Variation Of Material Properties Research Articles

Bonding properties of selected alien invasive wood species

Invasive alien plant species pose a significant challenge to European ecosystems. They displace native vegetation, damage agricultural land, and annually cost the European economy billions of euros. Many of them are removed daily and mainly burned, although some of them produce lignocellulosic material that could be used in place of native wood species. In this study, the bonding properties of selected invasive wood species in Slovenia were tested using standard methods. Wood lamellas were produced according to the SIST EN 205 standard from Ailanthus altissima, Aesculus hippocastanum, Robinia pseudoacacia, Gleditsia triacanthos and Acer negundo and glued with polyvinyl acetate (PVAc) and one-component polyurethane (PU) adhesive. The results showed that selected wood species can be bonded well with both adhesives (bond shear strengths from 7.2 to 15.1 N/mm2), although there were large variations due to the heterogeneity of the wood material. The differences in the shear strength of the bonds were mainly due to the different densities of the wood (479 to 702 kg/m3) species and the high variability in material properties (for example porosity from 0.54 to 0.68 and shear strength in tangential direction from 11.2 to 21.1 N/mm2), which are related to the anatomical characteristics of the individual wood species.

Monitoring of Älvkarleby test embankment dam using 3D electrical resistivity tomography for detection of internal defects

Electrical resistivity tomography (ERT) is a potential-based method for detecting internal erosion in the core of embankment dams using the electrodes installed outside. This study aims at evaluating the practical capability of ERT monitoring for detecting internal defects in embankment dams. A test embankment dam with in-built well-defined defects was built in Älvkarleby, Sweden, to assess different monitoring systems including ERT and the defect locations were unknown to the monitoring teams. Between 7500 and 14,000 ERT data points were acquired daily, which were used to create the distribution of electrical resistivity models of the dam using 3D time-lapse inversion. The inversion models revealed a layered resistivity structure in the core that might be related to variations in water content or unintentional variations in material properties. Several anomalous zones that were not associated with the defects were detected, which might be caused by unintentional variations in material properties, temperature, water content, or other installations. The results located two out of five defects in the core, horizontal and vertical crushed rock zones, with a slight location shift for the horizontal zone. The concrete block defect in the core was indicated, although not as distinctly and with a lateral shift. The two remaining defects in the core, a crushed rock zone at the abutment and a wooden block and a crushed rock zone in the filter, were not discovered. The results cannot be used to fully evaluate the capability of ERT in detecting internal erosion under typical Swedish conditions due to limited seepage associated with the defects. Furthermore, scale effects need to be considered for larger dams.

Thermoelastic response of multilayer cylinders: The direct integration and single-solid approach

An approach for solving the plane nonaxisymmetric thermoelasticity problems in multilayer cylinders is suggested from the single-solid standpoint, which implies considering a multilayer structure as a single unity with a step-wise variation of material properties. The direct integration method was used to the advantage of our approach by applying toward the compatibility equation in terms of strains instead of the one in terms of stresses. As a result, the governing equation is derived without involving generalized derivatives by the radial coordinate and then reduced to an integral equation of the second kind. Using the resolvent-kernel for solving the latter equation, the thermal stresses are computed as the Fourier series whose constituents are explicitly determined by the corresponding constituents of the thermal loading.

