Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies最新文献
Heat is often lost unused in industry, commerce and households and is considered a waste product — while there is a lot of energy potential in waste heat. As part of the project “Theasmart”, scientists and companies are exploring just these potentials to find out how the waste product heat can be used for further purposes through the use of an innovative smart materials technology. The goal of the project is the further qualification of shape memory alloys with special focus on thin hysteresis applications for energy harvesting. In certain applications, these metals can be used as a thermal drive, for example for thermal valves or as thermal air flow regulators. Energy efficiency in processes in industrial companies or households could be improved by their use of waste heat. By 2020, the development of thermally driven generators, so-called “energy harvesters”, and the identification of other areas of application is planned. This publication focuses on first steps towards a process tool which can be actuated by waste heat of a thermal annealing sub-process directly, or used as mechanical energy charging device combined with a releasing mechanism.
{"title":"Energy Harvesting in Discontinuous Processes by Shape Memory Alloys","authors":"A. Czechowicz, S. Langbein","doi":"10.1115/SMASIS2018-8162","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8162","url":null,"abstract":"Heat is often lost unused in industry, commerce and households and is considered a waste product — while there is a lot of energy potential in waste heat. As part of the project “Theasmart”, scientists and companies are exploring just these potentials to find out how the waste product heat can be used for further purposes through the use of an innovative smart materials technology. The goal of the project is the further qualification of shape memory alloys with special focus on thin hysteresis applications for energy harvesting. In certain applications, these metals can be used as a thermal drive, for example for thermal valves or as thermal air flow regulators. Energy efficiency in processes in industrial companies or households could be improved by their use of waste heat. By 2020, the development of thermally driven generators, so-called “energy harvesters”, and the identification of other areas of application is planned. This publication focuses on first steps towards a process tool which can be actuated by waste heat of a thermal annealing sub-process directly, or used as mechanical energy charging device combined with a releasing mechanism.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129840888","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}
F. Phillips, Daniel Martin, D. Lagoudas, R. Wheeler
Shape memory alloys (SMAs) are unique materials capable of undergoing a thermo-mechanically induced, reversible, crystallographic phase transformation. As SMAs are utilized across a variety of applications, it is necessary to understand the internal changes that occur throughout the lifetime of SMA components. One of the key limitations to the lifetime of a SMA component is the response of SMAs to fatigue. SMAs are subject to two kinds of fatigue, namely structural fatigue due to cyclic mechanical loading which is similar to high cycle fatigue, and functional fatigue due to cyclic phase transformation which typical is limited to the low cycle fatigue regime. In cases where functional fatigue is due to thermally induced phase transformation in contrast to being mechanically induced, this form of fatigue can be further defined as actuation fatigue. Utilizing X-ray computed microtomography, it is shown that during actuation fatigue, internal damage such as cracks or voids, evolves in a non-linear manner. A function is generated to capture this non-linear internal damage evolution and introduced into a SMA constitutive model. Finally, it is shown how the modified SMA constitutive model responds and the ability of the model to predict actuation fatigue lifetime is demonstrated.
{"title":"Modeling of Internal Damage Evolution During Actuation Fatigue in Shape Memory Alloys","authors":"F. Phillips, Daniel Martin, D. Lagoudas, R. Wheeler","doi":"10.1115/SMASIS2018-8068","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8068","url":null,"abstract":"Shape memory alloys (SMAs) are unique materials capable of undergoing a thermo-mechanically induced, reversible, crystallographic phase transformation. As SMAs are utilized across a variety of applications, it is necessary to understand the internal changes that occur throughout the lifetime of SMA components. One of the key limitations to the lifetime of a SMA component is the response of SMAs to fatigue. SMAs are subject to two kinds of fatigue, namely structural fatigue due to cyclic mechanical loading which is similar to high cycle fatigue, and functional fatigue due to cyclic phase transformation which typical is limited to the low cycle fatigue regime. In cases where functional fatigue is due to thermally induced phase transformation in contrast to being mechanically induced, this form of fatigue can be further defined as actuation fatigue. Utilizing X-ray computed microtomography, it is shown that during actuation fatigue, internal damage such as cracks or voids, evolves in a non-linear manner. A function is generated to capture this non-linear internal damage evolution and introduced into a SMA constitutive model. Finally, it is shown how the modified SMA constitutive model responds and the ability of the model to predict actuation fatigue lifetime is demonstrated.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122898453","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}
An exact solution that investigates the pre-buckling characteristics of nonlocal carbon nanotube (CNT)-based mass sensor subjected to thermal load under clamped-clamped boundary condition is determined. The uniform temperature rise is utilized to study thermal effects on the sensitivity of the mechanical resonator in pre-buckling configuration. Using Eringen’s nonlocal theory, along with the Hamilton’s principle, the governing equations considering small scale and geometric nonlinearity are derived. The influences of important parameters including nonlocal parameter, temperature change, length, and diameter of the CNT on the pre-buckling behavior and frequency shift of the CNT-based mass detector are also studied. Results show that these parameters have significant impact on the dynamic characteristics of the CNT-mass sensor.
