Variation Of Material Properties Research Articles

High-speed grinding: from mechanism to machine tool

AbstractHigh-speed grinding (HSG) is an advanced technology for precision machining of difficult-to-cut materials in aerospace and other fields, which could solve surface burns, defects and improve surface integrity by increasing the linear speed of the grinding wheel. The advantages of HSG have been preliminarily confirmed and the equipment has been built for experimental research, which can achieve a high grinding speed of more than 300 m/s. However, it is not yet widely used in manufacturing due to the insufficient understanding on material removal mechanism and characteristics of HSG machine tool. To fill this gap, this paper provides a comprehensive overview of HSG technologies. A new direction for adding auxiliary process in HSG is proposed. Firstly, the combined influence law of strain hardening, strain rate intensification, and thermal softening effects on material removal mechanism was revealed, and models of material removal strain rate, grinding force and grinding temperature were summarized. Secondly, the constitutive models under high strain rate boundaries were summarized by considering various properties of material and grinding parameters. Thirdly, the change law of material removal mechanism of HSG was revealed when the thermodynamic boundary conditions changed, by introducing lubrication conditions such as minimum quantity lubrication (MQL), nano-lubricant minimum quantity lubrication (NMQL) and cryogenic air (CA). Finally, the mechanical and dynamic characteristics of the key components of HSG machine tool were summarized, including main body, grinding wheel, spindle and dynamic balance system. Based on the content summarized in this paper, the prospect of HSG is put forward. This study establishes a solid foundation for future developments in the field and points to promising directions for further exploration.

Precision Calibration in Wire-Arc-Directed Energy Deposition Simulations Using a Machine-Learning-Based Multi-Fidelity Model

Thermal simulation is essential in wire-arc-directed energy deposition (W-DED) to accurately estimate temperature distributions, impacting residual stress and distortion in components. Proper calibration of simulation models minimizes inaccuracies caused by varying material properties, machine settings, and environmental conditions. The lack of standardized calibration methods further complicates thermal predictions. This paper introduces a novel calibration method integrating both machine learning, as the high-fidelity (HF) model, and response surface modeling, as the low-fidelity (LF) model, within a multi-fidelity (MF) framework. The approach utilizes Bayesian optimization to effectively explore the search space for optimal solutions. A two-tiered model employs the LF model to identify feasible regions, followed by the HF model to refine calibration parameters, such as thermal efficiency (η), convection coefficient (h), and emissivity (ε), which are difficult to determine experimentally. A three-factor Box–Behnken design (BBD) is applied to explore the design space, requiring only thirteen parameter configurations, conserving resources and enabling robust model training. The efficacy of this MF model is demonstrated in multi-layer W-DED calibration, showing strong alignment between experimental and simulated temperatures, with a mean absolute error (MAE) of 7.47 °C. This method offers a replicable framework for broader additive manufacturing processes.

Programmable Hydrogel-Based Soft Robotics via Encoded Building Block Design

Hydrogels have revolutionized the field of soft robotics with their ability to provide dynamic and programmable responses to different stimuli, enabling the fabrication of highly adaptable and flexible robots. This continual development holds significant promise for applications in biomedical devices, active implants, and sensors due to the biocompatibility of hydrogels. Actuation in hydrogel-based soft robotics relies on variations in material properties, structural design, or a combination of both to generate desired movements and behaviors. While such traditional approaches enable hydrogel actuation, they often rely on complex material design, bringing challenges to hydrogel fabrication and hindering practical use. Therefore, this work seeks to present a simplified and versatile approach for fabricating programmable single-component hydrogel-based soft robotics using an encoded building block design concept and 3D printing. A series of structural building blocks have been designed to achieve various actuation characteristics, including the direction, degree, and kinetics of actuation. By assembling these building blocks into various configurations, a broader range of actuation responses can be encoded, allowing for the fabrication of versatile, programmable soft robotics using a single uniform material through vat photopolymerization 3D printing. This approach enables adaptation to a wide range of applications, providing highly customizable encoding designs.