Characterization of Ti-6Al-4V Bar for Aerospace Fastener Pin Axial Forging

Ti-6Al-4V warm forged fasteners are a critical part of the aerospace industry, as they are used in vast quantities for mechanical joining of components for the fuselage, wing-skin and aero-engine. These components are produced in vast quantities at rapid production rates through multi-blow axial forging However the rate that they are manufactured means that manufacturers rely upon periodic part conformance testing to understand if the part is within tolerance or if any undesirable manufacturing defects such as cracks or underfilling are present. Thus, a right-first-time manufacturing approach is essential to minimize non-conformant scrap. An analysis of the Ti-6Al-4V supplied raw material for axial forging, in a variety of different bar diameter sizes and from different industrial suppliers, was conducted. This was to attempt to understand whether material property variation or operator variation was the root cause for some material behaving differently during the manufacture route. Experimental testing was performed through microstructure characterization and mechanical testing methods. The volume fraction of the β-phase was noted to be marginally higher in material with good forgeability. The hardness of the inner core of the bar appears to be a critical material property for the Ti-6Al4V bar, with an overly hard bar-core hindering forgeability of the bar. This is believed to be due to the hotter central region malleability being key for forgeability. Micro-void porosity was also noted which could lead to stress concentration locations, or crack initiation, and as such is a deleterious property for forgeability. The experienced forgeability of the Ti-6Al-4V bars have been demonstrated to be sensitive to rather small variation in measured microstructure and mechanical property. It is believed that cumulative impacts of small differences, 1% variation in α-phase volume fraction, small variations in elongation to failure, 1% variation in elastic modulus and microhardness profile variation at the center of the bar of less than 10 HV0.3, can combine to significantly impact the forgeability of Ti-6Al-4V bar.

A modeling strategy for the shear and flexural performance prediction of SFRC beams without stirrups accounting for the variability of properties

AbstractThe behavior of steel fiber reinforced concrete (SFRC) is highly dependent on mix design, where the properties of its components, such as cementitious matrix and fibers, impact the physical and mechanical properties of the composite. Incorporating steel fibers into concrete modifies its physical properties, potentially leading to fiber segregation and resulting in a nonuniform distribution of the fibers within the material. In experimental studies, an increase in fiber volume has been found to change the failure mode of SFRC beams from shear to flexure, and numerical investigations have shown that randomness in fiber distribution can significantly influence the behavior of the composite. Therefore, this study aims to develop a numerical model that considers variations in material properties to simulate the change in the failure mode of SFRC beams due to variations in fiber volume. The failure and yielding parameters were initially established and integrated into the concrete damaged plasticity (CDP). Subsequently, the response of both steel and concrete to tension or compression stresses was ascertained. Finally, the appropriate meshes and finite elements were chosen for the simulation. In the final approach, two methods were employed to introduce randomness into the beam. One divided the beam into different vertical segments: 30, 45, and 90, and the other combined vertical and horizontal segments: 30 vertical with 4 horizontal, 45 vertical with 5 horizontal, and 90 vertical with 10 horizontal. A script was developed in Python to automatically insert the properties. The numerical model successfully captured the change in failure mode from shear to flexure resulting from fiber volume increase. Vertical and horizontal segments increased toughness due to crack deviation, better predicting cracking pattern and loading capacity for the highest fiber contents.

Closed-loop control of [formula omitted]EDM surface quality with alternate on-machine metrology and in-process roughness prediction

As an established tool-based micro-machining technique, micro electrical discharge machining (μEDM) has received widespread attention from academia and industry for its capability of producing intricate micro-scale structures and features on various difficult-to-cut materials. However, assurance of the as-built surface quality and consistency has been a challenge due to the complex interactions among machining parameters and the unpredictable variability of material properties and the discharge process. In this paper, an efficient closed-loop control methodology based on an innovative application of on-machine metrology (OMM) and in-process roughness prediction (IPRP) is proposed for automatically ensuring the μEDM surface quality. The work takes into consideration process fundamentals related to size and distribution of craters by means of single crater experiments and pulse discrimination studies in order to analyze the influence of actual energy parameter on the evolving surface roughness. The OMM enables accurate in situ surface characterization while the IPRP, which is built on an online Bayesian regression model, allows for the prediction of surface quality according to the monitored process features. This IPRP model presents an increased predictive performance and confidence when more OMM data become available. Depending on the acquired quality outputs, a two-step quality control strategy involving energy adaptation and discharge stability regulation is implemented. This novel control strategy has proven to achieve a consistent areal surface roughness with variations smaller than 0.1 μm for a specific material and dielectric combination in μEDM milling.

