Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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Application of ANN Back-Propagation for Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.345.272 ◽

2013 ◽

Vol 345 ◽

pp. 272-276 ◽

Cited By ~ 1

Author(s):

Hong Yan Duan ◽

You Tang Li ◽

Zhi Jia Sun ◽

Yang Yang Zhang

Keyword(s):

Neural Network ◽

Carbon Steel ◽

Network Model ◽

Neural Network Model ◽

Network Architecture ◽

Back Propagation ◽

Training Data ◽

Design Parameters ◽

Ann Model ◽

The Neural Network

The fracture problems of medium carbon steel (MCS) under extra-low cycle bend torsion loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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Application of ANN Back-Propagation for An Alloy Reinforced Ceramics/Metal Composite under Extra-Low Cycle Bending Fatigue Loading

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.105-106.108 ◽

2010 ◽

Vol 105-106 ◽

pp. 108-111

Author(s):

Zhi Yuan Rui ◽

Hong Yan Duan ◽

Chun Li Lei ◽

Xing Chun Wei

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Propagation Model ◽

Training Data ◽

Design Parameters ◽

Metal Composite ◽

Ann Model ◽

The Neural Network

Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.

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Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

Materials Science Forum ◽

10.4028/www.scientific.net/msf.610-613.450 ◽

2009 ◽

Vol 610-613 ◽

pp. 450-453

Author(s):

Hong Yan Duan ◽

You Tang Li ◽

Jin Zhang ◽

Gui Ping He

Keyword(s):

Neural Network ◽

Neural Network Model ◽

Back Propagation ◽

Training Data ◽

Design Parameters ◽

Ann Model ◽

Bending Fatigue ◽

The Neural Network ◽

Rotating Bending Fatigue ◽

Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures

10.26434/chemrxiv.14428865 ◽

2021 ◽

Author(s):

Shubham Pandey ◽

Jiaxing Qu ◽

Vladan Stevanovic ◽

Peter St. John ◽

Prashun Gorai

Keyword(s):

Neural Network ◽

Total Energy ◽

Network Model ◽

Neural Network Model ◽

Density Functional ◽

Failure Modes ◽

Inorganic Materials ◽

Training Data ◽

The Neural Network ◽

Unique Graph

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties.

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Sensitivity Direction Learning with Neural Networks Using Domain Knowledge as Soft Shape Constraints

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/422 ◽

2021 ◽

Author(s):

Kazuyuki Wakasugi

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Network Architecture ◽

Domain Knowledge ◽

Neural Network Architecture ◽

Shape Constraints ◽

Direction Error ◽

The Neural Network ◽

Strong Candidate

If domain knowledge can be integrated as an appropriate constraint, it is highly possible that the generalization performance of a neural network model can be improved. We propose Sensitivity Direction Learning (SDL) for learning about the neural network model with user-specified relationships (e.g., monotonicity, convexity) between each input feature and the output of the model by imposing soft shape constraints which represent domain knowledge. To impose soft shape constraints, SDL uses a novel penalty function, Sensitivity Direction Error (SDE) function, which returns the squared error between coefficients of the approximation curve for each Individual Conditional Expectation plot and coefficient constraints which represent domain knowledge. The effectiveness of our concept was verified by simple experiments. Similar to those such as L2 regularization and dropout, SDL and SDE can be used without changing neural network architecture. We believe our algorithm can be a strong candidate for neural network users who want to incorporate domain knowledge.