Investigating the Sound Absorption Properties of Silk Cotton and Other Sound-Absorbent Materials

This research investigates the acoustic properties of various materials to mitigate sound transmission and enhance acoustic environments, focusing on silk cotton, cotton wool, chipped foam, and open-cell polyurethane foam. The study evaluates these materials for sound absorption and reduction, considering sound frequency and material density. Materials were tested for their sound reduction and absorption coefficients, with silk cotton emerging as the most effective across a broad frequency spectrum, particularly below 200 Hz and above 1000 Hz. The research demonstrated that silk cotton, with its high porosity and fine fibre structure, achieved significant sound reduction even at low densities The findings align with theoretical predictions of resonance frequencies, showing a strong correlation (r2 = 0.9988 for the sound box and r2 = 0.9894 for the sound pipe). Sound intensity and sound pressure levels were measured using a sound level meter, and data analysis was conducted using graphing and calculating software. The researcher employed modified laboratory methods to account for loose materials and equipment limitations, validating the use of theoretical equations to estimate sound absorption and reduction properties The study provides some insights into using fibrous materials like silk cotton for noise reduction and acoustic enhancement. These findings have practical implications for improving sound quality in various environments, such as schools, studios, and public spaces, contributing to noise reduction strategies and enhancing auditory experiences.

Quantum geometry in condensed matter

Abstract One of the most celebrated accomplishments of modern physics is the description of fundamental principles of nature in the language of geometry. As the motion of celestial bodies is governed by the geometry of spacetime, the motion of electrons in condensed matter can be characterized by the geometry of the Hilbert space of their wave functions. Such quantum geometry, comprising of Berry curvature and quantum metric, can thus exert profound influences on various properties of materials. The dipoles of both Berry curvature and quantum metric produce nonlinear transport. The quantum metric plays an important role in flat-band superconductors by enhancing the transition temperature. The uniformly distributed momentum-space quantum geometry stabilizes the fractional Chern insulators and results in the fractional quantum anomalous Hall effect. We here review in detail quantum geometry in condensed matter, paying close attention to its effects on nonlinear transport, superconductivity, and topological properties. Possible future research directions in this field are also envisaged.

NUMERICAL MODELING OF DETERMINING THE ROCKWELL HARDNESS NUMBER OF A STEEL SAMPLE

In steel Hardness is a mechanical property that indicates the resistance of a material to local plastic deformation caused by mechanical indentation. In engineering, hardness is mainly used to determine the properties of various materials. In mechanical solids, the study of the hardness of materials is usually limited to the study of metals and their mechanical behavior. To understand the mechanism of hardness in metal structures, a basic understanding of materials science is necessary. Accurate hardness measurement is of great practical importance in the technical diagnosis of potentially hazardous industrial equipment (pipelines, pressure vessels, metal structures). This is primarily due to the fact that this characteristic has a strong correlation with strength and yield strength, which largely determine the possibility of further operation of the equipment. At the same time, one of the main advantages of hardness measurement is the speed of its determination and the non-destructive nature of its control. A finite element model was developed for the indentation process, and the contact radius and contact pressure were obtained. The Rockwell hardness measurement process was modelled in accordance with the test methodology of DSTU ISO 6508-1:2013. The indentation process was modelled using ANSYS software with different contact models and different loads. The relevance of solving the problem of modelling the process of determining the surface hardness number using the FEM software ANSYS is determined by the high degree of automation of the solution process. During the modelling, it is assumed that the material of the sample into which the indenter of any shape is pressed is homogeneous isotropic and elastoplastic according to the kinematic plasticity model. This model corresponds to the behavior of steels. In addition, the indenter material is isotropic and linearly elastic. In the developed numerical model, the indenter is taken in the form of a tungsten carbide ball, which corresponds to the scheme for determining the hardness number on the "B" scale. In conditions of limited access to laboratory equipment, numerical modelling of the process of determining the hardness number allows students to demonstrate all stages of the test at a qualitatively new level, and further, to compare with the results of full-scale tests. Keywords: structural materials, Rockwell method, finite element model, indenter, hardness.

