Parameter Identification Procedure Research Articles

Parameter identification procedure for hysteretic shear-resistant properties of beech wood dowels

To evaluate the shear-resistant behavior of wooden dowels used in Blockhaus shear walls under cyclic load, 19 specimens under ten groups of conditions were prepared and tested. The failure modes, hysteresis curves, mechanical properties, stiffness degradations, and energy dissipation capacities of the specimens were studied. The test results showed that with the increase in the number of dowels, the initial stiffness and peak load of the specimens increased greatly. The diameter of the dowels had little influence on the mechanical properties of the specimens. Furthermore, the test findings demonstrated that the pretension load between the walls greatly enhanced the initial stiffness and energy dissipation capacity of specimens. A simplified finite element model was established in Opensees. Considering the effect of material variability, the parameters of single dowel shear spring and friction spring were identified by Genetic Algorithm with modified objective function in Matlab. The identified parameters were applied to the finite element model of the multi-dowel specimens. The simulation results were in good agreement with the test results, and the validity of the numerical model and parameter identification method was verified.

Distributed robust data-enabled predictive control based voltage control for networked microgrid system

The networked microgrid system (NMG) has become an effective alternative for integrating various types of distributed energy resources (DERs). However, because of the increasing share of DERs and high R/X ratio in the system, the uncertainty and variability of DERs' power generation may cause significant voltage violations in the system. To address these issues, this paper introduces a novel distributed data-driven voltage control strategy based on data-enabled predictive control (DeePC) to address voltage violations problems in the NMG. The DeePC algorithm uses only historical system trajectories, eliminating the need for parameter identification procedures of power system. Based on the system data model, the proposed controller coordinates MGs in the NMG in a distributed manner by employing a consensus version of the fast alternating direction method of the multiplier algorithm (FADMM), which achieves a better privacy and scalability. Simulation studies in a modified two-MG NMG demonstrate the effectiveness and superiority of the proposed algorithm.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

Direct model parameter identification of twisted string actuators using Nelder-Mead simplex method

A twisted string actuator (TSA) is a simple rotary-to-linear motion converter with a large transmission ratio. This paper presents a simple procedure for direct model parameters identification of TSAs, which accepts the model in initial ODE-based form, while the previous techniques required a complex reformulation of the model. Also, the identification process estimates all unknown or impossible-to-measure parameters directly (not in an arithmetic combination) in the original form of the mathematical model of the TSA, which is a benefit in comparison with common and classic adaptive parameter estimators. The proposed nonlinear identification procedure employs the Nelder–Mead simplex method to minimize the cost function as the core. Moreover, the identification results are verified in different working frequencies. The presented model parameter optimization procedure reduced the deviation between the mathematical model and the real TSA behavior for a set of optimum parameters up to [Formula: see text] in terms of the defined cost function. As an application for the mathematical model of the TSA with the identified parameters, a feedback linearization controller is designed for the position control of the actuator. The simulation and implementation of the controller prove the proper performance of the controller.

Modelling and identification methods for simulation of cable-suspended dual-arm robotic systems

This paper proposes rigid-body modelling and identification procedures for long-reach dual-arm manipulators in a cable-suspended pendulum configuration. The proposed model relies on a virtually constrained open kinematic chain and lends itself to be simulated through the most commonly used robotic simulators without explicitly account for the cables constraints and flexibility. Moreover, a dynamic parameters identification procedure is devised to improve the simulation model fidelity and reduce the sim-to-real gap for controllers deployment. We show the capability of our model to handle different cable configurations and suspension mechanisms by customising it for two representative cable-suspended dual-arm manipulation systems: the LiCAS arms suspended by a drone and the CRANEbot system, featuring two Pilz arms suspended by a crane. The identified dynamic models are validated by comparing their evolution with data acquired from the real systems showing a high (between 91.3% to 99.4%) correlation of the response signals. In a comparison performed with baseline pendulum models, our model increases the simulation accuracy from 64.4% to 85.9%. The simulation environment and the related controllers are released as open-source code.