RECTANGULAR HOLLOW T‐SHAPED JOINT WITH ECCENTRICITY MADE OF HIGH STRENGTH STEELS

AbstractThe presented research aims to investigate how the choice of steel grade affects the resistance of T‐shaped RHS‐to‐RHS structural connections, specifically those involving an offset of a brace toward the wall of a chord. To assess the mechanical resistance of these connections, the numerical non‐linear FE modelling with ABAQUS software were utilized, which is then validated against results obtained from the analytical model developed by Kalmykova & Wald [1]. Material modelling is carried out for steel grades S355, S460, and S690, using standardized parametric curves as outlined in EN 1993‐1‐8. It is evident that variations in material properties and the fy/fu ratio influence the behavior of these connections, particularly at the 3% deformation level.

Fast and accurate transcranial ultrasound simulation using the asymptotic model of the Civa Healthcare platform

To ensure its efficacy and safety, transcranial ultrasound therapy treatment planning requires accurate pressure field simulations and phase law corrections. Despite their long computation time and high memory usage, full numerical methods are often used since they are considered more accurate than semi-analytical methods. This work will present the so-called “pencil method“, a fast asymptotic model embedded in the CIVA HealthCare simulation platform. It allows computation in harmonic and impulse mode and the consideration of complex configurations, including solid obstacles, considering, at each interface, refractions and reflections with or without mode conversion of the acoustic field. This model was successfully compared to a recent collaborative work by Aubry et al. that presented a set of numerical benchmarks for transcranial propagation, to allow comparisons between various modeling tools. It was used to investigate the influence of parametric variation of skull material properties on the quality of acoustic focusing through the human skull. Its ability to predict the thermal rise at the intracranial target was validated against experimental data obtained ex-vivo through human skulls. Finally, works in progress will be shared about its connection to the open-source Kranion software developed at the FUS Foundation to facilitate comparison between clinical and simulated data.

Incorporating non-linear effects in fast semi-analytical thermal modelling of powder bed fusion

The usefulness of semi-analytical thermal models for predicting the connection between process, microstructure and properties in powder bed fusion has been well illustrated in recent years. Such an approach provides predictions that are orders of magnitude more computationally efficient than numerical approaches. However, the opportunity to make predictions that span several orders of magnitude in space and time comes at the cost of significant simplifications, limiting fully quantitative predictions without empirical calibration. This approach relies on the solution to a linear problem meaning that first order non-linear effects induced by e.g. the temperature dependence of material properties and surface boundary conditions, are not incorporated. Here, we revisit these limitations and highlight ways that temperature-varying material properties and radiative heat loss from the melt pool can be systematically accounted for without prior calibration. These corrections, made with an eye to minimizing additional computational overhead, bring the technique’s predictive capability much closer to that of higher fidelity thermal simulations without a significant computational cost. Quantitative comparisons to experiments are used to illustrate the important impact of including such corrections.

Regional variation of the cortical and trabecular bone material properties in the rabbit skull.

The material properties of some bones are known to vary with anatomical location, orientation and position within the bone (e.g., cortical and trabecular bone). Details of the heterogeneity and anisotropy of bone is an important consideration for biomechanical studies that apply techniques such as finite element analysis, as the outcomes will be influenced by the choice of material properties used. Datasets detailing the regional variation of material properties in the bones of the skull are sparse, leaving many finite element analyses of skulls no choice but to employ homogeneous, isotropic material properties, often using data from a different species to the one under investigation. Due to the growing significance of investigating the cranial biomechanics of the rabbit in basic science and clinical research, this study used nanoindentation to measure the elastic modulus of cortical and trabecular bone throughout the skull. The elastic moduli of cortical bone measured in the mediolateral and ventrodorsal direction were found to decrease posteriorly through the skull, while it was evenly distributed when measured in the anteroposterior direction. Furthermore, statistical tests showed that the variation of elastic moduli between separate regions (anterior, middle and posterior) of the skull were significantly different in cortical bone, but was not in trabecular bone. Elastic moduli measured in different orthotropic planes were also significantly different, with the moduli measured in the mediolateral direction consistently lower than that measured in either the anteroposterior or ventrodorsal direction. These findings demonstrate the significance of regional and directional variation in cortical bone elastic modulus, and therefore material properties in finite element models of the skull, particularly those of the rabbit, should consider the heterogeneous and orthotropic properties of skull bone when possible.

