Identification Of Material Properties Research Articles

Property Prediction of Bio-Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods.

Increasing the diversity of bio-based polymers is needed to address the combined problems of plastic pollution and greenhouse gas emissions. The magnitude of the problems necessitates rapid discovery of new materials; however, identification of appropriate chemistries maybe slow using current iterative methods. Machine learning (ML) methods could significantly expedite new material discovery and property identification. Here, PolyAGM, a ML algorithm using graph kernel methods, is introduced and used to predict the properties of block copolymers and identify the responsible structural 'motifs'. It applies a "fingerprinting" method to convert Graph representations of polymers into numerical vectors. The Graphs explicitly encode the entire copolymer of atoms and bonds such that the sequencing of chemical features and polymer chain length are included, alongside relevant stereochemical information. PolyAGM gives predictions for both thermal and mechanical properties that are in good agreement with experimental measurements. This work focuses on predicting the properties of bio-derived ABA-block polymer thermoplastic elastomers, but the general fingerprinting technique of PolyAGM should be relevant to other application fields.

Modular soft pneumatic actuator mimics elephant trunk locomotion

Robots support and facilitate tasks in all life fields. Soft robots specifically have the advantages of inherent compliance, safe interaction and flexible deformability. Soft pneumatic network (Pneu-Net) is a soft pneumatic actuator (SPA) composed of network of chambers that is actuated by pneumatic power. Soft Pneu-Net fits the human interface applications perfectly. In this paper, a bio-inspired modular based design for Pneu-Net actuator is developed. The actuator mimics the elephant trunk curling to be employed for rehabilitation of human hand fingers. The actuator is an integrated four Pneu-Net modules actuator which is attached to hand’s finger. The main introduced advantages in the new developed actuator are: providing four degrees of freedom (DoF) essential for finger’s motion by single compound actuator and developing a methodology for a modular soft Pneu-Net actuator that is efficiently reproducible. The actuator’s design is developed using computer aided design (CAD) software SOLIDWORKS. The design is simulated using finite element modeling (FEM) software ABAQUS. Fabrication process uses 3D printed molds. Soft material is molded in the 3D printed molds, forming actuator’s modules. Actuator’s modules are integrated by adhesion using the soft material. A proposed non-standard hyper-elastic material biaxial tension test is introduced as a quick material properties identification method that can produce a test table used for material identification in the FEM. Enhanced version for the actuator uses reinforcement fibers. Results show advances for the reinforced actuator, as it limits the unwanted actuator’s strain and deformation. The reinforced actuator shows improved energy efficiency reaches to 46%.

Phase change material properties identification for the design of efficient thermal management system for cylindrical Lithium-ion battery module

Performance and life of Lithium-ion battery packs in EVs and energy storage applications are limited by the thermal profile of cells during its life. Passive cooling technique based on Phase Change Material (PCM) is employed for mitigating thermal issues associated with Lithium-ion cells. The challenge with PCM materials is the trade-off between its latent heat of fusion, thermal conductivity and specific heat. Detailed CFD analysis is carried out to study the impact of PCM thermal properties on battery pack thermal profile and provide guidelines for PCM properties selection and usage of existing PCM materials. Impact of critical PCM properties such as latent heat (100–250 kJkg−1), thermal conductivity (0.2–23 Wm−1K−1), melting temperature and thickness of PCM material on thermal performance of a battery module during rapid discharge rate of 25 A (3.2C) is investigated. Battery module maximum temperature (Tmax) reaches 54°C and temperature difference (∆T) to 5.5°C while operating at 35°C with 124 kJkg−1and 0.2 Wm−1K−1. It is found that, PCM with higher latent heat (≥ 175 kJkg−1), thermal conductivity (≥ 3 Wm−1K−1), PCM thickness (≥ 3 mm) and appropriate phase change temperature 41-44°C exhibit improved heat dissipation for high discharge rates by effectively reducing the Tmax to 44.4°C and ∆T to 2.4°C (25 A and 35°C ambient). Further, ways to mitigate low latent heat capacity and thermal conductivity of PCM through increasing PCM material usage (i.e. inter cell distance) is emphasized. Results of the present study can be considered as a design tool for PCM development engineers which could be cost effective both in-terms of development time and effort.

