Identification Of Material Parameters Research Articles

An enhanced deep learning approach for vascular wall fracture analysis

AbstractThis work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.

Identification of Bimodular Material Parameters via a Semi-Closed Form Solution of the Brazilian Test

Identification of Bimodular Material Parameters via a Semi-Closed Form Solution of the Brazilian Test

Identification of material parameters at high strain rates using ballistic impact tests and inverse finite element analysis

Identification of material parameters at high strain rates using ballistic impact tests and inverse finite element analysis

The modal behaviour of a violin corpus

Violins incorporate numerous interfaces and anisotropic behaviour due to natural materials. Hence, the modelling of these instruments is extremely complex. The present work investigates the structural dynamics of a specific violin corpus (violin resonant body) with the aim of using numerical modelling to accurately understand the modal behaviour of the violin using a novel stepwise approach. In the first step, an inverse material parameter identification via experimental and numerical modal analysis of the raw woods is performed to obtain the material parameters of the individual woods. In the second step, an experimental modal analysis of the violin substructures is performed using 2D scanning laser Doppler vibrometry and building finite element models based on accurate 3D scans of the parts manufactured by a luthier. The experimentally obtained modal parameters of the individual parts are compared with the numerical results. In the third step, the whole violin corpus, i.e. the resonant body of the instrument, is assessed. The good agreement between the numerical and the experimental results demonstrates that the application of standard techniques of modal analysis on a very complex mechanical system, the violin body, is possible. Our methodology offers a novel approach to analysing individual components of bowed string instruments, facilitating a comprehensive vibrational analysis with implications for broader musical research, including luthier optimisation tools, digital reconstruction of historical violins, and advancements in instrument design for improved playability and tonal quality.

Identification of Vivo Material Parameters of Arterial Wall Based on Improved Niching Genetic Algorithm and Neural Networks

Cardiovascular diseases are seriously threatening human health and the incidence rate is high. Many scholars are devoted to studying arterial mechanical properties and material parameters. In this study, the bovine artery was selected as the experimental object and the uniaxial tensile test was carried out by cutting the specimens along its axial, circumferential and [Formula: see text] directions. The finite element software ABAQUS and hyperelastic Holzapfel Gasser Ogden (HGO) constitutive model were used to simulate the experimental process. Niche technology is introduced on the basis of genetic algorithm, and the program of Improved Niche Genetic Algorithm for material parameter identification is compiled based on Python language. In addition, BP Neural Network was constructed based on Tensorflow mathematical system. The material parameters of the constitutive model of bovine artery in different directions were identified by finite element method and experimental data. The results show that Improved Niche Genetic Algorithm and Neural Network, respectively, combined with finite element are both effective and accurate methods for predicting the parameters of arterial vascular hyperelastic materials, which can provide reference and help for the study of arterial vascular mechanical properties.

Orthotropic Material Parameters Identification Method of Stator Core and Windings in Electric Motors

The accurate equivalent orthotropic material parameters (OMPs) of stator core and windings are the basis of modal analysis in electric motors and the key to precisely predicting electromagnetic vibration and noise. A CNN (convolutional neural network)-MPGA (multi-population genetic algorithm) based OMPs identification method of stator core and windings for electric motors is proposed in this study. Firstly, the surrogate models between the material parameters and eigenmode frequencies are trained with CNN for stator core and windings. Then, MPGA is utilized to identify the equivalent OMPs of stator core and windings combined with tested eigenmode frequencies. Finally, two different classes of electric machines are selected to verify the method. The CNN-MPGA based method in this study can quickly and accurately identify OMPs of stator core and windings. It is significant for the calculation and suppression of electromagnetic noise for electric motors.

Investigation of fracture source mechanisms through full-field imaging and acoustic emission

The identification and understanding of fracture processes is a vital component in securing engineering structures. By collecting data through experiments or simulations, the identification of material parameters and the understanding of failure mechanisms can be investigated. In the context of in-laboratory experiments, this is done by provoking fracture process through destructive testing (TD) techniques, while collecting data by non-destructive testing (NDT) techniques and postmortem analysis. While NDT techniques are often used individually, increasing interest is given to collective investigations of source activities. Like this, limitations of one method can be compensated by the next. A multi-modal experimental setup is proposed for holistic understanding of source mechanisms. Combined time resolved AE, DIC and in-volume measurements with post-mortem fracture surface analysis are applied on PMMA tensile cracking experiments for the purpose of AE localization, crack tip detection and determination of crack front complexities and kinematics, respectively. Both high and lower amplitude AE signals were captured and respectively associated to localized dynamic instabilities and stable crack advance. Signals are analyzed individually using localization, waveform analysis, and AE descriptor classification. Crack front velocity heterogeneities are identified as valuable quantity to correlate with fracture induced AE responses. For dynamic instabilities, a linear relationship between AE absolute energy and crack propagation area was found. While identification and analyses of local and global dynamic instabilities has shown to be rather trivial, difficulties arose with respect to quasi-static related AE events.

