- 标题
- 摘要
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- 实验方案
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Light- and humidity-driven actuators with programmable complex shape-deformations
摘要： Programmable complex shape-deformation, multi-responsive property and convenient fabrication are all crucial for the development of actuators. However, a simultaneous realization of all these advantages has not been reported. Here, we report a new type of light- and humidity-driven actuator with programmable complex shape-deformations. The fabrication employs a laser printing technology. The light-driven actuation is based on a dual-mode actuation mechanism which utilizes the water adsorption/desorption properties. When irradiated by near infrared light, the actuator shows a large bending actuation with a curvature up to 2.1 cm-1. More importantly, programmable complex shape-deformations can be realized by printing patterns with different/gradient grayscale distributions on the actuators. The bending rates and amplitudes of actuators can be programmed and controlled. Complex 3D shapes, such as an anomalous tube and a helical cylinder, are obtained. In addition, the actuator can also perform a curvature of 1.3 cm-1 when driven by humidity. Finally, a series of smart biomimetic devices with programmable complex shape-deformations are demonstrated, including a self-adjustment iris responding to incident light, a biomimetic hand demonstrating a complex “OK” gesture, and a lotus with folding petals of different bending rates. This new-type actuator will have great potential in robotics and biomimetic applications.
关键词： programmable,shape-deformation,biomimetic,actuator
更新于2025-09-23 15:21:01
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pH and Thermo Dual-Responsive Fluorescent Hydrogel Actuator
摘要： As one of the most important smart materials, fluorescent hydrogel actuators can produce both color and shape changes under external stimuli. In the present work, an effective approach to develop a novel fluorescent hydrogel actuator with pH and thermo dual responsiveness is proposed. Through incorporating pH-responsive perylene tetracarboxylic acid (PTCA), which is a typical fluorescent moiety with aggregation-caused quenching (ACQ) effect, into an anisotropic poly(N-isopropylacrylamide)–polyacrylamide (PNIPAm-PAAm) structure, the obtained hydrogel exhibits stable thermoresponsive shape deformation and switchable fluorescence performance upon a pH trigger. Therefore, fluorescence-quenching-based and actuation-based information can be revealed when exposed to UV light and immersed into warm water, respectively. Moreover, the thermoresponsive actuating behavior can be applied to further hide the fluorescence-quenching-based images. The present work may provide new insights into the design and preparation of novel stimuli-responsive hydrogel actuators.
关键词： anisotropic structures,hydrogel actuators,fluorescence quenching,shape deformation
更新于2025-09-23 15:21:01
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Elliptical fibre dielectric waveguides: a transverse transmission line analysis
摘要： Non-rigid structure-from-motion (NRSfM) is the process of recovering time-varying 3D structures and poses of a deformable object from an uncalibrated monocular video sequence. Currently, most NRSfM algorithms utilize a non-degenerate assumption for non-rigid object deformations whereby the 3D structures of a non-rigid object can be assumed to be a linear combination of basis shapes with full rank three. Unfortunately, this assumption will produce extra degrees-of-freedom when the non-rigid object has some degenerate deformations with shape bases of rank less than three. These extra degrees-of-freedom will yield spurious shape deformations due to non-negligible noise in real applications, which will cause substantial reconstruction errors. To solve this problem, we propose a low-rank shape deformation model to represent 3D structures of degenerate deformations. When modeling degenerate deformations, the proposed model exploits the rank-deficient nature of degenerate deformations in addition to the low-rank property of non-rigid objects’ trajectories, thus providing a more accurate and compact representation compared with existing models. Based on this model, we formulate the NRSfM problem as two coherent optimization problems. These problems are solved with iterative non-linear optimization algorithms. Experiments on synthetic and motion capture data are conducted. The results exhibit the significant advantages of our approach over state-of-the-art NRSfM algorithms for the 3D recovery of non-rigid objects with degenerate deformations.