{"title":"Nonlocal Modeling and Behavior of Carbon Nanotube-Based Sensors in Thermal Environment","authors":"S. Ghaffari, S. Ceballes, A. Abdelkefi","doi":"10.1115/SMASIS2018-7987","DOIUrl":"https://doi.org/10.1115/SMASIS2018-7987","url":null,"abstract":"An exact solution that investigates the pre-buckling characteristics of nonlocal carbon nanotube (CNT)-based mass sensor subjected to thermal load under clamped-clamped boundary condition is determined. The uniform temperature rise is utilized to study thermal effects on the sensitivity of the mechanical resonator in pre-buckling configuration. Using Eringen’s nonlocal theory, along with the Hamilton’s principle, the governing equations considering small scale and geometric nonlinearity are derived. The influences of important parameters including nonlocal parameter, temperature change, length, and diameter of the CNT on the pre-buckling behavior and frequency shift of the CNT-based mass detector are also studied. Results show that these parameters have significant impact on the dynamic characteristics of the CNT-mass sensor.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122956525","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}
Fused deposition modeling (FDM) is highly commercialized Rapid Prototyping (RP) technology for its ability to build complex parts with low cost in a short period of time. The process parameters in the FDM play a vital role in the mechanical properties of the polymeric parts. Most of the research studies show that the variable parameters such as orientation, layer thickness, raster angle, raster width, and air gap are some of the key parameters that affect the mechanical properties of FDM-processed polymeric parts. However, no reports have been made regarding the influence of nozzle diameter with raster width on the tensile properties of FDM fabricated polymeric parts. This work was devoted to achieving improved and isotropic mechanical properties in polycarbonate (PC) and PC/carbon nanotube (PC/CNT) nanocomposites by investigating the effect of printing parameters in FDM process. The nozzle diameter to raster width ratio, α was found to significantly affect the mechanical properties. The printing direction dependency in tensile properties were studied with the ratio α < 1 and α≥ 1 at three different raster angles of 0°, 45°/−45° and 90°. For α < 1, Ultimate tensile strength and modulus of elasticity were higher for 0°, compared to 45°/−45° and 90° raster angles. However, for α ≥ 1, the ultimate tensile strength and the modulus of elasticity showed little dependency to print direction. This certainly determines the decrease in anisotropy at higher values of α. Mesostructure characterization with microscopy and image analysis were used to further explain the printing behavior and the resultant properties of the printed samples.
{"title":"Tensile Properties of 3D-Printed Polycarbonate/Carbon Nanotube Nanocomposites","authors":"K. Kalia, A. Ameli","doi":"10.1115/SMASIS2018-8048","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8048","url":null,"abstract":"Fused deposition modeling (FDM) is highly commercialized Rapid Prototyping (RP) technology for its ability to build complex parts with low cost in a short period of time. The process parameters in the FDM play a vital role in the mechanical properties of the polymeric parts. Most of the research studies show that the variable parameters such as orientation, layer thickness, raster angle, raster width, and air gap are some of the key parameters that affect the mechanical properties of FDM-processed polymeric parts. However, no reports have been made regarding the influence of nozzle diameter with raster width on the tensile properties of FDM fabricated polymeric parts.\u0000 This work was devoted to achieving improved and isotropic mechanical properties in polycarbonate (PC) and PC/carbon nanotube (PC/CNT) nanocomposites by investigating the effect of printing parameters in FDM process. The nozzle diameter to raster width ratio, α was found to significantly affect the mechanical properties. The printing direction dependency in tensile properties were studied with the ratio α < 1 and α≥ 1 at three different raster angles of 0°, 45°/−45° and 90°. For α < 1, Ultimate tensile strength and modulus of elasticity were higher for 0°, compared to 45°/−45° and 90° raster angles. However, for α ≥ 1, the ultimate tensile strength and the modulus of elasticity showed little dependency to print direction. This certainly determines the decrease in anisotropy at higher values of α. Mesostructure characterization with microscopy and image analysis were used to further explain the printing behavior and the resultant properties of the printed samples.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127913990","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}
Gabriella M. Shull, J. Hu, Justin Buschnyj, Henry Koon, J. Abel, S. Kodandaramaiah
