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

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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Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

Diyala Journal of Engineering Sciences ◽

10.24237/djes.2014.07409 ◽

2014 ◽

Vol 7 (4) ◽

pp. 132-143

Author(s):

ABBAS M. ABD ◽

SAAD SH. SAMMEN

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Correlation Coefficient ◽

Mean Squared Error ◽

Water Reservoir ◽

Prediction Method ◽

Training Data ◽

Ann Model ◽

Data Set ◽

Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.146-147.720 ◽

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.

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Penerapan Artificial Neural Network Terhadap Identifikasi Wajah Menggunakan Metode Backpropagation

Natural Science Journal of Science and Technology ◽

10.22487/25411969.2019.v8.i3.14599 ◽

2019 ◽

Vol 8 (3) ◽

Author(s):

Mimin Hendriani ◽

Rais ◽

Lilies Handayani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Correlation Coefficient ◽

Training Data ◽

Face Identification ◽

Training Methods ◽

Supervised Training ◽

R Value ◽

Artificial Neural

Backpropagation is one of the supervised training methods that causes an error in the output produced. Backpropagation neural networks will be carried out in 3 stages, namely feedforward from input training patterns, backpropagation from errors related to adjustment of weights. Updating the weight is done when the training results obtained have not been converged. The value of the goal error (MSE) is 0.0070579 which is achieved at epochs to 99994 from the provisions of 100000 iterations. Based on the plot regression, the training data resulted in a correlation coefficient value of up to 0.55321. The correlation coefficient value is concluded that the greater the R value produced, the better the level of accuracy in face identification carried out in this study

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

Materials Science Forum ◽

10.4028/www.scientific.net/msf.724.351 ◽

2012 ◽

Vol 724 ◽

pp. 351-354 ◽

Cited By ~ 2

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.

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Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v1i2.2384 ◽

2010 ◽

Vol 1 (2) ◽

pp. 418

Author(s):

Afan Galih Salman ◽

Yen Lina Prasetio

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Adaptive Learning ◽

Gradient Descent ◽

Learning Rate ◽

Training Data ◽

Testing Data ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Data Group

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3.

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IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v5i2.152 ◽

2018 ◽

Vol 5 (2) ◽

pp. 169

Author(s):

Muhammad Dedek Yalidhan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Information System ◽

Prediction Accuracy ◽

Confusion Matrix ◽

The Other ◽

Training Data ◽

Backpropagation Algorithm ◽

Testing Data ◽

Artificial Neural

Student’s graduation is one kind of the college accreditation elements by BAN-PT. Because of that. Information System is one of the department in STMIK Banjarbaru, there is no application has been implemented to predict imprecisely of student’s graduation time so far, which causes on time graduation percentage tend low every year. Therefore the accurate student’s graduation prediction can help the committe to choose the correct decisions in order to prevent the imprecisely of student’s graduation time. In this research, the backpropagation algorithm of artificial neural network will be implemented into the application with the output result as delayed and on time graduation. This reseach is using 318 data samples which the 70 % of it will be used as the training data and the other 30 % will be used as testing data. From the calculation of confusion matrix table’s the percentage of the prediction accuracy is 98.97 %.Keywords: student’s graduation, artificial neural network, backpropagation, confusion matrixKelulusan mahasiswa merupakan salah satu elemen dalam standar akreditasi perguruan tinggi oleh BAN-PT. Sistem Informasi adalah salah satu program studi yang ada di STMIK Banjarbaru, selama ini belum ada aplikasi yang diimplementasikan untuk memprediksi ketidaktepatan waktu kelulusan mahasiswanya yang menyebabkan angka kelulusan tepat waktu cenderung rendah setiap tahunnya. Oleh sebab itu, prediksi kelulusan mahasiswa yang akurat dapat membantu pihak Program Studi dalam mengambil keputusan-keputusan yang tepat untuk mencegah ketidaktepatan waktu kelulusan mahasiswanya. Pada penelitian ini, artificial neural network algoritma backpropagation diimplementasikan pada aplikasi yang dibuat dengan output lulus terlambat dan lulus tepat waktu. Penelitian ini menggunakan sebanyak 318 sampel data yang mana 70 % data digunakan sebagai data training dan 30 % data digunakan sebagai data testing. Dari hasil perhitungan tabel confusion matrix diperoleh persentase akurasi prediksi sebesar 98.97 %.Kata kunci: kelulusan mahasiswa, artificial neural network, backpropagation, confusion matrix

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Modeling of High Temperature Flow Stress of AZ80 Magnesium Alloy with Support Vector Machines and Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.486.227 ◽

2012 ◽

Vol 486 ◽

pp. 227-232

Author(s):

Yan Lou

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Temperature ◽

Support Vector Machines ◽

Flow Stress ◽

Strain Rate ◽

Support Vector ◽

Prediction Ability ◽

Vector Machines ◽

Artificial Neural

Support vector machines (SVM) and artificial neural network (ANN) were employed in modeling the flow stress of the AZ80 magnesium. The hot deformation behavior of extruded AZ80 magnesium was investigated by compression tests in the temperature 350-450 and strain rate range 0.01-50 s-1. The maximum relative errors at different temperatures and different strain rates between experimental and predicted flow stresses by SVM and ANN were compared. The results show the SVM derives statistical models have better similar prediction ability to those of ANN, especially at high strain rate. This indicates that SVM can be used as an alternative modeling tool for high temperature rheological behavior studies.

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Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽

10.3390/ma14113042 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3042

Author(s):

Sheng Jiang ◽

Mansour Sharafisafa ◽

Luming Shen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Strain Rate ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Rock Strength ◽

Strain Rates ◽

Filling Material ◽

Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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