Estimation Of Material Properties Research Articles

Time-dependent behaviour prediction of the prestressed HPC I-girder

A time-dependent behaviour prediction of the prestressed concrete I-girder made from high-performance concrete (HPC) is very difficult. Overestimation or underestimation of the deflection (camber) and prestress loss may be affecting their service life and even also a failure. In this study, time-dependent behaviour of the prestressed I-girder was predicted using experimentally measured material properties data of HPC, i.e., shrinkage, creep and modulus of elasticity. Four independent HPC mixes of M50 grade concrete were designed as per IS 10262 and considered as a reference mixes to study its influence on the time-dependent behaviour of prestressed I-girder. HPC Mix-I, was prepared only using ordinary Portland cement (OPC) while others Mix-II, Mix-III, and Mix-IV were prepared using OPC as the main binder with the addition of one supplementary cementitious material (SCM), i.e., fly ash (FA), silica fume (SF) and ground granulated blast-furnace slag (GGBS). A validated incremental time-step analysis method was adopted to predict deflections, fibre stresses and prestress losses up to 1106 days (3 years) of prestressed I-girder.A predicted time-dependent behaviour is a good agreement with field measured data when the analysis method is used experimentally measured properties of concrete. A comparative appraisal using estimated material properties from the existing material models (i.e., ACI, FIB, B3, GL, and ANN) with experimental test data has also been done to find their reliability for HPC prestressed I-girder. A time-step analysis method can provide an accurate prediction of the time-dependent behaviour of HPC prestressed I-girder, if the measured material properties considered as an input parameter.

Estimation of depth-dependent material properties of biphasic soft tissues through finite element optimization and sensitivity analysis

A knowledge of material properties of soft tissue, such as articular cartilage, is essential to assess its mechanical function. It is also increasingly more evident that the inhomogeneity of the tissues plays a significant role in its in vivo functioning. Hence, efficient and reliable tools are needed to accurately characterize the inhomogeneity of the soft tissue mechanical properties. The objective of this research is to propose a finite element optimization procedure to determine depth-dependent material properties of articular cartilage by processing experimental data. Cartilage is modeled as a biphasic continuum with a linear elastic solid phase. The optimization method is based on a sensitivity analysis where the sensitivity of the finite element results to a variation in the material properties is analytically evaluated. The elastic modulus and permeability of the tissue are assumed to vary either linearly or quadratically through the thickness of the cartilage layer. After adopting some initial estimates, these material properties are updated iteratively based on their sensitivities to the current results, and the difference between the actual experimental data and computational experimental data. The optimization method has been tested in two common experimental configurations of cartilage and found to be efficient to estimate the material properties.

A Simple Method for the Estimation of Hyperelastic Material Properties by Indentation Tests

본 논문에서는 압입시험을 통해서 초탄성 재료 물성치를 평가하는 간단한 방법을 제시하였다. 초탄성 재료 모델 중, 3개의 물성치(C10,C20,C30)를 가지는 Yeoh 모델을 선택하여 주연신률로 표현되는 변형률 에너지 밀도를 적용하였다. Yeoh 물성치를 변화시키며, 구형 압입시험 유한요소해석을 수행하여 압입자 반력-변위 곡선을 획득하였다. 압입자 반력-변위 곡선을 3차 다항식으로 근사하였고, 이 다항식을 물성치(C10,C20,C30)의 3차 곱으로 근사된 3차 다항식으로 표현하였다. 압입자 반력-변위 곡선 근사를 위해 회귀분석을 진행하여 수식들의 계수를 결정하였으며, 이 회귀식을 이용하여 초탄성 재료의 물성치를 평가 하였다. 초탄성 재료 물성치 평가를 수행하고 오차를 비교하여 유효성을 보여 주었다.