The analysis of pressurized FGM cylinders with arbitrarily varying material properties using strain based approach

This article aims to demonstrate the accuracy and robustness of the strain based approach in analyzing pressurized FGM cylinders with arbitrarily varying material properties. A sector in-plane finite element in the polar coordinate system based on this approach is employed. The obtained results are compared with the analytical solutions available in the literature, and a good accuracy was shown. In addition, this element can easily simulate the behavior of FGM without the need for dividing the cylinder into homogeneous layers. This investigation would be helpful in the design and analysis of hollow cylinders made of functionally graded materials.

Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs).

The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47.

Quantifying the impact of stiffness distributions on the dynamic behaviour of railway transition zones

Railway transition zones (RTZs) are regions where abrupt track stiffness changes occur that may lead to dynamic amplifications and subsequent track deterioration. The design challenges for these zones arise due to variations in material properties in both the depth (trackbed layers composed of different materials) and longitudinal directions of the track, as well as temporal variations in mechanical properties of materials due to several external factors over the operational period. This research aims to investigate the effects of these variations in material properties (i.e., of the resulting stiffness distributions in vertical and longitudinal directions) on the behaviour of RTZs, assess from this perspective the performance of a novel transition structure called the SHIELD, and establish a methodology for designing a robust solution to mitigate the dynamic amplifications in these zones. Results indicate that stiffness variations in both vertical and longitudinal directions significantly influence the dynamic behaviour of the RTZs. The study also suggests a permissible range of stiffness ratios to control the amplification of strain energy in the most critical components of RTZs, both in the initial state as well as during the operational phase (where material properties may vary over time). Moreover, the proposed methodology offers a valuable tool for the design and evaluation of RTZs and is applicable to various transition types and a broad spectrum of material properties.

The Casimir, Van der Waals, and electrostatic forces’ effects on the response of magneto-electro-elastic nanosensor/switch beams under thermal environment

This study investigates the impact of Casimir, Van der Waals, and electrostatic forces on nanomechanical switches’ thermomechanical and free vibration behavior. The analysis is conducted using a novel higher-order beam theory and the nonlocal strain gradient elasticity. The motion equations of the nanosensor/switch beam are derived using Hamilton’s principle and solved using Navier’s method for general boundary conditions. The nanoswitch is composed of electroelastic barium-titanate (BaTiO3) and magnetostrictive cobalt-ferrite (CoFe2O4) materials, which are modeled using a power-law approach to account for functionally graded material property variations across the beam’s thickness. The impact of different parameters, such as Casimir, Vander Waals, electrostatic forces, and variations in material composition, size parameters, and gap distance, on a nanoswitch system’s bucking and free vibration is comprehensively examined. With the intermolecular and electrostatic forces, the temperature dependency of barium-titanate and cobalt-ferrite nanoswitch materials, which have not been extensively studied in any previous research, is considered in the modeling of free vibration, and the buckling behavior of a nanoswitch for the first time. This research represents the first comprehensive analysis of these factors. Considering the investigated parameters, the study’s findings can provide helpful insights into developing micro/nano-electromechanical systems, including switches, sensors, and actuators.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Investigating the Impact of Productivity on Surface Roughness and Dimensional Accuracy in FDM 3D Printing

Over the past three decades, 3D printing technologies have undergone significant advancements, revolutionizing various industries, such as automotive. Additive manufacturing has played a crucial role in reducing product development time and enabling the direct printing of functional parts. One of the key advantages of additive manufacturing is its ability to provide greater design freedom compared to traditional manufacturing methods. This means that complex geometries and varying material properties can be easily printed. In this research paper, the author focused on investigating the impact of layer thickness and part orientation on the surface roughness and accuracy of printed parts. The main objective was to identify the optimal combination of layer thickness and part orientation that would yield the best results in terms of surface roughness and accuracy, considering printing time as a variable. The results from printing time, accuracy and surface roughness measurements showed that the vertical orientation is the superior for cylindrical orientation.

