Variation Of Material Properties Research Articles

Synthetic thermal image generation and processing for close proximity operations

The new scenarios foreseen in forthcoming space missions have increased interest towards optical-based relative navigation techniques, which have demonstrated efficacy in a variety of operational conditions. Although object detection methods have predominantly been used within the visible spectrum, optical payloads struggle in weak lighting conditions and are susceptible to overexposure. Consequently, thermal imaging systems are being investigated as a potential solution, as their integration into the current systems would greatly extend future mission capabilities. This study seeks to fill the gap in literature by assessing the performance of state-of-the-art object detection algorithms with images captured in the thermal spectrum. Given the scarcity of readily available thermal infrared (TIR) images captured in orbit, a novel rendering pipeline is implemented to generate physically accurate thermal images relevant to close-proximity scenarios. These synthetic representations feature a simplified target spacecraft against Earth and deep space backgrounds, including variations in illumination conditions, material properties, relative state, and scale. To ensure realistic outputs, the radiative field of the Earth is modelled based on satellite measurements collected in the cloud and Earth radiant energy system (CERES) database. To enrich the fidelity of the outputs, a thermal sensor model and the corresponding noise levels are introduced in the pipeline. The generated images are then used to test the performance of traditional object detection algorithms in discerning the region of interest (ROI) under different orbital scenarios. The results demonstrate the effectiveness of the selected methodologies in mitigating the influence of the Earth in the ROI extraction process, while also revealing a performance degradation due to the presence of multi-material targets.

Interaction between material and process parameters during 3D concrete extrusion process

Extrusion of cement-based materials in additive manufacturing is influenced by a complex interaction of various material and process parameters. This study aims to investigate the impact of rheological properties and printing parameters on the extrudability of cement-based materials as well as the interaction between rheological properties of print materials and extrusion speed, as a key printing process parameter. 20 different print materials with diverse rheological properties were extruded at 10 different extrusion speeds. The effect of material parameters (i.e. rheological properties) and process parameters (i.e., extrusion speed) and their interaction during the extrusion process were evaluated using filament continuity, conformity and consistency. At a constant printing speed, a higher yield stress adversely impacted filament continuity in mixtures with a yield stress of over 260 Pa, whereas viscosity showed minimal influence on filament quality within the tested range of the rheological properties. The results of this study highlighted a significant correlation between material and process parameters and that adapting process parameters to variations in material properties is crucial for meeting printing criteria. The filament consistency of the mixtures with high yield stress was more sensitive to change in the extrusion speed than that of mixtures with low yield stress. An opposite trend, however, was observed for filament conformity where low yield stress mixtures showed a higher sensitivity to the extrusion speed. A procedure was suggested and applied to determine the optimum extrusion speed. Optimal extrusion speeds enabled printing mixtures with a broader spectrum of rheological properties with an acceptable visual quality, filament conformity and consistency requirements. With extrusion speed adjustment, the extrudability window was expanded from dynamic yield stress of 108–126 Pa to 108–263 Pa and plastic viscosity of 8.2–12.3 Pa.s to 5.1–16.4 Pa.s.

A double-helix plane winding sensor with wire using transient plane source method

The transient plane source (TPS) method is widely used to measure thermophysical properties of various materials, and the high-precision double-helix sensor is a key technology. A novel sensor using an efficient, simple, and low-cost lacquered micron-wire-wound process is proposed to replace traditional complex micro- and nano-etching processes. The TPS test platform was built based on the Wheatstone Bridge theory. A new sensor sample of diameter 5.377 mm was wound with an enameled copper wire of diameter 0.063 mm. Owing to the characteristics of the process, a 1-mm round hole was bored inside the sensor. An analytical solution for the concentric ring heat transfer model of the sensor with a central circular hole structure was derived, and a two-dimensional concentric circle simulation model of the sensor was established. As the number of turns decreased, the average error between the analytical solution of the concentric ring model with a large center hole (LHAS) and that of the traditional concentric circle model (TAS), as well as LHAS and the simulation model, gradually increased. The maximum relative errors between LHAS and TAS, as well as between LHAS and the simulation model, were 23.69 % and 0.1 %, respectively, and the average errors of the absolute maximum were 11.86 % and −0.06 %, respectively. The LHAS exhibited excellent accuracy because of the structural characteristics of the novel sensor. The accuracy of LHAS has been validated by simulation and hot wire method. Temperature rise data of high-borosilicate glass and polytetrafluoroethylene samples at different powers were obtained. The thermal properties of the samples were determined by fitting LHAS and TAS data. The thermal conductivity errors measured by LHAS were smaller than those measured by TAS, the thermal conductivity and thermal diffusivity errors were less than 3.5 % at different powers, and the measured repeatability errors were less than 1 %, indicating the good application prospect of the proposed model.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.