Physical Model-Based State of Health Understanding of Severely Aged Lithium-Ion Batteries Under Real-World Automotive Operational Life

Lithium ion-battery (LIB) technology, featuring upstanding energy and power density, satisfying lifetime, high round-trip efficiency and very fast dynamics in a reasonably economic package, rapidly became the undisputed ruler of portable power and it is now the main driver of the electrification of transportation sector. However, despite the fully commercial development, understanding and predicting degradation of such devices is still a great challenge for the scientific and technical community, especially when dealing with real-life-operation induced aging. The reusability of such devices in a circular economy perspective is a hot topic to the sector to improve sustainability, but requires understanding “how” batteries are faded rather than only “how much” they are, to enable a physically consistent and second-life-related estimation of residual lifetime.In the present activity, a detailed analysis of high-power LFP (lithium iron phosphate) cells samples operated on IVECO hybrid buses are performed to assess their possible reusability, in the frame of a joint research cooperation. A large batch of cells with different ages (from 9 years old to brand new) and different positions inside the modules are investigated.State-of-art electrochemical diagnostics are performed embedded in a multi-measurement optimized protocol (full discharge, pulse test, electrochemical impedance spectroscopy) developed after a sensitivity optimization for a model-based improved parameter identification [1], as visible in Figure 1. Residual performances are analyzed and compared with limited on-board data collected from vehicles BMS and analyzed by means of an appositely-improved physical modelling platform.Both capacity and power capabilities have sensibly decreased over the cells lifetime (see Figure 2 for the full discharges at 0.1C, 25°C) with a clear pattern attributable to cells position beneath the modules, prompting the importance of a homogeneous thermal management during operation. Power-based state of health of the samples is higher than a capacity-based SoH, foreseeing a possible reuse of the samples in a power-intensive second-life. Residual performance interestingly feature a high consistency with data recently published in [2], relative to similar system despite operated in a completely different geographic area; this strengthens the generalizability of the results.Aged cells diagnostics were interpreted with a previously developed Newman P2D physical model provided with heath exchange, to identify the degradation mechanisms through the EIS-based parameter identification procedure [1] involving both thermodynamic and kinetic aspects.Interpretation of thermodynamic aging modes has been conducted on all the tested cell samples, based on 0.1C thermodynamic analysis and the differential voltage. Incremental capacity curves of aged cells have been reproduced with a PSO-based (particle-swarm-optimization) identification of three ageing parameters: loss of lithium inventory (LLI), mainly corresponding to solid electrolyte interphase (SEI) growth, and loss of active materials at negative (LAMn) and positive (LAMp) electrode, indicating loss of actives sites for the electrochemical reactions. Despite the severely low residual capacity, the model reproduces satisfyingly the features of the aged cells.Interestingly, a linear trend could be identified as common beneath all the samples involved in the analysis, indicating common aging mechanism appearing with different magnitude. As visible in Figure 3, the trend points out a two-steps aging path: (I) a first pathway mainly leading to LLI close to 20%, followed by (II) a second pathway with both LLI and LAMn occurring at the same time. In the literature, similar observations have been already performed by some authors in previous works, with a lower extent of degradation [3,4]. They agree in this interpretation: the first stage is usually associated to the growth of the SEI layer, while the second stage is usually associated to the onset of lithium plating (due to a dense SEI which inhibits the lithium intercalation into graphite). Confirmation of such interpretation is obtained by means of model based interpretation on non-equilibrium measurements, permitting estimation of physical parameters value and enabling identification of aging mechanisms, together with ex-situ measurements based on electrochemical and morpho chemical analyses.Additionally, several consistencies between on-line measurements, such as EIS, and equilibrium capacity loss of cells have been identified, theoretically discussed and tested under further accelerated aging tests, enabling possible strategies of SoH implementation based on fast and informative electrochemical measurements.

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

Prediction of magnetic energy loss of Giant magnetostrictive materials under different prestresses, AC excitations and DC biases

Magnetic energy losses of giant magnetostrictive material (GMM) significantly depend on a number of factors including prestresses, AC excitations, and DC biases. The existing magnetic energy loss prediction models require further optimization to account for the sensitive magnetic hysteresis of GMM. A magnetic energy loss prediction model that can adapt to different operating conditions is very essential for GMM applications. Herein, the dependency of magnetic energy losses to the operation conditions is interpreted by the parametric variations of the Jiles-Atherton model. A novel four-step parameter identification procedure with great generalizability is proposed. In particular, the combined effects of three external conditions on model parameters are considered. The measurement and simulation data of major hysteresis loops, symmetric minor hysteresis loops, and biased minor hysteresis loops under different operating conditions are compared, which verified the effectiveness of the modified model. Finally, the magnetic energy losses of biased minor hysteresis loops of GMM at different frequencies are calculated using the modified model and compared with the measured data. The average relative error is 5.59 %, and the maximum relative error is 12.05 %.