The ability to sense neural activity using electrodes has allowed scientists to use this information to temporarily restore movement in paralyzed individuals using brain-computer interfaces (BCI). However, current electrodes do not provide chronic recording of the brain due to the inflammatory response of the immune system caused by the large (∼ 20–80 μm) size of the shanks, and the mechanical mismatch of the shanks relative to the brain. Electrode designs are evolving to use small (< 15 μm) flexible neural probes to minimize inflammatory responses and enable chronic use. However, their flexibility limits the scalability — it is challenging to assemble 3D arrays of such electrodes, to insert the arrays of flexible neural probes into the brain without buckling, and to uniformly distribute them into large areas of the brain. Thus, we created Shape Memory Alloy (SMA) actuated Woven Neural Probes (WNPs). A linear array of 32 flexible insulated microwires were interwoven with SMA wires resulting in an ordered array of parallel electrodes. SMA WNPs were shaped to an initial constricted profile for reliable insertion into a tissue phantom. Following insertion, the SMA wires were used as actuators to unravel the constricted WNP to distribute electrodes across large volumes. We demonstrated that the WNPs could be inserted into the brain without buckling and record neural activity. In separate experiments, we showed that the SMA could mechanically distribute the WNPs via thermally induced actuation. This work thus highlights the potential of actuatable WNPs to be used as a platform for neural recording.
{"title":"Shape Memory Alloy Actuatable Woven Neural Probes","authors":"Gabriella M. Shull, J. Hu, Justin Buschnyj, Henry Koon, J. Abel, S. Kodandaramaiah","doi":"10.1115/SMASIS2018-8169","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8169","url":null,"abstract":"The ability to sense neural activity using electrodes has allowed scientists to use this information to temporarily restore movement in paralyzed individuals using brain-computer interfaces (BCI). However, current electrodes do not provide chronic recording of the brain due to the inflammatory response of the immune system caused by the large (∼ 20–80 μm) size of the shanks, and the mechanical mismatch of the shanks relative to the brain. Electrode designs are evolving to use small (< 15 μm) flexible neural probes to minimize inflammatory responses and enable chronic use. However, their flexibility limits the scalability — it is challenging to assemble 3D arrays of such electrodes, to insert the arrays of flexible neural probes into the brain without buckling, and to uniformly distribute them into large areas of the brain. Thus, we created Shape Memory Alloy (SMA) actuated Woven Neural Probes (WNPs). A linear array of 32 flexible insulated microwires were interwoven with SMA wires resulting in an ordered array of parallel electrodes. SMA WNPs were shaped to an initial constricted profile for reliable insertion into a tissue phantom. Following insertion, the SMA wires were used as actuators to unravel the constricted WNP to distribute electrodes across large volumes. We demonstrated that the WNPs could be inserted into the brain without buckling and record neural activity. In separate experiments, we showed that the SMA could mechanically distribute the WNPs via thermally induced actuation. This work thus highlights the potential of actuatable WNPs to be used as a platform for neural recording.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129218340","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}
Mustafa A. Ayad, Robert A. Nawrocki, R. Voyles, Junseok Lee, Hyowon Lee, Daniel Leon-Salas
Robotic Materials are materials that have sensing, computation and, possibly actuation, distributed throughout the bulk of the material. In such a material, we envision semiconducting polymer based sensing, actuation, and information processing for on-board decision making to be designed, in tandem, with the smart product that will be implemented with the smart material. Prior work in printing polymer semiconductors for sensing and cognition have focused on highly energetic inkjet printing. Alternatively, we are developing liquid polymer extrusion processes to work hand-in-hand with existing solid polymer extrusion processes (such as Fused Deposition Manufacturing - FDM) to simultaneously deposit sensing, computation, actuation and structure. We demonstrate the successful extrusion printing of conductors and capacitors to impedance-match a new, higher-performance organic transistor design that solves the cascading problem of the device previously reported and is more amenable to liquid extrusion printing. Consequently, these printed devices are integrated into a sheet material that is folded into a 3-D, six-legged walking machine with attached electric motor.
