scholarly journals Improving the Actuation Speed and Multi-Cyclic Actuation Characteristics of Silicone/Ethanol Soft Actuators

Actuators ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 62 ◽  
Author(s):  
Boxi Xia ◽  
Aslan Miriyev ◽  
Cesar Trujillo ◽  
Neil Chen ◽  
Mark Cartolano ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Short Term Memory ◽  
Conductive Filler ◽  
Thermally Driven ◽  
Thermally Conductive ◽  
Ethanol Evaporation ◽  
Long Short Term Memory ◽  
Soft Actuators ◽  
Robotic Applications ◽  
Soft Composite

The actuation of silicone/ethanol soft composite material-actuators is based on the phase change of ethanol upon heating, followed by the expansion of the whole composite, exhibiting high actuation stress and strain. However, the low thermal conductivity of silicone rubber hinders uniform heating throughout the material, creating overheated damaged areas in the silicone matrix and accelerating ethanol evaporation. This limits the actuation speed and the total number of operation cycles of these thermally-driven soft actuators. In this paper, we showed that adding 8 wt.% of diamond nanoparticle-based thermally conductive filler increases the thermal conductivity (from 0.190 W/mK to 0.212 W/mK), actuation speed and amount of operation cycles of silicone/ethanol actuators, while not affecting the mechanical properties. We performed multi-cyclic actuation tests and showed that the faster and longer operation of 8 wt.% filler material-actuators allows collecting enough reliable data for computational methods to model further actuation behavior. We successfully implemented a long short-term memory (LSTM) neural network model to predict the actuation force exerted in a uniform multi-cyclic actuation experiment. This work paves the way for a broader implementation of soft thermally-driven actuators in various robotic applications.

Study on Thermal Conductivity of Polyetheretherketone/Thermally Conductive Filler Composites

2007 ◽  
Vol 124-126 ◽  
pp. 1079-1082 ◽  
Author(s):  
Sung Ryong Kim ◽  
Dae Hoon Kim ◽  
Dong Ju Kim ◽  
Min Hyung Kim ◽  
Joung Man Park
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Carbon Fiber ◽  
Conductive Filler ◽  
Laser Flash ◽  
Flash Method ◽  
Phase System ◽  
Two Phase ◽  
Nielsen Theory ◽  
Thermally Conductive

Thermal properties of PEEK/silicon carbide(SiC) and PEEK/carbon fiber(CF) were investigated from ambient temperature up to 200°C measured by laser flash method. Thermal conductivity was increased from 0.29W/m-K without filler up to 2.4 W/m-K with at 50 volume % SiC and 3.1W/m-K with 40 volume % carbon fiber. Values from Nielsen theory that predicts thermal conductivity of two-phase system were compared to those obtained from experiment.

Insulating and Thermally Conductive Composite Filled with Boron Nitride Particles

2011 ◽  
Vol 391-392 ◽  
pp. 282-286 ◽  
Author(s):  
Jun Peng Li ◽  
Shu Hua Qi ◽  
Fan Xie
Keyword(s):  
Thermal Conductivity ◽  
Thermal Stability ◽  
Boron Nitride ◽  
Filler Content ◽  
Glass Fibers ◽  
High Surface ◽  
Conductive Filler ◽  
Volume Resistivity ◽  
Conductive Composites ◽  
Thermally Conductive

A new kind of thermally conductive composites reinforced by glass fibers with boron nitride (BN) as thermally conductive filler was prepared in heat press molding. Thermal conductivity of the composites was found to increase with increasing in filler content. But impact strength and flexural strength reach the top point, 385.05KJ/m2 and 912.6481MPa, with content of 50wt% and 20wt% respectively. The thermal conductivity of 0.8385 W/mK was obtained at 50wt% filler content. Experimental dates show that mixed matrix of epoxy (EP) and polyimide (PI) displays high thermal stability and can improve thermal stability compared to pure epoxy obviously at 50wt% PI content. Additionally, the obtained composites possess high surface resistivity and volume resistivity, which are suitable for substrate materials.

scholarly journals Carbonization of Polydopamine-Coating Layers on Boron Nitride for Thermal Conductivity Enhancement in Hybrid Polyvinyl Alcohol (PVA) Composites

Polymers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1410 ◽  
Author(s):  
Youjin Kim ◽  
Jooheon Kim
Keyword(s):  
Thermal Conductivity ◽  
Boron Nitride ◽  
Polyvinyl Alcohol ◽  
Composite Films ◽  
Analytical Techniques ◽  
Conductive Filler ◽  
Sustainable Chemistry ◽  
Conductivity Enhancement ◽  
Nanocrystalline Graphite ◽  
Thermally Conductive

Inspired by mussel adhesion proteins, boron nitride (BN) particles coated with homogeneous polydopamine (BNPDA) were prepared, and through an annealing process, a carbonized PDA layer on the surface of BN was obtained, which exhibited a nanocrystalline graphite-like structure. The effect of carbonization of PDA coating layer on BN particles was characterized by various analytical techniques including SEM, TEM, Raman spectroscopy, and XPS. When the resulting particles were used as a thermally conductive filler for polyvinyl alcohol (PVA) composite films, enhanced thermal conductivity was observed compared to raw BN composite due to the ordered structure and improved solubility in water. Furthermore, the homogeneous dispersion of the filler and excellent flexibility of the modified composite film with 21 wt % filler may be attributed to compatibility with the PVA chain. As the whole fabrication process did not use toxic chemicals (mainly water was used as the solvent), it may contribute to green and sustainable chemistry.

scholarly journals Study on Performance Optimization and Physical Properties Testing of a New Type of High Thermal Conductive Damping Coating

2021 ◽  
Vol 2083 (2) ◽  
pp. 022001
Author(s):  
Wei Chen ◽  
Sheng Hu ◽  
RuiDun Zhao ◽  
Yine Xie ◽  
Hao Cao
Keyword(s):  
Thermal Conductivity ◽  
Performance Optimization ◽  
Heat Dissipation ◽  
Fiber Type ◽  
Conductive Filler ◽  
Sound Insulation ◽  
Acrylic Emulsion ◽  
Network Chain ◽  
Thermally Conductive ◽  
New Type

Abstract Surface coating of damping paint is a common method to suppress structural vibration and reduce noise, but damping paint has poor thermal conductivity which limits it’ s application to transformers, reactors and other equipment that have high requirements for heat dissipation. In this paper, a new type of high thermal conductivity damping coating is prepared by emulsion polymerization, among which, a polyurethane emulsion with internal cross-linking structure and an acrylic emulsion with polymerization function are used as main agents, mica powder is used as the main damping function filler. By adjusting the proportion of non-metallic thermal conductive filler Al2O3 and thermal conductive fiber to explore the influence of different thermal conductive fillers on the thermal conductivity and damping performance of the damping coating. The paint is applied to aluminum and iron plates, and the sound insulation capacity is tested to study the influence of paint thickness, fiber addition, fiber type, viscoelasticity, and temperature aging on the sound insulation performance of damping sound insulation panels. The test results show that by adding thermally conductive filler Al2O3 and thermally conductive fibers, a thermally conductive network chain is formed inside the damping coating, which greatly improves the thermal conductivity of the coating while ensuring the damping performance and the effect of vibration and noise reduction.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
De Novo ◽  
De Novo Drug Design ◽  
Generative Process ◽  
New Methods ◽  
Multiparameter Optimization ◽  
Long Short Term Memory ◽  
New Compounds

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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