Histology-informed multiscale modeling of human brain white matter

In this study, we propose a novel micromechanical model for the brain white matter, which is described as a heterogeneous material with a complex network of axon fibers embedded in a soft ground matrix. We developed this model in the framework of RVE-based multiscale theories in combination with the finite element method and the embedded element technique for embedding the fibers. Microstructural features such as axon diameter, orientation and tortuosity are incorporated into the model through distributions derived from histological data. The constitutive law of both the fibers and the matrix is described by isotropic one-term Ogden functions. The hyperelastic response of the tissue is derived by homogenizing the microscopic stress fields with multiscale boundary conditions to ensure kinematic compatibility. The macroscale homogenized stress is employed in an inverse parameter identification procedure to determine the hyperelastic constants of axons and ground matrix, based on experiments on human corpus callosum. Our results demonstrate the fundamental effect of axon tortuosity on the mechanical behavior of the brain’s white matter. By combining histological information with the multiscale theory, the proposed framework can substantially contribute to the understanding of mechanotransduction phenomena, shed light on the biomechanics of a healthy brain, and potentially provide insights into neurodegenerative processes.

A two-stage approach for material parameters identification in spark plasma sintering

A fully coupled electrical-thermo-mechanical model is essential to an in-depth understanding of the spark plasma sintering (SPS) process, where the mechanical modeling is heavily dependent on the constitutive equations and their material parameters. This paper proposes a two-stage approach that integrates direct experimental measurements with numerical simulations, providing a more comprehensive and reliable basis for identifying the material parameters in SPS. We develop a finite element-based fully coupled process model in COMSOL Multiphysics® and validate it experimentally. The parameter identification procedure achieves an average prediction error below 1.5% when applied to copper and nickel SPS at different heating rates from 25 to 100 K/min. This robust numerical–experimental approach establishes a transferable technique that can be used for other materials commonly used in SPS.

Prediction of carbon nanotubes reinforced interphase properties in fuzzy fibre reinforced polymer via inverse analysis and optimisation

This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven Optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase properties were parametrised and became input parameters in the Goal-Driven Optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic Algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally.

A constitutive modeling approach to simulate time‐dependent phenomena in rubber‐like materials

AbstractIndustrial rubber materials and similar rubber‐like materials exhibit complex mechanical behavior including nonlinear stress‐strain behavior up to large strains, hysteresis, Mullins effect, permanent set, as well as time‐dependent effects, like relaxation and rate dependency. The modeling of all these phenomena is a challenging task. One constitutive model that is capable to capture many effects in the large strain regime is the so‐called model of rubber phenomenology (MORPH). However, it is not able to capture any of the mentioned time‐dependent phenomena. Thus, this contribution deals with the extension of this model to simulate relaxation and rate dependency of rubber‐like materials. Thereby, a basic modeling approach is proposed, where only a slight change in the original system of tensorial equations is necessary. This approach is further extended by applying not only a constant relaxation time, but also loading history‐depended relaxation properties. For numerical simulations, a time discretization has to be applied to the constitutive equations, which is briefly shown. Finally, the extended MORPH model is applied to two different materials: an industrial rubber material and a polyurethane based adhesive. In both cases, the extended MORPH model is adjusted to uniaxial tension test data by parameter identification procedures. It will be shown that basic time‐dependent phenomena of rubber‐like materials can be captured well with the proposed extensions.

Estimation of iron losses in Terfenol-D for variable DC-biased and AC excitation conditions

Giant magnetostrictive material (GMM) is well known for its hysteresis behaviors related to multiple influencing factors, such as bias magnetic field and excitation level. This feature greatly challenges the loss prediction of giant magnetostrictive transducers. The existing loss prediction models need further improvement to cover the sensitive magnetic properties of GMM. In this paper, the variations of the iron losses with the operation conditions are interpreted by the variations of the Jiles–Atherton model parameters, and a concise parameter identification procedure with great generalizability is proposed. The iron losses of Terfenol-D under variable excitation levels and the DC biases are further investigated. With the proposed procedure, the introduced eight additional modification parameters for the original Jiles–Atherton model are easy to obtain, and merely a few hysteresis loops of Terfenol-D are involved. An experimental setup is established for further demonstration. Comparisons between the measured and calculated results indicate the effectiveness of the proposed approach. Throughout the bias-excitation space within the frequency range under investigation, the iron losses can be precisely estimated with an acceptable error of less than 5%.

