BP neural network prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction

2006 ◽  
Vol 419 (1-2) ◽  
pp. 214-217 ◽  
Author(s):  
Qiang Li ◽  
Jing-Yuan Yu ◽  
Bai-Chun Mu ◽  
Xu-Dong Sun
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Thermal Explosion ◽  
Bp Neural Network ◽  
Niti Shape Memory Alloy ◽  
Porous Niti ◽  
Neural Network Prediction ◽  
Network Prediction

Modeling and Analysis of Compressive Properties of Porous NiTi Shape Memory Alloy Using Artificial Neural Network

2008 ◽  
Vol 41-42 ◽  
pp. 135-140 ◽  
Author(s):  
Qiang Li ◽  
Xu Dong Sun ◽  
Jing Yuan Yu ◽  
Zhi Gang Liu ◽  
Kai Duan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Thermal Explosion ◽  
Process Parameters ◽  
Niti Shape Memory Alloy ◽  
Compressive Properties ◽  
Porous Niti ◽  
Bp Model

Artificial neural network (ANN) is an intriguing data processing technique. Over the last decade, it was applied widely in the chemistry field, but there were few applications in the porous NiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosion experiments were used to build a three-layer BP (back propagation) neural network model. According to the registered BP model, the effect of process parameters including heating rate ( ), green density ( ) and particle size of Ti ( d ) on compressive properties of reacted products including ultimate compressive strength ( v D σ ) and ultimate compressive strain (ε ) was analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the properties analysis and process parameters design of the porous NiTi SMA prepared by thermal explosion method.

scholarly journals Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6593
Author(s):  
Meng Zhan ◽  
Junsheng Liu ◽  
Deli Wang ◽  
Xiuyun Chen ◽  
Lizhen Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Shape Memory Alloy ◽  
Prediction Model ◽  
Shape Memory ◽  
Structural Vibration ◽  
Absorption Effect ◽  
Structural Vibration Control ◽  
Neural Network Prediction ◽  
Network Prediction

The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%.

scholarly journals The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension

2011 ◽  
Vol 1 ◽  
pp. 163-167
Author(s):  
Da Ke Wu ◽  
Chun Yan Xie
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Input Data ◽  
Bp Network ◽  
Neural Network Prediction ◽  
Infected Tomato ◽  
Network Prediction ◽  
Hidden Nodes

Leafminer is one of pest of many vegetables, and the damage may cover so much of the leaf that the plant is unable to function, and yields are noticeably decreased. In order to get the information of the pest in the vegetable before the damage was not serious, this research used a BP neural network to classify the leafminer-infected tomato leaves, and the fractal dimension of the leaves was the input data of the BP neural network. Prediction results showed that when the number of FD was 21 and the hidden nodes of BP neural network were 21, the detection performance of the model was good and the correlation coefficient (r) was 0.836. Thus, it is concluded that the FD is an available technique for the detection of disease level of leafminer on tomato leaves.

scholarly journals Mechanical Properties of NiTi Shape Memory Alloy Particle/Epoxy Composites

2006 ◽  
Vol 55 (2) ◽  
pp. 230-236 ◽  
Author(s):  
Run-Xin ZHANG ◽  
Qing-Qing NI ◽  
Toshiaki NATSUKI ◽  
Ken KURASHIKI ◽  
Masaharu IWAMOTO
Keyword(s):  
Mechanical Properties ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Epoxy Composites ◽  
Niti Shape Memory Alloy ◽  
Alloy Particle

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

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