Porcine growth plate experimental study and estimation of human pediatric growth plate properties

Growth plate (GP) is a type of tissue widely found in child's immature skeleton. It may have significant influence on the overall injury pattern since it has distinguishing mechanical properties compared to the surrounding bony tissue. For more accurate material modeling and advanced pediatric human body modeling, it is imperative to investigate the material property of GPs in different loading conditions. In this study, a series of tensile and shearing experiments on porcine bone-GP-bone units were carried out. Total 113 specimens of bone-GP-bone unit from the femoral head, distal femur, and proximal tibia of four 20-weeks-old piglets were tested, under different strain rates (average 0.0053 to 1.907 s−1 for tensile tests, and 0.0085 to 3.037 s−1 for shearing tests). Randomized block ANOVA was conducted to determine the effects of anatomic region and strain rate on the material properties of GPs. It was found that, strain rate is a significant factor for modulus and ultimate stress for both tensile and shearing tests; the ultimate strains are not sensitive to the input factors in both tensile and shearing tests; the GPs at knee region could be grouped due to similar properties, but statistically different from the femoral head GP.Additionally, the tensile test data from the experimental study were comparing to the limited data obtained from tests on human subjects reported in the literature. An optimal conversion factor was derived to correlate the material properties of 20-week-old piglet GPs and 10 YO child GPs. As a result, the estimated material properties of 10 YO child GPs from different regions in different loading conditions became available given the conversion law stays legitimate. These estimated material properties for 10 YO child GPs were reported in the form of tensile and shearing stress-strain curves and could be subsequently utilized for human GP tissue material modeling and child injury mechanism studies.

Dynamic Analysis of a Large Freestanding Rock Tower (Castleton Tower, Utah)

AbstractWe acquired a unique ambient vibration dataset from Castleton Tower, a 120 m high bedrock monolith located near Moab, Utah, to resolve dynamic and material properties of the landform. We identified the first two resonant modes at 0.8 and 1.0 Hz, which consist of mutually perpendicular, linearly polarized horizontal ground motion at the top of the tower. Damping ratios for these modes were low at ∼1%. We successfully reproduced field data in 3D numerical eigenfrequency simulation implementing a Young’s modulus of 7 GPa, a value ∼30% lower than measured on core samples. Our analysis confirms that modal deformation at the first resonant frequencies closely resembles that of a cantilever beam. The outcome is that with basic estimates of geometry and material properties, the resonant frequencies of other freestanding rock monoliths can be estimated a priori. Such estimates are crucial to evaluate the response of rock towers to external vibration inputs.

Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model.

Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3% for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.

Estimation of Material Properties of Carbon Nanotubes Using Finite Element Method

Abstract The paper deals with estimation of material properties of single-walled carbon nanotubes (SWCNTs). The SWCNTs are simulated as frames, where carbon atoms are replaced by nodes and interatomic interactions are replaced by beams. The tension and torsion loading is applied on SWCNTs for determining the elastic modulus, Poisson’s ratio, shear modulus and membrane stiffness of SWCNTs. The simulations for obtaining elongations and torsion angles of SWCNTs are accomplished by the finite element method.

Testing non-magnetic materials using Oberst Beam Method utilising electromagnetic excitation

The use of a non-contact electromagnetic excitation system is highly recommended in the literature to identify the mechanical properties of materials using the Oberst Beam Method. However, it is not possible to test a specimen made of non-magnetic material using the Oberst beam test rig, comprising of an electromagnetic exciter, unless the specimen is modified using some magnetic particles or small discs made of a ferromagnetic material. Although doing so makes it possible to perform the test, this results in an undesirable modification to the test specimen, leading to unquantified levels of errors in the estimated material properties. This study proposes an approach for eliminating the adverse effects of such mass modification to the test specimen, and also allows subsequent removal of the electromagnetic stiffening effects produced by the electromagnetic exciter. The proposed method is validated using both finite element (FE) simulations and experimental data. Results confirm that the proposed method for the removal of the adverse effects of mass modification, combined with the subsequent removal of the electromagnetic stiffening effects, is very effective, making it possible to determine the material properties of non-magnetic materials with a very good accuracy.

