger bandwidth","authors":"Mostafa K. Hassan, Alwathiqbellah Ibrahim","doi":"10.1117/12.2658518","DOIUrl":"https://doi.org/10.1117/12.2658518","url":null,"abstract":"The efficiency of the energy harvesters can be improved by increasing the harvester bandwidth. Towards this, we presented a Two-Degree of Freedom (2-DOF) Vibro-impact Triboelectric Energy Harvester by combining multi-modality and piecewise linearity of two close resonant frequencies. The harvester structure consists of a primary cantilever beam attached to a secondary cantilever beam through a tip mass. The secondary beam is attached in the opposite direction to the primary beam. The bottom surface of the secondary beam acts as an upper electrode of a triboelectric generator. A lower electrode with bonded Polydimethylsiloxane (PDMS) insulator is attached at some gap separation distance underneath the upper electrode to create an impact structure. When the system vibrates, an impact between the triboelectric layers generates an alternating electrical signal. A 2-DOF system with lumped parameter theoretical model was developed to extract the governing equations. The structure’s dynamic behavior at different excitation levels, separation distance, and surface charge density were investigated theoretically. As a result, we achieved a wider bandwidth for the designed energy harvester. The proposed harvester demonstrated an increase in the maximum output voltage by more than 300 percent, and 250 percent increase in the bandwidth, by changing the excitation level from 0.1g to 0.7g. The result of this study can pave the way for an efficient energy harvester that can scavenge ambient vibrations over a wide range of excitation frequencies.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"9 1","pages":"124830X - 124830X-11"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90439877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hardware implementation of nonstationary structural dynamics forecasting","authors":"Puja Chowdhury, Austin Downey, J. Bakos, S. Laflamme, Chao Hu","doi":"10.1117/12.2658036","DOIUrl":"https://doi.org/10.1117/12.2658036","url":null,"abstract":"High-rate time series forecasting has applications in the domain of high-rate structural health monitoring and control. Hypersonic vehicles and space infrastructure are examples of structural systems that would benefit from time series forecasting on temporal data, including oscillations of control surfaces or structural response to an impact. This paper reports on the development of a software-hardware methodology for the deterministic and low-latency time series forecasting of structural vibrations. The proposed methodology is a software-hardware co-design of a fast Fourier transform (FFT) approach to time series forecasting. The FFT-based technique is implemented in a variable-length sequence configuration. The data is first de-trended, after which the time series data is translated to the frequency domain, and frequency, amplitude, and phase measurements are acquired. Next, a subset of frequency components is collected, translated back to the time domain, recombined, and the data's trend is recovered. Finally, the recombined signals are propagated into the future to the chosen forecasting horizon. The developed methodology achieves fully deterministic timing by being implemented on a Field Programmable Gate Array (FPGA). The developed methodology is experimentally validated on a Kintex-7 70T FPGA using structural vibration data obtained from a test structure with varying levels of nonstationarities. Results demonstrate that the system is capable of forecasting time series data 1 millisecond into the future. Four data acquisition sampling rates from 128 to 25600 S/s are investigated and compared. Results show that for the current hardware (Kintex-7 70T), only data sampled at 512 S/s is viable for real-time time series forecasting with a total system latency of 39.05 μs in restoring signal. In totality, this research showed that for the considered FFT-based time series algorithm the fine-tuning of hyperparameters for a specific sampling rate means that the usefulness of the algorithm is limited to a signal that does not shift considerably from the frequency information of the original signal. FPGA resource utilization, timing constraints of various aspects of the methodology, and the algorithm accuracy and limitations concerning different data are discussed.