Variation Of Material Properties Research Articles

Deep SVBRDF Acquisition and Modelling: A Survey

AbstractHand in hand with the rapid development of machine learning, deep learning and generative AI algorithms and architectures, the graphics community has seen a remarkable evolution of novel techniques for material and appearance capture. Typically, these machine‐learning‐driven methods and technologies, in contrast to traditional techniques, rely on only a single or very few input images, while enabling the recovery of detailed, high‐quality measurements of bi‐directional reflectance distribution functions, as well as the corresponding spatially varying material properties, also known as Spatially Varying Bi‐directional Reflectance Distribution Functions (SVBRDFs). Learning‐based approaches for appearance capture will play a key role in the development of new technologies that will exhibit a significant impact on virtually all domains of graphics. Therefore, to facilitate future research, this State‐of‐the‐Art Report (STAR) presents an in‐depth overview of the state‐of‐the‐art in machine‐learning‐driven material capture in general, and focuses on SVBRDF acquisition in particular, due to its importance in accurately modelling complex light interaction properties of real‐world materials. The overview includes a categorization of current methods along with a summary of each technique, an evaluation of their functionalities, their complexity in terms of acquisition requirements, computational aspects and usability constraints. The STAR is concluded by looking forward and summarizing open challenges in research and development toward predictive and general appearance capture in this field. A complete list of the methods and papers reviewed in this survey is available at computergraphics.on.liu.se/star_svbrdf_dl/.

Research Paper on Analytical Investigation of the Effect of 3d Printing Process Parameters on Anisotropic Mechanical Behaviour of Additively Constructed Concrete

This research identifies key challenges in additively constructed concrete, including the need for understanding the scalability of mechanical behaviour from small to large structures, addressing variability in material properties, and predicting failure mechanisms. It highlights the need for improved finite element modelling for accurate 3DCP simulation, and further investigation into concrete foundation interactions and initial imperfections. The study acknowledges difficulties in simulating complex shapes and calls for refinement of the numerical model for better predictive accuracy. It also emphasizes the need to understand long-term behaviour, durability, and the impact of the 3DCP process on microstructural morphology and mechanical properties. The influence of pores and time gaps between layers on the overall capacity of the specimen, and the development of a model considering different properties of 3D concrete material are also identified as areas for future research.

Influence of Infill Patterns on the Shape Memory Effect of Cold-Programmed Additively Manufactured PLA.

In four-dimensional additive manufacturing (4DAM), specific external stimuli are applied in conjunction with additive manufacturing technologies. This combination allows the development of tailored stimuli-responsive properties in various materials, structures, or components. For shape-changing functionalities, the programming step plays a crucial role in recovery after exposure to a stimulus. Furthermore, precise tuning of the 4DAM process parameters is essential to achieve shape-change specifications. Within this context, this study investigated how the structural arrangement of infill patterns (criss-cross and concentric) affects the shape memory effect (SME) of compression cold-programmed PLA under a thermal stimulus. The stress-strain curves reveal a higher yield stress for the criss-cross infill pattern. Interestingly, the shape recovery ratio shows a similar trend across both patterns at different displacements with shallower slopes compared to a higher shape fixity ratio. This suggests that the infill pattern primarily affects the mechanical strength (yield stress) and not the recovery. Finally, the recovery force increases proportionally with displacement. These findings suggest a consistent SME under the explored interval (15-45% compression) despite the infill pattern; however, the variations in the mechanical properties shown by the stress-strain curves appear more pronounced, particularly the yield stress.

Discrete and continuum modelling of rock-ice avalanches: vertical segregation and its feedback on flow mobility

Rock-ice avalanches are rapid, catastrophic mass flows consisting of massive rock and ice particles in alpine regions. Varying material properties of rock and ice particles cause them to spontaneously mix and segregate during the flow. However, the impacts of segregation on the mobility remain poorly understood and are overlooked in current rock-ice avalanche models. Here, we address modeling of segregation and its mobility feedback from both discrete and continuum points of view. Using the discrete element method, we simulate rock-ice avalanches, and the segregation that occur therein, as steady gravity-driven flows of particles different in size, density, and surface friction. Segregation results in sharp particle concentration gradients which, when coupled with the large surface friction difference between rocks and ice, lead to transitions in the flow kinematic profiles. We reveal that rocks and ice contact probabilities are key to modelling the effective friction and volume fraction of the mixture flows. Continuum equations are proposed to model the concentration dependence of the pressure, while segregation-induced flow velocity profiles are modelled using the non-local granular fluidity framework that accounts for microscopic frictional interactions. The findings are expected to promote more sophisticated modeling of rock-ice avalanches necessary for hazard risk reduction and mitigation.