A numerical study of double flow focusing micro-jets

Purpose Double flow-focusing nozzles (DFFNs) form a coaxial flow of primary liquid with micro-crystalline samples, surrounded by secondary liquid and focusing gas. This paper aims to develop an experimentally validated numerical model and assess the performance of micro-jets from a DFFN as a function of various operating parameters for the water–ethanol–helium system, revealing the jet's stability, diameter, length and velocity. Design/methodology/approach The physical model is formulated in the mixture-continuum formulation, which includes coupled mass, momentum and species transport equations. The model is numerically formulated within the finite volume method–volume of fluid approach and implemented in OpenFOAM to allow for a non-linear variation of the fluid's material properties as a function of the mixture concentration. The numerical results are compared with the experimental data. Findings A sensitivity study of jets with Reynolds numbers between 12 and 60, Weber numbers between 4 and 120 and capillary numbers between 0.2 and 2.0 was performed. It was observed that jet diameters and lengths get larger with increased primary and secondary fluid flow rates. Increasing gas flow rates produces thinner, shorter and faster jets. Previously considered pre-mixed and linear mixing models substantially differ from the accurate representation of the water–ethanol mixing dynamics in DFFNs. The authors demonstrated that Jouyban–Acree mixing model fits the experimental data much better. Originality/value The mixing of primary and secondary liquids in the jet produced by DFFN is numerically modelled for the first time. This study provides novel insights into mixing dynamics in such micro-jets, which can be used to improve the design of DFFNs.

Evaluating the Performance of Ensemble Machine Learning Algorithms Over Traditional Machine Learning Algorithms for Predicting Fire Resistance in FRP Strengthened Concrete Beams

In recent years, fiber-reinforced polymers (FRP) have emerged as a highly effective solution for strengthening reinforced concrete (RC) structures. However, accurately assessing the fire resistance of FRP-strengthened members remains a significant challenge due to the limited guidance available in current building codes, often leading to conservative and cost-intensive evaluations. Experimental testing and numerical analysis required for such assessments are resource-demanding, highlighting the need for more efficient methods. This study investigates the application of machine learning (ML) techniques to predict the fire resistance of FRP-strengthened RC beams. Twelve ML models, including eight ensemble methods and four traditional approaches, were employed. The models were trained using a comprehensive dataset comprising over 21,000 data points obtained from numerical simulations and experimental tests. The dataset captured variations in geometric configurations, insulation strategies, loading conditions, and material properties. To enhance predictive accuracy, Bayesian optimization and k-fold cross-validation were applied for model tuning, while the Shapley Additive Explanations (SHAP) method was utilized to assess the relative importance of features influencing fire resistance. Among the models tested, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), and Gradient Boosting (GRB) demonstrated superior performance, achieving accuracy rates exceeding 92%. Key factors identified as significantly affecting fire resistance included loading ratio, area of tensile reinforcement, insulation depth, concrete cover thickness, and FRP area. The findings underscore the potential of ensemble ML techniques over traditional methods for accurately predicting the fire resistance of FRP-strengthened RC beams, offering critical insights for optimizing design practices and enhancing structural fire safety.

Analysis of the seismic fragility of slender piers on prestressed concrete viaducts

This research aims to study the seismic fragility evaluations of slender piers with single-column sections on prestressed concrete viaducts to investigate how pier height, prestressing force, and ground motion intensity are related, not only in terms of mean values but also in their probability density functions (PDFs), which inherently imply parameters such as dispersion indices. A detailed nonlinear numerical analysis was performed using a finite element model that incorporated the realistic response of concrete, steel, and infrm-FB element types for the columns. To this end, fragility curves were developed based on a probabilistic procedure that accounted for the variability of material properties, pier geometry, and seismic loading. The results show that the fragility function of high piers is hardly dependent on the shear span efficiency. Conversely, slender piers exhibit an increased level of fragility when the effect is confined to a constant prestressing force. This study highlights the need to account for the nonlinearity of pier members and variation in seismic loading when estimating the seismic fragility of slender piers. The conclusions of this study are beneficial for designing and retrofitting strategies for prestressed concrete viaducts in seismic regions.

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.

Deep learning coupled Bayesian inference method for measuring the elastoplastic properties of SS400 steel welds by nanoindentation experiment

