Identification Of Material Model Parameters Research Articles

Examination of machine learning method for identification of material model parameters

In this work, we compare two methods of using artificial neural network (ANN) to determine the optimal parameters of material model by results of high-speed plate impact experiments. The first ANN is trained to determine the free surface velocity history based on the impact parameters and material model parameters; this ANN is used as a fast emulator of model to speed-up the statistical Bayesian calibration of the model parameters. The second ANN directly determines the material model parameters based on the impact parameters and the free surface velocity history, because this ANN is trained by inverse data set; this approach for inverse problem is proposed for the first time and can be more preferable for fast-track expert systems in engineering applications. Both approaches are successfully used to determine the optimal parameters of material model in the case of impact of copper and aluminum plates at different temperatures and impact velocities with experimental data from the literature. A simple relaxation plasticity model with a constant relaxation time is used in the present research as an example; more accurate coincidence with the experimental data and wider applicability of the ANN-based approach can be expected for a more realistic plasticity model.

Coupling physics-informed neural networks and constitutive relation error concept to solve a parameter identification problem

Identification of material model parameters using full-field measurement is a common process both in industry and research. The constitutive equation gap method (CEGM) is a very powerful strategy for developing dedicated inverse methods, but suffers from the difficulty of building the admissible stress field. In this work, we present a new technique based on physics-informed neural networks (PINNs) to implement a CEGM optimization process. The main interest is to easily construct the admissible stress thanks to automatic differentiation (AD) associated with PINNs. This new method combines the high quality of the CEGM with the numerical effectivity of the PINNs and realizes the identification of material properties in a more concise way. We compare two variants of the developed method with the classical identification strategies on simple two-dimensional (2D) cases and illustrate its effectiveness in three-dimensional (3D) problems, which is of interest when dealing with tomographic images. The results indicate that the proposed method has good performance while avoiding complex calculation procedures, showing its great potential for practical applications.

Numerical Identification of Material Model Parameters of UHPFRC Slab under Blast Loading

The reliability of numerical simulations of the structural response of nonhomogeneous materials to high velocity loadings is highly dependent on the used material model and parameters. For nonhomogeneous materials, such as fibres, reinforced concrete is widely used for the Winfrith model, but the question of appropriate material parameters for Ultra-High Performance Fibre Reinforcement Concrete (UHPFRC) under high velocity loadings is still open. The article deals with possible method of inverse identification of material parameters of a UHPFRC slab under blast loading for a Winfrith material model. Possible application is in the field of numerical simulation of protective or critical infrastructure response to blast loading. Experimental measurement of the time–deflection curve through laser scanning using the triangulation method gave us input data for an inverse identification phase conducted in Optislang software. Obtained material parameters from a given range are optimized for blast loading and their Pearson’s correlation coefficient provides us information about their significance for simulation.

Two stage identification approach of material model parameters for forming thermomechanical processes

ABSTRACT The problem of proper identification of material model parameters is crucial for simulations of any material forming thermomechanical process and it directly affects the results of computations. The problem of material model parameters identification should be characterized by high effectiveness, especially for industry application. Thus, the main goal of the work was to develop the identification strategy dedicated parameters identification. The strategy consists of two stages that are a compromise between high accuracy and low computation costs of identification. The strategy was applied for material model based on an internal variable (IVM) defined with a delay differential equation. The results of calculations allowed to design the identification strategy for IVM and they were presented in the paper.

Study on Identification of Material Model Parameters from Compact Tension Test on Concrete Specimens

Identification of a concrete material model parameters using optimization is based on a calculation of a difference between experimentally measured and numerically obtained data. Measure of the difference can be formulated via root mean squared error that is often used for determination of accuracy of a mathematical model in the field of meteorology or demography. The quality of the identified parameters is, however, determined not only by right choice of an objective function but also by the source experimental data. One of the possible way is to use load-displacement curves from three-point bending tests that were performed on concrete specimens. This option shows the significance of modulus of elasticity, tensile strength and specific fracture energy. Another possible option is to use experimental data from compact tension test. It is clear that the response in the second type of test is also dependent on the above mentioned material parameters. The question is whether the parameters identified within three-point bending test and within compact tension test will reach the same values. The presented article brings the numerical study of inverse identification of material model parameters from experimental data measured during compact tension tests. The article also presents utilization of the modified sensitivity analysis that calculates the sensitivity of the material model parameters for different parts of loading curve. The main goal of the article is to describe the process of inverse identification of parameters for plasticity-based material model of concrete and prepare data for future comparison with identified values of the material model parameters from different type of fracture tests.