Accelerated discovery of multi-property optimized Fe–Cu alloys

The next generation of rotating electrical machines requires materials with an optimized combination of magnetic, electrical and mechanical properties. High-throughput experiments can accelerate the discovery of relevant materials. Past work has focused on complex multicomponent alloys in which it is difficult to pinpoint the effect of each component. On the other hand, in this work, Fe–Cu binary alloys were used as a model materials system to determine the feasibility of novel materials discovery through a rapid determination of multiple properties. The microstructures, magnetic, electrical, and mechanical properties of Fe–Cu alloys were rapidly determined. With increasing Cu content in Fe-xCu from 0 to 40 wt.%, the phases present changed from single phase BCC to a two-phase FCC + BCC microstructure. There was a decline in the saturation magnetization (Ms) from 211.3 to 118 emu/g. The hardness value increased from 166.3 HV to 238.5 HV, the coercivity (Hc) ranged from 14.6 to 45.7 Oe, and the resistivity varied between 27.3 and 43.0 μΩ·cm. The increased content of a Cu-rich phase led to a decrease in Ms and grain size, and higher Hc and hardness. Using an accelerated methodology, a significant variation in material properties was determined. Three compositions, i.e., Fe–3Cu, Fe–4Cu and Fe–10Cu, with a good balance of properties were identified for potential use in energy applications.

Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling

Uncertainty quantification is a critical aspect of machine learning models for material property predictions. Gaussian Process Regression (GPR) is a popular technique for capturing uncertainties, but most existing models assume homoscedastic aleatoric uncertainty (noise), which may not adequately represent the heteroscedastic behavior observed in real-world datasets. Heteroscedasticity arises from various factors, such as measurement errors and inherent variability in material properties. Ignoring heteroscedasticity can lead to lower model performance, biased uncertainty estimates, and inaccurate predictions. Existing Heteroscedastic Gaussian Process Regression (HGPR) models often employ complicated structures to capture input-dependent noise but may lack interpretability. In this paper, we propose an HGPR approach that combines GPR with polynomial regression-based noise modeling to capture and quantify uncertainties in material property predictions while providing interpretable noise models. We demonstrate the effectiveness of our approach on both synthetic and physics-based simulation datasets, including mechanical properties (effective stress) of porous materials. We also introduce an approximated expected log predictive density method for model selection, which eliminates the need for retraining the model during leave-one-out cross-validation, allowing for efficient hyperparameter tuning and model evaluation. By capturing heteroscedastic behavior, enhancing interpretability, and improving model selection, our approach contributes to the development of more robust and reliable machine learning models for material property predictions, enabling informed decision-making in material design and optimization.

Photo- thermo-induce changes in As2S3- and As2S3: Au-nanoparticles filled porous glass composite: Optics and the molecular structure simulation of the responsible chalcogenide transformations

In this study we continue a more detailed investigation of the photoinduced effect in nanoporous glass impregnated with As2S3 NPs and Au NPs and its influence on the material's optical properties. The goal of this research is establishing the quantum chemical peculiarities of these processes taking into account the impact of gold nanoparticles on the photoinduced structural transformations of As-S component using Raman spectroscopy and theoretically investigating the molecular structure of arsenic sulfide As-S through ab initio quantum chemical calculations. These support the possibility of further variations in optical properties of such composite materials and photo-thermo driven elements made on their basis.

Cross-Sectional Preparation of Complex Energy Devices and Their Characterization By Advanced Microscopic Methods