Developments in the estimation of tensile strength by small punch testing

The Small Punch (SP) test has shown in a number of applications that it can be successfully used for material ranking and material property estimation, especially where standard tests cannot be applied due to sampling location or the amount of material available. The most sought material properties are the ultimate tensile strength (Rm) and proof strength (Rp02) for the classical SP tests and the equivalent creep stress σ for the SPC creep testing. In the case of SP testing the force to stress conversion is classically done by correlating the Rm to descriptive (test set-up dependent) variables such as measured maximum force divided by the product of displacement at the maximum and the disk thickness. Naturally, if the test set-up or the test samples are not according to the standardized dimensions or low material ductility imposes crack growth instead of plastic deformation, these formulations cannot be applied. In this paper the classical formulations are studied and modifications in the formulations and in the extraction of the best descriptive variables for estimating Rm are proposed. The assessments are done on a range of materials using both standardized flat SP samples as well as curved (tube section) samples. It is claimed that the equivalent stress in both SP and SPC tests can robustly be estimated with the same type of equation, at least for ductile and semi-ductile ferritic/martensitic and austenitic steels. The same equations can further be applied on non-standard test samples and test set-ups using FEA determined conversion factors correcting for curvature. The tensile strengths of ductile P91 steel and 46% cold worked 15-15Ti cladding steel, with clearly reduced ductility, are successfully estimated in a broad temperature range. The determination of tensile strength by small punch testing of engineering steels in general and for nuclear claddings in specific has successfully been shown to give robust and accurate estimates.

Estimation of mechanical material properties using an isotropic three-dimensional elastic plate model

This paper develops an inverse method to simultaneously estimate complex Young’s and shear moduli of a rectangular-shaped solid material. This new method exploits a relatively simple experiment and is designed for a frequency range of approximately 1 kHz–10 kHz. A fully elastic spatial domain model of a plate is derived and corresponding experimental measurements are collected. An error function is defined using the residual between the model displacements and the measured displacements, which allows a metric for a search over a space where the complex model values of Young’s and shear moduli are allowed to vary. When this error function is minimized, the parameters used to create the model are considered the best estimate possible. An experiment is included to illustrate the estimation process, and it is shown that the real parts of both moduli compare favorably to previously available measurements. Finally, the model is compared to the experimental measurements to illustrate its accuracy when it is formulated using the material property estimates obtained using this method.

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

Naturally glossy: Gloss perception, illumination statistics, and tone mapping.

Recognizing materials and understanding their properties is very useful—perhaps critical—in daily life as we encounter objects and plan our interactions with them. Visually derived estimates of material properties guide where and with what force we grasp an object. However, the estimation of material properties, such as glossiness, is a classic ill-posed problem. Image cues that we rely on to estimate gloss are also affected by shape, illumination and, in visual displays, tone-mapping. Here, we focus on the latter two. We define some commonalities present in the structure of natural illumination, and determine whether manipulation of these natural “signatures” impedes gloss constancy. We manipulate the illumination field to violate statistical regularities of natural illumination, such that light comes from below, or the luminance distribution is no longer skewed. These manipulations result in errors in perceived gloss. Similarly, tone mapping has a dramatic effect on perceived gloss. However, when objects are viewed against an informative (rather than plain gray) background that reflects these manipulations, there are some improvements to gloss constancy: in particular, observers are far less susceptible to the effects of tone mapping when judging gloss. We suggest that observers are sensitive to some very simple statistics of the environment when judging gloss.

On the estimation of cyclic material properties – Part 1: Quality of known estimation methods

AbstractA large variety of methods for estimating cyclic material properties from quasi-static material properties can be found throughout the literature. Therefore, the user usually faces the problem of determining which methods would best suit his purpose. To help answer this question, a massive database containing test results from quasi-static and cyclic tests is gathered and used for a quality rating of the different methods. This rating results in the recommendation to use the uniform material law for steel materials, the modified park-song's method for wrought aluminum and the method of variable slopes 2006 for thin steel sheets. In addition, the accuracy of these estimation methods is compared to experimental results using deviation ranges in the total strain life curve and in the cyclic stress-strain curve.

