Identification Of Material Model Research Articles

Identification of the extended standard linear solid material model by means of experimental dynamical measurements

A novel algebraic procedure for the non-parametric identification of the material model by means of dynamical test measurements is proposed. An extended Standard Linear Solid (SLS) material model is taken into account to model the material linear visco-elastic behavior. It consists of the series arrangement of fractional Kelvin model elements adopting real parameters and integer and non-integer order time differential operators, and of hysteretic Kelvin model elements adopting complex parameters and integer order time differential operators. Hysteretic Kelvin model elements are introduced to take account of the material hysteretic behavior. The material E(j⋅ω) complex modulus, is analytically modeled as the ratio of pseudo polynomials (non-integer power terms) in the j⋅ω Fourier variable. A multi-step, iterative, material model identification technique is here proposed to identify the unknown polynomial coefficients and the non-integer exponent values starting from E(j⋅ω) material discrete estimates from input-output dynamical measurements made on a beam specimen at different ω frequency values. Computational, nonphysical SLS elements resulting from the application of the identification procedure can be found and eliminated, so that a low order optimal model result. Some results obtained by applying the proposed identification technique with real experimental measurements are shown and discussed.

An Approach for Material Model Identification of a Composite Coating Using Micro-Indentation and Multi-Scale Simulations

While deposited thin film coatings can help enhance surface characteristics such as hardness and friction, their effective incorporation in product design is restricted by the limited understanding of their mechanical behavior. To address this, an approach combining micro-indentation and meso/micro-scale simulations was proposed. In this approach, micro-indentation testing was conducted on both the coating and the substrate. A meso-scale uniaxial compression finite element model was developed to obtain a material model of the coating. This material model was incorporated within an axisymmetric micro-scale model of the coating to simulate the indentation. The proposed approach was applied to a Ti/SiC metal matrix nanocomposite (MMNC) coating, with a 5% weight of SiC nanoparticles deposited over a Ti-6Al-4V substrate using selective laser melting (SLM). Micro-indentation testing was conducted on both the Ti/SiC MMNC coating and the Ti-6Al-4V substrate. The results of the meso-scale finite element indicated that the MMNC coating can be represented using a bi-linear elastic-plastic material model, which was incorporated within an axisymmetric micro-scale model. Comparison of the experimental and micro-scale model results indicated that the proposed approach was effective in capturing the post-indentation behavior of the Ti/SiC MMNC coating. This methodology can also be used for studying the response of composite coatings with different percentages of reinforcements.

Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization.

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.

Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification.

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.

Material model identification from set of experiments and validation by DIC

A methodology for calibration and validation of material models using more simultaneous experiments is presented. The procedure contains the selection of suitable parameters for identification using a sensitivity analysis, parameters identification, analysis of usability of the material model, and validation of the material model. Three elastoplastic models are evaluated in this study: the bilinear isotropic hardening model with Von Mises yield condition, the bilinear isotropic hardening model with the Hill anisotropic yield condition, and the nonlinear isotropic hardening model with Hill anisotropic yield condition. Five proportional as well as nonproportional monotonic loading experiments conducted on aluminium alloy 2124-T851 are used for the presentation of the inverse approach application, whereas four of them are the source of identification. The validation of calibrated material models is done based on the strain field measurement realised by the Digital Image Correlation method. The nonlinear isotropic hardening rule together with Hill yield condition brings the best description of stress–strain behaviour of the material under investigation.

On the modeling of a visco-hyperelastic polymer gel under blunt ballistic impacts

In this paper, the modeling of a polymer gel used as target medium in blunt ballistic experiments is presented. A new visco-hyperelastic law based on the Mooney–Rivlin model is proposed and implemented in a numerical simulation software. The material model identification relies on mechanical characterization experiments performed at room temperature through tensile and compressive tests over a wide range of strain rates (0.002–1500 s−1). Indeed, these experiments highlight a significant strain rate sensitivity but also a non-homogeneous strain and a barreling effect during compressive experiments. Hence, constitutive modeling of the material behavior cannot be directly determined. Tensile and compressive data are exploited with a direct and indirect identification process. An optimization by inverse technique, using finite element modeling of static and dynamic compressive tests and a global response surface method, is employed to accurately reproduce loading conditions and identify the model parameters. Finally, the proposed visco-hyperelastic law is validated through comparison with experimental data from blunt ballistic impacts over various projectile velocities.