Energy devices such as batteries, fuel cells, and solar cells are composed of multiple functional components with varying material properties. Achieving a fundamental understanding of structure-property relationships and systematically enhancing performance, while also investigating device degradation and failure mechanisms, requires high-quality cross-sections of entire devices or substantial regions. Given that many devices contain different classes of materials, including liquid and/or air-sensitive components, working under cryogenic or inert conditions is necessary to maintain their original state. To facilitate cross-sectional characterization of these devices from the macroscopic down to the atomic level, we use a range of preparation tools. A self-designed cryo-cutter allows us to efficiently produce cross-sections of entire devices, such as pouch cells, in a single step, enabling optical microscopy (OM) observation at the centimeter scale while preserving their pristine state. For finer scales, cryo-ultramicrotomy and focused ion beam (FIB) techniques are regularly employed. These methods can prepare high-quality thin samples under 50 nm for investigations using OM, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) at atomic resolution. In this presentation, we demonstrate the scale-bridging capabilities of these preparation techniques applied to various devices and their components in conjunction with advanced electron microscopic and spectroscopic methods. Examples include devices such as batteries (Fig. 1) and complex proton exchange membrane (PEM) fuel cells, as well as battery components (Fig. 2). The techniques used are ideal for assessing the integrity of entire devices and the interfaces of individual layers and components, including their morphology, contact, composition, and chemical bonding states. Acknowledgement: Part of this work was performed at the Micro-and Nanoanalytics Facility (MNaF) of the University of Siegen. Figure 1

Uncertainty Analysis of Tensile Strength of Scarf Adhesive Joints Using Numerical Method

This study uses three-dimensional finite element (FE) analysis to investigate stress distribution and fatigue strength in adhesive scarf joints, which are complex due to their oblique geometry and varying material properties. ANSYS was used to model stress components within the adhesive and at the adhesive-adherent interface. The FE models, created with eight-node solid elements, utilized fine meshing to ensure accurate stress representation. A mesh size of 0.025 mm was optimal. The models, assuming linearly elastic materials, were validated by comparing simulated deformations with experimental results. Findings enhance understanding of local stress states, improving adhesive joint design reliability. Key Words: Adhesive scarf joints, finite element analysis, stress distribution, fatigue strength, ANSYS,

Characterizing the cyclic behavior of piping T-joint connections

Failures in piping systems during earthquakes have resulted in operational disruptions and financial losses for critical facilities, such as nuclear power plants and hospital infrastructure. Past earthquake events have shown that failures in piping systems occur primarily at discontinuities, such as T-joints, elbows, and nozzles. Therefore, it is important to understand the behavior of piping joints under cyclic loading to mitigate the impact of such failures. This manuscript investigates the behavior of piping T-joints under cyclic loading. The behavior of these joints under cyclic loading is greatly influenced by the material properties of the piping components. Any variation in material properties can alter the responses of the piping joints, eventually affecting the seismic fragility of the piping system. This manuscript uses data from laboratory experiments to characterize and quantify uncertainties in material properties that influence the seismic fragility of piping systems. Additionally, this manuscript uses the data from laboratory experiments to validate a new limit state for characterizing the failure of piping joints under cyclic loading. In addition, it explores the effects of uncertainties in material properties on the performance function used to characterize the new limit state for failures at T-joint connections. In summary, this study provides insights into the influence of uncertainty in material properties on the seismic fragility of a piping system with an intent to improve the resiliency of piping systems in critical infrastructure facilities.

Shell element-based prediction of process-induced deformation considering the different fabric parameters and stacking sequences of CFRP woven composites

Herein, we present a finite element method (FEM) based curing analysis for predicting the process-induced deformation (PID) of CFRP woven composites using shell-type elements. This method can efficiently consider various fabric parameters and stacking sequences, even for complex structures. For woven composite plates and L-shaped flanges, FEM-based PID prediction using shell elements reduced computation times by 50–59% compared to solid elements, while maintaining acceptable accuracy with relative errors of 0.17–7.35%. Furthermore, the impacts of various fabric parameters and stacking sequences on the PID of woven composites are analyzed in detail. The main finding is that the variation in material properties due to fabric parameters and stacking sequences significantly impacts the PID of woven composite structures. This prediction method can be applied in the design of composite structures and molds, thereby enhancing the dimensional precision and performance of composite structures.

Effect of crosslinker molecular weight on the characteristics of thermoelectric ionogels

Abstract The present study investigates an ionic thermoelectric gel with adjustable functionality through the manipulation of crosslinker molecular weight. The thermoelectric properties of 2-hydroxyethyl acrylate (2-HEA) ionogels, prepared using PEGDA200, PEGDA575, and PEGDA1000 as crosslinkers, were thoroughly examined. Furthermore, dynamic thermal mechanical analysis (DMA) and X-ray diffraction (XRD) techniques were employed to elucidate the underlying reasons for variations in the thermoelectric material properties resulting from changes in crosslinker molecular weight. Notably, the thermoelectric gel synthesized with PEGDA575 exhibited a remarkable thermal power output of 19.19 mV•K-1 along with a tensile capacity of 231%. Additionally, the developed ionic thermoelectric capacitor demonstrated an impressive energy density of 4.288 J•m−2, thereby showcasing its potential application in flexible low-grade heat energy recovery devices.