Fracture parameter identification by Digital Image Correlation and Finite Fracture Mechanics for millimeter-scale samples

In situ wedge splitting tests are conducted on millimeter-size PMMA samples. A method to determine the material tensile strength and critical energy release rate (ERR) from digital image correlation (DIC) and a full finite element implementation of the coupled criterion (CC) is proposed. The best material parameter identification is achieved when the CC implementation based on measured displacements on a contour of a subdomain is performed, providing the crack initiation length remains small compared to the subdomain size.

On the Material Constitutive Behavior of the Aortic Root in Patients with Transcatheter Aortic Valve Implantation

BackgroundTranscatheter aortic valve implantation (TAVI) is a minimally invasive procedure used to treat patients with severe aortic valve stenosis. However, there is limited knowledge on the material properties of the aortic root in TAVI patients, and this can impact the credibility of computer simulations. This study aimed to develop a non-invasive inverse approach for estimating reliable material constituents for the aortic root and calcified valve leaflets in patients undergoing TAVI. MethodsThe identification of material parameters is based on the simultaneous minimization of two cost functions, which define the difference between model predictions and cardiac-gated CT measurements of the aortic wall and valve orifice area. Validation of the inverse analysis output was performed comparing the numerical predictions with actual CT shapes and post-TAVI measures of implanted device diameter.ResultsA good agreement of the peak systolic shape of the aortic wall was found between simulations and imaging, with similarity index in the range in the range of 83.7% to 91.5% for n.20 patients. Not any statistical difference was observed between predictions and CT measures of orifice area for the stenotic aortic valve. After TAVI simulations, the measurements of SAPIEN 3 Ultra (S3) device diameter were in agreement with those from post-TAVI angio-CT imaging. A sensitivity analysis demonstrated a modest impact on the S3 diameters when altering the elastic material property of the aortic wall in the range of inverse analysis solution.ConclusionsOverall, this study demonstrates the feasibility and potential benefits of using non-invasive imaging techniques and computational modeling to estimate material properties in patients undergoing TAVI.

Cohesive Zone Modelling and the Fracture Process of Cement-Based Composites

Computational modelling of quasi-brittle fracture in cement-based composites needs to cover both i) the damage caused by micro-fractured zones, referring to some nonlocal strain-stress relations, respecting quite different behaviour of such composites in tension and compression, and ii) the initiation and propagation of macroscopic cracks, exploiting the cohesive zone model, handled by some modification of the finite element technique, together with the discretization in time. A fundamental issue for such model is the introduction of a traction-separation (stress-displacement) relationship. This contribution pays particular attention to the design, identification and estimation of material parameters for the traction separation law suitable for predicting the deformation behaviour of samples of materials and structures.

Comparison of Grain-Growth Mean-Field Models Regarding Predicted Grain Size Distributions.

Mean-field models have the ability to predict the evolution of grain size distribution that occurs through thermomechanical solicitations. This article focuses on a comparison of mean-field models under grain-growth conditions. Different microstructure representations are considered and discussed, especially regarding the consideration of topology in the neighborhood construction. Experimental data obtained with a heat treatment campaign on 316L austenitic stainless steel are used for the identification of material parameters and as a reference for model comparisons. Mean-field models are also applied to both mono- and bimodal initial grain size distributions to investigate the potential benefits of introducing neighborhood topology in microstructure prediction models. This article demonstrates that improvements in the predictions can be obtained in monomodal cases for topological models. In the bimodal test, no comparison with experimental data was performed as no data were available. But relative comparisons between models indicated few differences in the predictions. Although of interest, the consideration of neighborhood topology in grain-growth mean-field models generally results in only small improvements compared to classical mean-field models, especially in terms of implementation complexity.

A hybrid algorithm of particle swarm optimization and finite element method to identify local mesoscopic damage of concrete-like materials

Meso-scale material modeling and parameter identification are still unsettled issues in the current multi-scale methods for the field of computational mechanics. In this investigation, a hybrid algorithm of particle swarm optimization and finite element method coupled with continuum damage mechanics is proposed to forecast local mesoscopic elasticity modulus and damage of concrete-like materials based on the macroscopic stress-strain relationship data curve. The algorithm establishes a bridge between mesoscopic mechanical analysis and macroscopic mechanical analysis, which can also guarantee the consistency of analysis in different scales. A representative numerical case is conducted to support the algorithm. Additionally, the effect of different divisions of representative volume element is analyzed based on the algorithm, and the recommended size of representative volume element is 5 mm. The prediction results match well with the corresponding macroscopic experimental results and mesoscopic experimental results based on the chosen recommended size of representative volume element. The effectiveness of the algorithm is verified, which can support an effective numerical tool to optimize and identify mesoscopic mechanical parameters and damage for multi-scale material modeling and analysis.

Finite element model correction method based on surrogate model with multiple working conditions and multiple measurement points

The finite element model inversion method has been widely used in recent years for iterative adjustment of finite element model parameters. However, the models constructed in the existing literature are weak and time consuming to adapt to the environment, which makes it difficult to adapt to the current needs of numerical simulations. To address the problem of large uncertainty in the material parameters of real objects and the difficulty of constructing finite element simulation models, a surrogate-based model correction method was proposed for multi-condition and multi-measurement point finite element models. The innovative use of the working condition parameter as one of the training parameters of the surrogate model to construct the optimal mathematical model for parameter correction of the finite element model to variable working conditions. To reduce the number of finite element model calls and speed up the convergence process, an Minimizing Prediction-CV-Voronoi parallel infill sampling method for the surrogate model was proposed to overcome the problems of easily falling into local optima and slow convergence when solving after constructing the surrogate model. The proposed parallel infill sampling method was tested using the test functions. The finite element model correction method with multiple working conditions and multiple measurement points was applied for material parameter correction and identification of aluminum alloys. The superiority of the proposed parallel point addition method in terms of the solution accuracy and speed was demonstrated. The results show that the multi-measurement points have a significant effect on improving the model correction effect, and the constructed multi-condition surrogate model can make fast predictions for arbitrary conditions and has strong environmental adaptability. The finite element model correction method proposed in this paper, with strong environmental adaptability, high accuracy and fast iteration, has been tested to be very effective.

Computational modeling of viscoelastic backsheet materials for photovoltaics

Backsheet is the outermost layer of the photovoltaic (PV) laminate which consists of polymers such as Polyethylene terephthalate (PET) or Polyvinyl fluoride (PVF). The viscoelastic response of these materials significantly affects the durability of the PV module. In this study, the viscoelastic response of commercially available backsheet materials is experimentally characterized and computationally modeled. An extensive viscoelastic experimental study on backsheet materials is carried out, considering the temperature-dependent properties to characterize the mechanical properties. Based on an experimental campaign, small-strain viscoelastic models based on the Prony-series (PS) and Fractional Calculus (FC) are herein proposed. The form of the constitutive equations for both models is outlined, and the finite element implementation is described in detail. Following the identification of the relevant material parameters, models are validated with experimental data, showing good predictability. A comparative study of model responses under different loading conditions is also reported to assess the advantages and disadvantages of both models. Such an extensive experimental study and constitutive modeling will help design and simulate a more comprehensive digital-twin model of PV modules, as illustrated by the benchmark problems.

Identification of material parameters for the Vawter-Fung lung tissue constitutive model and assessment in human body model for impact loading

The widely used Vawter-Fung (VF) lung tissue constitutive model, originally developed to model respiration, was assessed for applicability to impact human body models (HBMs). A review of the mechanical properties of lung tissue demonstrated existing parameter sets for the VF model encompassed a wide range of stiffness relative to experimental data. Consistent experimental datasets of lung tissue for uniaxial and biaxial tension were identified, and new parameters were fit to the VF model. A thoracic pendulum impact using a contemporary HBM was used to assess existing literature parameter sets, and the new parameters. The VF model parameters presented in this study produced uniaxial and biaxial tension response with improved hyperelastic response compared to experimental data and previously reported parameters. The VF surface tension component did not contribute substantially to the lung response in impact. The proposed VF model parameters were numerically stable for impact simulations and use in HBMs.

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson’s ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson’s ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance?In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.

Deformation-Induced Surface Roughening of an Aluminum-Magnesium Alloy: Experimental Characterization and Crystal Plasticity Modeling.

The deformation-induced surface roughening of an Al-Mg alloy is analyzed using a combination of experiments and modeling. A mesoscale oligocrystal of AA5052-O, obtained by recrystallization annealing and subsequent thickness reduction by machining, that contains approx. 40 grains is subjected to uniaxial tension. The specimen contains one layer of grains through the thickness. A laser confocal microscope is used to measure the surface topography of the deformed specimen. A finite element model with realistic (non-columnar) shapes of the grains based on a pair of Electron Back-Scatter Diffraction (EBSD) scans of a given specimen is constructed using a custom-developed shape interpolation procedure. A Crystal Plasticity Finite Element (CPFE) framework is then applied to the voxel model of the tension test of the oligocrystal. The unknown material parameters are determined inversely using an efficient, custom-built optimizer. Predictions of the deformed shape of the specimen, surface topography, evolution of the average roughness with straining and texture evolution are compared to experiments. The model reproduces the averaged features of the problem, while missing some local details. As an additional verification of the CPFE model, the statistics of surface roughening are analyzed by simulating uniaxial tension of an AA5052-O polycrystal and comparing it to experiments. The averaged predictions are found to be in good agreement with the experimentally observed trends. Finally, using the same polycrystalline specimen, texture-morphology relations are discovered, using a symbolic Monte Carlo approach. Simple relations between the Schmid factor and roughness can be inferred purely from the experiments. Novelties of this work include: realistic 3D shapes of the grains; efficient and accurate identification of material parameters instead of manual tuning; a fully analytical Jacobian for the crystal plasticity model with quadratic convergence; novel texture-morphology relations for polycrystal.

A multi-material-oriented modeling framework to characterize and predict mechanical self-healing

In the family of smart materials termed as “self-healing materials”, there is a prominent number of them sharing many similarities in the way that healing is affecting their post-damaged mechanical response. In this work, a generic and multi-material phenomenological-based healing formulation is proposed to investigate and characterize the self-healing effect in the mechanical response of materials exhibiting strong nonlinearities like rate-dependent plasticity, visco-damage initiation and evolution. The proposed healing formulation uses objective experimental measures such as resting time, loading rate and damage level. This formulation is integrated in a combined computational–experimental procedure, the so-called Generic Healing-Oriented Multi-Material Modeling Framework (GHOM3) that facilitates (i) the understanding of a strongly nonlinear response, (ii) a robust material characterization and (iii) the predictive simulation via the finite element analysis. As study case, the intrinsic self-healable mechanical response of a highly nonlinear asphalt-based composite matrix is characterized at room temperature using the proposed framework. As additional novelty in the framework, an ultra-fast optimization-based material parameter identification process is developed using a master–slave parallelization paradigm that leads to save up to 90% of data processing time. The framework is put into practice to virtually predict the mechanical response influenced by the healing process of the validated material in a dog-bone specimen under Load–Unload–Resting–Reload at multiple loading rates and damage levels. The implemented approach gives direct access to the entire damage and healing histories, which are experimentally inaccessible, as well as providing an alternative definition of the healing indices commonly used in experimentation.

ESG analytics in investment decision making: in search for material information

Subject. The article considers approaches to determining information that is material for corporate reporting and analyses the way they affect the usefulness of the information presented to stakeholders and required for decision making. The choice of a particular approach determines the identification and formulation of material topics, information sources, and the target audience. Users need to understand the principal approach of the company to determining material information in corporate reporting in order to avoid controversies and unreasonable decisions.Objectives. The purpose of our study was to analyse approaches to defining material information for non-financial reporting and assess their impact on the usefulness of the information for stakeholders, primarily for investors.Research methods. The research involved using methods of comparative, logical, and financial analysis. The materials analysed were standards of financial and non-financial reporting, including frameworks and guidelines. The analysis was performed in order to define the concept of material information and requirements for it as well as suggested methods of identification of material topics and parameters to be presented in corporate reports of organisations. The following standards were analysed: GRI standards; standards by the International Sustainability Standards Board (ISSB); standards of the Sustainability Accounting Standards Board (SASB); Corporate Sustainability Reporting Directive (CSRD), and recommendations on disclosure of financial consequences of climate change of the Task Force on Climate-related Financial Disclosures (TCFD). The analysis of information disclosure practices was performed based on the sustainability statements of leading Russian companies operating in oil and gas industry and in steel industry for 2019-2021. We also used data from ESG ratings and rankings of Russian rating agencies.Results and discussion. The results of our study showed that while basic approaches to defining materiality suggested by leading international initiatives in corporate reporting have a lot in common, there are still crucial differences. The main difference is the focus on either the impact of external factors on the company (financial materiality) or the impact of the company on the society and environment (impact materiality). The understanding of these differences can help stakeholders to integrate the ESG information in the decision making process and use it to substantiate the decisions made. The problems revealed by the study, as well as the dynamic nature of the assessment of materiality of ESG data make it difficult to regulate the process of non-financial disclosure as compared to the disclosure of financial data. At the same time, the existing methods of determining material ESG information, taking into account industry specifics and based on the analysis of the organisation’s activities, its context, and requirements of the stakeholders, make it possible to find a practical solution to the problem.Conclusions. The conclusions and results of the study can be used to develop recommendations on public non-financial reporting for Russian companies taking into account the requirements of stakeholders.