Improving Model Parameters in Vibrating Systems Using Neumann Series

A method is presented to improve the estimates of material properties, dimensions, and other model parameters for linear vibrating systems. The method improves the estimates of a single model parameter of interest by finding parameter values that bring model predictions into agreement with experimental measurements. A truncated Neumann series is used to approximate the inverse of the dynamic stiffness matrix. This approximation avoids the need to directly solve the equations of motion for each parameter variation. The Neumman series is shown to be equivalent to a Taylor series expansion about nominal parameter values. A recursive scheme is presented for computing the associated derivatives, which are interpreted as sensitivities of displacements to parameter variations. The convergence of the Neumman series is studied in the context of vibrating systems, and it is found that the spectral radius is strongly dependent on system resonances. A homogeneous viscoelastic bar in longitudinal vibration is chosen as a test specimen, and the complex-valued Young's modulus is chosen as an uncertain parameter. The method is demonstrated on simulated experimental measurements computed from the model. These demonstrations show that parameter values estimated by the method agree with those used to simulate the experiment when enough terms are included in the Neumann series. Similar results are obtained for the case of an elastic plate with clamped boundary conditions. The method is also demonstrated on experimental data, where it produces improved parameter estimates that bring the model predictions into agreement with the measured response to within 1% at a point on the bar across a frequency range that includes three resonance frequencies.

Successfully estimating tensile strength by small punch testing

The Small Punch (SP) test is a relatively simple test well suited for material ranking and material property estimation in situations where standard testing is not possible or considered too material consuming. The material tensile properties, e.g. the ultimate tensile strength (UTS) and the proof strength are usually linearly correlated to the force-deflection behaviour of a SP test. However, if the test samples and test set-up dimensions are not according to standardized dimensions or the material ductility does not allow the SP sample to deform to the pre-defined displacements used in these correlations, the standard formulations can naturally not be used. Also, in cases where no supporting UTS data is available the applied correlation factors cannot be verified. In this paper a formulation is proposed that enables the estimation of UTS without supporting uniaxial tensile strength data for a range of materials, both for standard type and for curved (tube section) samples. The proposed equation was originally developed for estimating the equivalent stress in small punch creep but is also found to robustly estimate the UTS of several ductile ferritic, ferritic/martensitic and austenitic steels. It is also shown that the methodology can be further applied on non-standard test samples and test set-ups and to estimate the properties of less ductile materials such as 46% cold worked 15-15Ti cladding steel tubes. In the case of curved samples the UTS estimates have to be corrected for curvature to match the corresponding flat specimen behaviour. The geometrical correction factors are dependent on tube diameters and wall thicknesses and were determined by finite element simulations. The outcome of the testing and simulation work shows that the UTS can be robustly estimated both for flat samples as well as for thin walled tube samples. The usability of the SP testing and assessment method for estimating tensile strength of engineering steels in general and for nuclear claddings in specific has been verified.

Deep learning for estimating porous material properties from full-waveform data

This research focuses on both developing computational tools to model transient wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media and estimating material properties from the recorded full-waveform data. Numerical simulation of wave-dominated problems is computationally demanding. Efficient parallelization, capability to handle complex geometries, and sufficient numerical accuracy are some of the requirements for a suitable full-waveform simulation technique. On the other hand, the robustness and prediction accuracy are needed from the method used to solve the corresponding inverse problem. In this work, the discontinuous Galerkin method is used to solve the forward model while the convolutional neural networks is used to solve the estimation problem. Two-dimensional model problems with simulated data are presented. In the numerical experiments, the primary unknowns are estimated while the remaining parameters which are of less interest are successfully marginalized.This research focuses on both developing computational tools to model transient wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media and estimating material properties from the recorded full-waveform data. Numerical simulation of wave-dominated problems is computationally demanding. Efficient parallelization, capability to handle complex geometries, and sufficient numerical accuracy are some of the requirements for a suitable full-waveform simulation technique. On the other hand, the robustness and prediction accuracy are needed from the method used to solve the corresponding inverse problem. In this work, the discontinuous Galerkin method is used to solve the forward model while the convolutional neural networks is used to solve the estimation problem. Two-dimensional model problems with simulated data are presented. In the numerical experiments, the primary unknowns are estimated while the remaining parameters which are of less interest are successfully marginalized.

