Identification Of Material Properties Research Articles

Material properties identification. Comparison of two techniques

Present paper describes the comparison of two different material properties identification techniques in order to investigate a possibility of combination of two techniques into one powerful and fast tool for material properties identification. Both methods are theoretically described and main options are given in this article. Some experiments wherein aluminium material properties are identified are carried out. Methods are qualified, conclusions and proposals are given.

Identification of Hybrid Polymer Material STERED and Basic Material Properties Used in Road Substructures or Pavements.

Modern road construction uses a large number of polymer-based materials. Material composition depends on their roles. Among the most important functions of road body materials is to transfer all loads safely to the subgrade. A thorough understanding of material properties in various climates is crucial for this purpose. In the automotive industry, polymer residues from recycling can be used to make innovative materials, such as STERED, a hybrid polymer composite. Drawing on the porous nature of this material, this paper investigates its mechanical behavior. For road construction, the compressive properties of the material are most important. The paper presents the results of a detailed analysis and experimental research of the STERED material from in-lab tests. Successful research will lead to the inclusion of the material in road body compositions with excellent retention properties, vibration damping, and potential in circular economy.

Automated Visual Inspection

In manufacturing, where satisfying increasing customer demands is critical, quality is of the utmost importance for any organization. Evaluating the quality of a product may be tedious and error- prone, even for skilled operators. Though computer vision automates visual evaluation, it provides temporary solutions. The Lean manufacturing method has been created to overcome this. Statistical pattern recognition, image processing, object identification, and other activities are integrated and automated by computer vision, a branch of artificial intelligence. Though computational limitations now restrict its application, it has potential to spread to other domains such as product design, defect diagnostics, automation of manufacturing procedures, and material property identification. In the future, this discipline may hold answers to a myriad of problems thanks to the ongoing advancement of research and development, which includes reinforcement learning

Investigation of the Motion of a Spherical Object Located at Soft Elastic and Viscoelastic Material Interface for Identification of Material Properties

Measuring the properties of soft viscoelastic materials is challenging. Here, the motion of a spherical object located at the soft elastic and viscoelastic material interface for the identification of material properties is thoroughly investigated. Formulations for different loading cases were derived. First, the theoretical models for a spherical object located at an elastic medium interface were derived, ignoring the medium viscosity. After summarizing the model for the force reducing to zero following the initial loading, we developed mathematical models for the force reducing to a lower non-zero value or increasing to a higher non-zero value, following the initial loading. Second, a similar derivation process was followed to evaluate the response of a spherical object located at a viscoelastic medium interface. Third, by performing systematic analyses, the theoretical models obtained via different approaches were compared and evaluated. Fourth, the measured and predicted responses of a spherical object located at a gelatin phantom interface were compared and the viscoelastic material properties were identified. It was seen that the frequency of oscillations of a spherical object located at the sample interface during loading was 10–15% different from that during unloading in the experimental studies here. The results showed that different loading cases have immense practical value and the formulations for different loading cases can provide an accurate determination of material properties in a multitude of biomedical and industrial applications.

Experimental characterization and numerical analysis of CFRPs at cryogenic temperatures

The thermo-mechanical performance of carbon fiber reinforced polymers (CFRPs) is affected by temperature changes due to the dissimilar nature of the constituents and dissimilar properties. For instance, under cryogenic conditions, the effective mechanical performance of the composite system can be significantly reduced. In this work, dynamic mechanical analysis (DMA) tests and coupon mechanical tests at room and cryogenic temperatures (through immersion in liquid nitrogen), are combined with computational micromechanics to analyze the development of micro-residual stresses from curing to cryogenic conditions, and their influence on the mechanical performance of a unidirectional CFRP. The results confirm the stiffness increase in the matrix and in the composite under cryogenic conditions, but the high tensile triaxial stress state that is developed during cooling reduces the overall composite strength, despite the increase of the matrix strength with decreasing temperature. To capture the failure modes and the evolution of the properties appropriately, the elastic-plastic response and the pressure dependent failure model are formulated to appropriately represent the shear response of the composite system and matrix failure under complex triaxial stress states, enabling a complete analysis and the identification of effective material properties, based on a reduced number of simple tests on the neat resin and on the composite system at different temperatures, which otherwise would be impossible to obtain considering the dimensional and technological restrictions of performing mechanical tests at cryogenic conditions.