Identification of material model for Finite Element Modelling of quenching process

Identification of material model for Finite Element Modelling of quenching process

Methodology for experimental verification of steel armour impact modelling

We present a novel methodology to experimentally verify constitutive models and numerical algorithms used in terminal ballistics of small arms ammunition. The methodology comprises of the following elements: identification of material models in a set of independent tests, terminal ballistics testing of conditions covering the most important cases of bullet–target interactions, while providing enough data to assess the scatter of parameters measured in the experiments and to create a measure characterising deviation of modelling results from the experiments. To meet the objectives of this study, 7.62mm armour-piercing ammunition was used to perforate steel armour plates at ordnance velocity. Several parameters that characterise bullet velocity and path, plate deformation and ductility were measured and used as reference data for the verification of models. Relatively complex and simple constitutive and failure models implemented in the Finite Elements (FE) code LS DYNA were used. Finally, solid Lagrange and hybrid solid/Sooth Particles Hydrodynamics (SPH) discretisation methods with detailed models of the bullet and target are presented and different findings are compared with ballistic test results. The methodology shows a significant efficiency in the assessment of the adequacy of models. In this study, stress triaxiality and strain rate based models were found to give results in good agreement with experimental results, and several physical mechanisms are well predicted.

Identification Problem of Internal Variables Model of Material

The paper deals with the identification of material model based on the internal variable. The model with one internal variable, which was average dislocation density, was considered. Identification was performed using inverse analysis (IA) of uniaxial compression tests. In this work IA was transformed to an optimization task and the goal function was defined as difference (in Euclid's norm) between measured and calculated parameters: loads in plastometric tests (used to identify flow stress) and stresses in stress relaxation tests (used to identify recrystallization kinetics). Exploring a possibility of making the identification more reliable by application the Sensitivity Analysis (SA) was the main objective of the work. The IA was preceded by SA of the model output with respect to the model parameters to select an efficient optimization algorithm and/or eliminate local minima. Selected results of identification for different materials are presented in the paper, as well.

Computer System for Identification of Material Models on the Basis of Plastometric Tests

Abstract The paper describes the hybrid computer system dedicated to identification of models of materials subjected to thermomechanical processing. The functionalities of the system consist of plastometric tests data processing and application of the inverse analysis. The latter functionality is realized unconventionally, instead of the finite element method the metamodel is implemented using artificial neural network. The metamodels, used for simulations of the plastometric tests, are imported to the proposed computer system as external plugins, what guarantees flexibility and possibility of further development. On the other hand, application of rich optimization libraries assures the best possible solution of the problem. Basic principles of the inverse analysis with metamodels and mentioned optimization procedures are described in the paper. Selected examples of identification of models for various metallic materials recapitulate the paper.

Application of inverse analysis with metamodelling for identification of metal flow stress

