International Journal of Mechanics and Materials in Design最新文献

Rigorous protocols must be followed when mounting ball bearings to avoid structural damage and subsequent malfunctioning or unexpected failures. Unconventional mounting procedures may produce excessive contact pressures between the elements of the bearing, therefore the whole process must be well-understood and modelled to prevent unwanted effects. Specifically for angular ball bearings, fitting axial forces should always be applied over the raceway subjected to the shrink-fit to avoid contact forces arising on the ball. In the present study, such an axial force is applied unconventionally, such that the axial force is transferred to the shrink-fit raceway through the balls. In this scenario, the evaluation of the contact areas and the pressure distributions is accomplished by exploiting both analytical and FEM approaches, supported by bespoke experimental tests to determine the relevant frictional coefficients and mounting forces. The study demonstrated how analytical methods can successfully replace more demanding FEM-based tools for the evaluation of the bearing mounting force and contact pressure and extent. FEM modelling can, however, be more accurate when dealing with more generic boundary conditions and more intricate geometrical features involved.

Highly integrated electrical components produce intensive heat while in use, which will seriously impact their performance if not properly designed. In this study, an end-to-end heat dissipation structure topology optimization prediction framework considering physical mechanisms was established by using the convolutional neural network (CNN) and the moving morphable components (MMC) method. Aiming at the sparsity of physical field matrix caused by the initial component distribution in MMC method, a CNN model was established taking the temperature gradient information of both homogeneous material and initial component layout as input. Compared with other seven input forms, the CNN model in this study considers both the initial component layout and the physical field information of the structure, which can predict the topology configuration of heat dissipation structure more accurately. In addition, an improved penalty mean square error (PMSE) function was proposed by introducing a penalty factor, which improved the prediction ability of the CNN model on the structural boundary and ensured more accurate and efficient structural heat dissipation performance. Several 2D and 3D numerical examples verified the effectiveness of the proposed framework and the dual temperature gradient input model. The overall framework provides a new method for the innovative and efficient heat dissipation structure topology optimization in packaging structure of electronic equipment.

We investigated the application of ensemble learning approaches in geotechnical stability analysis and proposed a compound explainable artificial intelligence (XAI) fitted to ensemble learning. 742 sets of data from real-world geotechnical engineering records are collected and six critical features that contribute to the stability analysis are selected. First, we visualized the data structure and examined the relationships between various features from both a statistical and an engineering standpoint. Seven state-of-the-art ensemble models and several classical machine learning models were compared and evaluated on slope stability prediction using real-world data. Further, we studied model fusion using the stacking strategy and the performance of model fusion that contributes to slope stability prediction. The results manifested that the ensemble learning model outperformed the classical single predictive models, with the CatBoost model yielding the most favourable results. To dive deeper into the credibility and explainability of CatBoost composed of multiple learners, the compound XAI fitted to CatBoost was formulated using feature importance, sensitivity analysis, and Shapley additive explanation (SHAP), which further strengthened the credibility of ensemble learning in geotechnical stability analysis.

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The literature contains numerous articles devoted to examining the mechanical behavior of nanowires (NWs) using molecular dynamics simulations. Many of these investigations have selected improper strain rates leading to erroneous results concerning ductile–brittle transition. In this study, we tested this hypothesis and proved that such transition in the material behavior existed due to the improper selection of strain rates which eventually changes the propagation velocity of phonons in the conducted atomistic simulations. In the current study, we subjected gold nanowires (Au NWs) with a diameter of 100 Å and lengths ranging from 25 to 1000 Å to varied strain rates. Specifically, we examined the effect of the rate of deformation of the NW upon its mechanical behaviour by dividing its length into several stations along its entire length to capture the strain distribution in each segment along that length. Five orders of magnitudes of strain rates were applied in our work for studying the influence of rate of deformation on the strain distribution along the NW length. The results of our molecular dynamics simulations show that smaller strain rates were necessary for modeling relatively long (> 150 Å) NWs to ensure the transmission of the applied loads through the entire NW length to suppress phonon drag effect. On the other hand, relatively short (< 25 Å) NWs experience large variations in the axial strain along the NW length; with smaller strains near the ends and higher strains at the middle section. As a result, relatively short NWs exhibit higher elastic moduli than longer ones and the NW length’s effect diminishes at lengths exceeding 150 Å. Location of necking, under the application of higher strain rate, shifts away from the loading end of NW towards its middle portion with the decrease in the NW length due to the phonon drag. The slope of the stress–strain curves was found to significantly depend on the NW length, and thus, using the same strain rate over a large range of NW lengths will lead to erroneous results.