A novel machine learning-based approach for nonlinear analysis and in-situ assessment of masonry

Predicting the local and global mechanical response of masonry structures or estimating their in-situ properties are critical and challenging for the design or assessment of these structures. This article presents a fast prediction/assessment model, developed using a conditional generative adversarial neural network (cGAN) to address this challenge. For the first time, the model shows to be capable of establishing a relationship between masonry microstructural features and full-field local/global mechanical response, and vice-versa, overpassing the path dependency of the non-linear mechanical problems. The strain maps and reaction forces of masonry panels are predicted at any load level from images of masonry panels, which embedded information regarding the materials properties and loading scenarios only in colours, without having any prior knowledge of the material properties and constitutive laws and without the need to access these fields in previous loading levels. Also, it is shown that the mechanical properties of masonry constituents can be predicted from full-field strain maps and loading scenarios using the developed model. This promises to become a revolutionary metamodel for the expensive finite element simulations of masonry or to support in-situ/laboratory experimental testing in the identification of masonry material properties.

Functionally graded cylinders: Vibration analysis

AbstractThe problems of steady‐state and free oscillations of functionally graded hollow cylinders are considered in the present article, with the cylinders' properties considered changing along the radial or longitudinal coordinates. The stress–strain state was described using the model of an isotropic body with the variable Lamé parameters and density. The cylinders were assumed to be subjected to the normal and tangential loads. In order to obtain the numerical problem solutions, the finite element method was employed. The effect of variable properties on the amplitude–frequency characteristics and the resonant frequencies values for various cylinders types is estimated. Some features, which can be used in solving new inverse coefficient problems on material properties identification based on acoustic sounding data, are revealed.

A Variational Formulation of Physics-Informed Neural Network for the Applications of Homogeneous and Heterogeneous Material Properties Identification

A new approach for solving computational mechanics problems using physics-informed neural networks (PINNs) is proposed. Variational forms of residuals for the governing equations of solid mechanics are utilized, and the residual is evaluated over the entire computational domain by employing domain decomposition and polynomials test functions. A parameter network is introduced and initial and boundary conditions, as well as data mismatch, are incorporated into a total loss function using a weighted summation. The accuracy of the model in solving forward problems of solid mechanics is demonstrated to be higher than that of the finite element method (FEM). Furthermore, homogeneous and heterogeneous material distributions can be effectively captured by the model using limited observations, such as strain components. This contribution is significant for potential applications in non-destructive evaluation, where obtaining detailed information about the material properties is difficult.

A novel deep-learning-based objective function for inverse identification of material properties

Inverse methods are critical in exploring the constitutive models of materials. In the traditional finite element model updating method, an objective function is often established in the form of mean square error, which is simple and easy to use. However, this approach does not fully consider the prior knowledge of the material, leading to low computational stability and sensitivity to noise. To address these limitations, a novel objective function that considers deep semantic information and mechanical constraints was proposed based on deep learning techniques in this study. Compared with the traditional method, the proposed objective function can significantly reduce the effect of noise on inversion performance without any loss in computational efficiency. The method was validated through simulated experiments and successfully applied to the inverse identification of damage properties of real nuclear graphite materials under simple and complex stress states. The results demonstrated that the proposed objective function can significantly enhance the stability of the finite element model updating method, leading to a rapid convergence with high accuracy.

Identification of Viscoelastic Material Properties of a Layer Through a Constrained Sandwich Beam in the Frequency Domain

Identification of Viscoelastic Material Properties of a Layer Through a Constrained Sandwich Beam in the Frequency Domain

Coupling physics-informed neural networks and constitutive relation error concept to solve a parameter identification problem

Identification of material model parameters using full-field measurement is a common process both in industry and research. The constitutive equation gap method (CEGM) is a very powerful strategy for developing dedicated inverse methods, but suffers from the difficulty of building the admissible stress field. In this work, we present a new technique based on physics-informed neural networks (PINNs) to implement a CEGM optimization process. The main interest is to easily construct the admissible stress thanks to automatic differentiation (AD) associated with PINNs. This new method combines the high quality of the CEGM with the numerical effectivity of the PINNs and realizes the identification of material properties in a more concise way. We compare two variants of the developed method with the classical identification strategies on simple two-dimensional (2D) cases and illustrate its effectiveness in three-dimensional (3D) problems, which is of interest when dealing with tomographic images. The results indicate that the proposed method has good performance while avoiding complex calculation procedures, showing its great potential for practical applications.

