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Yu Sun - One of the best experts on this subject based on the ideXlab platform.
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modeling the Constitutive Relationship of al 0 62mg 0 73si alloy based on artificial neural network
Metals, 2017Co-Authors: Ying Han, Yu Sun, Shun Yan, Hua ChenAbstract:In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683–783 K and strain-rate range of 0.001–1 s−1. According to the obtained true stress–true strain curves, the Constitutive Relationship of the alloy was revealed by establishing the Arrhenius-type Constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol−1. The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type Constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress.
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modeling the Constitutive Relationship of powder metallurgy ti 47al 2nb 2cr alloy during hot deformation
Journal of Materials Engineering and Performance, 2015Co-Authors: Yu Sun, Junshuai RenAbstract:In the present work, the isothermal compression tests of PM alloy Ti-47Al-2Nb-2Cr were carried out in the temperature range of 950-1200 °C. A Gleeble 1500D thermosimulation machine was used, and samples were tested at strain rates ranging from 10−3 to 10−1 s−1. Based on the obtained flow stress curves, the hot deformation behavior was presented. The Constitutive Relationship of powder metallurgy (PM) Ti-47Al-2Nb-2Cr alloy was developed using an Arrhenius-type Constitutive model that involves strain compensation in addition to an artificial neural network model. The accuracy and reliability of the developed models were quantified in terms of statistical parameters such as correlation coefficient and absolute value of relative error. It was found that deformation temperature and strain rate have obvious effects on the flow characteristics, and the flow stress increases with the increasing strain rate and the decreasing temperature. Moreover, the proposed models possess excellent prediction capability of flow stresses for the present alloy during hot deformation. Compared with the traditional Arrhenius-type model, the backpropagation neural network model is more accurate when presenting the isothermal compressing deformation behavior at elevated temperatures for PM Ti-47Al-2Nb-2Cr alloy.
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modeling the Constitutive Relationship of cr20ni25mo4cu superaustenitic stainless steel during elevated temperature
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2012Co-Authors: Ying Han, Guanjun Qiao, Yu Sun, Dening ZouAbstract:Abstract Isothermal compression tests at temperatures of 1273–1473 K and strain rates ranging from 0.01 to 10 s −1 were preformed on Cr 20 Ni 25 Mo 4 Cu superaustenitic stainless steel to reveal the hot deformation characteristics. In order to give a precise prediction of flow behavior, the obtained experimental data was employed to derive the Constitutive Relationship incorporating the effect of strain. It is found that the effect of temperature and strain rate on flow stress is significant and their Relationship can be represented by the Zener–Hollomon parameter including Arrheuins term. The material constant in the model, such as α , n , Q and ln A functioned by the strain is identified using sixth order polynomial. The flow stresses calculated by the developed model are reasonable agreement with the experimental ones, which indicates that the Constitutive Relationship can effectively describe the high temperature flow behavior of Cr 20 Ni 25 Mo 4 Cu superaustenitic stainless steel and can be used to numerically analyze the hot deformation process of the present material.
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modeling Constitutive Relationship of ti17 titanium alloy with lamellar starting microstructure
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2012Co-Authors: Weidong Zeng, Yu Sun, Kaixuan Wang, Yunjin Lai, Yigang ZhouAbstract:Abstract The isothermal compression tests of Ti17 titanium alloy with lamellar starting microstructure were conducted on a Gleeble-1500 thermo-mechanical simulator at the deformation temperatures ranging from 780 to 860 °C with an interval of 20 °C and the strain rates of 0.001, 0.01, 0.1, 1.0 and 10.0 s−1 with the height reduction of 40 and 60%. The typical flow curves exhibit softening at all the deformation conditions, even at low strain rate (0.001 s−1), which have been considered that the flow softening results from adiabatic shear bands at high strain rates and lamellar globularization at low strain rates. On the basis of the experimental data, the artificial neural network model was proposed to develop the Constitutive Relationship of Ti17 alloy with lamellar starting microstructure. In the present investigation, the input parameters of ANN model are strain, strain rate and deformation temperature. The output parameter of ANN model is the flow stress. The comparison of experimental flow stresses with predicted value by ANN model and calculated value by regression model was carried out. It is found that the predicted flow stresses obtained from ANN were in a better agreement with the experimental values, indicating that it is available and novel to establish the Constitutive Relationship of Ti17 alloy using the technique of artificial neural network.
