Performance optimization of shape memory epoxy polymers based on machine learning

2021 ◽  
Author(s):  
Bei Liu ◽  
Kai Jin ◽  
Jie Tao ◽  
Hao Wang ◽  
Dan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Shape Memory ◽  
Performance Optimization ◽  
Epoxy Polymers

scholarly journals Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

Polymer ◽  
2021 ◽  
Vol 214 ◽  
pp. 123351
Author(s):  
Cheng Yan ◽  
Xiaming Feng ◽  
Collin Wick ◽  
Andrew Peters ◽  
Guoqiang Li
Keyword(s):  
Machine Learning ◽  
Shape Memory ◽  
Shape Memory Polymers ◽  
Training Dataset

Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism

2019 ◽  
Vol 32 (17) ◽  
pp. 13107-13115 ◽  
Author(s):  
Rashid Ali ◽  
Ali Nauman ◽  
Yousaf Bin Zikria ◽  
Byung-Seo Kim ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Performance Optimization ◽  
Channel Access

scholarly journals Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritaban Dutta ◽  
Cherry Chen ◽  
David Renshaw ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Shape Memory ◽  
Video Data ◽  
The Body ◽  
Restricted Boltzmann Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Boltzmann Machine ◽  
High Level

AbstractExtraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

scholarly journals Machine learning based approach for shape memory polymer behavioural characterization

Array ◽  
2020 ◽  
Vol 7 ◽  
pp. 100036
Author(s):  
Ritaban Dutta ◽  
David Renshaw ◽  
Cherry Chen ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Shape Memory ◽  
Shape Memory Polymer

scholarly journals Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Pei Liu ◽  
Haiyou Huang ◽  
Stoichko Antonov ◽  
Cheng Wen ◽  
Dezhen Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Optimization

scholarly journals G-NETWORKS AND THEIR APPLICATIONS TO MACHINE LEARNING, ENERGY PACKET NETWORKS AND ROUTING: INTRODUCTION TO THE SPECIAL ISSUE

2017 ◽  
Vol 31 (4) ◽  
pp. 381-395 ◽  
Author(s):  
Mehmet Ufuk Caglayan
Keyword(s):  
Machine Learning ◽  
Performance Optimization ◽  
Saint Paul ◽  
Network Routing ◽  
Packet Networks ◽  
The Novel ◽  
Special Issue ◽  
Research Directions ◽  
Cloud Management ◽  
Theoretical Developments

This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novelEnergy Packet Networksformalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

scholarly journals Machine Learning Enhanced Dynamic Response Modelling of Superelastic Shape Memory Alloy Wires

Materials ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 304
Author(s):  
Niklas Lenzen ◽  
Okyay Altay
Keyword(s):  
Machine Learning ◽  
Shape Memory Alloy ◽  
Strain Rate ◽  
Shape Memory ◽  
Thermodynamic Parameters ◽  
Expert Knowledge ◽  
Material Behavior ◽  
Model Parameters ◽  
Engineering Structures ◽  
Wide Range

Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. Therefore, they are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. However, the thermodynamically coupled SMA behavior is highly sensitive to strain rate. For an accurate modelling of the material behavior, a wide range of parameters needs to be determined by experiments, where the identification of thermodynamic parameters is particularly challenging due to required technical instruments and expert knowledge. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was specifically designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. For the generation of training data, a macroscopic constitutive SMA model was adapted considering strain rate effects. After training, the ANN can identify the searched model parameters from cyclic tensile stress–strain tests. The proposed approach is applied on superelastic SMA wires and validated by experiments.

scholarly journals Machine Learning based Self-sensing the Stiffness of Shape Memory Coil Actuator

2021 ◽  
Author(s):  
Bhagoji B. Sul ◽  
K. Dhanalakshmi
Keyword(s):  
Machine Learning ◽  
Shape Memory ◽  
Electrical Resistance ◽  
Statistical Parameter ◽  
Automated Method ◽  
Displacement Sensors ◽  
Stiffness Characteristic ◽  
Almost All ◽  
And Control ◽  
Intelligent Model

Abstract Self-sensing actuation (SSA) assists in sensing the vital property of the shape memory coil which can be used to monitor and control the actuation. The stiffness characteristic of the shape memory coil is sensed during actuation which plays a significant role in development of Intelligent Robotics in defense systems. The electrical property of shape memory coil such as electrical resistance changes due to martensitic phase transformation which is further used to sense the mechanical properties such as strain, stress, temperature, length, and force. Nowadays electrical properties are used to sense the stiffness of the shape memory coil. As of now, there is no well-established analytical model to predict the stiffness of sensing during actuation accurately. Therefore, Machine Learning (ML) based data-driven intelligent model is proposed in this paper for auto-sensing of the stiffness. The experimental facility has been developed for the collection of data with respect to diverse Joule heating currents. To determine the experimental data values of stiffness and electrical resistance of shape memory coil is a cumbersome task. Hence we have proposed an automated method to predict the stiffness of the shape memory coil using ML methods. The Classical Polynomial and Feedforward Neural Network (FFNN) models are developed for analyzing the stiffness of the shape memory coil. It is found that FFNN model outperforms the other ML based model by attaining 95.2650 % accuracy. The FFNN model is also able to explain almost all the predicted stiffness values which are experimentally recorded. The FVU (Fraction Variance Unexplained) statistical parameter explains the prediction of FFNN with the value of 0.0842. The great advantage of the ML model is to replace two sensors (Force and displacement sensors) with one soft sensor (ML model). It will be useful in the controlling robotics and other devices which require high precision in data generated by the sensors.

Sign in / Sign up

Export Citation Format

Share Document