Additively manufactured polyethylene terephthalate scaffolds for scapholunate interosseous ligament reconstruction

The regeneration of the ruptured scapholunate interosseous ligament (SLIL) represents a clinical challenge. Here, we propose the use of a Bone-Ligament-Bone (BLB) 3D-printed polyethylene terephthalate (PET) scaffold for achieving mechanical stabilisation of the scaphoid and lunate following SLIL rupture. The BLB scaffold featured two bone compartments bridged by aligned fibres (ligament compartment) mimicking the architecture of the native tissue. The scaffold presented tensile stiffness in the range of 260 ± 38 N/mm and ultimate load of 113 ± 13 N, which would support physiological loading. A finite element analysis (FEA), using inverse finite element analysis (iFEA) for material property identification, showed an adequate fit between simulation and experimental data. The scaffold was then biofunctionalized using two different methods: injected with a Gelatin Methacryloyl solution containing human mesenchymal stem cell spheroids (hMSC) or seeded with tendon-derived stem cells (TDSC) and placed in a bioreactor to undergo cyclic deformation. The first approach demonstrated high cell viability, as cells migrated out of the spheroid and colonised the interstitial space of the scaffold. These cells adopted an elongated morphology suggesting the internal architecture of the scaffold exerted topographical guidance. The second method demonstrated the high resilience of the scaffold to cyclic deformation and the secretion of a fibroblastic related protein was enhanced by the mechanical stimulation. This process promoted the expression of relevant proteins, such as Tenomodulin (TNMD), indicating mechanical stimulation may enhance cell differentiation and be useful prior to surgical implantation. In conclusion, the PET scaffold presented several promising characteristics for the immediate mechanical stabilisation of disassociated scaphoid and lunate and, in the longer-term, the regeneration of the ruptured SLIL.

Toward Pactive (Passive +Active) Sensing for Improved Identification of Material Properties

Subsurface electromagnetic sensing techniques that can measure material properties of hidden layers are useful for applications such as security screening. A system combining active (radar) and passive (radiometer) sensing into one unit, or a pactive sensor, can address drawbacks of extant single mode sensors by reducing measurement ambiguity and resolving features better, thereby leading to improved identification of the hidden layer. This work investigates a technique to noninvasively extract the complex permittivity and thickness of hidden dielectrics using a pactive sensor. A proof-of-concept demonstration of an optimization-based inversion technique is used to extract the properties of an unknown, hidden layer in a multi-layered dielectric structure. The technique uses active data to estimate dielectric constant and thickness, and passive data to estimate loss tangent. The experimental setup is selected to represent a simplified single-voxel security scenario consisting of multiple dielectric layers backed by a human phantom layer (heated water). Tests using K-band prototype radar and radiometer systems yielded results with less than 5% error in the extracted dielectric constant, loss tangent and thickness of the unknown layer.

Experimental and Numerical Investigation of Novel Acoustic Liners and Their Design for Aero-Engine Applications

This paper presents a combined experimental and numerical investigation on a novel liner concept for enhanced low-frequency and broadband acoustic attenuation. In particular, two different realizations, derived from conventional Helmholtz resonators (HR) and plate resonators (PR) are investigated, which both deploy flexible materials with material inherent damping. In this context, a comprehensive experimental investigation was carried out focusing the identification and evaluation of various geometric parameters and material properties on the acoustics dissipation and related properties of various materials in a simplified setup of a single Helmholtz resonator with flexible walls (FHR concept). Furthermore, a parameter study based on analytical models was performed for both liner concepts, taking into account material as well as geometric parameters and their effects on transmission loss. In addition, design concepts that enable cylindrical or otherwise curved liner structures and the corresponding manufacturing technologies are presented, while considering essential structural features such as drainage. With respect to the potential application in jet engines, a structural–mechanical analysis considering the relevant load cases to compare and discuss the mechanical performance of a classical HR and the FHR concept liner is presented. Finally, both concepts are evaluated and possible challenges and potentials for further implementation are described.

Machine learning accelerates the materials discovery

As the big data generated by the development of modern experiments and computing technology becomes more and more accessible, the material design method based on machine learning (ML) has opened a new paradigm for materials science research. With its ability to automatically solve complex tasks, machine learning is being used as a new method to help discover the relevance of materials, understand materials' properties, and accelerate the discovery of materials. This paper first introduces the general process of machine learning in materials science. Secondly, the applications of machine learning in material properties prediction, classification and identification, auxiliary micro-scale characterization, phase transformation research and phase diagram construction, process optimization, service behavior evaluation, accelerating the development of computational simulation technology, multi-objective optimization and inverse design of materials are reviewed. Finally, we discuss the main challenges and possible solutions in machine learning, and predict the potential research directions.