关键词： 3D reconstruction.,Degenerate deformations,non-rigid structure from motion,low-rank shape deformation model
更新于2025-09-23 15:19:57
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Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning
摘要： Non-rigid structure-from-motion (NRSfM) is the process of recovering time-varying 3D structures and poses of a deformable object from an uncalibrated monocular video sequence. Currently, most NRSfM algorithms utilize a non-degenerate assumption for non-rigid object deformations whereby the 3D structures of a non-rigid object can be assumed to be a linear combination of basis shapes with full rank three. Unfortunately, this assumption will produce extra degrees-of-freedom when the non-rigid object has some degenerate deformations with shape bases of rank less than three. These extra degrees-of-freedom will yield spurious shape deformations due to non-negligible noise in real applications, which will cause substantial reconstruction errors. To solve this problem, we propose a low-rank shape deformation model to represent 3D structures of degenerate deformations. When modeling degenerate deformations, the proposed model exploits the rank-deficient nature of degenerate deformations in addition to the low-rank property of non-rigid objects’ trajectories, thus providing a more accurate and compact representation compared with existing models. Based on this model, we formulate the NRSfM problem as two coherent optimization problems. These problems are solved with iterative non-linear optimization algorithms. Experiments on synthetic and motion capture data are conducted. The results exhibit the significant advantages of our approach over state-of-the-art NRSfM algorithms for the 3D recovery of non-rigid objects with degenerate deformations.
关键词： Degenerate deformations,non-rigid structure from motion,3D reconstruction,low-rank shape deformation model
更新于2025-09-23 15:19:57
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[IEEE 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - Berlin, Germany (2019.7.23-2019.7.27)] 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Evaluation of the influence of cyclic loading on a laser sintered transtibial prosthetic socket using Digital Image Correlation (DIC)
摘要： Non-rigid structure-from-motion (NRSfM) is the process of recovering time-varying 3D structures and poses of a deformable object from an uncalibrated monocular video sequence. Currently, most NRSfM algorithms utilize a non-degenerate assumption for non-rigid object deformations whereby the 3D structures of a non-rigid object can be assumed to be a linear combination of basis shapes with full rank three. Unfortunately, this assumption will produce extra degrees-of-freedom when the non-rigid object has some degenerate deformations with shape bases of rank less than three. These extra degrees-of-freedom will yield spurious shape deformations due to non-negligible noise in real applications, which will cause substantial reconstruction errors. To solve this problem, we propose a low-rank shape deformation model to represent 3D structures of degenerate deformations. When modeling degenerate deformations, the proposed model exploits the rank-deficient nature of degenerate deformations in addition to the low-rank property of non-rigid objects’ trajectories, thus providing a more accurate and compact representation compared with existing models. Based on this model, we formulate the NRSfM problem as two coherent optimization problems. These problems are solved with iterative non-linear optimization algorithms. Experiments on synthetic and motion capture data are conducted. The results exhibit the significant advantages of our approach over state-of-the-art NRSfM algorithms for the 3D recovery of non-rigid objects with degenerate deformations.
关键词： Degenerate deformations,non-rigid structure from motion,3D reconstruction,low-rank shape deformation model
更新于2025-09-16 10:30:52
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[ASME ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Quebec City, Quebec, Canada (Sunday 26 August 2018)] Volume 1B: 38th Computers and Information in Engineering Conference - Predicting Manufactured Shapes of a Projection Micro-Stereolithography Process via Convolutional Encoder-Decoder Networks
摘要： Projection micro-stereolithography (P-μSLA) processes have been widely utilized in three-dimensional (3D) digital fabrication. However, various uncertainties of a photopolymerization process often deteriorates the geometric accuracy of fabrication results. A predictive model that maps input shapes to actual outcomes in real-time would be immensely beneficial for designers and process engineers, permitting rapid design exploration through inexpensive trials-and-errors, such that optimal design parameters as well as optimal shape modification plan could be identified with only minimal waste of time, material, and labor. However, no computational model has ever succeeded in predicting such geometric inaccuracies to a reasonable precision. In this regard, we propose a novel idea of predicting output shapes from input projection patterns of a P-μSLA process via deep neural networks. To this end, a convolutional encoder-decoder network is proposed in this paper. The network takes a projection image as the input and returns a predicted shape after fabrication as the output. Cross-validation analyses showed the root-mean-square-error (RMSE) of 10.72 μm in average, indicating noticeable performance of the proposed convolutional encoder-decoder network.
关键词： P-μSLA,convolutional encoder-decoder network,Projection micro-stereolithography,shape deformation prediction,deep neural networks
更新于2025-09-10 09:29:36