Artificial Neural Network for Predicting the Flow Behaviors of Hot Compressed 2124-T851 Aluminum Alloy

2010 ◽  
Vol 146-147 ◽  
pp. 720-723
Author(s):  
Yong Cheng Lin ◽  
Xiao Min Chen ◽  
Yu Chi Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Flow Stress ◽  
Strain Rate ◽  
Learning Algorithm ◽  
Ann Model ◽  
Wide Range ◽  
Feed Forward Network ◽  
Artificial Neural

The compressive deformation experiments of 2124-T851 aluminum alloy were carried out over a wide range of temperature and strain rate. An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the flow behaviors of hot compressed 2124-T851 aluminum alloy and working conditions. The input parameters of the model consist of strain rate, forming temperature and deformation degree whereas flow stress is the output. A three layer feed-forward network with 15 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. Good performance of the ANN model is achieved. The predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.

Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network

2012 ◽  
Vol 724 ◽  
pp. 351-354 ◽  
Author(s):  
Zhao Hui Zhang ◽  
Dong Na Yan ◽  
Jian Tao Ju ◽  
Ying Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Flow Stress ◽  
Austenitic Stainless Steel ◽  
Strain Rate ◽  
Flow Behavior ◽  
Constitutive Relationship ◽  
Ann Model ◽  
Artificial Neural

The high temperature flow behavior of as-cast 904L austenitic stainless steel was studied using artificial neural network (ANN). Isothermal compression tests were carried out at the temperature range of 1000°C to 1200°C and strain rate range of 0.01 to 10s1. Based on the experimental flow stress data, an ANN model for the constitutive relationship between flow stress and strain, strain rate and deformation temperature was constructed by back-propagation (BP) method. Three layer structured network with one hidden layer and nine hidden neurons was trained and the normalization method was employed in training process to avoid over fitting. Modeling results show that the developed ANN model exhibits good performance for predicting the flow stresses of the 904L steel. Therefore, it can be used to reflect the hot deformation behavior in a wide working window.

Application of artificial neural network to loan recovery prediction

2016 ◽  
Vol 9 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Amos Olaolu Adewusi ◽  
Tunbosun Biodun Oyedokun ◽  
Mustapha Oyewole Bello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Type I ◽  
Ann Model ◽  
Content Type ◽  
Credit Evaluation ◽  
Artificial Neural

Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel

2011 ◽  
Vol 695 ◽  
pp. 361-364 ◽  
Author(s):  
Ying Han ◽  
Guan Jun Qiao ◽  
Dong Na Yan ◽  
De Ning Zou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Flow Stress ◽  
Strain Rate ◽  
Hot Deformation ◽  
Deformation Behavior ◽  
Martensitic Stainless Steel ◽  
Ann Model ◽  
Hot Deformation Behavior

The hot deformation behavior of super 13Cr martensitic stainless steel was investigated using artificial neural network (ANN). Hot compression tests were carried out at the temperature range of 950°C to 1200°C and strain rate range of 0.1–50s–1at an interval of an order of magnitude. Based on the limited experimental data, the ANN model for the constitutive relationship existed between flow stress and strain, strain rate and deformation temperature was developed by back-propagation (BP) neural network method. A three layer structured network with one hidden layer and ten hidden neurons was trained and the normalization method was employed in training for avoiding over fitting. Modeling results show that the developed ANN model can efficiently predict the flow stress of the steel and reflect the hot deformation behavior in the whole deforming process.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

Modeling the Hot Deformation Behaviors of As-Extruded 7075 Aluminum Alloy by an Artificial Neural Network with Back-Propagation Algorithm

2017 ◽  
Vol 36 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Guo-zheng Quan ◽  
Zhen-yu Zou ◽  
Tong Wang ◽  
Bo Liu ◽  
Jun-chao Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Strain Rate ◽  
Hot Deformation ◽  
Back Propagation ◽  
Bp Algorithm ◽  
7075 Aluminum Alloy ◽  
Ann Model ◽  
Deformation Behaviors