Thermal buckling in multi-directional porous plates: The effects of material grading and aspect ratio

In the present study, a trigonometric shear deformation plate theory was employed to perform a thermal buckling analysis of multi-directional functionally graded (FG) plates. During the manufacturing of the multi-directional graded plate, the formation of pores is abounded. Hence, the effect of porosity on the buckling performance was investigated by considering the variation of porosity in the plate for power law gradation variation of material properties. The adverse effect of porosity on the material properties was taken into account by employing the rule of mixture relation. Finite element results show that the thermal expansion coefficient is unaffected by the presence of porosity. For simply supported boundary conditions, the non-linear governing equations are solved for different thermal loads such as uniform, linear, and non-linear. A parametric study was performed in which the effect of grading parameters, aspect ratio, and side-to-thickness ratio under variable temperature change was studies. Critical material grading indices for multi-directional plates have been identified that help researchers and industry personnel in fabrication planning.

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts (https://data.mendeley.com/datasets/k5hdc9cz7w/1).

DIAGNOSTICS OF INSTRUMENTAL MATERIALS BASED ON THE RESULTS OF MODELING OF COATINGS OF INSTRUMENTAL MATERIAL

Computer modeling makes it possible to predict the adhesion properties of various materials and monolayers in the material at the atomic level. As a result of this approach, it is possible to diagnose the operational properties of the tool material according to the calculated values of the adhesion energy of the layers in the tool coating. The issues of studying the adhesive properties of monolayers successively deposited on a hard alloy VK8 are considered. The adhesive properties of Ti, TiN and (Ti, Cr, Al)N layers with each other and with WC92-Co8 hard alloy are investigated. The dependence of the adhesion energy of titanium to WC and Co surfaces depending on the thickness (from one to three atomic layers) of the deposited titanium layer has been studied. For four variants of the compound structure (Ti, Cr, Al)N the adhesion energy of this compound to the TiN surface is calculated. An approach to predicting rational coatings has been developed. It consists in the calculation (control) of the adhesion energy and the use of the adhesion energy value to diagnose a rational coating option for an instrumental hard alloy VK8.

Effect of Capsule Thickness and Material Properties on the Central Optical Power of Natural Human Lens

Background: A thin membrane capsule covers the eye lens and links the lens and the applied forces by the ciliary muscles. The capsule converts the tension of the concentrated zonules to distributed surface tractions over the cortex surface during the lens accommodation. The gradual changes of the capsule geometry and material properties with age and its important role in the design of intraocular lens implants were the motivation of extensive researches on describing the capsule biomechanical behavior. Aim: The present work aimed to study the lens accommodation response to different capsule thicknesses and material properties at different ages. Materials and Methods: A material and geometry parametric study was performed, drawing some guidelines on the choice of lens capsule thickness and biaxial/uniaxial material parameters and exploring the response sensitivity of the finite element model at different age groups Results: The 16-, 35- and 48-year-old lenses were considered for lens accommodation simulation. The sensitivity of lens accommodation was studied at each age group by considering constant thickness (cnst-t) and variable thickness (var-t) capsules and biaxial (Biax) and uniaxial (Uniax) material characteristics tests. The lens was stretched through zonules, and corresponding absolute changes in central optical power (COP) were measured. Conclusion: After the stretch, the lens anterior and posterior curvatures increased, producing a change in COP. The Biax models underestimated the ΔCOP compared to the Uniax models. The 16-year lens model was more sensitive to material properties than thickness variation, while thickness variation was more relevant to the ΔCOP of the 35-year lens model. The 48-year model had the least sensitivity to capsule thickness and material property variations.