{"title":"NUCLEOs: Toward Rapid-Prototyping of Robotic Materials That Can Sense, Think and Act","authors":"Mustafa A. Ayad, Robert A. Nawrocki, R. Voyles, Junseok Lee, Hyowon Lee, Daniel Leon-Salas","doi":"10.1115/SMASIS2018-8245","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8245","url":null,"abstract":"Robotic Materials are materials that have sensing, computation and, possibly actuation, distributed throughout the bulk of the material. In such a material, we envision semiconducting polymer based sensing, actuation, and information processing for on-board decision making to be designed, in tandem, with the smart product that will be implemented with the smart material. Prior work in printing polymer semiconductors for sensing and cognition have focused on highly energetic inkjet printing. Alternatively, we are developing liquid polymer extrusion processes to work hand-in-hand with existing solid polymer extrusion processes (such as Fused Deposition Manufacturing - FDM) to simultaneously deposit sensing, computation, actuation and structure. We demonstrate the successful extrusion printing of conductors and capacitors to impedance-match a new, higher-performance organic transistor design that solves the cascading problem of the device previously reported and is more amenable to liquid extrusion printing. Consequently, these printed devices are integrated into a sheet material that is folded into a 3-D, six-legged walking machine with attached electric motor.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128409251","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}
Xuefeng Zhao, Shengyuan Li, Hongguo Su, Lei Zhou, K. Loh
Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.
{"title":"Image-Based Comprehensive Maintenance and Inspection Method for Bridges Using Deep Learning","authors":"Xuefeng Zhao, Shengyuan Li, Hongguo Su, Lei Zhou, K. Loh","doi":"10.1115/SMASIS2018-8268","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8268","url":null,"abstract":"Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128762741","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}
In this paper, we report a low-frequency and wide-bandwidth piezoelectric energy transducer. The transducer is designed based on a piezoelectric plate, a truss mechanism, a spring-mass system and a stopper. The spring-mass system receives kinetic energy from excitation and induces forces, which are further transmitted, amplified by the truss mechanism and applied onto the piezoelectric plate. The stopper is added to truncate the amplitude of the mass. The mass and the stopper interact through impacts. The impact force triggers dynamic bifurcation in the transducer. By taking advantage of the superharmonic resonances and nonlinearity born from the bifurcation, the transducer is able to work efficiently with a wide bandwidth. Through experiment studies on a fabricated prototype, the lowest resonant frequency is around 3.2Hz with the peak-peak voltage output up to 55V. The bandwidth of the transducer is approximately 4.5Hz out of our targeted frequency domain [2.5Hz, 10Hz], broadened by up to 20 times compared to that of the linear system without the stopper.
{"title":"Experimental Studies on a Compressive-Mode Piezoelectric Transducer for Low-Frequency and Wide-Band Energy Harvesting","authors":"Zhongjie Li, H. Naguib","doi":"10.1115/SMASIS2018-8197","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8197","url":null,"abstract":"In this paper, we report a low-frequency and wide-bandwidth piezoelectric energy transducer. The transducer is designed based on a piezoelectric plate, a truss mechanism, a spring-mass system and a stopper. The spring-mass system receives kinetic energy from excitation and induces forces, which are further transmitted, amplified by the truss mechanism and applied onto the piezoelectric plate. The stopper is added to truncate the amplitude of the mass. The mass and the stopper interact through impacts. The impact force triggers dynamic bifurcation in the transducer. By taking advantage of the superharmonic resonances and nonlinearity born from the bifurcation, the transducer is able to work efficiently with a wide bandwidth. Through experiment studies on a fabricated prototype, the lowest resonant frequency is around 3.2Hz with the peak-peak voltage output up to 55V. The bandwidth of the transducer is approximately 4.5Hz out of our targeted frequency domain [2.5Hz, 10Hz], broadened by up to 20 times compared to that of the linear system without the stopper.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116921669","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}