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modeling of Constitutive Relationship of ti 25v 15cr 0 2si alloy during hot deformation process by fuzzy neural network
Materials & Design, 2010Co-Authors: Yuanfei Han, Weidong Zeng, Yongqing Zhao, Xuemin Zhang, Yu SunAbstract:In this paper, an adaptive fuzzy-neural network model has been established to model the Constitutive Relationship of Ti–25V–15Cr–0.2Si alloy during high temperature deformation. The network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained at deformation temperatures of 900–1100 °C, strain rates of 0.01–10 s−1, and height reduction of 50%. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. It has shown that the predicted values are in satisfactory agreement with the experimental results and the maximum relative error is less than 10%. It proved that the fuzzy-neural network was an easy and practical method to optimize deformation process parameters.
Cai Jian - One of the best experts on this subject based on the ideXlab platform.
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Constitutive Relationship OF L-SECTION CFT WITH BINDING BARS
Engineering mechanics, 2008Co-Authors: Cai JianAbstract:The interactions within the components of L-section concrete-filled steel tube (CFT) with binding bars subjected to axial load are analyzed. By reasonably setting the effective confined areas of L-section CFT with binding bars, the L-section is divided into a square and two rectangular sections. The rectangular sections have binding bars while the square section has no one. The logical assumptions on the characters and boundary conditions of the cutting section are suggested. The Constitutive Relationship of confined concrete is adopted to build the equivalent Constitutive Relationship of the core concrete confined by L-section steel tube with binding bars and the parameters are confirmed through experimental data. The experimental specimens are calculated by the proposed Constitutive Relationship. The calculating results of load-strain curves are in good agreement with the experimental ones.
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Constitutive Relationship OF RECTANGULAR CFT COLUMNS WITH BINDING BARS
Engineering mechanics, 2008Co-Authors: Cai JianAbstract:A Constitutive Relationship model for rectangular concrete-filled steel tube columns with binding bars (rectangular CFT-WB) is developed based on the Constitutive model for square CFT-WB and the analysis of mechanism of rectangular CFT-WB. The Constitutive model considers the difference between concrete confinement effect provided by broad faces and that provided by narrow faces of the steel tube and the difference in the binding bar arrangement in different directions. The failure criterion for concrete based on true triaxial compression test is used to predict the ultimate strength of concrete core. The parameters of the model are calibrated against the test results. Finally, a calculation of complete load-stress Relationship curves is conducted for some certain experimental specimens using this Constitutive Relationship. The comparison between the results of calculation and those of corresponding test shows that they agree well with each other.
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Constitutive Relationship of Square CFT
Science Technology and Engineering, 2007Co-Authors: Cai JianAbstract:A Constitutive Relationship of concrete core confined by square steel tube is put forward based on the analysis on interaction mechanics within components of square CFT and the conception of equivalent lateral compressive stress. The Constitutive Relationship takes similar form of the Constitutive model of confined concrete while its parameters such as coefficient of ultimate strain, effectively lateral compressive stress, and transverse stress of steel tube and so on are modified to fit square CFT with experimental results. Finally, the experimental specimens are calculated with the proposed Constitutive Relationship and the theoretical calculating load-strain curves are shown in good agreement with the experimental ones. The proposed Constitutive Relationship can evaluate ultimate strength and deformation of square CFT effectively.
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Constitutive Relationship OF SQUARE CFT WITH BINDING BARS
Engineering mechanics, 2006Co-Authors: Cai JianAbstract:A Constitutive Relationship for square concrete-filled steel tube with binding bars (square CFT-WB) is put forward based on the analysis of interaction within the components of square CFT-WB and the conception of equivalent lateral compressive stress. The Constitutive Relationship takes a similar form of the Constitutive model of confined concrete while its parameters are modified to fit square CFT-WB according to experimental results. Finally, a calculation of complete load-stress Relationship curves is conducted for some certain experimental specimens using this Constitutive Relationship. The comparison between the results of calculation and the results of experiment shows that they agree well with each other.
Nader Tabatabaee - One of the best experts on this subject based on the ideXlab platform.
