scholarly journals On the optimisation efficiency for the inverse identification of constitutive model parameters

2021 ◽  
Author(s):  
Miguel Guimarães Oliveira ◽  
João Miguel Peixoto Martins ◽  
Bernardete Coelho ◽  
Sandrine Thuillier ◽  
António Andrade-Campos
Keyword(s):  
Constitutive Model ◽  
Mechanical Tests ◽  
Model Parameters ◽  
Inverse Identification ◽  
Full Field ◽  
Material Parameters ◽  
Gradient Based ◽  
Identification Techniques ◽  
Optimisation Algorithms ◽  
Optimisation Technique

The development of full-field measurement techniques paved the way for the design of new mechanical tests. However, because these mechanical tests provide heterogeneous strain fields, no closed-form solution exists between the measured deformation fields and the constitutive parameters. Therefore, inverse identification techniques should be used to calibrate constitutive models, such as the widely known finite element model updating (FEMU) and the virtual fields method (VFM). Although these inverse identification techniques follow distinct approaches to explore full-field measurements, they all require using an optimisation technique to find the optimum set of material parameters. Nonetheless, the choice of a suitable optimisation technique lacks attention and proper research. Most studies tend to use a least-squares gradient-based optimisation technique, such as the Levenberg-Marquardt algorithm. This work analyses optimisation algorithms, gradient-based and -free algorithms, for the inverse identification of constitutive model parameters. To avoid needless implementation and take advantage of highly developed programming languages, the optimisation algorithms available in optimisation libraries are used. A FEMU based approach is considered in the calibration of a thermoelastoviscoplastic model. The material parameters governing strain hardening, temperature and strain rate are identified. Results are discussed in terms of efficiency and the robustness of the optimisation processes.

Measurement and Calibration of the Parameters of Hypoplasticity Model for an Iraqi Soil

2020 ◽  
Vol 857 ◽  
pp. 243-252
Author(s):  
Aysar Hassan Subair ◽  
Ala Nasir Aljorany
Keyword(s):  
Constitutive Model ◽  
Void Ratio ◽  
Initial Stresses ◽  
Model Parameters ◽  
State Variables ◽  
Soil Behavior ◽  
Dense Sand ◽  
Hypoplastic Model ◽  
Material Parameters ◽  
Wide Range

There are many constitutive models that have been used to model the mechanical behavior of soils. Some of these models are either unable to represent important features such as the strain softening of dense sand or required many parameters that can be hard to obtain by standard laboratory tests. Because of that, a more reliable constitutive model, which is capable to capture the main features of the soil behavior with easily obtained parameters, is required. The Hypoplasticity model is considered as a promising constitutive model in this respect. It is considered as a particular class of rate non-linear constitutive model at which the stress increment is expressed in a tensorial equation as a function of strain increment, actual stress, and void ratio. The hypoplastic model required only eight material parameters (critical friction angle critical, maximum and minimum void ratio respectively), granular stiffness hs and the model constants n, α, β). The appealing feature of the hypoplastic model is that the material parameters are separated from the state variables (void ratio and the initial stresses). This feature enables the model to simulate the soil behavior under a wide range of stresses and densities with the same set of material parameters. In this research, a brief description of the Hypoplasticity model is presented. Detailed discussions regarding the measurement and calibration of the model parameters of an Iraqi soil are then exposed. It is concluded that only Consolidated Drained (CD) triaxial test, oedometer test, and the well-known limit density tests are needed to get all the parameters of the hypoplasticity model.

scholarly journals A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

2019 ◽  
Author(s):  
Harshad M Paranjape ◽  
Kenneth I. Aycock ◽  
Craig Bonsignore ◽  
Jason D. Weaver ◽  
Brent A. Craven ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Inference ◽  
Constitutive Model ◽  
Probabilistic Approach ◽  
Surface Strain ◽  
Image Correlation ◽  
Full Field ◽  
Material Parameters ◽  
Superelastic Deformation ◽  
Calibration Approach

We implement an approach using Bayesian inference and machine learning to calibrate the material parameters of a constitutive model for the superelastic deformation of NiTi shape memory alloy. We use a diamond-shaped specimen geometry that is suited to calibrate both tensile and compressive material parameters from a single test. We adopt the Bayesian inference calibration scheme to take full-field surface strain measurements obtained using digital image correlation together with global load data as an input for calibration. The calibration is performed by comparing these two experimental quantities of interest with the corresponding results from a simulation library built with the superelastic forward finite element model. We present a machine learning based approach to enrich the simulation library using a surrogate model. This improves the calibration accuracy to the extent permitted by the accuracy of the underlying material model and also improves the computational efficiency. We demonstrate, verify, and partially validate the calibration results through various examples. We also demonstrate how the uncertainty in the calibrated superelastic material parameters can propagate to a subsequent simulation of fatigue loading. This approach is versatile and can be used to calibrate other models of superelastic deformation from data obtained using various modalities. This probabilistic calibration approach can become an integral part of a framework to assess and communicate the credibility of simulations performed in the design of superelastic NiTi articles such as medical devices. The knowledge obtained from this calibration approach is most effective when the limitations of the underlying model and the suitability of the training data used to calibrate the model are understood and communicated.

