scholarly journals Inverse Strategies for Identifying the Parameters of Constitutive Laws of Metal Sheets

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
P. A. Prates ◽  
A. F. G. Pereira ◽  
N. A. Sakharova ◽  
M. C. Oliveira ◽  
J. V. Fernandes
Keyword(s):  
Finite Element ◽  
Cost Function ◽  
Optimization Algorithm ◽  
Yield Criterion ◽  
Kinematic Hardening ◽  
Constitutive Laws ◽  
Constitutive Law ◽  
Identification Procedure ◽  
Metal Sheets ◽  
The Cost

This article is a review regarding recently developed inverse strategies coupled with finite element simulations for the identification of the parameters of constitutive laws that describe the plastic behaviour of metal sheets. It highlights that the identification procedure is dictated by the loading conditions, the geometry of the sample, the type of experimental results selected for the analysis, the cost function, and optimization algorithm used. Also, the type of constitutive law (isotropic and/or kinematic hardening laws and/or anisotropic yield criterion), whose parameters are intended to be identified, affects the whole identification procedure.

Plastic Deformation Theory Application in Finite Element Analysis

2012 ◽  
Vol 594-597 ◽  
pp. 2723-2726
Author(s):  
Wen Shan Lin
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Deformation Theory ◽  
Three Dimensional ◽  
Constitutive Laws ◽  
Constitutive Law ◽  
Element Analysis ◽  
Finite Element Program ◽  
Theory Of Plasticity ◽  
Element Program

In the present study, the constitutive law of the deformation theory of plasticity has been derived. And that develop the two-dimensional and three-dimensional finite element program. The results of finite element and analytic of plasticity are compared to verify the derived the constitutive law of the deformation theory and the FEM program. At plastic stage, the constitutive laws of the deformation theory can be expressed as the linear elastic constitutive laws. But, it must be modified by iteration of the secant modulus and the effective Poisson’s ratio. Make it easier to develop finite element program. Finite element solution and analytic solution of plasticity theory comparison show the answers are the same. It shows the derivation of the constitutive law of the deformation theory of plasticity and finite element analysis program is the accuracy.

Effect of Material Model on Finite Element Modeling of Aerospace Alloys

2013 ◽  
Vol 554-557 ◽  
pp. 151-156 ◽  
Author(s):  
Mehdi Saboori ◽  
Javad Gholipour ◽  
Henri Champliaud ◽  
Augustin Gakwaya ◽  
Jean Savoie ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Material Model ◽  
Constitutive Laws ◽  
Material Behavior ◽  
Constitutive Law ◽  
Expansion Process ◽  
Free Expansion ◽  
Element Modeling ◽  
Bulge Height

Increasing acceptance and use of hydroforming technology within the aerospace industry requires a comprehensive understanding of critical issues such as the material characteristics, friction condition and hydroformability of the material. Moreover, the cost of experiments that can be reduced by accurate finite element modeling (FEM), which entails the application of adapted constitutive laws for reproducing with confidence the material behavior. In this paper, the effect of different constitutive laws on FEM of tubular shapes is presented. The free expansion process was considered for developing the FEM. Bulge height, thickness reduction and strains were determined at the maximum bulge height using different constitutive models, including Hollomon, Ludwik, Swift, Voce, Ludwigson. In order to minimize the effect of friction, the free expansion experiments were performed with no end feeding. The simulation results were compared with the experimental data to find the appropriate constitutive law for the free expansion process.

scholarly journals Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4362
Author(s):  
Subramaniam Saravana Sankar ◽  
Yiqun Xia ◽  
Julaluk Carmai ◽  
Saiprasit Koetniyom
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Fuel Consumption ◽  
Optimization Algorithm ◽  
Internal Combustion Engines ◽  
Optimization Method ◽  
Computational Time ◽  
Weight Factor ◽  
Driving Cycles ◽  
The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

scholarly journals Parameter estimation of DC motor through whale optimization algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 83 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu
Keyword(s):  
Experimental Data ◽  
Cost Function ◽  
Optimization Algorithm ◽  
Dc Motor ◽  
Whale Optimization Algorithm ◽  
Model Parameters ◽  
Adaptive Model ◽  
Flux Dynamics ◽  
Whale Optimization ◽  
The Cost

This article estimates the unknown dc motor parameters by adapting the adaptive model with the reference model created by experimental data onto armature current and speed response from separately excited dc motor .The field flux dynamics, which is usually ignored, is included to model the dynamics of the motor. The block diagram including the flux dynamics and model parameters is considered as the adaptive model. The integral time square error between the instant experimental data and the corresponding adaptive model data is taken as cost function. The Whale optimization algorithm is used to minimize the cost function. Additionally, to improve the performances of optimization algorithm and for accurate result, the experimental data is divided into three intervals which form the three inequality constraints. A fixed penalty value is added to the cost function for violating these constraints. The effectiveness of estimation with two different methods is validated by convergence curve.

scholarly journals Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 1630-1634
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Cost Function ◽  
Optimization Algorithm ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification ◽  
The Neural Network ◽  
Training Examples ◽  
The Cost

Since Remote sensing images are used for a variety of applications, classification of such images is essential for extracting information. However, due to the lack of sufficient training examples, classification of remote sensing images is more complex. Also, in traditional method of classification, the statistical procedure uses only the gray value of the digital images to extract information. Neural network (NN) has a better impact in this domain. The proposed work uses an integrated NN and cuckoo Optimisation Algorithm (COA) for classification. The NN picks, organise and constructs the data to be trained into a network. The network is then trained and tested. The COA is combined with NN to aid the task of classification and to calculate the cost function. The optimization algorithm provides an appropriate feature suitable for the neural network classifier to produce final output. This hybridized technique presents the effective advantages of the neural network and further investigate the classification of the multispectral remote sensible images. The performance and error rate of the system is better when compared with other classical methods. The observed results illustrate the effectiveness of the COA to optimize the cost function. As training parameters in Neural network is found to be NP hard, utilization of COA involve its responsibility in optimizing the learning rate of NN.

scholarly journals An efficient and robust VUMAT implementation of elastoplastic constitutive laws in Abaqus/Explicit finite element code

2018 ◽  
Vol 19 (3) ◽  
pp. 308 ◽  
Author(s):  
Lu Ming ◽  
Olivier Pantalé
Keyword(s):  
Finite Element ◽  
Plastic Strain ◽  
Constitutive Laws ◽  
Constitutive Law ◽  
Computational Time ◽  
Plastic Strain Rate ◽  
Integration Scheme ◽  
Elastoplastic Material ◽  
Finite Element Software ◽  
Return Mapping

This paper describes the development of an efficient and robust numerical algorithm for the implementation of elastoplastic constitutive laws in the commercial non-linear finite element software Abaqus/Explicit through a VUMAT FORTRAN subroutine. In the present paper, while the Abaqus/Explicit uses an explicit time integration scheme, the implicit radial return mapping algorithm is used to compute the plastic strain, the plastic strain rate and the temperature at the end of each increment instead of the widely used forward Euler approach. This more complex process allows us to obtain more precise results with only a slight increase of the total computational time. Corrector term of the radial return scheme is obtained through the implementation of a safe and robust Newton–Raphson algorithm able to converge even when the piecewise defined hardening curve is not derivable everywhere. The complete method of how to implement a user-defined elastoplastic material model using the radial return mapping integration scheme is presented in details with the application to the widely used Johnson–Cook constitutive law. Five benchmark tests including one element tests, necking of a circular bar and 2D and 3D Taylor impact tests show the efficiency and robustness of the proposed algorithm and confirm the improved efficiency in terms of precision, stability and solution CPU time. Finally, three alternative constitutive laws (the TANH, modified TANH and Bäker laws) are presented, implemented through our VUMAT routine and tested.

Analytical Solutions for Circular Bars Subjected to Large Strain Plastic Torsion

1990 ◽  
Vol 57 (2) ◽  
pp. 298-306 ◽  
Author(s):  
K. W. Neale ◽  
S. C. Shrivastava
Keyword(s):  
Closed Form ◽  
Analytical Solutions ◽  
Large Strain ◽  
Kinematic Hardening ◽  
Flow Theory ◽  
Constitutive Laws ◽  
Isotropic Hardening ◽  
Large Strains ◽  
Inelastic Behavior ◽  
Rate Dependent

The inelastic behavior of solid circular bars twisted to arbitrarily large strains is considered. Various phenomenological constitutive laws currently employed to model finite strain inelastic behavior are shown to lead to closed-form analytical solutions for torsion. These include rate-independent elastic-plastic isotropic hardening J2 flow theory of plasticity, various kinematic hardening models of flow theory, and both hypoelastic and hyperelastic formulations of J2 deformation theory. Certain rate-dependent inelastic laws, including creep and strain-rate sensitivity models, also permit the development of closed-form solutions. The derivation of these solutions is presented as well as numerous applications to a wide variety of time-independent and rate-dependent plastic constitutive laws.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

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