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A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures

10.26434/chemrxiv.14428865.v1 ◽

2021 ◽

Author(s):

Shubham Pandey ◽

Jiaxing Qu ◽

Vladan Stevanovic ◽

Peter St. John ◽

Prashun Gorai

Keyword(s):

Neural Network ◽

Total Energy ◽

Network Model ◽

Neural Network Model ◽

Density Functional ◽

Failure Modes ◽

Inorganic Materials ◽

Training Data ◽

The Neural Network ◽

Unique Graph

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Application of ANN Back-Propagation for Fracture Design Parameters of Medium Carbon Steel in Extra-Low Cycle Axial Fatigue Loading

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.345-346.445 ◽

2007 ◽

Vol 345-346 ◽

pp. 445-448

Author(s):

Hong Yan Duan ◽

You Tang Li ◽

Shuai Tan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Fatigue Loading ◽

Medium Carbon Steel ◽

Training Data ◽

Design Parameters ◽

Artificial Neural

The fracture problems of medium carbon steel under extra-low cycle axial fatigue loading were studied using artificial neural network in this paper. The training data were used in the formation of training set of artificial neural network. The artificial neural network model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. Training artificial neural network model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The artificial neural network model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The result show that the training model has good performance, and the experimental data and predicted data from artificial neural network are in good coherence.

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Using Convolution and Deep Learning in Gomoku Game Artificial Intelligence

Parallel Processing Letters ◽

10.1142/s0129626418500111 ◽

2018 ◽

Vol 28 (03) ◽

pp. 1850011

Author(s):

Peizhi Yan ◽

Yi Feng

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Network Model ◽

Neural Network Model ◽

Traditional Approach ◽

Training Data ◽

Game Tree ◽

The Neural Network ◽

Testing Data ◽

Game Board

Gomoku is an ancient board game. The traditional approach to solving the Gomoku game is to apply tree search on a Gomoku game tree. Although the rules of Gomoku are straightforward, the game tree complexity is enormous. Unlike many other board games such as chess and Shogun, the Gomoku board state is more intuitive. That is to say, analyzing the visual patterns on a Gomoku game board is fundamental to play this game. In this paper, we designed a deep convolutional neural network model to help the machine learn from the training data (collected from human players). Based on this original neural network model, we made some changes and get two variant neural networks. We compared the performance of the original neural network with its variants in our experiments. Our original neural network model got 69% accuracy on the training data and 38% accuracy on the testing data. Because the decision made by the neural network is intuitive, we also designed a hard-coded convolution-based Gomoku evaluation function to assist the neural network in making decisions. This hybrid Gomoku artificial intelligence (AI) further improved the performance of a pure neural network-based Gomoku AI.

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Using Models of Collective Neural Networks for Classification of the Input Data Applying Simple Voting

The Journal of Social Sciences Research ◽

10.32861/jssr.spi5.333.339 ◽

2018 ◽

pp. 333-339

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Network Architecture ◽

Input Data ◽

Bootstrap Method ◽

Classification Models ◽

The Neural Network ◽

Voting Method

This paper deals with the use of neural networks in binary classification problems based on the simple voting method. It specifies that the accuracy of the neural network classification depends both on the choice of the network architecture and on the partitioning of data into training and test sets. It is noted that the process of building a neural network model is probabilistic in nature. To eliminate this drawback and improve the accuracy of classification, the need to combine several models in the form of a collective of neural networks is actualized. To build such a model, it is proposed to use the 0.632-bootstrap method. To aggregate individual solutions formed at the output of each neural network, it is proposed to use a single-choice simple voting. The choice of the model structure in the form of a single-layer Perceptron is justified, and its mathematical model is presented. Using the evaluation data of the functional state of a drunk human as an example, the results of an experimental assessment of the bootstrap error and the accuracy of the neural network model are presented. It is concluded that it is possible to achieve a higher accuracy of classification based on the neural network model when aggregating the results of all bootstrap models using the simple voting method. The accuracy of the constructed model is compared with the accuracy of other classification models. The accuracy of the constructed model was 96.7%, which on average exceeded the accuracy of other classification models by 6.6%. Thus, the neural network collective model is an effective tool for classifying input data using the simple voting method.

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Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837236 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 108-116 ◽

Cited By ~ 1

Author(s):

Mostafa H. Tawfeek ◽

Karim El-Basyouny

Keyword(s):

Neural Network ◽

Linear Regression ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Safety Management ◽

Estimation Method ◽

Estimation Model ◽

The Neural Network ◽

Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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