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A thermodynamic-based large deformation viscoplastic Constitutive Relationship for asphalt concrete compaction
International Journal of Solids and Structures, 2019Co-Authors: Mohammad M. Karimi, Masoud K. Darabi, Nader TabatabaeeAbstract:Abstract This research proposes a large deformation, time-dependent viscoplastic Constitutive Relationship to enhance the prediction of the compaction degree of asphalt concrete materials under laboratory and field conditions. A large-deformation thermodynamic-based framework is presented. The Helmholtz free energy and rate of energy dissipation functions were assumed to derive rate-dependent Constitutive Relationships to relate multi-axial state of stresses to the recoverable and non-recoverable deformation response of asphalt concrete during compaction. A straightforward method that allows the calibration of the proposed model against laboratory compaction data (e.g., data from Superpave Gyratory Compactor; SGC) is presented. Numerical algorithms associated with the proposed Constitutive Relationship were implemented in the finite element (FE) code Abaqus via the user material subroutine UMAT. The model is calibrated against SGC deformation data at different number of gyrations (time). The calibrated model was utilized to predict the field compaction of asphalt concrete. Comparisons of the model predictions and field measurements showed that the model is capable of predicting the compaction of asphalt concrete materials both in the laboratory and in the field.
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Development of a stress-mode sensitive viscoelastic Constitutive Relationship for asphalt concrete: experimental and numerical modeling
Mechanics of Time-Dependent Materials, 2017Co-Authors: Mohammad M. Karimi, Nader Tabatabaee, H. Jahanbakhsh, Behnam JahangiriAbstract:Asphalt binder is responsible for the thermo-viscoelastic mechanical behavior of asphalt concrete. Upon application of pure compressive stress to an asphalt concrete specimen, the stress is transferred by mechanisms such as aggregate interlock and the adhesion/cohesion properties of asphalt mastic. In the pure tensile stress mode, aggregate interlock plays a limited role in stress transfer, and the mastic phase plays the dominant role through its adhesive/cohesive and viscoelastic properties. Under actual combined loading patterns, any coordinate direction may experience different stress modes; therefore, the mechanical behavior is not the same in the different directions and the asphalt specimen behaves as an anisotropic material. The present study developed an anisotropic nonlinear viscoelastic Constitutive Relationship that is sensitive to the tension/compression stress mode by extending Schapery’s nonlinear viscoelastic model. The proposed Constitutive Relationship was implemented in Abaqus using a user material (UMAT) subroutine in an implicit scheme. Uniaxial compression and indirect tension (IDT) testing were used to characterize the viscoelastic properties of the bituminous materials and to calibrate and validate the proposed Constitutive Relationship. Compressive and tensile creep compliances were calculated using uniaxial compression, as well as IDT test results, for different creep-recovery loading patterns at intermediate temperature. The results showed that both tensile creep compliance and its rate were greater than those of compression. The calculated deflections based on these IDT test simulations were compared with experimental measurements and were deemed acceptable. This suggests that the proposed viscoelastic Constitutive Relationship correctly demonstrates the viscoelastic response and is more accurate for analysis of asphalt concrete in the laboratory or in situ.
He Yang - One of the best experts on this subject based on the ideXlab platform.
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A new Constitutive Relationship for alloy TC11
Journal of Materials Engineering and Performance, 1997Co-Authors: Qiang Liu, He YangAbstract:Engineers need Constitutive Relationships for planning engineering of high quality forgings. Accordingly, accurately describing the mechanical performance of material deformation is of decisive importance. In this paper, a new method is presented, in which a four-layer backpropagation neural network is built to acquire the Constitutive Relationship of the TC11 alloy based on the homogeneous compression test. Temperature, effective strain, and effective strain rate are used as the input vectors of the neural network, and the output of the neural network is the flow stress. After the network is trained with experimental data, it correctly reproduces the flow stress in the sampled data. Furthermore, when the network is presented with nonsampled data, it also correctly predicts the flow stress.
Y G Zhou - One of the best experts on this subject based on the ideXlab platform.
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modeling Constitutive Relationship of ti40 alloy using artificial neural network
Materials & Design, 2011Co-Authors: Yuyao Sun, W D Zeng, Yongqing Zhao, X M Zhang, Y Shu, Y G ZhouAbstract:Abstract Constitutive Relationship equation reflects the highly non-linear Relationship of flow stress as function of strain, strain rate and temperature. It is a necessary mathematical model that describes basic information of materials deformation and finite element simulation. In this paper, based on the experimental data obtained from Gleeble-1500 Thermal Simulator, the Constitutive Relationship model for Ti40 alloy has been developed using back propagation (BP) neural network. The predicted flow stress values were compared with the experimental values. It was found that the absolute relative error between predicted and experimental data is less than 8.0%, which shows that predicted flow stress by artificial neural network (ANN) model is in good agreement with experimental results. Moreover, the ANN model could describe the whole deforming process better, indicating that the present model can provide a convenient and effective way to establish the Constitutive Relationship for Ti40 alloy.