Direct material property determination: One‐dimensional formulation utilising full‐field deformation measurements

AbstractA direct approach is described to determine the elastic modulus distribution in a nominally heterogeneous material subject to tensile/compression loading and primarily experiencing deformations in the axial direction. The formulation is developed for uniaxial applications using basic theoretical constructs, resulting in a computational framework that has a matrix form [A] {E} = {R}, where the [A] matrix components are known functions of measured axial strains and axial positions, {R} components are known functions of axial body forces, applied loads and reactions and {E} components are the unknown elastic moduli at discrete locations along the length of the specimen. For a series of one‐dimensional (1D) material property identification procedure with known axial strains at discrete locations and various levels of random noise, results are presented to demonstrate the accuracy and noise sensitivity of the methodology. Finally, experimental measurements for a heterogeneous bone specimen are compared to our 1D model predictions, demonstrating that the predictions are in very good agreement with independent estimates at each load level of interest along the length of the bone specimen.

Deep learning in frequency domain for inverse identification of nonhomogeneous material properties

The inverse identification of nonhomogeneous material properties from measured displacement/strain fields, especially when noise exists, is crucial for both engineering and material science. The conventional physics-based solutions either require time-consuming iterative calculations, or are sensitive to noise. While the new machine learning methods either need excess data for high-dimensional matchups, or mainly apply to case-by-case analyses with informed physics. In this paper, to solve the complex matchup between the measured displacement/strain fields and the randomly distributed modulus field rapidly and robustly, a novel method of deep learning in frequency domain is proposed, with discrete cosine transform (DCT) to achieve frequency domain transformation as well as dimensionality reduction and convolutional neural network (CNN) to implement learning in frequency domain. Results show that our method not only has high prediction accuracy on zero-noise samples (with L1-error of 4.249%) but also presents great robustness to noise (with L1-error of 5.085% on large-noise samples). Besides, by our method, only one-time training on a dataset with mixed noise is basically enough to deal with arbitrary levels of noise (with L1-errors below 5.202%), improving the efficiency significantly in practical applications. Moreover, our method can be directly transferred to neighbor sampling spaces with different sampling points, showing a great generalization. The study provides a powerful approach for inverse identification of material properties and promises for wide applications such as real-time elastography and high-throughput non-destructive evaluation techniques.

Identifying Varying Thermal Diffusivity of Inhomogeneous Materials Based on a Hybrid Physics-Informed Neural Network

Inhomogeneous materials are characterized by uneven material properties with changing dimensions. Effective and precise identification of nonuniform material properties is essential for studying material inhomogeneity. In this study, we propose a hybrid physics-informed neural network (PINN) to identify the inhomogeneous thermal diffusivity only using temperature data collected in a transient heat conduction problem. A PINN and a fully connected neural network are combined in the proposed model. The PINN model is trained to learn the law of physics governed by the transient heat conduction equation. The identification network is trained to uncover the thermal diffusivity variation with changing dimensions. The proposed model successfully identifies two types of inhomogeneity: temperature-dependent and space-dependent thermal diffusivity, and both two-dimensional and three-dimensional heat conduction problems are investigated. The proposed model is examined to be robust to small datasets and noisy training data. In addition to heat conduction problems, the proposed model can be adopted to identify the material properties of various types of inhomogeneous materials.

On the Use of High-Order Shape Functions in the SAFE Method and Their Performance in Wave Propagation Problems

In this research, high-order shape functions commonly used in different finite element implementations are investigated with a special focus on their applicability in the semi-analytical finite element (SAFE) method being applied to wave propagation problems. Hierarchical shape functions (p-version of the finite element method), Lagrange polynomials defined over non-equidistant nodes (spectral element method), and non-uniform rational B-splines (isogeometric analysis) are implemented in an in-house SAFE code, along with different refinement strategies such as h-, p-, and k-refinement. Since the numerical analysis of wave propagation is computationally quite challenging, high-order shape functions and local mesh refinement techniques are required to increase the accuracy of the solution, while at the same time decreasing the computational costs. The obtained results reveal that employing a suitable high-order basis in combination with one of the mentioned mesh refinement techniques has a notable effect on the performance of the SAFE method. This point becomes especially beneficial when dealing with applications in the areas of structural health monitoring or material property identification, where a model problem has to be solved repeatedly.

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green’s matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc.