Variation Of Material Properties Research Articles

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

Abstract In this paper, we propose a deep-learning algorithm, Data-driven simulation assisted Physics-learned Artificial Intelligence (DPAI), to simulate heat diffusion in
large-grain polycrystalline material. The DPAI model utilizes an encoder-decoder
architecture with convolutional long short-term memory (ConvLSTM), which captures
the spatio-temporal representation from input sequences. The DPAI model learns the
physics of heat diffusion in the material from training datasets. This model is trained
with Finite Element (FE) simulation datasets consisting of a varying number of grains
in a microstructure of polycrystalline material with a single-point heat source at the
center. The arbitrary plane of the 3D microstructure of these materials is represented
using 2D Voronoi tessellations. These configurations are used to model the geometry
of the 2D Computer-Aided Design (CAD) model. Each cell of the Voronoi tessellation
represents one grain of the microstructure. This CAD model is used as the input to
the FE solver for solving heat diffusion equations. To model the near-realistic material
anisotropy and accurately measure temperature differences at cell boundaries, a smaller
mesh size is required in FE modeling, which takes considerable solver time. Therefore,
the proposed model significantly reduces the computational time while maintaining
reasonable accuracy compared to conventional FE simulation. The effectiveness of
the trained DPAI model is examined by modeling larger domain problems involving a
greater number of grains and varying material properties.

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Machine learning techniques for predictive modelling and uncertainty quantification of the mechanical properties of woven carbon fibre composites

Carbon fibre composites owe their exceptional mechanical properties to the characteristics of their constituents; fibres and resin. However, uncertainty in these individual constituent properties can introduce significant variations in the macroscale mechanical performance of composites. These variations are amplified in the presence of various plies in the composite and pose a challenge to quantify during manufacturing stages. In this study, uncertain constituent material properties are propagated to the macroscale composite through multiscale homogenisation to compute the varied resultant thin-shell stiffness matrices. An in-house-built automated simulation environment has been used to create a physics-conforming trainable database for model training, testing, validation and uncertainty quantification using Gaussian Process Regression (GPR), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR). Through rigorous comparative analyses, the superior performance of GPR is demonstrated when compared to alternative techniques with R² ≥ 0.99 and NRMSE < 10⁻⁷. Further, the findings approve the validity of GPR for extrapolatory predictions. For instance, a randomly sampled [-5, 5] % variation of constituent material properties leads to extreme uncertainties in macroscale ± 0.067 %, for axial stiffness with excellent GPR predictive accuracy of R² = 0.99. This slightly increases to ± 0.14 % for a variation of [-10, 10] %. Moreover, the extreme uncertainties indicate that the direct axial and bending stiffnesses are more sensitive to variations in constituent properties than others. Additional investigations presented in the paper produce predictive models and uncertainty bounds for all stiffness entries that are of high importance for macroscale property control during manufacturing.

Probabilistic Finite Element Analysis of Human Rib Biomechanics: A Framework for Improved Generalizability.

In dynamic impact events, thoracic injuries often involve rib fractures, which are closely related to injury severity. Previous studies have investigated the behavior of isolated ribs under impact loading conditions, but often neglected the variability in anatomical shape and tissue material properties. In this study, we used probabilistic finite element analysis and statistical shape modeling to investigate the effect of population-wide variability in rib cortical bone tissue mechanical properties and rib shape on the biomechanical response of the rib to impact loading. Using the probabilistic finite element analysis results, a response surface model was generated to rapidly investigate the biomechanical response of an isolated rib under dynamic anterior-posterior load given the variability in rib morphometry and tissue material properties. The response surface was used to generate pre-fracture force-displacement computational corridors for the overall population and a population sub-group of older mid-sized males. When compared to the experimental data, the computational mean response had a RMSE of 4.28N (peak force 94N) and 6.11N (peak force 116N) for the overall population and sub-group respectively, whereas the normalized area metric when comparing the experimental and computational corridors ranged from 3.32% to 22.65% for the population and 10.90% to 32.81% for the sub-group. Furthermore, probabilistic sensitivities were computed in which the contribution of uncertainty and variability of the parameters of interest was quantified. The study found that rib cortical bone elastic modulus, rib morphometry and cortical thickness are the random variables that produce the largest variability in the predicted force-displacement response. The proposed framework offers a novel approach for accounting biological variability in a representative population and has the potential to improve the generalizability of findings in biomechanical studies.