Bone is a highly adaptive biological structure. Following Wolff’s law, bone realigns and grows to adapt to its mechanical environment. This leads to structural heterogeneity of trabecular bone and orthotropic symmetry of the elastic properties. Determining the bone alignment and material properties for living patients is difficult and involves implantation of force and displacement sensors on the bone to determine the compliance and stiffness properties. Micro-computed tomography along with finite element modeling have been limited to the vertebrae of donor cadavers to evaluate trabecular architecture, material properties, and density. Here, an adaptive structure topology optimization algorithm is presented and used to predict trabecular architecture. The algorithm predicts the optimal structure by minimizing the global compliance. The lumbar 1 (L1) vertebra is used as an example. Loads common to L1 vertebrae are applied and bone volume fraction measurements that can be taken easily from living patients through bone mineral density scans are used as the only inputs. The mathematical model is an adaptation of “99 Line Topology Optimization Code Written in Matlab” developed by Sigmund (2001). Bone is locally assumed to be isotropic with an elastic modulus of 13 GPa and the Poisson ratio of 0.3 applied to each element. The resulting structural heterogeneity results in global orthotropic relations. The model uses bone volume fraction and the loading orientation as inputs and gives the corresponding ideal bone structure geometry as an output. The trabecular structure can be predicted solely from the results of a bone mineral density scan. Finite element analysis of the optimized structure is then conducted and the global material properties are determined. While this model is for two-dimensional examples representing planes within the vertebral bone, it is extended to three-dimensional modeling to develop the cortical bone geometry and define the total volume. Matlab is then used to run the topology optimization simulation. The ideal structure is defined by optimizing for a prescribed displacement field of the system following the implementation of a gradient descent optimization method. The results are compared to published values from a combined experimental and numerical procedure. The procedure on sectioned vertebrae reported average ratios between elastic moduli of E1/E2 = 5.2, E1/E3 = 8.8, and E2/E3 = 1.4. Results between the models and the previously published data yield similar transversely isotropic symmetry in the elastic moduli of trabecular bone. However, the elastic moduli ratios are not quite in agreement. Improving the accuracy of the boundary conditions and loading of the finite element model may improve the correlation between the optimization models and published data.
骨骼是一种高度适应性的生物结构。根据沃尔夫定律，骨骼会重新排列并生长以适应其机械环境。这导致骨小梁的结构不均一性和弹性特性的正交异性对称性。确定活体患者的骨对齐和材料特性是困难的，并且需要在骨上植入力和位移传感器来确定顺应性和刚度特性。微计算机断层扫描和有限元建模仅限于供体尸体的椎骨，以评估小梁结构、材料特性和密度。本文提出了一种自适应结构拓扑优化算法，并将其用于预测小梁结构。该算法通过最小化全局顺应性来预测最优结构。以腰椎(L1)为例。施加L1椎体的共同载荷，并且通过骨密度扫描可以轻松地从活着的患者身上获得的骨体积分数测量作为唯一输入。该数学模型改编自Sigmund(2001)开发的“99 Line Topology Optimization Code Written in Matlab”。局部假设骨是各向同性的，弹性模量为13gpa，每个元素的泊松比为0.3。由此产生的结构非均质性导致了全局正交各向异性关系。该模型使用骨体积分数和加载方向作为输入，并给出相应的理想骨结构几何形状作为输出。骨小梁结构可以仅从骨密度扫描的结果来预测。然后对优化后的结构进行有限元分析，确定材料的整体性能。虽然该模型用于表示椎骨内平面的二维示例，但它可以扩展到三维建模，以开发皮质骨几何形状并定义总体积。然后使用Matlab运行拓扑优化仿真。采用梯度下降优化方法，对系统规定的位移场进行优化，确定理想结构。结果与已发表的实验和数值结合计算结果进行了比较。经椎体切片后，E1/E2弹性模量的平均比值为5.2,E1/E3 = 8.8, E2/E3 = 1.4。模型和先前发表的数据之间的结果在小梁骨弹性模量方面产生类似的横向各向同性对称性。然而，弹性模量比并不完全一致。提高有限元模型边界条件和载荷的精度可以提高优化模型与已发表数据的相关性。
{"title":"An Adaptive Structure Topology Optimization Approach Applied to Vertebral Bone Architecture","authors":"A. DiCarlo, J. Gallagher","doi":"10.1115/SMASIS2018-8131","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8131","url":null,"abstract":"Bone is a highly adaptive biological structure. Following Wolff’s law, bone realigns and grows to adapt to its mechanical environment. This leads to structural heterogeneity of trabecular bone and orthotropic symmetry of the elastic properties. Determining the bone alignment and material properties for living patients is difficult and involves implantation of force and displacement sensors on the bone to determine the compliance and stiffness properties. Micro-computed tomography along with finite element modeling have been limited to the vertebrae of donor cadavers to evaluate trabecular architecture, material properties, and density. Here, an adaptive structure topology optimization algorithm is presented and used to predict trabecular architecture. The algorithm predicts the optimal structure by minimizing the global compliance. The lumbar 1 (L1) vertebra is used as an example. Loads common to L1 vertebrae are applied and bone volume fraction measurements that can be taken easily from living patients through bone mineral density scans are used as the only inputs. The mathematical model is an adaptation of “99 Line Topology Optimization Code Written in Matlab” developed by Sigmund (2001). Bone is locally assumed to be isotropic with an elastic modulus of 13 GPa and the Poisson ratio of 0.3 applied to each element. The resulting structural heterogeneity results in global orthotropic relations. The model uses bone volume fraction and the loading orientation as inputs and gives the corresponding ideal bone structure geometry as an output. The trabecular structure can be predicted solely from the results of a bone mineral density scan. Finite element analysis of the optimized