Nanoindentation experiment has shown broad application prospects due to its ability to measure the mechanical properties of various materials at multiple scales. In this paper, a deep learning coupled Bayesian inverse approach is proposed for measuring the elastoplastic parameters of SS400 steel welds by nanoindentation experiment. The nanoindentation experiments were performed on the SS400 steel welds, including base metal (BM), weld zone (WZ), and heat affected zone (HAZ), and the experiment load–displacement (P-h) curves were obtained. The hyper-parameters tunable artificial neural network (ANN) was established to correlate elastoplastic parameters with indentation P-h curves. Based on Bayesian inference theory, the posterior density function for estimating the unknown material parameters was established. Transitional Markov chain Monte Carlo was used for sampling from the posterior density function, and the elastoplastic properties in different regions of SS400 steel welds were identified. The advantage of the established measuring method is that the hyper-parameters optimized ANN model can provide the very accurate forward relationship between material properties and indentation P-h curves. Besides, the inverse Bayesian framework can quantify the potential uncertainty of the identified elastoplastic parameters. The measured elastoplastic properties of the base metal of SS400 steel show good agreement with tensile experiment data, of which the maximum measuring error is less than 12%. The measured elastoplastic properties in WZ and HAZ are also proved to be effective. The uncertainty of the identified elastoplastic parameters of SS400 steel welds can be quantified by posterior marginal distribution, using Mean and Variance values. The results proved that the proposed inverse measuring method is reliable and effective.

Optimisation of open pit slope design considering groundwater effects using particle swarm optimisation and scaled boundary finite element method

Slopes are a crucial structures in open pit mines. Their design has implications on the economic, safety and environmental operation of the mining industry. Designing stable slopes can be challenging due to the complexities introduced by the stratigraphy and hydrology of the strata. With rising commodity costs and inflation rates, mining operating costs are increasing. Reducing operational costs is necessary for mining industries to remain competitive. While steepening the pit slope can decrease stripping materials and save money, it also increases the risk associated with slope surges. Therefore, optimising slopes is crucial for both financial and safety reasons. Numerical models such as the finite element method experience challenges in mesh generation of heterogeneous systems characterised by varying material properties and stratigraphies. Moreover, the need for repetitive geometry update necessitates recursive mesh regeneration that increases the computational burden. Moreover, previous slope optimisation studies focus solely on dry conditions. To consider the complex condition of hydrology along with heterogeneity in the soil stratigraphy, this study develops an optimisation procedure by combining the particle swarm optimisation algorithm and the scaled boundary finite element with an image-based meshing technique to optimise slopes with groundwater and achieve the desired factor of safety (FoS). The method changes the slope design parameters and the phreatic surface of groundwater simultaneously, considering user-defined parameters while iteratively re-meshing the optimisation processes. Several cases are presented, demonstrating the optimisation of bench width, bench angle, backfill parameters, and groundwater pumping levels.

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.

Effect of circular and elliptical cutouts on the free vibration analysis of sandwich FGM plate under the elastic foundations

The present article investigates the effect of circular and elliptical cutouts on vibrational characteristics of porous sandwich functionally graded material (SFGM) plates with FGM core resting on Pasternak elastic foundation. The structural kinematics of the plate are formulated based on nonpolynomial-based higher-order shear deformation theory (HSDT). Geometric discontinuities have been incorporated in terms of circular and elliptical cutouts in the SFGM plates. The sandwich structure is considered to be comprised of two homogeneous face sheets and a porous FGM core with various cutout shapes and sizes. The variation of material properties from the homogeneous face sheet to the FGM core has been carefully modeled using modified power law distribution. Further, a C0 continuous finite element formulation with a four-noded, isoparametric quadrilateral element with seven degrees of freedom (DOFs) per node has been employed to accomplish the results. The accuracy of the present results has been demonstrated through convergence and validation studies. A comprehensive study has been carried out to investigate the influence of volume fraction index, even and uneven porosity distribution, circular and elliptical cutouts, as well as H-elliptical and V-elliptical type cutout shapes on the frequency parameter of SFGM plates with the elastic foundation. The numerical results demonstrate the aforementioned parameters significantly impact the vibrational frequency of the SFGM plates.

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.

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.

Fractal-Dependent Growth of Solidlike Condensates.

The phenomenon of droplet growth occurs in various industrial and natural processes. Recently, the discovery of liquidlike condensates within cells has sparked an increasing interest in understanding their growth behaviors. These condensates exhibit varying material properties that are closely related to many cellular functions and diseases, particularly during the phase transition from liquidlike droplets to solidlike aggregates. However, how the liquid-to-solid phase transition affects the growth of condensates remains largely unknown. In this study, we investigate the growth of peptide-RNA condensates, which behave as either liquidlike droplets or solidlike aggregates depending on the RNA sequences. Dynamic light scattering experiments show that solidlike condensates grow surprisingly faster, with their hydrodynamic diameters increasing over time as d_{h}(t)∼t^{1/2}, contrasting with d_{h}(t)∼t^{1/3} for liquidlike droplets. By combining theoretical analysis and simulations, we demonstrate that this accelerated growth is caused by the noncoalescence aggregation of solidlike condensates and thus formation of percolated swollen structures with a decreased fractal dimension. Moreover, we demonstrate that the accelerated growth can be slowed down by introducing agents that can revert solidlike condensates back to their liquidlike states, such as urea or specific RNAs. Together, our work reveals a fractal-dependent growth mechanism of condensates, with useful insights for understanding the aging of condensates and modulating their aggregation behaviors in synthetic and biological systems.

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.