Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

Quan, Guo-zheng; Jia, Peng; Wang, Xuan

doi:10.3390/app6030066

Cited by 44 publications

(14 citation statements)

References 32 publications

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“…As one can see in Figure 5, the values of A 3 , n 2 , α, and Q 3 constants are significantly dependent on true strain due to changes in the microstructure during deformation. To include the strain (ε) influence too, the constitutive Equation (3) was modified by using fourth-degree polynomial dependence of the material constants [28,29] As one can see in Figure 6a, the average difference between calculated and experimental flow stress values is 3.3%. In addition, the accuracy of the constitutive model was checked by the modeling of the samples' compression at a strain rate of 1 s −1 using the FEM method.…”

Section: Constitutive Model Of Hot Deformation Behaviormentioning

confidence: 99%

Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel

et al. 2020

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This study describes deformation behavior and fracture during compression and tension at high temperatures of ferritic/martensitic 13CrMoNbV steel. Hot compression and tensile tests were carried out in the temperature range of 1100–1275 °C with a thermomechanical simulator Gleeble 3800. The true stress and ultimate tensile strength decrease with an increase in the deformation temperature. The modified Arrhenius-type constitutive model was built for 13CrMoNbV ferritic/martensitic steel using the experimental stress–strain compression data. The modified Rice and Tracy ductile fracture criteria were calculated using finite element simulation of the tensile test at different temperatures. The comparison between experimental and computed force vs. displacement curves shows high predictability of the deformation and fracture models for ferritic/martensitic 13CrMoNbV steel.

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Section: Constitutive Model Of Hot Deformation Behaviormentioning

confidence: 99%

Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel

et al. 2020

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“…The progress in neural networks has greatly stimulated research. Studies [24] have reported the automatic classification systems of defects, elimination of the adverse effects of environmental parameters in capacitive pressure sensors [25], and pattern recognition for pipeline leakage localization systems [26]. Neural networks trained by certain samples can process enormous amounts of data and recognize the patterns of test samples in a short period.…”

Section: Introductionmentioning

confidence: 99%

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

Qi¹,

et al. 2020

JMSE

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The reconstruction algorithm for the probabilistic inspection of damage (RAPID) is aimed at localizing structural damage via the signal difference coefficient (SDC) between the signals of the present and reference conditions. However, tomography is only capable of presenting the approximate location and not the length and angle of defects. Therefore, a new quantitative evaluation method called the multiple back propagation neural network (Multi-BPNN) is proposed in this work. The Multi-BPNN employs SDC values as input variables and outputs the predicted length and angle, with each output node depending on an individual hidden layer. The cracks of different lengths and angles at the center weld seam of offshore platforms are simulated numerically. The SDC values of the simulations and experiments were normalized for each sample to eliminate external interference in the experiments. Then, the normalized simulation data were employed to train the proposed neural network. The results of the simulations and experimental verification indicated that the Multi-BPNN can effectively predict crack length and angle, and has better stability and generalization capacity than the multi-input to multi-output back propagation neural network.

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“…It is chemically a complex superalloy whose main components of the alloying elements are Cr, Mo, W, Ti, Ta, Al. Nimonic 80A alloys are commonly used in jet engine, gas turbine because of their high creep strength, oxidation resistance and strong resistance to high temperature corrosion [1][2][3]. It is known that some materials have higher hardness as they deform plastically.…”

Section: Introductionmentioning

confidence: 99%

Confirmation of Johnson-Cook Model Parameters for Nimonic 80A alloy by Finite Element Method

Korkmaz¹,

Günay²

2020

Politeknik Dergisi

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Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Korkmaz, M.E and Günay, M., "Confirmation of johnson-cook model parameters for nimonic 80A alloy by finite element method", Politeknik Dergisi, 23(3): 625-632, (2020).Erişim linki (To link to this article): http://dergipark.org.tr/politeknik/archive Highlights  The determination of material constitutive model (Johnson-Cook) of Nimonic 80A superalloy.  Three different types of compression tests (quasi-static, dynamic and high temperatures) in order to determine the equation parameters.  The confirmation of Johnson-Cook parameters of Nimonic 80A superalloy.  A suggestion of finite element simulation of any plastic deformation processes such as forging, rolling and deep drawing by using JC parameters of Nimonic 80A material as a next study. Graphical AbstractThe aim of the study is to confirm the predetermined J-C model parameters for Nimonic 80A superalloys using finite element method. Figure. Experimental procedure AimThe aim of the study is to confirm the predetermined J-C model parameters for Nimonic 80A superalloys using finite element method. Design & MethodologyJC parameters of Nimonic 80A nickel-based superalloys were identified via quasi-static tests, dynamic tests and different temperature tests. OriginalityBy considering the literature, it is obviously seen that there is no study on simulation of plastic deformation processes based on finite element method for Nimonic 80A superalloy. FindingsThe mean deviation between experimental results and simulation results obtained with FEM were calculated as average of %3.23. ConclusionConsequently, the Johnson-Cook model parameters of Nimonic 80A was confirmed based on overall results. Hence, it was concluded that JC model parameters of the material can be confidently used for any plastic deformation processes. ABSTRACTNimonic 80A superalloy is frequently used due to its high creep resistance, oxidation resistance and high resistance to high temperature corrosion. On the other hand, due to compatibility of simulation of plastic deformation processes, Johnson-Cook model is chosen among the materials models such as Zerille Armstrong, Bordner Partom, Steinberg-Guinan etc. In this study, primarily, quasi-static compression tests were performed for 10-3, 10-2 and 10-1 s-1 strain rates at room temperature. Secondly, dynamic compression tests were secondly conducted at high strain rates ranging from 370 to 954 s-1 using the Split Hopkinson Pressure Bar (SHPB) apparatus. Then, the compression tests were conducted at a temperature level from 24~200 °C at the reference strain rate. Johnson-Cook model parameters of Nimonic 80A were determined by analyzing the data obtained from the tests. Lastly, the compression simulations with finite element method (FEM) were performed in ANSYS Workbench to confirm the accuracy of the parameters. In the light of the results, it was determined that there is an average of %3.23 deviation between the experimental and the simulation values. The result showed that acc...

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Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

Cited by 44 publications

References 32 publications

Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel

Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

Confirmation of Johnson-Cook Model Parameters for Nimonic 80A alloy by Finite Element Method

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