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"24 1","pages":"1248316 - 1248316-10"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75337392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Torque sensing and energy harvesting based on phase shift in two-point magnetic plucking","authors":"Y. Lo, C. Y. Chang, Y. Shu","doi":"10.1117/12.2657948","DOIUrl":"https://doi.org/10.1117/12.2657948","url":null,"abstract":"A self-powered SECE (synchronized electric charge extraction)-based energy sensor is developed and applied to measuring shaft torque. The design is based on the setup of two-point magnetic plucking allowing the torque-induced phase angles between two pairs of magnets. The result shows the realization of broadband energy harvesting due to inducing mixed resonant modes of vibration from frequency up-conversion and enhancement by SECE. In addition, torque sensing is achieved by measuring the variation of modal amplitude of voltage response against the phase shift angles. For the case of torque sensing operated at the second resonant mode, the phase angle against the voltage is multi-valued. A solution for the unique sensing is to develop a CNN (convolution neural network) classifier capable of distinguishing various voltage waveforms from different phase angles. The prediction agrees reasonably with experiment.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"1 1","pages":"124830S - 124830S-10"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88675506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of wind-induced vibration control and energy harvesting of traffic signal structures","authors":"Feng Qian, Ihab Ragai, Xiaochun Nie, Yabin Liao","doi":"10.1117/12.2658532","DOIUrl":"https://doi.org/10.1117/12.2658532","url":null,"abstract":"Traffic signal support structures are slender, highly flexible, and lightly damped. Therefore, they are particularly susceptible to wind-induced vibrations, which result in repeated load stresses and fatigue failures. A tuned energy harvesting inerter damper (TEHID)is proposed to reduce wind-induced vibrations of traffic signal support structures and convert the wasted vibration energy into electricity. The TEHID creates a large inertia mass by converting the low-frequency vibration motion of the light head to a high-speed rotation thereby eliminating the need for a large physical mass and accommodation space required by the conventional tuned mass damper (TMD). This paper focuses on the nonlinear dynamics modeling of the wind-induced vibration control and energy harvesting system for traffic signal support structures. The traffic signal structure is modeled as an L-shaped beam with multi-segments and the TEHID is simplified as a three-element device consisting of a spring, a damper, and an inerter. The nonlinear equations and the boundary conditions governing the motion of the integrated vibration control and energy harvesting system are derived from the energy method and presented herein. Modal analysis is conducted and the derived natural frequencies and mode shapes are compared with the finite element simulation results to validate the analytical model.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"19 1","pages":"1248310 - 1248310-12"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90262870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation of double impact 2DOF triboelectric energy harvesting for extended bandwidth","authors":"M. Hassan, Alwathiqbellah Ibrahim","doi":"10.1117/12.2658526","DOIUrl":"https://doi.org/10.1117/12.2658526","url":null,"abstract":"Increasing the bandwidth of the vibration energy harvesters is one of the research emphases to maximize the energy harvested from the ambient. Here we design a Two-Degree of Freedom Vibro-impact Triboelectric energy harvester with a double-impact configuration, which combines multi-modality and piecewise linearity to improve the harvesting bandwidth of triboelectric energy harvesters. The harvester structure consists of primary and secondary cantilever beams with two integrated energy harvesters. The two beams are designed to operate at close natural frequencies, and under the effect of the impact, triboelectricity is generated, and the bandwidths of the resonators are combined to create a wide bandwidth. The double impact system is investigated numerically to examine the structure’s dynamic behavior at different excitation levels, separation distance, and surface charge density to extract an optimal parameter for achieving a wide combined bandwidth. The system demonstrates the capability of connecting multi-modality and piecewise linearity to significantly broaden the triboelectric energy harvester’s bandwidth.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"33 1","pages":"124830Y - 124830Y-14"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90478198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an understanding of piezoresistivity in filled polymer composites","authors":"L. Ritchie, S. Rosset, I. Anderson","doi":"10.1117/12.2658164","DOIUrl":"https://doi.org/10.1117/12.2658164","url":null,"abstract":"Filled polymer composites are capable of combining the favourable mechanical properties of polymers with desirable electrical properties of filler particles. Carbon-black elastomer nanocomposites are capable of conducting electricity while maintaining high stretchability. Despite these materials having been studied and utilised for a number of decades, the relationship between their internal structure and their macroscopic properties is still not fully understood. A major feature of their behaviour is significant piezoresistivity, which can be a nuisance in certain applications and a benefit in others. It is known that there is a relationship between the piezoresistivity of the material and the percolation threshold. However, the exact mechanisms underlying this behaviour are not rigorously understood. This work utilises Monte Carlo modelling to propose and examine ways in which the structure of the internal nanoparticle network, and the evolution of said network with strain could help to explain the piezoresistivity of these materials. Hopefully, a more detailed understanding of this mechanism will lead to an improved capability to customise it for various applications.