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11 results for “Soft sensors”
Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots
<p>Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Scale Soft Sensor for steel semi-products [CSS4]
<p> </p> <p>During production of steel bars in hot rolling mills there is a formation of scale on the surface of the products. It is differentiated between two scale types: primary scale and secondary scale. During the reheating of the product in the furnace the scale is called primary scale. After reheating and before rolling the scale is removed by a descaler, for instance with high water pressure. After rolling, while the product is located on the cooling bed, the secondary scale grows up.</p>
Dataset for paper entitled "Development of Fully Shielded Soft Inductive Tactile Sensors"
<p>This dataset includes all the experimental and FE results presented in the IEEE ICECS 2019 paper "Development of Fully Shielded Soft Inductive Tactile Sensors" (DOI: 10.1109/ICECS46596.2019.8964922).<br> URL of IEEE Xplore:<br> https://ieeexplore.ieee.org/abstract/document/8964922</p> <p>List of data in this dataset:<br> Fig-2-FE modeling-FS-SITS.xlsx<br> Fig-4-Exp_characterization-FS-SITS.xlsx<br> Fig-5-Exp_Demo-FS-SITS.xlsx</p> <p>All the data included in this dataset were collected by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
A Sensorized Soft Pneumatic Actuator Fabricated with Extrusion-Based Additive Manufacturing
<p>Soft pneumatic actuators with a channel network (pneu-net) based on thermoplastic elastomers are compatible with fused deposition modeling (FDM). However, conventional filament-based fused deposition modeling (FDM) printers are not well suited for thermoplastic elastomers with a shore hardness (Sh < 70A). Therefore, in this study, a pellet-based FDM printer was used to print pneumatic actuators with a shore hardness of Sh18A. Additionally, the method allowed the in situ integration of soft piezoresistive sensing elements during the fabrication. The integrated piezoresistive elements were based on conductive composites made of three different styrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers, each with a carbon black (CB) filler with a ratio of 1:1. The best sensor behavior was achieved by the SEBS material with a shore hardness of Sh50A. The dynamic and quasi-static sensor behavior were investigated on SEBS strips with integrated piezoresistive sensor composite material, and the results were compared with TPU strips from a previous study. Finally, the piezoresistive composite was used for the FDM printing of soft pneumatic actuators with a shore hardness of 18 A. It is worth mentioning that 3 h were needed for the fabrication of the soft pneumatic actuator with an integrated strain sensing element. In comparison to classical mold casting method, this is faster, since curing post-processing is not required and will help the industrialization of pneumatic actuator-based soft robotics</p>
Case study of a rapid prototyping method for optimizing soft gripper structures with integrated piezoresistive sensors
<p>Closed-loop control systems and monitoring the activities of soft robots in the natural environment require sensing elements in soft actuator modules. In this study, additive manufacturing is used for sensorized soft actuator modules to investigate the influence of the Shore hardness and design aspects of an open-source tendon-based gripper structure, in a time-efficient way. Additionally, the placement of the piezoresistive sensing element (tension or compression side on the bending soft gripper) was investigated. A user-friendly method, based on thermoplastic material extrusion, has been explored to improve the future design optimization in of active soft robotic structures successfully. A higher Shore hardness resulted in a higher total deflection and a higher force to bend the gripper structure. By increasing the geometrical stiffness of the gripper printed with low Shore hardness, the total deflection was increased, but the force needed to activate the movement was higher in comparison to high Shore hardness and low geometrical stiffness. Moreover, the sensing element on the substrate of higher Shore hardness, leads to low drift, monotonic response, with good sensitivity, independent of the sampling rate. The gripper of higher Shore hardness had a larger functional range, being capable of gripping small and larger objects.</p>
A dataset of throat-related events collected with a soft skin-attachable vibration sensor
<p>This dataset is collected using a skin-attachable sensor placed on the laryngeal prominence. The sensor measures various throat-related events, including coughing, speaking, swallowing, and throat clearing. The dataset is divided into a training set and a test set, each containing different types and quantities of events.</p> <p> </p> <p><strong>Training set</strong></p> <p>The training set comprises the following throat-related events:</p> <p>1. Coughing: 1,984 instances<br>2. Speaking (English): 2,107 instances<br>3. Swallowing: 2,028 instances<br>4. Throat Clearing: 2,089 instances</p> <p> </p> <p><strong>Test set</strong></p> <p>The test set includes a diverse range of events to evaluate the model's performance:</p> <p>1. Coughing: 150 instances<br>2. Speaking (multiple languages): 333 instances (comprising English, French, German, Korean, and Spanish)<br>3. Swallowing: 161 instances<br>4. Throat Clearing: 148 instances</p> <p> </p> <p>This dataset offers labeled throat-related events for training and testing machine learning models.</p>
Conventional vs. Sensor Guided Soft Tissue Balancing in TKA RCT
ClinicalTrials.gov study NCT02976428. IPD Sharing: NO. Countries: 1. Publications: 2.
Supporting information of the article: Stretchable reflective coating for soft optical waveguides and sensors
Open the record for dataset details and reuse information.
Dataset Soft sensor development for cyclist power prediction using Deep Learning
Open the record for dataset details and reuse information.
Supplementary Materials for Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks
<p>Videos accompanying the paper Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.