Back Propagation Artificial Neural Network Approach to Predict the Flow Stress in Isothermal Tensile Test of Medium Carbon Steel Material

2020 ◽  
Vol 977 ◽  
pp. 163-168
Author(s):  
Mohanraj Murugesan ◽  
Dong Won Jung
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Flow Stress ◽  
Carbon Steel ◽  
Back Propagation ◽  
Medium Carbon Steel ◽  
Ann Model ◽  
Medium Carbon ◽  
Average Absolute Relative Error ◽  
Steel Material

Isothermal tensile test of medium carbon steel material was conducted on a computer controlled servo-hydraulic testing machine at the deformation temperatures (923 to 1223 K) and the strain rates (0.05 to 1.0 s-1). Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For the model training and testing purpose, deformation temperature, strain rate and strain data were considered as inputs and in addition, the flow stress data were used a targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination (R2) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.997 and 0.913%, respectively. In addition, by evaluating each test conditions, it was found that the average absolute relative error based on an ANN model varied from 0.55% to 1.36% and moreover, the results showed the better predictability compared with the measured data. Overall, the trained BP-ANN model is found to be much more efficient and accurate by means of flow stress prediction with respect to the experimental data for an entire tested conditions.

scholarly journals Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1315 ◽  
Author(s):  
Mohanraj Murugesan ◽  
Muhammad Sajjad ◽  
Dong Won Jung
Keyword(s):  
Experimental Data ◽  
Flow Stress ◽  
Carbon Steel ◽  
Medium Carbon Steel ◽  
Medium Carbon ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Steel Material ◽  
Stress Prediction ◽  
Bp Model

The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

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

2013 ◽  
Vol 345 ◽  
pp. 272-276 ◽  
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.

scholarly journals Prediction of Flow Stress of Annealed 7075 Al Alloy in Hot Deformation Using Strain-Compensated Arrhenius and Neural Network Models

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 5986
Author(s):  
Hongbin Yang ◽  
Hengyong Bu ◽  
Mengnie Li ◽  
Xin Lu
Keyword(s):  
Neural Network ◽  
Flow Stress ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Al Alloy ◽  
Ann Model ◽  
Neural Network Models ◽  
Prediction Ability ◽  
7075 Al Alloy ◽  
Average Absolute Relative Error

Hot compression experiments of annealed 7075 Al alloy were performed on TA DIL805D at different temperatures (733, 693, 653, 613 and 573 K) with different strain rates (1.0, 0.1, 0.01 and 0.001 s−1.) Based on experimental data, the strain-compensated Arrhenius model (SCAM) and the back-propagation artificial neural network model (BP-ANN) were constructed for the prediction of the flow stress. The predictive power of the two models was estimated by residual analysis, correlation coefficient (R) and average absolute relative error (AARE). The results reveal that the deformation parameters including strain, strain rate, and temperature have a significant effect on the flow stress of the alloy. Compared with the SCAM model, the flow stress predicted by the BP-ANN model is in better agreement with experimental values. For the BP-ANN model, the maximum residual is only 1 MPa, while it is as high as 8 MPa for the SCAM model. The R and AARE for the SCAM model are 0.9967 and 3.26%, while their values for the BP-ANN model are 0.99998 and 0.18%, respectively. All these reflect that the BP-ANN model has more accurate prediction ability than the SCAM model, which can be applied to predict the flow stress of the alloy under high temperature deformation.

The Influence of Quench Tempering and Carburazing Treatment towards Mechanical Properties and Microstructure of Medium Carbon Steel for Automotive Application

2016 ◽  
Vol 842 ◽  
pp. 99-102
Author(s):  
Abdul Aziz ◽  
Muhammad Fitrullah ◽  
Suryana ◽  
Febri Firmansyah
Keyword(s):  
Mechanical Properties ◽  
Carbon Steel ◽  
Medium Carbon Steel ◽  
Automotive Application ◽  
Medium Carbon ◽  
Steel Material ◽  
Produce Steel ◽  
Hardness Values ◽  
Carburizing Process

Gear is one of the machine components that is widely used in industrial and automotive fields. In machinery process, gear has a very important function to forward speed, power, or torque from one engine component to other components as a mechanical drive. Today a lot of development to obtain a good quality of gear, due to many gears were damage, worn out, and broken because they were not strong enough to resist friction and pressure. In addition, broken gears due to pressure and friction make them did not last long. To increase the hardness value of gear, then it needs though material that can be used when the gear reach optimum rotation. The material that is widely used for gear application is medium carbon steel. The medium carbon steel is a metal material that has carbon composition ranging from 0.30 to 0.59%. This medium carbon steel has hardness value of 174.501 HVN without treatment. The process of quench tempering and carburizing are conducted to increase hardness and toughness value of the material. The hardness value of gear is 140 HVN. The result of the research showed the hardness value at various temperature 780°C, 810°C, and 840°C. The optimum hardness values ​is 165.355 HVN at the temperature of 840°C. Medium carbon steel is expected to be an alternative to produce steel material with certain mechanical properties. This research also conducted heat treatment in austenite area and then detained with holding time of 20, 40, and 60 minutes. Furthermore, quench tempering was conducted and followed by carburizing to obtain a ferrite phase and coarse pearlite and to increase hardness value after quech tempering. It is expected that after quech tempering and carburizing process, steel with better mechanical properties can be produced. This research obtained the increase of hardness value and the number of pearlite and ferrite.

Development of Constitutive Relationships Using Compression Testing of a Medium Carbon Steel

1992 ◽  
Vol 114 (1) ◽  
pp. 116-123 ◽  
Author(s):  
K. P. Rao ◽  
E. B. Hawbolt
Keyword(s):  
Flow Stress ◽  
Carbon Steel ◽  
Strain Rate ◽  
Hot Deformation ◽  
Activation Parameters ◽  
Medium Carbon Steel ◽  
Power Relationship ◽  
Process Variables ◽  
Element Analysis ◽  
Medium Carbon

Empirical or semi-empirical stress-strain relationships are of limited applicability because (i) they require a large number of constants to represent the effect of process variables, (ii) they are not able to adequately describe the typical hot deformation characteristics i.e., strain hardening at lower strains and steady state flow stress at higher strains, and (iii) they are not able to provide reliable extrapolation. In the present study, flow curves for hot deformation of a medium carbon steel in compression were obtained using a computer controlled thermo-mechanical simulator. The flow stress data were analyzed using three Arrhenius-type equations, each representing the flow stress in terms of strain rate and temperature at different strain levels. It was found that the hyperbolic-sine equation represented the data very well; each of the different activation parameters of this equation varied systematically with strain, and could be satisfactorily described using a power relationship. Using these proposed relationships the flow stress can be described in terms of the process variables—strain, strain rate and temperature—in an explicit fashion of use in finite-element analysis of hot deformation processes.

scholarly journals Experimental data on mechanical properties evaluation of medium carbon steel quenched in different waste media

Data in Brief ◽  
2018 ◽  
Vol 20 ◽  
pp. 1224-1228 ◽  
Author(s):  
P.P. Ikubanni ◽  
O.O. Agboola ◽  
A.A. Adediran ◽  
A.A. Adeleke ◽  
B.T. Ogunsemi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Carbon Steel ◽  
Medium Carbon Steel ◽  
Medium Carbon

Study on the Flow Stress Curve of the Warm Deformation of Medium Carbon Steel

2012 ◽  
Vol 535-537 ◽  
pp. 517-520 ◽  
Author(s):  
Zhi Jie Li ◽  
Yan Peng ◽  
Hong Min Liu ◽  
Li Zi Xiao ◽  
Su Fen Wang ◽  
...  
Keyword(s):  
Flow Stress ◽  
Carbon Steel ◽  
Strain Rate ◽  
Peak Stress ◽  
Medium Carbon Steel ◽  
Warm Deformation ◽  
Medium Carbon ◽  
Stress Curve ◽  
Flow Stress Curve ◽  
Gleeble 3500

The warm compression experiment of medium carbon steel was conducted using the Gleeble-3500 thermal/mechanical simulator system. By the experiment, the warm deformation of medium carbon steel was studied within the temperature (500~700°C) and the strain rate (0.001~10s-1). The results indicate that the flow stress was increasing with the lowering temperature and the higher strain rate. And the stress-strain curves could be divided into four parts, including four stage of the Strain-Hardening, the First Softening, the Strong Softening, and the Steady Deformation. Dynamic recovery softening has little effect on the flow stress. The peak stress was caused by kink and fracture of the lamellar cementite. Strong softening stage was longer than other one, while its softening influence was stronger compared with hot deformation.

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