Simultaneous Determination of Young’s Modulus and Residual Stress in PECVD a-SiC from Postbuckling Vibration of MEMS Beams

Silicon carbide is a highly desirable material for MEMS sensor applications due to its robustness against harsh environments, especially high temperature and chemically corrosive environments. It is also an excellent material for high frequency applications due to its high Young’s modulus (E). Unfortunately, it is also a very difficult material to grow on silicon as crystalline SiC. In comparison, it is much easier to grow SiC by PECVD and integrate into a MEMS stack. However, the PECVD grown SiC is amorphous and its material properties vary considerably depending on the process parameters. For example, the Young’s modulus of PECVD grown a-SiC has been reported in the range of 138-196 GPa. This large variation makes it difficult to design MEMS structures with desirable characteristics using a specific value of E. The other significant process induced uncertainty is due to the residual stress (σ) in the structure. In order to have reliable response from the micromechanical structures made of such SiC, it is important to have reliable values of both the residual stress and the Young’s modulus. In this work, we report a method for simultaneous determination of E and σ by designing the test structures in the form of fixed-fixed beams and using their frequency response in buckled state. We exploit the invariance of even mode frequencies on residual stress to uncouple the effects of E and σ on the frequency, thus making it possible to determine E, along with another possible unknown- the underetch length. We subsequently use this information to find the residual stress from the odd mode frequencies. This evaluation requires a single sweep of frequency response from the buckled beam. The only condition here is that the beams have to be designed such that they result in buckled structures after release. This condition is essential, as only the buckled state guarantees the invariance of even modes on residual stress. Compared to other methods for determination of these properties, the advantage here is that we use a single method of measurement – LDV based frequency response measurement and a single type of test structures. Since the proposed method does not involve electrostatic actuation, in general it is applicable for dielectric thin films. In order to ensure repeatability, we have used measured results from more than 100 test structures. For the procedure to work, we need at least one of each even and odd mode frequencies to estimate the unknown parameters. Since in our experiment we are able to measure first four frequencies, we have two modes each from odd and even category. Thus there are total four modal combinations of measured data available to proceed with estimation of material properties, i.e. 2nd – 1st, 2nd – 3rd, 4th – 1st, 4th – 3rd. However not all of these combinations are always equally feasible. We have developed a good understanding of the circumstances, which favours use of one combination against the others.

Soil Compaction Assessment Using Spectral Analysis of Surface Waves (SASW)

Compaction is a process of soil densification in earthworks via by pressing the soil particles with air being expelled from the soil mass, thereby increasing its unit weight. Thus, it is important to evaluate the quality of soil compaction as prescribed in the technical requirement. SASW method is widely used for estimating material properties in layered structures based on the dispersion characteristics of Rayleigh Waves. The small scale at dimension area of 1.0 m width x 1.0 m length x 0.9 m depth was excavated and back filled with laterite soil. The soil was compacted for every layer at 0.3 m thickness. Each layer of soil compaction was conducted compaction test using core cutter methods and SASW test to determine the density and shear wave velocity. The phase velocity for layer 1 was between 112 m/s and 114 m/s, layer 2 was between 67 m/s and 74 m/s and layer 3 was between 74 m/s and 97 m/s. The result shows that the compacted soil layers are not fulfilled the quality of compacted soil layers where supposedly the expected shear wave velocity for the compacted layers should be higher than 180 m/s which is classified as stiff soil.

Estimating Material Properties Under Extreme Conditions by Using Bayesian Model Calibration with Functional Outputs

SummaryDynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto-correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment-specific nuisance input parameters on estimates of the physical parameters. The Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the procedure results in simple, fast and valid inferences on the material properties for tantalum.

Estimation of state and material properties during heat-curing molding of composite materials using data assimilation: A numerical study

Accurate simulations of carbon fiber-reinforced plastic (CFRP) molding are vital for the development of high-quality products. However, such simulations are challenging and previous attempts to improve the accuracy of simulations by incorporating the data acquired from mold monitoring have not been completely successful. Therefore, in the present study, we developed a method to accurately predict various CFRP thermoset molding characteristics based on data assimilation, a process that combines theoretical and experimental values. The degree of cure as well as temperature and thermal conductivity distributions during the molding process were estimated using both temperature data and numerical simulations. An initial numerical experiment demonstrated that the internal mold state could be determined solely from the surface temperature values. A subsequent numerical experiment to validate this method showed that estimations based on surface temperatures were highly accurate in the case of degree of cure and internal temperature, although predictions of thermal conductivity were more difficult.