structure is then conducted and the global material properties are determined. While this model is for two-dimensional examples representing planes within the vertebral bone, it is extended to three-dimensional modeling to develop the cortical bone geometry and define the total volume. Matlab is then used to run the topology optimization simulation. The ideal structure is defined by optimizing for a prescribed displacement field of the system following the implementation of a gradient descent optimization method. The results are compared to published values from a combined experimental and numerical procedure. The procedure on sectioned vertebrae reported average ratios between elastic moduli of E1/E2 = 5.2, E1/E3 = 8.8, and E2/E3 = 1.4. Results between the models and the previously published data yield similar transversely isotropic symmetry in the elastic moduli of trabecular bone. However, the elastic moduli ratios are not quite in agreement. Improving the accuracy of the boundary conditions and loading of the finite element model may improve the correlation between the optimization models and published data.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114150548","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}
M. Todd, W. Gregory, C. Key, Mike Yeager, J.-Y. Ye
In many structural applications the use of composite material systems in both retrofit and new design modes has expanded greatly. The performance benefits from composites such as weight reduction with increased strength, corrosion resistance, and improved thermal and acoustic properties, are balanced by a host of failure modes whose genesis and progression are not yet well understood. As such, structural health monitoring (SHM) plays a key role for in-situ assessment for the purposes of performance/operations optimization, maintenance planning, and overall life cycle cost reduction. In this work, arrays of fiber Bragg grating optical strain sensors are attached to glass-epoxy solid laminate composite specimens that were subsequently subjected to specific levels of fully reversed cyclic loading. The fatigue loading was designed to impose strain levels in the panel that would induce damage to the laminate at varying numbers of cycles. The objectives of this test series were to assess the ability of the fiber Bragg grating sensors to detect fatigue damage (using previously developed SHM algorithms) and to establish a dataset for the development of a prognostic model to be applied to a random magnitude of fully reversed strain loading. The prognostic approach is rooted in the Failure Forecast Method, whereby the periodic feature rate-of-change was regressed against time to arrive at a failure estimate. An uncertainty model for the predictor was built so that a probability density function could be computed around the time-of-failure estimate, from which mean, median, and mode predictors were compared for robustness.
{"title":"Composite Laminate Fatigue Damage Detection and Prognosis Using Embedded Fiber Bragg Gratings","authors":"M. Todd, W. Gregory, C. Key, Mike Yeager, J.-Y. Ye","doi":"10.1115/SMASIS2018-8182","DOIUrl":"https://doi.org/10.1115/SMASIS2018-8182","url":null,"abstract":"In many structural applications the use of composite material systems in both retrofit and new design modes has expanded greatly. The performance benefits from composites such as weight reduction with increased strength, corrosion resistance, and improved thermal and acoustic properties, are balanced by a host of failure modes whose genesis and progression are not yet well understood. As such, structural health monitoring (SHM) plays a key role for in-situ assessment for the purposes of performance/operations optimization, maintenance planning, and overall life cycle cost reduction. In this work, arrays of fiber Bragg grating optical strain sensors are attached to glass-epoxy solid laminate composite specimens that were subsequently subjected to specific levels of fully reversed cyclic loading. The fatigue loading was designed to impose strain levels in the panel that would induce damage to the laminate at varying numbers of cycles. The objectives of this test series were to assess the ability of the fiber Bragg grating sensors to detect fatigue damage (using previously developed SHM algorithms) and to establish a dataset for the development of a prognostic model to be applied to a random magnitude of fully reversed strain loading. The prognostic approach is rooted in the Failure Forecast Method, whereby the periodic feature rate-of-change was regressed against time to arrive at a failure estimate. An uncertainty model for the predictor was built so that a probability density function could be computed around the time-of-failure estimate, from which mean, median, and mode predictors were compared for robustness.","PeriodicalId":117187,"journal":{"name":"Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198512","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}
Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies
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