Identification, uncertainty quantification and validation of orthotropic material properties for additively manufactured polymers

The mechanical behavior of polymeric parts manufactured by fused filament fabrication is often characterized as orthotropic because of the layer-wise extrusion of the material. To perform reliable simulations of the mechanical response of these parts subjected to certain loads, the material parameters have to be known. This leads to the task of material parameter identification from suitable experimental data. In this work, the identification of the material parameters for orthotropic material behavior is studied in detail with a focus on the specific characteristics of additively manufactured parts. This comprises working out the differences that arise compared to the parameter identification for classical orthotropic composite materials. Moreover, the material parameters are identified from tensile and shear test information together with full-field displacement data from digital image correlation measurements. Then, the obtained parameters are compared for analytical and numerical approaches, where a non-linear least-squares method with finite elements is drawn on. The parameters are not only identified, but also an uncertainty analysis is carried out and, subsequently, the different methods are validated. Further, a clear derivation of commonly applied analytical equations is provided with a special focus on the underlying assumptions. It turns out that the analytical parameter identification procedures cannot be easily transferred to numerical procedures, especially when using full-field data, because of correlations between the parameters. Moreover, the identified parameters are employed to prove the reasonability of orthotropic material behavior for specimens manufactured by fused filament fabrication. Finally, during validation it turns out that all parameter identification methods provide a good agreement with experimental data, showing only small differences between the results of the different calibration strategies.

Identification of viscoelastic material properties by ultrasonic angular measurements in double through-transmission.

Recent advances in additive manufacturing (AM) of viscoelastic materials have paved the way toward the design of increasingly complex structures. In particular, emerging biomedical applications in acoustics involve structures with periodic micro-architectures, which require a precise knowledge of longitudinal and transverse bulk properties of the constituent materials. However, the identification of the transverse properties of highly soft and attenuating materials remains particularly challenging. Thereby, the present work provides a methodological framework to identify the frequency-dependent ultrasound characteristics (i.e., phase velocity and attenuation) of viscoelastic materials. The proposed approach relies on an inverse procedure based on angular measurements achieved in double through-transmission, referred as θ-scan. Toward this goal, a forward modeling of the double transmitted waves through a homogeneous solid is proposed for any incidence angle based on the global matrix formalism. The experimental validation is conducted by performing ultrasound measurements on two types of photopolymers that are commonly employed for AM purposes: a soft elastomer (ElasticoTM Black) and a glassy polymer (VeroUltraTM White). As a result, the inferred dispersive ultrasound characteristics are of interest for the computational calibration and validation of models involving complex multi-material structures in the MHz regime.

Exploring the relationship between bulk Young’s Modulus of materials and milling efficiency during wet bead milling of pharmaceutical compounds

Wet bead milling (WBM) is one of the main approaches for manufacturing long acting injectable (LAI) suspensions, wherein the particle size of an Active Pharmaceutical Ingredient (API) is reduced in a liquid vehicle via grinding. A common challenge observed during WBM is long milling time to achieve target particle size, resulting in poor milling efficiency. The objective of this work was to identify potential API attributes predictive of milling efficiency during WBM. In this study, physical and mechanical properties of nine APIs were characterized. Formulations with these APIs were manufactured using WBM. Bulk Young’s Modulus was identified to have a significant influence on the rate of particle attrition. The rank order of Young’s Moduli of the APIs was consistent with that of milling efficiency, estimated by an empirical function defined in this study called Milling Resistance (ϕ), representing the holistic impact of milling time, tip speed, bead loading, and batch to chamber volume ratio. The identification of such intrinsic material properties, which provide an early evaluation of potential manufacturing risks, is beneficial to product development, as these assessments can be performed with limited quantities of materials and help identify and design out scale-up challenges.

Sub-global equilibrium method for identification of elastic parameters based on digital image correlation results

In this work, a new, simple method is presented, which enables identification of material properties of solids basing on the digital image correlation (DIC) measurements. It may be considered as a simplified alternative of low computational complexity for the well-known finite element model updating (FEMU) method and virtual fields method (VFM). The idea of the introduced sub-global equilibrium (SGE) method is to utilize the fundamental concept and definition of internal forces and its equilibrium with appropriate set of external forces. This makes the method universal for the use in the description of a great variety of continua. The objective function is the measure of imbalance, namely the sum of squares of residua of equilibrium equations of external forces and internal forces determined for finite-sized part of the sample. It is then minimized with the use of the Nelder–Mead downhill simplex algorithm. The efficiency of the proposed SGE method is shown for two types of materials: 310 S austenitic steel and carbon-fiber-reinforced polymer (CFRP). The proposed method was also verified based on FE analysis showing error estimation.

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.