scholarly journals Estimating Johnson-Cook Material Parameters using Neural Networks

2021 ◽  
Vol 53 ◽  
pp. 680-689
Author(s):  
Nesar Ahmed Titu ◽  
Matt Baucum ◽  
Timothy No ◽  
Mitchell Trotsky ◽  
Jaydeep Karandikar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Material Parameters

ANSLC Artifitial Neural Strain Life Curves

2008 ◽  
Author(s):  
Chalid el Dsoki ◽  
Holger Hanselka ◽  
Heinz Kaufmann ◽  
Andreas Ro¨big
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Parameters ◽  
Cost Effective ◽  
Material Behaviour ◽  
Easy Method ◽  
Linear Flow ◽  
Material Parameters ◽  
Experimental Effort

A durable design for linear flow split sheet components requires suitable methods and transferability criteria which are not yet available for dentritic structures. Knowledge of the cyclic material behaviour is essential for this. For this reason, the cyclic material parameters are determined as a function of the product’s properties (level of deformation, microstructure, surface finish, residual stresses) and different loading parameters. However, since the determination of the cyclic parameters is associated with considerable experimental effort and costs, a cost-effective and easy method is sought to determine these parameters. A very promising approach for this is the application of artificial neural networks (ANN) [1, 2, 3, 4, 5] since they have the ability to generate the influences on the fatigue strength from the manufacturing and environmental parameters using sensibly selected input parameters. They offer the possibility to access acquired knowledge and to thus construct a multidimensional map based on a few tests.

A Novel Constitutive Parameters Identification Procedure for Hyperelastic Skeletal Muscles Using Two-Way Neural Networks

2021 ◽  
pp. 2150060
Author(s):  
Yang Li ◽  
Jianbing Sang ◽  
Xinyu Wei ◽  
Zijian Wan ◽  
G. R. Liu
Keyword(s):  
Neural Networks ◽  
Skeletal Muscles ◽  
Work Load ◽  
Hyperelastic Material ◽  
Load Prediction ◽  
Material Parameters ◽  
Penalty Factor ◽  
Comparison Results ◽  
Predicted Values ◽  
Constitutive Parameters

Muscle soreness can occur after working beyond the habitual load, especially for people engaged in high-intensity work load. Prediction of hyperelastic material parameters is essentially an inverse process, which possesses challenges. This work presents a novel procedure that combines nonlinear finite element method (FEM), two-way neural networks (NNs) together with experiments, to predict the hyperelastic material parameters of skeletal muscles. FEM models are first established to simulate nonlinear deformation of skeletal muscles subject to compressions. A dataset of nonlinear relationship between nominal stress and principal stretch of skeletal muscles is created using our FEM models. The dataset is then used to establish two-way NNs, in which a forward NN is trained and it is in turn used to train the inverse NN. The inverse NN is used to predict the hyperelastic material parameters of skeletal muscles. Finally, experiments are carried out using fresh skeletal muscles to validate the predictions in great detail. In order to examine the accuracy of the two-way NNs predicted values against the experimental ones, a decision coefficient [Formula: see text] with penalty factor is introduced to evaluate the performance. Studies have also been conducted to compare the present two-way NNs approach with the other existing methods, including the directly (one-way) inverse problem NN, and improved niche genetic algorithm (INGA). The comparison results show that two-way NNs model is an accurate approach to identify the hyperelastic parameters of skeletal muscles. The present two-way NNs method can be further expanded to the predictions of constitutive parameters of other type of nonlinear materials.

Identification of viscoplastic material parameters from spherical indentation data: Part I. Neural networks

2006 ◽  
Vol 21 (3) ◽  
pp. 664-676 ◽  
Author(s):  
E. Tyulyukovskiy ◽  
N. Huber
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Phenomenological Model ◽  
Numerical Solutions ◽  
Element Model ◽  
Finite Element Simulations ◽  
Initial Slope ◽  
Spherical Indentation ◽  
Viscoplastic Material ◽  
Material Parameters

In this paper, a new method for the identification of material parameters is presented. Neural networks, which are trained on the basis of finite element simulations, are used to solve the inverse problem. The material parameters to be identified are part of a viscoplasticity model that has been formulated for finite deformations and implemented in the finite element code ABAQUS. A proper multi-creep loading history was developed in a previous paper using a phenomenological model for viscoplastic spherical indentation. Now, this phenomenological model is replaced by a more realistic finite element model, which provides fast computation and numerical solutions of high accuracy at the same time. As a consequence, existing neural networks developed for the phenomenological model have been extended from a power law hardening with two material parameters to an Armstrong–Frederick hardening rule with three parameters. These are the yield stress, the initial slope of work hardening, and maximum hardening stress of the equilibrium response. In addition, elastic deformation is taken into account. The viscous part is based on a Chaboche-like overstress model, consisting of two material parameters determining velocity dependence and overstress as a function of the strain rate. The method has been verified by additional finite element simulations. Its application for various metals will be presented in Part II, [J. Mater. Res.21, 677 (2006)].

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