Empowered parameter identification procedure for anaerobic digestion lumped model, stability and reliability analysis

Mathematical modeling of Anaerobic Digestion is fundamental for the prediction of crucial quantities such as CH4 and CO2 content. Among many possibilities, the AMOCOHN model, an upgraded version of the AMOCO model, represents a simplified mathematical model and is qualified for control purposes, avoiding long calculation time while assuring precise enough results. Their differences rely on the parameter identification procedure. While AMOCO model calibration is linear-based, AMOCOHN is nonlinear-based. Because of its inherent sensitivity to fluctuations, this produces some deceptive results in terms of coefficients assessment when the regression is performed with a nonlinear algorithm. This work aims to simplify and improve the robustness and stability of the AMOCOHN identification procedure by integrating an adaptation of the same dual-step approach originally proposed in AMOCO. The results, illustrated by a comparison between literature and ADM1 simulation data, reveal a mean Radj2 of 0.98, demonstrating its effectiveness while preserving the model’s simplicity and flexibility.

APPLICATION OF SPLINE FUNCTIONS AND WALSH FUNCTIONS IN PROBLEMS OF PARAMETRIC IDENTIFICATION OF LINEAR NONSTATIONARY SYSTEMS

Context. In this article, a generalized parametric identification procedure for linear nonstationary systems is proposed, which uses spline functions and orthogonal expansion in a series according to the Walsh function system, which makes it possible to find estimates of the desired parameters by minimizing the integral quadratic criterion of discrepancy based on solving a system of linear algebraic equations for a wide class of linear dynamical systems. The accuracy of parameter estimation is ensured by constructing a spline with a given accuracy and choosing the number of terms of the Walsh series expansion when solving systems of linear algebraic equations by the A. N. Tikhonov regularization method. To improve the accuracy of the assessment, an algorithm for adaptive partitioning of the observation interval is proposed. The partitioning criterion is the weighted square of the discrepancy between the state variables of the control object and the state variables of the model. The choice of the number of terms of the expansion into the Walsh series is carried out on the basis of adaptive approximation of non-stationary parameters in the observation interval, based on the specified accuracy of their estimates. The quality of the management of objects with variable parameters is largely determined by the accuracy of the evaluation of their parameters. Hence, obtaining reliable information about the actual nature of parameter changes is undoubtedly an urgent task.
 Objective. Improving the accuracy of parameter estimation of a wide class of linear dynamical systems through the joint use of spline functions and Walsh functions.
 Method. A generalized parametric identification procedure for a wide class of linear dynamical systems is proposed. The choice of the number of terms of the expansion into the Walsh series is made on the basis of the proposed algorithm for adaptive partitioning of the observation interval.
 Results. The results of modeling of specific linear non-stationary systems confirm the effectiveness of using the proposed approaches to estimating non-stationary parameters.
 Conclusions. The joint use of spline functions and Walsh functions makes it possible, based on the proposed generalized parametric identification procedure, to obtain analytically estimated parameters, which is very convenient for subsequent use in the synthesis of optimal controls of real technical objects. This procedure is applicable to a wide class of linear dynamical systems with concentrated and distributed parameters.

Fractional-order model and experimental verification of granules-beam coupled vibration

Granular media generally exhibit fluid phase, solid phase, or two coexisting phases, and the mechanism of the phase transition is not clear enough. Therefore, unlike the airflow-structure or fluid–structure coupled vibrations, the granules-structure coupled vibrations have not yet been theoretically modeled, and the interest in such coupled vibrations, which has just started in recent years, is mainly focused on experiments and simulations. In order to develop a theoretical model of the coupled vibrations of granular media and beam structures, in this paper, a novel concept called the inertia-viscous effect is introduced in terms of terminology and mathematical expression, and surprisingly, its constitutive relation can be summarized in a unified framework based on fractional calculus, together with Hooke spring, Newtonian dashpot, and Abel sticky pot. Furthermore, the existence of this effect in the granules-beam coupled vibrations is experimentally confirmed, and more importantly, the effect is more suitable for modeling by fractional-order derivatives rather than the conventional integer-order ones due to the frequency-dependent characteristics of the effect. Based on this, the theoretical model of the granules-beam coupled vibrations and the associated parameters identification procedure are successfully developed, in which a fractional-order model is proposed to describe the additional dynamic load (ADL) of the granular media on the beam. The numerical and experimental results suggest that the system response can be accurately predicted by the developed theoretical model without the computational cost as in the discrete element methods, which is the main contribution of this work. Finally, the effectiveness and robustness of the developed theoretical model are further verified by the results of numerous parametric experiments, and importantly, by combining these results with the analytical results, the mechanism of ADL is revealed, thus providing insights for modeling coupled vibrations between other engineering structures and granular media. From another perspective, this work can also be seen as a novel application of fractional calculus in the dynamics of mechanical systems.