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"10 1","pages":"124820G - 124820G-10"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82103281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental realization of physical reservoir computing-based mechano-intelligence in self-adaptive phononic metastructures","authors":"Yuning Zhang, Aditya Deshmukh, Kon-Well Wang","doi":"10.1117/12.2657277","DOIUrl":"https://doi.org/10.1117/12.2657277","url":null,"abstract":"This research experimentally investigates the integration of mechano-intelligence into mechanical metastructures for self-adaptive wave control. We created a phononic metastructure prototype utilizing periodic buckled beam modules that has highly adjustable wave propagation characteristics via length reconfiguration using a linear displacement actuator. By utilizing the physical reservoir computing framework, we show that the proposed metastructure can recognize and self-adapt to different inputs by making decisions on appropriate actuations to reconfigure itself to achieve an intelligent wave blocking task. Overall, this research provided a promising approach for constructing and integrating functional mechano-intelligence in structures harnessing physical computing and learning, and created a new direction for the next generation of adaptive structures and material systems.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"31 1","pages":"124830B - 124830B-6"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84397351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging machine learning for arrays of soft sensors","authors":"S. Rosset, Samuel Belk, M. H. Mahmoudinezhad, Iain A. Anderson","doi":"10.1117/12.2661433","DOIUrl":"https://doi.org/10.1117/12.2661433","url":null,"abstract":"Sensor arrays are ubiquitous. They capture images in digital cameras, record the swipes of our fingers on the screens of our phones and tablets, or map pressure distribution over an area. Soft capacitive sensors have long been proposed to make electronic pressure-sensing skins. However, although different designs of entirely soft capacitive sensors have been proposed, large arrays of those sensors are challenging to produce. Indeed, arrays require high-resolution patterning of electrodes, and routing of long and thin electrical connections. These two tasks remain difficult or costly for the high-resistivity compliant electrodes of dielectric elastomer sensors. Instead of relying on the complex patterning of arrays to provide location resolution, we propose to use a plain, unstructured sensor with a single pair of electrodes but rely on computing power to infer pressure location and amplitude from clever sensing signals. Here, we propose a new machine-learning-based approach, which enables us to identify pressure location on a continuous 1D sensor split into 5 sensing zones with an accuracy greater than 98 %. We also demonstrate that we can identify pressure location and qualitative pressure magnitude (soft, medium, hard) on a 3-zone sensor with 99% accuracy.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"75 1","pages":"1248208 - 1248208-10"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83835665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurements of the second-order elastic constants of powder-bed fusion 17-4 PH AM steel using ultrasound","authors":"Shuai Ju, Haifeng Zhang","doi":"10.1117/12.2656960","DOIUrl":"https://doi.org/10.1117/12.2656960","url":null,"abstract":"Additive manufacturing (AM) of metals has drawn attention by both researchers and industries in recent decades for replacing aspects of conventional steel fabrication due to lessened cost and higher production flexibility. However, the characterization of the properties of AM steel remains scarce. This paper presents the ultrasonic determination of the second-order elastic constants of 17-4 PH steel produced using powder bed fusion AM. Both longitudinal and shear wave phase velocities are used to solve for the elastic constants. The present values of second-order elastic constants are compared with traditionally manufactured 17-4 PH. Porosity effects on second-order elastic constants of additive-manufactured 17-4 PH steel are also analyzed.","PeriodicalId":89272,"journal":{"name":"Smart structures and materials. Nondestructive evaluation for health monitoring and diagnostics","volume":"15 1","pages":"124831A - 124831A-20"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85708581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}