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

AbstractFunctionally graded (FG) carbon nanotubes (CNTs) reinforced composite possessing continuously varied material properties have received extensive concern in the area of aviation, automotive, military, and spaceflight. The nonlinear bending of FG CNT composite is performed by developing a novel meshless model on the base of Smoothed Particle Hydrodynamics (SPH) method. The equivalent temperature relative material properties of composite is established in the light of the extended rule of mixture model. Different gradient dispersions of CNTs are taken into account and modeled. The governing equations are explored using the Mindlin theory and discretized though a symmetric SPH method with high prediction ability. The developed model is verified by analyzed several examples and compared to the solution obtained in literature. Influences of CNT dispersion, content, plate aspect ratio, boundary conditions and lamination angle on the bending response are elaborated. The present study provides an alternative potential method to solve the mechanical performances of FG CNT composites based on the meshless SPH formulation.Highlights Thermo‐mechanics of FG‐CNTRC is firstly solved by SPH method. The model is verified by solving the benchmarks in literature. Nonlinear bending behaviors of FG‐CNTRC are demonstrated. The minimum deflection take place in FG‐X type composite. For laminate plate, the smallest deformation occurs in 0° CNTRC.

Batch-to-Batch Variation in Laser-Inscribed Graphene (LIG) Electrodes for Electrochemical Sensing

Laser-inscribed graphene (LIG) is an emerging material for micro-electronic applications and is being used to develop supercapacitors, soft actuators, triboelectric generators, and sensors. The fabrication technique is simple, yet the batch-to-batch variation of LIG quality is not well documented in the literature. In this study, we conduct experiments to characterize batch-to-batch variation in the manufacturing of LIG electrodes for applications in electrochemical sensing. Numerous batches of 36 LIG electrodes were synthesized using a CO2 laser system on polyimide film. The LIG material was characterized using goniometry, stereomicroscopy, open circuit potentiometry, and cyclic voltammetry. Hydrophobicity and electrochemical screening (cyclic voltammetry) indicate that LIG electrode batch-to-batch variation is less than 5% when using a commercial reference and counter electrode. Metallization of LIG led to a significant increase in peak current and specific capacitance (area between anodic/cathodic curve). However, batch-to-batch variation increased to approximately 30%. Two different platinum electrodeposition techniques were studied, including galvanostatic and frequency-modulated electrodeposition. The study shows that formation of metallized LIG electrodes with high specific capacitance and peak current may come at the expense of high batch variability. This design tradeoff has not been discussed in the literature and is an important consideration if scaling sensor designs for mass use is desired. This study provides important insight into the variation of LIG material properties for scalable development of LIG sensors. Additional studies are needed to understand the underlying mechanism(s) of this variability so that strategies to improve the repeatability may be developed for improving quality control. The dataset from this study is available via an open access repository.

Surface Interaction and Wear Due to Sliding Electrical Contact of Materials

This article reports a research study on sliding electrical contacts of copper–diamond materials, involving modeling and validation, contact status simulations, and parametric studies. The simulation model includes the analyses of both mechanical and electrical heat sources, evaluations of heat partition and temperature, and the capture of material thermal softening, plastic deformation, and material removal. The simulation model is validated through result comparison with experimental average temperature and measured wear. With this model, temperature variation and surface wear accumulation were numerically predicted under the influences of material property variations. Factors influencing wear of the copper surfaces were explored, and parameter sensitivity was investigated by means of the Taguchi L18 matrix. The results reveal that thermally related parameters of the contact interface strongly affect the wear of the copper pin, and thermal conductivity of its mating surface material is of critical importance. The research also suggests that the electromagnetic field change due to switching the polarity of the power supply does not affect the friction and wear results; however, the polarity change influences material electrochemistry, which results in a difference in friction and wear.

Investigating the Impact of Blunt Force Trauma: A Probabilistic Study of Behind Armor Blunt Trauma Risk.

Evaluating Behind Armor Blunt Trauma (BABT) is a critical step in preventing non-penetrating injuries in military personnel, which can result from the transfer of kinetic energy from projectiles impacting body armor. While the current NIJ Standard-0101.06 standard focuses on preventing excessive armor backface deformation, this standard does not account for the variability in impact location, thorax organ and tissue material properties, and injury thresholds in order to assess potential injury. To address this gap, Finite Element (FE) human body models (HBMs) have been employed to investigate variability in BABT impact conditions by recreating specific cases from survivor databases and generating injury risk curves. However, these deterministic analyses predominantly use models representing the 50th percentile male and do not investigate the uncertainty and variability inherent within the system, thus limiting the generalizability of investigating injury risk over a diverse military population. The DoD-funded I-PREDICT Future Naval Capability (FNC) introduces a probabilistic HBM, which considers uncertainty and variability in tissue material and failure properties, anthropometry, and external loading conditions. This study utilizes the I-PREDICT HBM for BABT simulations for three thoracic impact locations-liver, heart, and lower abdomen. A probabilistic analysis of tissue-level strains resulting from a BABT event is used to determine the probability of achieving a Military Combat Incapacitation Scale (MCIS) for organ-level injuries and the New Injury Severity Score (NISS) is employed for whole-body injury risk evaluations. Organ-level MCIS metrics show that impact at the heart can cause severe injuries to the heart and spleen, whereas impact to the liver can cause rib fractures and major lacerations in the liver. Impact at the lower abdomen can cause lacerations in the spleen. Simulation results indicate that, under current protection standards, the whole-body risk of injury varies between 6 and 98% based on impact location, with the impact at the heart being the most severe, followed by impact at the liver and the lower abdomen. These results suggest that the current body armor protection standards might result in severe injuries in specific locations, but no injuries in others.

Roentgenoscopy of laser-induced projectile impact testing.

Laser-induced projectile impact testing (LIPIT) based on synchrotron imaging is proposed and validated. This emerging high-velocity, high-strain microscale dynamic loading technique offers a unique perspective on the strain and energy dissipation behavior of materials subjected to high-speed microscale single-particle impacts. When combined with synchrotron radiation imaging techniques, LIPIT allows for in situ observation of particle infiltration. Two validation experiments were carried out, demonstrating the potential of LIPIT in the roentgenoscopy of the dynamic properties of various materials. With a spatial resolution of 10 µm and a temporal resolution of 33.4 µs, the system was successfully realized at the Beijing Synchrotron Radiation Facility 3W1 beamline. This innovative approach opens up new avenues for studying the dynamic properties of materials in situ.

Why Do Small Earth Dams Deteriorate: Insights from Physical Investigations in the West African Sahel

In West Africa, the construction of small earth dams is common against water scarcity. Burkina Faso, an inland country in West Africa, is home to 1001 dams that serve agricultural and pastoral needs. These embankments are predominantly made of compacted laterite, a cost-effective material abundant in over 2/3 of the country. However, these dams degrade over time, hindering their functionality. This study aims to establish a catalog of typical degradation occurring on small dams in Burkina Faso, which is virtually non-existent in the region while identifying and analyzing the potential causes. The study uses a diagnostic analysis followed up with technical visits on a representative sample of 24 dams in the Centre and Centre-South regions as a basis for future studies. The results reveal that these dams were constructed between 1965 and 2018, with capacities ranging from 150,000 to 4,740,000 m3. 33% of these dams have undergone total failure, likely attributed to factors such as internal erosion, pore overpressures, settlement, and deformation. Although 67% of the dams remain functional, their structural integrity could be improved. Erosion observed in riprap indicates vulnerability during high flood periods. Additionally, the absence of proper maintenance, as shown by the vegetation development weakening embankments, contributes to deterioration. The analysis also suggests that variability in construction techniques and lateritic material properties across time and regions may further exacerbate degradation. These findings inform infrastructure improvements and policy development for sustainable water resource management in Burkina Faso and similar regions.

Voxel Design of Grayscale DLP 3D-Printed Soft Robots.

Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D-printed structures due to the complexities arising from the voxel-level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP-printed soft robots is developed by combining a grayscale-dependent hyperelastic constitutive model and a voxel-based finite-element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel-based finite-element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion(DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3Dprinting.

Channeled spectroscopic ellipsometry enabled by physics-informed tandem untrained neural networks

Ellipsometry is a powerful metrology technique for characterizing the optical properties of various materials. Channeled spectroscopic ellipsometry (CSE) has shown great promise among the different types of ellipsometry due to its simple setup and rapid performance. Furthermore, CSE modulates the polarization parameters of thin films into a spectrum, thus transforming the measurement process into a demodulation problem. However, conventional CSE faces challenges in measurement accuracy and computational efficiency, with strict hardware and calibration requirements. Inspired by physics-informed machine learning, we propose CSE enabled by the physics-informed tandem untrained neural networks (PITUNN), which does not require training, exhibits high computational efficiency and partially alleviates the strict requirements for hardware and calibration accuracy. We also demonstrate the effectiveness of CSE enabled by the PITUNN and its ability to handle system errors and random noise through simulations and experiments on thin films of different thickness and materials.

Additive manufacturing with varying material properties of thermosetting reactive polymers: a framework and comparison of different modes for implementing material transitions

Additive Manufacturing with Varying Material Properties enables controlled spatial variation of material properties in 3D-printed components, facilitating custom-tailored characteristics, added functionalities and reduced assembly processes. To promote this approach in building façade applications, this paper presents a novel framework for Additive Manufacturing with varying material properties using a thermosetting reactive polymer, specifically polyurethane. By dynamically changing the polyurethane’s chemical composition, the material properties can be precisely controlled. The framework’s individual aspects, including the material, hardware setup and computational system, are described in detail. Additionally, the research explores the implementation of material transitions with this framework, highlighting three different modes (horizontal, vertical and multi plane) and their impact on print time and material consumption. The paper concludes by discussing the potential of this approach for building façade applications, addressing current challenges and outlining future research directions.

Thermal modal analysis of hypersonic composite wing on transient aerodynamic heating

Considering the influence of thermal stress and material property variations, this study employs the Navier–Stokes equations and Fourier heat conduction law to establish a semi-implicit time-domain numerical analysis method for hypersonic aerothermal-structural coupling. Study the temporal variation pattern of different regions of the composite material wing under aerodynamic heating. Using the obtained transient temperature field of the wing, the thermal modal of the wing at different time points is calculated using the finite element method. Additionally, it conducts an analysis and discussion on the factors influencing the thermal modal. Composites can be effectively utilized as thermal protection materials for aircraft. During the aerodynamic heating process, the leading edge temperature reaches thermal equilibrium first, followed by the trailing edge, and the belly plate experiences a slower thermal response. Temperature rise significantly affects higher-order modes, with the change in material properties during the early stages of heating being the dominant factor. This leads to a faster decrease in natural frequency. As heat conduction progresses, the influencing factors of thermal stresses gradually increase, and the natural frequency decreases slowly or even rises.

Sensitivity of cartilage mechanical behaviour to spatial variations in material properties

Articular cartilage tissue exhibits a spatial dependence in material properties that govern mechanical behaviour. A mathematical model of cartilage tissue under one dimensional confined compression testing is developed for normal tissue that takes account of these variations in material properties. Modifications to the model representative of a selection of mechanisms driving osteoarthritic cartilage are proposed, allowing application of the model to both physiological and pathophysiological, osteoarthritic tissue. Incorporating spatial variations into the model requires the specification of more parameters than are required in the absence of these variations. A global sensitivity analysis of these parameters is implemented to identify the dominant mechanisms of mechanical response, in normal and osteoarthritic cartilage tissue, to both static and dynamic loading. The most sensitive parameters differ between dynamic and static mechanics of the cartilage, and also differ between physiological and osteoarthritic pathophysiological cartilage. As a consequence changes in cartilage mechanics in response to alterations in cartilage structure are predicted to be contingent on the nature of loading and the health, or otherwise, of the cartilage. In particular the mechanical response of cartilage, especially deformation, is predicted to be much more sensitive to cartilage stiffness in the superficial zone given the onset of osteoarthritic changes to material properties, such as superficial zone increases in permeability and reductions in fixed charge. In turn this indicates that any degenerative changes in the stiffness associated with the superficial cartilage collagen mesh are amplified if other elements of osteoarthritic disease are present, which provides a suggested mechanism-based explanation for observations that the range of mechanical parameters representative of normal and osteoarthritic tissue can overlap substantially.

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Guided wave propagation (GWP) is commonly employed for the design of SHM systems. However, GWP is sensitive to variations in the material properties, often leading to false alarms. To address this issue, uncertainty quantification (UQ) is employed to improve the reliability of the predictions. Computational structural mechanics is a useful tool for the simulation of GWP. Even so, the application of UQ methods requires numerous solutions, while large-scale, transient GWP simulations increase the computational cost. In this paper, we propose a machine learning (ML)-based reduced order model (ROM), annotated as BO-ML-ROM, to decrease the computational time of the GWP simulation. The ROM is integrated with a Bayesian optimization (BO) framework to adaptively sample the data for the ROM training. The results showed that BO outperforms one-shot sampling methods, both in terms of accuracy and speed. The developed ROM, which is orders of magnitude faster than the original solver, is then applied for forward uncertainty quantification of the GWP in an aluminum plate and a composite panel under varying material properties. The UQ analysis reveals the higher impact of the uncertainties in the wave reflections. Furthermore, to compute the influence of the material properties on the predictions, a sensitivity analysis (SA) is performed using the variance-based Sobol’ indices and the Shapley additive explanations, where it is shown that the uncertainties in the elastic properties have a varying influence across time. The predicted results reveal the efficiency of BO-ML-ROM for the simulation of the GWP and demonstrate its utility for UQ and SA.

