Application of Artificial Neural Network Based on GA-BP Algorithm to Predicting Mechanical Properties of TC4

2010 ◽  
Vol 146-147 ◽  
pp. 1698-1701
Author(s):  
Zhe Zhe Hou ◽  
Yan Liang Du
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Optimization Methods ◽  
Calculation Results ◽  
Tc4 Alloy ◽  
Artificial Neural

On the basis of numerous experimental results the effect of heat treatment on mechanical properties of TC4 alloy is studied. A computer model expressing the relationships between heat treatment and mechanical properties has been established with a back propagation feed forword artificial neural network method. The optimization methods based on artificial neural network and the genetic algorithm, using binary system, optimize the weight and threshold by the genetic algorithm. The calculation results show that the model has good learning precision and generalization and it can be used for predicting the mechanical properties of TC4 alloy.

Application of Artificial Neural Network for Prediction of Heat Treated Sintered Steels Properties

2008 ◽  
Vol 273-276 ◽  
pp. 323-328 ◽  
Author(s):  
H. Khorsand ◽  
M. Arjomandi ◽  
H. Abdoos ◽  
S.H. Sadati
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Initial Density ◽  
Back Propagation ◽  
Heat Treated ◽  
Algorithm Comparison ◽  
Artificial Neural

Heat treatment is an important method for improving the mechanical properties of industrial parts that are made through the powder metallurgy. Most PM steels are subjected to hardening and tempering, and it is due to this treatment that tempered martensite is formed. After heat treatment, these steel’s mechanical properties are affected by the heat treatment parameters and the initial density. In this paper, in order to make an evaluation of the effect of the above parameters, FN-0205 PM steel with various densities is heat treated in different austenite conditions and tempering time. Their mechanical properties are then evaluated and recorded. Afterwards, this data obtained by experimental procedure are predicted for various conditions. The method employed here is the well-known feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. Comparison between predicted values and experimental data, in the present study, indicate that the predicted results from this model are in good agreement with the experimental values.

Improving the Mechanical Properties of Wire-Rope Silk Scaffold by Artificial Neural Network in Tendon and Ligament Tissue Engineering

2015 ◽  
Vol 10 (3) ◽  
pp. 155892501501000 ◽  
Author(s):  
Elham Naghashzargar ◽  
Dariush Semnani ◽  
Saeed Karbasi
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Wire Rope ◽  
Elongation At Break ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Silk Yarn

Finding an appropriate model to assess and evaluate mechanical properties in tissue engineered scaffolds is a challenging issue. In this research, a structurally based model was applied to analyze the mechanics of engineered tendon and ligament. Major attempts were made to find the optimum mechanical properties of silk wire-rope scaffold by using the back propagation artificial neural network (ANN) method. Different samples of wire-rope scaffolds were fabricated according to Taguchi experimental design. The number of filaments and twist in each layer of the four layered wire-rope silk yarn were considered as the input parameters in the model. The output parameters included the mechanical properties which consisted of UTS, elongation at break, and stiffness. Finally, sensitivity analysis on input data showed that the number of filaments and the number of twists in the fourth layer are less important than other input parameters.

scholarly journals A hybrid artificial neural network - genetic algorithm for load shedding

2020 ◽  
Vol 10 (3) ◽  
pp. 2250 ◽  
Author(s):  
Le Trong Nghia ◽  
Quyen Huy Anh ◽  
Phung Trieu Tan ◽  
N Thai An
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Load Shedding ◽  
Software Systems ◽  
Training Time ◽  
Power Load ◽  
Artificial Neural ◽  
Input Variables

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.

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