Analysis of axisymmetric thermoelastic diffusive half-space with variable material properties using hyperbolic two-temperature model

The aim of the current study is to investigate the variable thermal conductivity and diffusivity impact on the transient response of an axisymmetric thermodiffusive elastic half-space under axisymmetric thermal, mechanical, and concentration loads in the light of hyperbolic two-temperature generalized fractional thermoelastic diffusion model. With the help of Kirchhoff’s transform, the governing equations are converted into linear form. The resulting equations are solved by imposing the combined Laplace–Hankel transform. In converted space, the solutions for different field quantities are presented in compact form. The copper material is considered for numerical computation. By employing a mathematical inversion procedure, the solutions are converted into the original space. Numerically computed solutions for various physical quantities are illustrated in the form of graphs to show the impact of variable thermal conductivity and diffusivity, hyperbolic two temperature, and fractional parameters. Validation of the obtained results is also presented.

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

In this paper, we propose Data-driven simulation-assisted Physics-learned Artificial Intelligence (DPAI), a deep-learning algorithm to simulate heat diffusion in large-grain polycrystalline materials. The DPAI model captures the spatio-temporal representation of heat diffusion in the material from input sequences from the training dataset. The training dataset consists of various temperature plots of polycrystalline materials taken from Finite Element (FE) simulations having varying numbers of grains oriented in random directions 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. Voronoi 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 an input to the FE 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 Deep learning model significantly reduces the computational time while maintaining accuracy as compared to conventional numerical techniques. After training, the effectiveness of the trained DPAI model is examined by modeling larger domain problems involving a greater number of grains and varying material properties. The simulation result is qualitatively compared with the experiment. A scaled-up version of the microstructure is represented using Unidirectional Carbon Fiber laminate. The laminate is heated with a point heat source and the temperature plots are captured using Infrared Camera.

Machine learning techniques for quality assurance in additive manufacturing processes

Additive manufacturing (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts remains a significant challenge due to the complexity of the process and inherent variability in material properties. This review investigates the use of artificial intelligence (AI) to enhance quality assurance in AM processes, focusing on specific machine learning techniques such as convolutional neural networks for defect detection, support vector machines for classification of material properties, and reinforcement learning for real-time process optimization. The AI-driven methodologies are applied to predict defects, optimize process parameters, and monitor real-time production quality, utilizing large datasets generated from sensors and in-situ monitoring systems. The study demonstrates significant improvements in the accuracy of defect detection, the reliability of material property classification, and the efficiency of process optimization. In addition, it addresses challenges such as data pre-processing, model interpretability, and integration with existing AM systems. The findings highlight the potential of AI to transform quality assurance in AM and outline future research directions for further integration and enhancement of AI techniques in AM.

Comprehensive investigation on modelling of low-velocity impact damage response of composite laminates − Experimental correlation and assessment

Low velocity impact (LVI) experiments are performed to provide correlations with the simulation results based on numerical modeling of the intralaminar matrix damage and interlaminar delamination damage of composite laminates. The good agreement between the numerical simulation and experimental results provided validation of the numerical modeling approach. The validated numerical modeling is then used in the parametrical assessment of the influence of material properties and model- related parameters on the global LVI deformation responses and the evolution characteristics of the intralaminar and interlaminar damage. It is observed that the element deletion method is not suitable to resolve the element distortion problem induced by the progressive damage. The numerical convergence problem caused by element distortion can be resolved with appropriate selection of threshold value of the critical damage parameter without element deletion. The parametrical numerical investigations showed that the variations of material properties and parameters related to interlaminar damage exhibited significant influence on the LVI global deformation responses and progressive damage evolutions. Meanwhile, insignificant influence of the material properties related to the intralaminar damage was observed for the normal variations within material scatters.

