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您的检索式：作者名="H.Vafaeenezhad"
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| 1 | Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network(ANN)model显示文摘The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium(Mg-Al-Ca)alloy by both constitutive equation and ANN model.Toward this end,hot compression experiments were performed in 250-450℃and in strain rates of 0.001-1 s^(−1).The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain,strain rate and temperature into account.Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation,in relatively high strain rate and low temperature values,the precision of the models become decreased due to activation of twinning phenomenon.At that moment and for a better evaluation of twinning effect during deformation,a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy.Then,the performance of the two suggested models has been assessed using a statistical criterion.The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior. | S.Aliakbari Sani G.R.Ebrahimi H.Vafaeenezhad A.R.Kiani-Rashid | 2018 | Journal of Magnesium and Alloys2018，6，2： | 12 |
| 2 | A novel approach to determine residual stress field during FSW of AZ91 Mg alloy using combined smoothed particle hydrodynamics/neuro-fuzzy computations and ultrasonic testing显示文摘The faults in welding design and process every so often yield defective parts during friction stir welding(FSW).The development of numerical approaches including the finite element method(FEM)provides a way to draw a process paradigm before any physical implementation.It is not practical to simulate all possible designs to identify the optimal FSW practice due to the inefficiency associated with concurrent modeling of material flow and heat dissipation throughout the FSW.This study intends to develop a computational workflow based on the mesh-free FEM framework named smoothed particle hydrodynamics(SPH)which was integrated with adaptive neuro-fiizzy inference system(ANFIS)to evaluate the residual stress in the FSW process.An integrated SPH and ANFIS methodology was established and the well-trained ANIS was then used to predict how the FSW process depends on its parameters.To verify the SPH calculation,an itemized FSW case was performed on AZ91 Mg alloy and the induced residual stress was measured by ultrasonic testing.The suggested methodology can efficiently predict the residual stress distribution throughout friction stir welding of AZ91 alloy. | A.R.Eivani H.Vafaeenezhad H.R.Jafarian J.Zhou | 2021 | Journal of Magnesium and Alloys2021，9，4： | 2 |
| 3 | Experimental and numerical study on plasma nitriding of AISI P20 mold steel显示文摘In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures(450℃, 500℃, and 550℃) and over a range of time periods(2.5, 5, 7.5, and 10 h), and at a fixed gas N_2:H_2 ratio of 75vol%:25vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The hardness profile of the samples was identified, and the microhardness profile from the surface to the sample center was recorded. The results show that ε-nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardness and hardness depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface were all used as network inputs; Vickers hardness measurements were given as the output of the model. The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel. | N.Nayebpashaee H.Vafaeenezhad Sh.Kheirandish M.Soltanieh | 2016 | International Journal of Minerals,Metallurgy and Materials2016，23，9： | 2 |
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