Surrogate analytical models of damage localization in metal matrix composites

Several parameters entering the constitutive laws of metal matrix composites are not amenable to direct measurement. Their determination is therefore demanded to indirect identification procedures, based on the comparison between the output of some experimental works and the results of the iterative simulation of the performed tests. Largely repetitive numerical analyses can also support the optimized design of composite microstructures, the understanding of the actual stress-transfer mechanisms, and virtual testing. Surrogate analytical models can replace effectively non-linear finite element (FE) computations in parametric studies and identification procedures, reducing execution times and costs without compromising the accuracy of the results. In this context, a frequently used approach consists of the interpolation, by means of radial basis functions (RBFs), of data filtered by proper orthogonal decomposition (POD). This methodology is often used to reproduce the smooth output of different mechanical systems. This work is rather focused on the homogenized response of damaging metal matrix composites subjected to strain localization phenomena, and explores the accuracy achievable in these situations by the POD-RBF approximation of more traditional FE analyses. In all considered cases, the POD-RBF results are accurate, and able to distinguish between apparently similar situations. The approach is flexible, and the performance of the surrogate models can be tailored to the requirements of each application. In particular, various analytical approximations can be introduced to support the design of new microstructures and material couplings, and to understand the role of any material and/or geometry imperfections resulting from the production processes.

Design and testing of a decision tree algorithm for early failure detection in steel truss bridges

This paper develops a methodology for damage identification in steel truss bridges that uses vibration-based monitoring data and a model-based decision tree algorithm. The methodology resorts to a calibrated FE model with an optimization-based parameter identification procedure to simulate and analyze all the potential damages that might affect the structure. The effect of environmental conditions on the modal parameters is also accounted for, which is modeled as structure stiffness variations using the Young’s modulus and forecasted using a surrogate modeling strategy. The feasibility of the methodology is demonstrated on a full-scale bridge in Vilagarcía de Arousa, Spain. The underlying hypotheses used in the algorithm implementation were validated, and the error ponderation and selection bound employed to detect and identify damage were optimized. The results show an average success rate of 95.0% and an average false positive rate of 1.0% in identifying damage indicating its robustness to be extrapolated to other case studies.

Efficient identification of material parameters for an orthotropic hyperelastic nonlinear material model for textile membranes

A recently proposed orthotropic nonlinear material model for general classes of woven fabrics is adapted to glass-PTFE and PES-PVC fabrics. The nonlinear model contains three governing stiffness-related material properties that will be shown to be separately and uniquely identified using a new optimization procedure. The parameter identification procedure is based on the separation into nonlinear model and linear material parameters. In a first step, the model parameters are optimized by minimizing a least-square functional which measures variations of the material parameters identified for each deformation state independently. Once the optimized model parameters are determined, the associated stiffness-related material parameters are uniquely identified. As a result, a highly efficient method is obtained, which allows for the independent identification of model and material parameters. The model parameters are shown to be kept fixed for a specific class of textile membrane materials and only the stiffness-related properties need to be adjusted for a new production batch or a new material from this class. By comparing differently identified parameters, the performance of the proposed procedure is analyzed in a numerical simulation of a roof structure.

Parameter identification and uncertainty quantification of the thermal conductivity tensor for transversely isotropic composite materials

AbstractThe identification of material parameters with a nonlinear least‐squares approach and finite elements is an established task in solid mechanics. There, full‐field experimental data can be employed in the material parameter identification procedure. The focus of this contribution is on the application of full‐field temperature data, measured with infrared thermography, to identify the coefficients of the thermal conductivity tensor of unidirectional fiber‐reinforced composites. Because of the unidirectional fiber reinforcement, transversely isotropic thermal behavior is assumed and the corresponding two thermal conductivities are identified from full‐field data. Beforehand performing the experiments, numerical re‐identifications are used to optimize the experimental procedure in a sensitivity analysis. Further, the uncertainties of the identified material parameters as well as the uncertainties of subsequent simulations are computed with the Gaussian error propagation concept.