Fabrication of bulk-sized W-1.1%TiC alloy with helium bubble retention via powder metallurgical route incorporated with helium ambient mechanical alloying

In the field of materials irradiation, it has been noted that incorporating helium (He) element into metals may have the beneficial effect of improving both strength and ductility in the high temperature region where creep occurs. In order to fabricate such new functional materials and apply them to a wide range of fields, it is necessary to establish new fabrication methods for bulk-size materials. In this study, bulk-sized W-1.1%TiC alloys containing He bubbles were fabricated by a powder metallurgical route using mechanical alloying in a He atmosphere and subsequent thermo-mechanical processing. Transmission electron microscopy observations showed that this fabrication process successfully incorporated nano-sized He bubbles into W-1.1%TiC. Since this fabrication process is free of radioactivation and irradiation-induced defects, it will facilitate research into the fundamental understanding of the independent effects of He bubbles on the physical and mechanical properties of various materials concerned.

Predicting the elastic properties of Norway spruce by its morphology

Models for the elastic material properties of wood aim to predict the mechanical response of wooden structures to external loading. Traditionally, the variability of these properties in trees is described by a taxonomy of growth defects that is typically based on visual indicators in the material. This includes curvature, knots, and spiral grain models. Existing meso- and macro-scale models fail to describe the uncertainty connected to the local heterogeneity of the material. In this paper, we propose a novel meso-scale model that describes the natural variability of Norway spruce morphology and material properties based on random field theory. Our approach removes the need for a taxonomy of growth defects and enables uncertainty quantification of the stiffness and density in a straightforward fashion using simulations. This may enhance confidence for stiffness-graded applications, where the dynamic resonant behavior of wood structures is relevant and growth anomalies are present. Further, our stochastic models can generate images that realistically mimic wood patterns, which is relevant for applications like synthetic wood panels and flooring.

Thermal vibration analysis of bi-directionally stepped porous functionally graded plates with segment-specific material property variation supported by Kerr foundation

This paper presents a comprehensive analysis of the nonlinear dynamic response of stepped rectangular plates made of functionally graded porous material (FGPM), supported by the Kerr foundation, in a thermal environment. A novel analytical framework is developed to explore geometric and material variations. The study investigates structural variations in plate thickness with abrupt changes in uni- or bi-directional orientations, examining both single and double stepped thickness profiles. Material properties vary with thickness, featuring distinct horizontal discontinuities across plate segments. Porosity distributions, both even and uneven, are addressed using modified mixture rules. Nonlinear kinematic relationships are established using Reddy’s third-order shear deformation plate theory and von Kármán’s nonlinear geometric assumptions, with equations of motion solved via Galerkin’s technique. This improved model effectively addresses non-continuous thickness variation through integral calculus, enhancing computational efficiency. Validation is achieved by comparing outcomes with published literature and Finite Element Analysis (FEA). The study investigates the influence of material properties, elastic foundation, boundary conditions, and geometric parameters on the free vibration and nonlinear behaviors of the plates. Some of the key findings include: increasing the thickness of the stepped segment significantly heightens the fundamental frequency while reducing vibrational amplitudes; optimizing the location of the stepped segment directly impacts the plate’s fundamental frequency and vibrational amplitudes; and as the load factor increases, the difference between linear and nonlinear deflection becomes evident. Therefore, accurate FGPM stepped plate design requires incorporating nonlinear terms in the strain–displacement relationships. Suggestions for future model modifications are also discussed, contributing to advancements in structural design and analysis.

Influence of Spatially Varying Boundary Conditions Based on Material Heterogeneity

When conducting numerical analyses, boundary conditions are generally applied homogeneously, neglecting the inherent heterogeneity of the material being represented. Whilst the heterogeneity is often considered within the medium, its influence on the response at the boundary should also be accounted for. In this study, A novel approach to applying heterogeneous boundary conditions in the simulation of physical systems is presented, particularly focusing on moisture transport in unsaturated soils. The proposed method divides the surface into blocks or “macro-elements” and scales the boundary conditions based on the variation of material properties within these blocks. The principle of using overlapping kernel functions allows local effects to be considered, impacting neighbouring regions. To demonstrate the efficacy of the approach, a set of analyses were conducted that considered infiltration into a body of unsaturated soil, with various configurations of material properties and boundary conditions. The numerical simulations indicate that the application of scaled boundary conditions leads to a more natural and realistic response in the system. The applied method is independent on the numerical techniques employed in the simulation process, making it adaptable to existing computational codes, offering flexibility in capturing complex behaviours, and providing insights into how heterogeneity influences the system’s overall response.