scholarly journals Identifiability of Tissue Material Parameters from Uniaxial Tests using Multi-start Optimization

2020 ◽  
Author(s):  
Babak N. Safa ◽  
Michael H. Santare ◽  
C. Ross Ethier ◽  
Dawn M. Elliott
Keyword(s):  
Experimental Data ◽  
Constitutive Model ◽  
Constitutive Models ◽  
Data Fitting ◽  
Tissue Mechanics ◽  
Lateral Strain ◽  
Model Parameters ◽  
Fiber Reinforced ◽  
Material Parameters ◽  
Tissue Material

AbstractDetermining tissue biomechanical material properties from mechanical test data is frequently required in a variety of applications, e.g. tissue engineering. However, the validity of the resulting constitutive model parameters is the subject of debate in the field. Common methods to perform fitting, such as nonlinear least-squares, are known to be subject to several limitations, most notably the uniqueness of the fitting results. Parameter optimization in tissue mechanics often comes down to the “identifiability” or “uniqueness” of constitutive model parameters; however, despite advances in formulating complex constitutive relations and many classic and creative curve-fitting approaches, there is no accessible framework to study the identifiability of tissue material parameters. Our objective was to assess the identifiability of material parameters for established constitutive models of fiber-reinforced soft tissues, biomaterials, and tissue-engineered constructs. To do so, we generated synthetic experimental data by simulating uniaxial tension and compression tests, commonly used in biomechanics. We considered tendon and sclera as example tissues, using constitutive models that describe these fiber-reinforced tissues. We demonstrated that not all of the model parameters of these constitutive models were identifiable from uniaxial mechanical tests, despite achieving virtually identical fits to the stress-stretch response. We further show that when the lateral strain was considered as an additional fitting criterion, more parameters are identifiable, but some remain unidentified. This work provides a practical approach for addressing parameter identifiability in tissue mechanics.Statement of SignificanceData fitting is a powerful technique commonly used to extract tissue material parameters from experimental data, and which thus has applications in tissue biomechanics and engineering. However, the problem of “uniqueness” or “identifiability” of the fit parameters is a significant issue, limiting the fit results’ validity. Here we provide a novel method to evaluate data fitting and assess the uniqueness of results in the tissue mechanics constitutive models. Our results indicate that the uniaxial stress-stretch experimental data are not adequate to identify all the tissue material parameters. This study is of potential interest to a wide range of readers because of its application for the characterization of other engineering materials, while addressing the problem of uniqueness of the fitted results.

scholarly journals Full-field inverse identification of elasto-plastic model parameters for ductile cast iron

2021 ◽  
pp. 104056
Author(s):  
Christian Gebhardt ◽  
Tobias Sedlatschek ◽  
Alexander Bezold ◽  
Christoph Broeckmann
Keyword(s):  
Cast Iron ◽  
Ductile Cast Iron ◽  
Model Parameters ◽  
Inverse Identification ◽  
Full Field ◽  
Plastic Model

scholarly journals A General Temperature-Dependent Stress–Strain Constitutive Model for Polymer-Bonded Composite Materials

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1393
Author(s):  
Xiaochang Duan ◽  
Hongwei Yuan ◽  
Wei Tang ◽  
Jingjing He ◽  
Xuefei Guan
Keyword(s):  
Composite Materials ◽  
Constitutive Model ◽  
Model Parameters ◽  
Temperature Dependent ◽  
Stress Strain ◽  
Proposed Model ◽  
Single Temperature ◽  
Testing Data ◽  
Bonded Composite ◽  
Tension And Compression

This study develops a general temperature-dependent stress–strain constitutive model for polymer-bonded composite materials, allowing for the prediction of deformation behaviors under tension and compression in the testing temperature range. Laboratory testing of the material specimens in uniaxial tension and compression at multiple temperatures ranging from −40 ∘C to 75 ∘C is performed. The testing data reveal that the stress–strain response can be divided into two general regimes, namely, a short elastic part followed by the plastic part; therefore, the Ramberg–Osgood relationship is proposed to build the stress–strain constitutive model at a single temperature. By correlating the model parameters with the corresponding temperature using a response surface, a general temperature-dependent stress–strain constitutive model is established. The effectiveness and accuracy of the proposed model are validated using several independent sets of testing data and third-party data. The performance of the proposed model is compared with an existing reference model. The validation and comparison results show that the proposed model has a lower number of parameters and yields smaller relative errors. The proposed constitutive model is further implemented as a user material routine in a finite element package. A simple structural example using the developed user material is presented and its accuracy is verified.

Sign in / Sign up

Export Citation Format

Share Document