Modeling of Annealing Heat Treatment Parameters for Zr Alloy Tube by ANN-Ga

2020 ◽  
Vol 1001 ◽  
pp. 207-211
Author(s):  
Xing Xing Tong ◽  
Xue Wen Tong
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Mechanical Property ◽  
Network Architecture ◽  
Heat Treatments ◽  
Annealing Heat Treatment ◽  
Alloy Heat ◽  
Artificial Neural Network Ann ◽  
Zr Alloy ◽  
Annealing Heat

In this paper, there are tow part of module for predicting the Annealing heat treatments of Zr tube. The artificial neural network (ANN) were used for relationship between mechanical property and annealing parameters. The genetic algorithm (GA) were used for Annealing heat treatments of Zr tube. The best ANN network architecture is 2-8-3, and the optimum values of momentum factor is 0.8 while the Crossover is also 0.8 by ANN-GA, which can be efficiently track the effect of annealing Heat treatment on properties for Zr-4 alloy. Keywords: Zr alloy, Heat Treatment, mechanical propert

scholarly journals Effects of Full Annealing Heat Treatment on Long-term Creep Strength of 2.25Cr-1Mo Steel Welded Joint

2003 ◽  
Vol 21 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Kazuhiro KIMURA ◽  
Takashi WATANABE ◽  
Hiromichi HONGO ◽  
Masayoshi YAMAZAKI ◽  
Jun-ichi KINUGAWA ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Creep Strength ◽  
Welded Joint ◽  
Annealing Heat Treatment ◽  
Full Annealing ◽  
Term Creep ◽  
Annealing Heat

Stable and accurate a posteriori LES of 2D turbulence with convolutional neural networks: Backscatter analysis and generalization via transfer learning

2021 ◽  
Author(s):  
Yifei Guan ◽  
Ashesh Chattopadhyay ◽  
Adam Subel ◽  
Pedram Hassanzadeh
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Architecture ◽  
Turbulence Modeling ◽  
Scale Effects ◽  
A Priori ◽  
A Posteriori ◽  
Subgrid Scale ◽  
Artificial Neural Network Ann ◽  
Short Term Prediction

<p>In large eddy simulations (LES), the subgrid-scale effects are modeled by physics-based or data-driven methods. This work develops a convolutional neural network (CNN) to model the subgrid-scale effects of a two-dimensional turbulent flow. The model is able to capture both the inter-scale forward energy transfer and backscatter in both a priori and a posteriori analyses. The LES-CNN model outperforms the physics-based eddy-viscosity models and the previous proposed local artificial neural network (ANN) models in both short-term prediction and long-term statistics. Transfer learning is implemented to generalize the method for turbulence modeling at higher Reynolds numbers. Encoder-decoder network architecture is proposed to generalize the model to a higher computational grid resolution.</p>

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Precipitates Formation in Ti-Ni Equiatomic Alloys due to Annealing Heat Treatment

2010 ◽  
Vol 643 ◽  
pp. 49-54 ◽  
Author(s):  
Carlos Augusto Nascimento Oliveira ◽  
Euclides Apolinário Cabral De Pina ◽  
Cezar Henrique Gonzalez ◽  
Carlos José de Araújo ◽  
U.S.L. Filho ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Martensitic Transformation ◽  
Shape Memory ◽  
Martensitic Transformations ◽  
Smart Material ◽  
X Ray Diffraction ◽  
X Ray ◽  
Metallurgical Investigation ◽  
Annealing Heat Treatment ◽  
Annealing Heat

The use of smart material such as Ti-Ni in actuators application requires an intense mechanical and metallurgical investigation to understand its behavior. This paper studies martensitic transformation using DSC and X-ray diffraction techniques to compare shape memory parameters in Ti-50.2%Ni (A1) and equiatomic Ti-50.0%Ni (A2) Alloys. The as as-received samples were submitted to annealing at 400°C and 500°C for 24 hours then quenched in at 25°C. The influence of heat treatment on martensitic transformations temperatures and the appearance of R-phase were analyzed using DSC and X-ray diffraction.

Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 221-225 ◽  
Author(s):  
Cheng Long Xu ◽  
Sheng Li Lv ◽  
Zhen Guo Wang ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Fatigue Life ◽  
Network Architecture ◽  
Fatigue Tests ◽  
Data Sets ◽  
Absolute Relative Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

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