Arash Jenab

Bio: Arash Jenab is an academic researcher from University of Windsor. The author has contributed to research in topics: Strain rate & Hardening (metallurgy). The author has an hindex of 7, co-authored 9 publications receiving 183 citations. Previous affiliations of Arash Jenab include Sharif University of Technology.

The Use of ANN to Predict the Hot Deformation Behavior of AA7075 at Low Strain Rates

Abstract: In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s−1 and temperature range of 200-450 °C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance in predicting the hot deformation behavior of 7075 aluminum alloy.

An extended model for void growth and coalescence - application to anisotropic ductile fracture

Abstract: A model for the axisymmetric growth and coalescence of small internal voids in elastoplastic solids is proposed and assessed using void cell computations. Two contributions existing in the literature have been integrated into the enhanced model. The first is the model of Gologanu-Leblond-Devaux, extending the Gurson model to void shape effects. The second is the approach of Thomason for the onset of void coalescence. Each of these has been extended heuristically to account for strain hardening. In addition, a micromechanically-based simple constitutive model for the void coalescence stage is proposed to supplement the criterion for the onset of coalescence. The fully enhanced Gurson model depends on the flow properties of the material and the dimensional ratios of the void-cell representative volume element. Phenomenological parameters such as critical porosities are not employed in the enhanced model. It incorporates the effect of void shape, relative void spacing, strain hardening, and porosity. The effect of the relative void spacing on void coalescence, which has not yet been carefully addressed in the literature. has received special attention. Using cell model computations, accurate predictions through final fracture have been obtained for a wide range of porosity, void spacing, initial void shape, strain hardening, and stress triaxiality. These predictions have been used to assess the enhanced model. (C) 2000 Elsevier Science Ltd. All rights reserved.

On the potential of recurrent neural networks for modeling path dependent plasticity

Abstract: The mathematical description of elastoplasticity is a highly complex problem due to the possible change from elastic to elasto-plastic behavior (and vice-versa) as a function of the loading path. Advanced physics-based plasticity models usually feature numerous internal variables (often of tensorial nature) along with a set of evolution equations and complementary conditions. In the present work, an attempt is made to come up with a machine-learning based model that can replicate the predictions anisotropic Yld2000-2d model with homogeneous anisotropic hardening (HAH). For this, a series of modeling problems of increasing complexity is formulated and sequentially addressed using neural network models. It is demonstrated that basic fully-connected neural network models can capture the characteristic non-linearities in the uniaxial stress-strain response such as the Bauschinger effect, permanent softening or latent hardening. A neural network with gated recurrent units (GRUs) and fully-connected layer is proposed for the modeling of plane stress plasticity for arbitrary loading paths. After training and testing the model through comparison with the Yld2000-2d/HAH model, the recurrent neural network model is also used to model the multi-axial stress-strain response of a two-dimensional foam. Here, the comparison with the results from unit cell simulations provided another validation of the proposed data-driven modeling approach.

Effects of initial aging time on processing map and microstructures of a nickel-based superalloy

Abstract: Hot compressive deformation behaviors of the aged nickel-based superalloy are studied under the deformation temperature range of 920–1040 °C and strain rate range of 0.001–1 s−1. Based on the experimental data, the processing maps are developed and correlated with the deformed microstructures of the studied nickel-based superalloy. The effects of initial aging time on the processing map and microstructures are discussed in detail. It is found that the processing map and microstructures are sensitive to the initial aging time. When the initial aging time is shorter than 12 h, the spherical and short needle-shaped δ phases ( Ni 3 Nb ) can stimulate the occurrence of dynamic recrystallization and improve the hot workability, as well as decrease the final forging temperature of the studied nickel-based superalloy. However, when the initial aging time is increased to 24 h, the excessive long needle-shaped δ phases appear and become the potential locations of wedge cracking, which easily leads to flow instability during hot deformation. The aged superalloy under 900 °C for 9 h or 12 h is suitable for the hammer forging process. The optimum deformation parameters for the hammer forging process are 1010–1040 °C and 0.1–1 s−1. The aged superalloy under 900 °C for 9 h can be used for the conventional die forging. Furthermore, the forging temperature should be controlled in the range of 980–1040 °C, and the strain rate should be lower than 0.1 s−1. The solution-treated superalloy or the aged superalloy under 900 °C for 6 h or 9 h is suitable for the isothermal die forging, and the optimum hot deformation parameters is 980–1040 °C and near 0.001 s−1.