The problem of effectiveness of the inverse algorithms used for identification of material model is investigated in the paper. Identification of flow stress models in metal forming processes is considered. This identification is usually performed by coupling the Finite element (FE) model with optimisation techniques which leads to long computing times. A proposition of application of the metamodel in the inverse analysis is presented in the paper. Metamodel is an alternative for the FE model. Artificial neural network was used as a metamodel of the axisymmetrical compression test. Experiments were performed on the Gleeble 3800 simulator for various materials and inverse calculations with the metamodel were performed. Validation of the results confirmed with higher degree of accuracy of the proposed approach.Dans cet article, on examine le problème d’efficacité des algorithmes inverses utilisés dans l’identification de modèle de matériau. On considère l’identification de modèles de contrainte d’écoulement dans les procédés de traitement du métal. Cette identification est habituellement effectuée en couplant le modèle d’EF à des techniques d’optimisation, ce qui mène à de longues durées de calculs. Dans cet article, on propose l’application du métamodèle dans l’analyse inverse. Le métamodèle est une substitution du modèle d’EF. On a utilisé le réseau neuronal artificiel comme métamodèle de l’essai de compression axisymétrique. On a effectué des expériences avec le simulateur Gleeble 3800 pour des matériaux variés et l’on a effectué des calculs inverses à l’aide du métamodèle. La validation des résultats a confirmé le très bon degré d’exactitude de cette approche.

Material Model Identification for DC04 Based on the Numerical Modelling of the Polycrystalline Microstructure and Experimental Data

Sheet-bulk-metal forming processes require an accurate material model which is derived in this contribution. The microscopic model is based on a simulation of a real microstructure. A validation on the macroscopical scale is performed through the reproduction of the experimentally calculated yield surface based on the homogenised structural response of a corresponding deformed representative volume element (RVE). The microstructural material model is also compared with a macroscopical phenomenological model based on logarithmic strains. The homogenised microscopic model and the phenomenological macroscopic model are in good agreement with the evolution of the stresses and strains obtained during the experiments.

Deviations between FE Simulation and Experiments in the SPIF Process

Single Point Incremental Forming (SPIF) is a modern and flexible alternative to traditional forming techniques. It thanks its flexibility to the fact that it does not require a dedicated tool set to operate. Numerical simulation of the SPIF process requires an accurate FE model. In the past several attempts have been undertaken to use inverse methods for sheet metal SPIF material model identification based on shearing, tensile and indenting tests. The basic idea of this paper is that the results of inverse methods can be improved by using the SPIF process itself as experimental data source. A SPIF experiment dedicated for material identification on a simple geometry using large step sizes is presented and compared with the FE simulation of the forming process based on an initial guess for the material behavior.

Numerical identification of material model for C–Mn steel using micro-indentation test

Identification of the elastoplastic material model for C–Mn steel, using finite element model of micro-indentation test developed by the authors and proposed algorithm of inverse analysis, is one of the objectives of the project. The micro-indentation experiment is widely described in the present paper, especially those parts, which are meaningful in getting input data for direct, further application in the numerical model of micro-indentation test and in the inverse procedure. Finite element solution connected with the inverse algorithm, which is based on the simplex method, is used to search for the unknown parameters of material model. Validation of the developed inverse algorithm is the particular objective of the present work. The present paper shows that material model determined using the inverse analysis is in agreement with that obtained from the tensile test. The results coincide also with the data available in the literature.

On Identification of Material Models When Nonrepeatability of Test Data is Present: Application to Textile Composites

Engineering test data occasionally violate assumptions underlying standard material model identification. Consequently, one has to apply appropriate remedies with respect to each violation to enhance the reliability of identified material parameters. This paper generalizes the use of the signal-to-noise weighting scheme when heteroscedasticity of test data are suspected. Different mathematical and practical aspects of the approach are discussed. Additionally, the ensuing weighted identification process is simplified to an equivalent standard form by means of a space transformation. Finally, the approach is applied to the identification of a nonlinear material model for textile composites, on both qualitative and quantitative levels.

An Unified Computational Framework for Parameter Identification of Material Models in Finite Inelasticity

Parameter identification processes concern the determination of parameters in a material model in order to fit experimental data. We provide a distinct, unified algorithmic setting of a generic class of material models and discuss the associated gradient–based optimization problem. Gradient–based optimization algorithms need derivatives of the objective function with respect to the material parameter vector κ . In order to obtain the necessary derivatives, an analytical sensitivity analysis is pointed out for the unified class of algorithmic material models. The quality of the parameter identification is demonstrated for a representative example.