AbstractIn order to investigate the hot deformation behaviors of as-extruded 7075 aluminum alloy, the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and 723 K and the strain rates of 0.01, 0.1, 1 and 10 s−1 on a Gleeble 1500 thermo-mechanical simulator. The flow behaviors showing complex characteristics are sensitive to strain, strain rate and temperature. The effects of strain, temperature and strain rate on flow stress were analyzed and dynamic recrystallization (DRX)-type softening characteristics of the flow behaviors with single peak were identified. An artificial neural network (ANN) with back-propagation (BP) algorithm was developed to deal with the complex deformation behavior characteristics based on the experimental data. The performance of ANN model has been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). A comparative study on Arrhenius-type constitutive equation and ANN model for as-extruded 7075 aluminum alloy was conducted. Finally, the ANN model was successfully applied to the development of processing map and implanted into finite element simulation. The results have sufficiently articulated that the well-trained ANN model with BP algorithm has excellent capability to deal with the complex flow behaviors of as-extruded 7075 aluminum alloy and has great application potentiality in hot deformation processes.

scholarly journals Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding based on Hot Compression Tests

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 804
Author(s):  
Sansan Ding ◽  
Qingyu Shi ◽  
Gaoqiang Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Flow Stress ◽  
Hot Compression ◽  
Compression Tests ◽  
6061 Aluminum Alloy ◽  
Friction Stir ◽  
Artificial Neural

The purpose of this paper is to report quantitative data and models for the flow stress for the computer simulation of friction stir welding (FSW). In this paper, the flow stresses of the commercial 6061 aluminum alloy at the typical temperatures in FSW are investigated quantitatively by using hot compression tests. The typical temperatures during FSW are determined by reviewing the literature data. The measured data of flow stress, strain rate and temperature during hot compression tests are fitted to a Sellars–Tegart equation. An artificial neural network is trained to implement an accurate model for predicting the flow stress as a function of temperature and strain rate. Two models, i.e., the Sellars–Tegart equation and artificial neural network, for predicting the flow stress are compared. It is found that the root-mean-squared error (RMSE) between the measured and the predicted values are found to be 3.43 MPa for the model based on the Sellars–Tegart equation and 1.68 MPa for the model based on an artificial neural network. It is indicated that the artificial neural network has better flexibility than the Sellars–Tegart equation in predicting the flow stress at typical temperatures during FSW.

scholarly journals Predictive Neural Network in Multipurpose Self-Tuning Controller

2020 ◽  
Vol 14 (2) ◽  
pp. 114-120
Author(s):  
Oleksiy Bondar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Adaptive Controller ◽  
Development Environment ◽  
Wide Range ◽  
Artificial Neural ◽  
Arbitrary Precision ◽  
The Right

AbstractA very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Flow Stress Prediction of Ti-6Al-4V Alloy at Elevated Temperature Using Artificial Neural Network

2014 ◽  
Vol 612 ◽  
pp. 83-88 ◽  
Author(s):  
Nitin Kotkunde ◽  
Aditya Balu ◽  
Amit Kumar Gupta ◽  
Swadesh Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Strain Rate ◽  
Correlation Coefficient ◽  
Differential Evolution Algorithm ◽  
Training Data ◽  
Uniaxial Tensile ◽  
Testing Data ◽  
Artificial Neural

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. In this work, experimental flow stress have been predicted for Ti-6Al-4V alloy using isothermal uniaxial tensile tests ranging from 323K to 673K at an interval of 50K and strain rates 10-5, 10-4, 10-3 and 10-2 s-1. Based on the input variables strain, strain rate and temperature, a back propagation neural network model has been developed to predict the flow stress as output. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network enhanced with differential evolution algorithm is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. Correlation coefficient for training and testing data is found to be 0.9997 and 0.9985 respectively. Based on the correlation coefficient, it indicates that predicted flow stress by using artificial neural network is in good agreement with experimental results.

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