Multiquadric Collocations for Nonlinear Vibration of Functionally Graded Plates of Ti–6Al–4V Metal

While Finite Element Method (FEM) has proven reliable for analyzing linear or nonlinear stresses in materials, structures, and fluid flows, its reliance on mesh generation can escalate project simulation costs. In contrast, mesh- free techniques offer an alternative by directly generating system algebraic equations for the entire issue domain, bypassing the need for a preset mesh. This study focuses on investigating the nonlinear free vibration response of shear-deformable Functionally Graded Material (FGM) plates. These plates are expected to exhibit varying material properties, following a power-law distribution linked to the volume fractions of constituent materials as plate thickness increases. Utilizing shear deformation theories and von Karman nonlinear kinematics, the study seeks a mathematical solution for the real physical issues encountered in these plates. To address these challenges, a meshless method employing the Multi Quadric Radial Basis Function (MQRBF) is employed for evaluation. The computed results reveal insights into how different material properties and amplitudes influence the nonlinear free vibration response of composite plates. Comparing these outcomes to prior research demonstrates promising progress in this area.

Analytical and FE modeling of a bi-directional functionally graded Timoshenko beam under thermomechanical loading

Functionally graded materials (FGMs) have variable volume fractions in different directions, making structural analysis challenging due to shear deformation. This study investigates the behavior of a two-directional functionally graded material (TDFGM) Timoshenko beam under thermomechanical loads. Varied material properties in both axial and transverse directions are analyzed using a mathematical approach and a finite element model. The mathematical approach involves transforming the system of two ordinary differential equations to a single fourth-order differential equation to determine deflection and rotation. Additionally, a bilinear quadrilateral element is used to validate the mathematical solution. The results show that increasing grading parameters reduced transverse deflection, with a maximum reduction of 38.97% for CF beams and 39.26% for SS beams when the gradation index az increased from 0 to 1 under mechanical loading. Under combined mechanical and thermal loading, higher temperatures on the beam’s hot side lead to increased deflection, with notable increases at higher gradation indices. Moreover, increased maximum deflection by 6.29% for CF beams and 4.05% for SS beams when considering temperature-dependent properties. It was observed that when TDFGM Timoshenko beams were heated for thermal loading, the SS beam deflected in the opposite direction of the applied mechanical load. The results agree with previous research and indicate alignment between mathematical and finite element solutions. The impact of boundary conditions on the deflection of Timoshenko beams made of TDFGM is discussed. The introduced methodology enhances TDFGM beam bending analysis, allowing control over behavior through grading parameters and boundary conditions selection.

Adaptive PF-CZM for multiphysics fracture analysis in functionally graded materials

Functionally graded materials (FGM) hold significant relevance in engineering due to their tailored material property gradation, designed for specific engineering applications. The challenge in fracture analysis of FGM stems from the spatial variation of material properties, which complicates the prediction of crack topology. Local and global refinement strategies are impractical for fracture analysis in FGM due to the unpredictable nature of crack topology, which renders local refinement infeasible. Additionally, global refinement is not advisable as it leads to a significant increase in degrees of freedom, adversely affecting computational efficiency.The novelty of this research lies in the incorporation of spatial variation in both the length scale and material properties, enhancing the realism of FGM domain modeling. To preserve the required length scale, it is necessary to adopt a minimum mesh size, which consequently results in a substantial increase in the degrees of freedom and, thereby, escalates the computational cost. To address these challenges, the study employs an adaptive mesh refinement (AMR) algorithm integrated with a phase-field cohesive zone model (PF-CZM), providing a robust solution for accurate fracture analysis in FGM. Based on the ideas stemming from the need for efficient and realistic modeling, the AMR-PF-CZM framework refines the mesh efficiently in regions of crack growth based on crack-driving energy and phase field variable, thereby eliminating the need for pre-refinement. The findings demonstrate a 76%–85% increase in computational efficiency and accuracy of the AMR-PF-CZM approach compared to the non-adaptive PF-CZM. Furthermore, the developed algorithm’s applicability to dynamic fracture and multi-physics problems, specifically addressing mechanical and thermal fracture in FGM, underscores the importance of this approach in capturing complex fracture phenomena.

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.