Direct identification of nonlinear damage behavior of composite materials using the constitutive equation gap method

This paper aims at improving the reliability of the identification of parameters that govern a non-linear damage law for orthotropic materials, the non-linearity considered being due to the inherent damage induced by in-plane shear loading. The adopted identification technique is based on the minimization of the constitutive equation gap (CEG). It is built upon the quadratic deviation between the measured and computed strain field. The paper explains how an appropriate use of the CEG method can improve the identification of material model parameters from strain field measured by an optical technique of full-field measurement. The minimization problem is solved by the modified Levenberg–Marquardt method. In the present work, the experimental data are replaced by numerical ones provided by the finite element (FE) simulations. These latter are performed on an orthotropic glass/epoxy composite material. The obtained parameters based on these numerical data are considered as reference ones. A sensitivity analysis is proposed as an attempt to examine the influence of some parameters, among which the mesh refinement, the initial set of parameters, the experimental noise, and the orthotropy ratio. Two types of noise simulating the measurement errors are tested: a random noise simulated by a Gaussian white noise and a bias noise. The obtained results demonstrated the feasibility and applications of the proposed method. The use of the CEG method gives results of good quality.

Identification of the parameters of an elastic material model using the constitutive equation gap method

Today, the identification of material model parameters is based more and more on full-field measurements. This article explains how an appropriate use of the constitutive equation gap method (CEGM) can help in this context. The CEGM is a well-known concept which, until now, has been used mainly for the verification of finite element simulations. This has led to many developments, especially concerning the techniques for constructing statically admissible stress fields. The originality of the present study resides in the application of these recent developments to the identification problem. The proposed CEGM is described in detail, then evaluated through the identification of heterogeneous isotropic elastic properties. The results obtained are systematically compared with those of the equilibrium gap method, which is a well-known technique for the resolution of such identification problems. We prove that the use of the enhanced CEGM significantly improves the quality of the results.

A goal-oriented field measurement filtering technique for the identification of material model parameters

The post-processing of experiments with nonuniform fields is still a challenge: the information is often much richer, but its interpretation for identification purposes is not straightforward. However, this is a very promising field of development because it would pave the way for the robust identification of multiple material parameters using only a small number of experiments. This paper presents a goal-oriented filtering technique in which data are combined into new output fields which are strongly correlated with specific quantities of interest (the material parameters to be identified). Thus, this combination, which is nonuniform in space, constitutes a filter of the experimental outputs, whose relevance is quantified by a quality function based on global variance analysis. Then, this filter is optimized using genetic algorithms.

In vivo characterization of the mechanical properties of human skin derived from MRI and indentation techniques

The human skin is an exceedingly complex and multi-layered material. This paper aims to introduce the application of the finite element analysis (FEA) to the in vivo characterization of the non-linear mechanical behaviour of three human skin layers. Indentation tests combined with magnetic resonance imaging (MRI) technique have been performed on the left dorsal forearm of a young man in order to reveal the mechanical behaviour of all skin layers. Using MRI images processing and a pre and post processor allows to make numerically individualized 2D model which consists of three skin layers and the muscles. FEA has been applied to simulate indentation tests. Neo-Hookean slightly compressible material model of two material constants (C10, K) has been used to model the mechanical behaviour of the three skin layers and the muscles. The identification of material model parameters was done by applying Levenberg–Marquardt algorithm (LMA). Our methodology of identification provides a range of values for each constant. Range of values of different material properties of epidermis, dermis, hypodermis are respectively, C10E = 0.12 ± 0.06 MPa, C10D = 1.11 ± 0.09 MPa, C10H = 0.42 ± 0.05 KPa, K E = 5.45 ± 1.7 MPa, K D = 29.6 ± 1,28 MPa, K H = 36.0 ± O.9 KPa.