Exploring the Mineral Composition, Structure, and Function of a Freshwater Bivalve Adhesive

AbstractEtheria elliptica is a freshwater oyster that produces an adhesive that hardens underwater, allowing them to attach to other oysters, rocks, wood, and other natural substrates, producing complex oyster bed communities. Despite the importance of the adhesive in oyster bed formation, little is known about its chemical composition, structure, or mechanical properties. Through a combination of scanning electron microscopy (SEM), x‐ray photoemission electron microscopy (X‐PEEM), infrared (IR) spectroscopy, microhardness testing, and nanoindentation, it is found that the oyster adhesive consists entirely of aragonite nanoparticles that clump together into crystallites of varied shape, size, and orientation. The crystallite clumping density varies from the exterior of the adhesive to the interior, with the adhesive closest to the prismatic layer generally being denser. With this variation in density comes a variation in material properties with the exterior adhesive layer being softer and more flexible and the interior adhesive layer being harder and less elastic, or more similar to the prismatic and nacre shell structures produced by the E. elliptica. The resultant material exhibits varied materials properties, despite similar mineral composition.

Creating a Virtual Shadow of the Manufacturing of Automotive Components

Within the automotive industry, there is an increasing demand for a paradigm shift in terms of which materials are used for the manufacturing of the automotive body. Global climate goals are forcing a rapid adaption of new, advanced, sustainable material grades such as the fossil free steels and materials containing higher scrap content. With the introduction of these new and untested materials, methods for accounting for variation in material properties are needed directly in the press lines.The following study will focus on creating an initial virtual shadow of the manufacturing of a Volvo XC90 inner door panel through the application of Artificial Neural Networks (ANN). The virtual shadow differs from the concept of the digital twin by only being a virtual representation of the production line, with training data generated exclusively by numerical simulations, and having no automated communication with the physical press line control system. The virtual shadow can be used as an assistance to the press line operators to see how different press line settings and material parameter variations will impact the quality of the stamped component. The study aims to validate the virtual shadow through accurate predictions of the material draw-in measured in the physical press line.

Technological progress in Russian military sports suits: the history of the use of materials and fabrics from the end of the 19th century to the beginning of the 21st century

The subject of the study is technology in military sports suits of Russian military personnel. The object of the study is the progress in the production technologies of military sports suits. The author examines in detail the history of the use of various materials and fabrics for the production of military sports suits, from the end of the 19th century to the beginning of the 21st century. Special attention is paid to the analysis of the properties of various materials. The author shows that fabrics with high strength, wear resistance and good breathability are most suitable for military sports suits of Russian servicemen. The fabrics used for the production of military uniforms must meet a number of requirements. At the same time, such properties of fabrics as crease, drapery, hydroscopicity, dust capacity, heat-shielding properties, shrinkage, thread shedding, thread expansion in seams, sliding should be taken into account. The main research methods were theoretical methods – the method of analysis, generalization, systematization, classification of literary sources on the research topic. The novelty of the research consists in a comprehensive analysis of the basic properties, characteristics and requirements for fabrics used for the manufacture of military suits. A retrospective analysis and comparison of costumes from different eras were carried out. The main conclusion of the study is that from the end of the 19th century to the 21st century, the use of fabrics for military clothing has undergone significant changes. It is shown that at the end of the 19th century, military clothing was mainly made from natural fabrics such as cotton, wool and linen. With the advent of synthetic fibers in the 20th century, fabrics became more durable, lightweight, quick-drying and resistant to external factors. In the 21st century, with the development of fabric production technologies for military clothing, special mixtures of natural and synthetic fibers, as well as membrane materials, began to be used to provide high protection, comfort and functionality for military personnel. The author's special contribution to the study is the generalization of the results of various studies, the comparison of the main characteristics of fabrics used in various historical times with modern fabrics and materials.

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application