Triply periodic minimal surfaces (TPMS) lattice structures present outstanding properties such as lightweight, high strength, energy absorption, and wave propagation control, which are extensively investigated in recent years. However, one of the main challenges when designing TPMS is the proper selection of cell type and volume ratio in order to obtain the desired properties for specific applications. To this aim, this work provides a comprehensive numerical study of bandgap’s formation in the sub-2 kHz frequency range for the seven major cell type TPMS structures, including Primitive, Gyroid, Neovius, IWP, Diamond, Fischer–Koch S, and FRD, for a comprehensive range of volume ratios. Results show that these seven TPMS structures present a complete bandgap between the 3rd and 4th dispersion curves. The width of the bandgap is strongly dependent of the TPMS lattice and the widest bandgaps are seen on the Neovius and Primitive-based lattice (reaching a maximum width of 0.458 kHz and 0.483 kHz, respectively) for volume ratios over 0.3. Below this volume ratio, the bandgap of the Primitive structure becomes negligible, and the Neovius and IWP structures are the best candidates among the 7 tested TPMS cases. The central frequency of the bandgaps is less sensitive to the lattice and are predominantly tailored by the volume ratio. With this study, we demonstrate that the proper selection of the periodic cell type and volume ratio can tailor the bandwidth of complete bandgaps from a tens of Hz up to 0.48 kHz, while the central frequency can be selected from 0.72 to 1.81 kHz according to the volume ratio. The goal of this study is to serve as a database for the Primitive, Gyroid, Neovius, IWP, Diamond, Fischer–Koch S, and FRD TPMS structures for metamaterial designers.

To research impacts of mass points and elastic supports on dynamic and aeroelastic properties of plate structures, a unified dynamic model concerning the plate structures with concentrated mass point or elastic support subjected to supersonic airflow is established in this paper. The energy approach is utilized to deduce energy functions of the dynamic system, and the nonlinear dynamic equations are further formulated based on the variational principle. Furthermore, several numerical calculations are implemented to validate the proposed formulations, and satisfactory agreements are exhibited between the calculated vibration and flutter solutions and data from the software and literature. Subsequently, impacts of the mass point and elastic support on vibration and flutter properties of panel structures are also presented and the detailed mechanisms are explained. It can be found that aeroelastic stability properties of panel structures are significantly raised with the location of the concentrated mass point or elastic support placed reasonably. This study provides a simple method for the flutter suppression of plates, which can be used in the mechanical design of these plate structures for the better dynamic performances.

Spline couplings allow for a certain amount of misalignment and relative sliding between their internal and external components. However, the misalignment could cause serious uneven load distribution and aggravate the wear of a spline coupling. So far, the effects of misalignment on the load distribution of the spline coupling aren't fully understood. To solve the above problem, an improved dynamic model of the spline coupling is established, which introduces the static misalignment caused by installation and manufacturing errors and the dynamic misalignment introduced by the dynamic vibration displacement between the internal and external splines. The classical potential energy method is adopted to derive the meshing stiffness, and then the equivalent stiffness and meshing excitation force of the spline coupling with misalignment is obtained. The accuracy of the method proposed has been proved by software. The load distribution of the spline coupling with various misalignments is studied. The results show that: the misalignment would cause serious uneven load distribution, especially the static parallel misalignment. Meanwhile, the dynamic misalignment has a small effect on the load distribution, which can be ignored during load distribution analysis. The improved model can be widely applied to rotor systems connected by spline couplings.

Materials are of prime importance for designing and manufacturing structures and components in numerous industries, including aviation, aerospace, military, automotive, machine construction, electronics, and telecommunications, among others. Throughout the industrial transformations in human history, it is evident that the materials industry had the most significant impact on scientific and technological progress. In recent years, the Fourth Industrial Revolution has altered the infrastructure and character of production in a number of global industries. Materials science has been contributing a significant and essential role in the global competitiveness of all industries, particularly those utilizing electronic domains such as semiconductors, microprocessors, and sensors for industrial and social applications. Consequently, nanoscale materials with exceptional properties have garnered the interest of numerous researchers. One of these phenomena in dielectric materials is flexoelectricity. This phenomenon was discovered in the 1950s of the previous century, but it wasn't until the early 2000s, when materials science and other disciplines flourished, that many researchers began to focus on it. In recent years, the applicability of flexoelectric materials has increased across all disciplines. In addition, as a consequence of the importance of novel electrical materials to the flexoelectric effect, the research problem for this material broadly and the analysis of the mechanical responses of flexoelectric structures are being investigated and developed at a rapid rate. This paper provides an overview of the flexoelectric phenomenon, together with potential applications and recommendations for further study. The article’s content will serve as a valuable resource for scientists interested in dielectric materials with unique electromechanical effects, which are extensively used in contemporary electronic disciplines.

The Kriging model with numerical simulation can analyze reliability efficiently, but its extension in multi-mode system is troubled by the hard quantification of correlations among Kriging models of multiple limit state functions. For this issue, a new learning strategy (NLS) is proposed by considering correlation effect. Firstly, NLS accurately derives the cumulative distribution function (CDF) boundary of the system Kriging model, and it considers the correlations among Kriging models of all system modes. By this CDF boundary, NLS derives the upper bound probability of the system Kriging model misjudging candidate sample state, on which the most contributive sample is selected to improve the capability of system Kriging model judging system state. Secondly, NLS only adds most contributive sample to the training set of the most easily identified mode to avoid computational cost on updating the Kriging models of unimportant modes. Thirdly, by employing the upper bound of expected relative error of failure probability estimated by prediction and prediction mean of system Kriging model, a convergence criterion is used to improve efficiency under acceptable accuracy. The superiorities, including in selecting training point, updating mode and convergence criterion, of NLS over the up-to-date methods for analyzing the system reliability are demonstrated by examples.

In this paper we propose a new optical-based technique to identify the constitutive relation coefficients of the hyperelastic material using a hybrid optimisation approach. This technique can be used in place of traditional mechanical testing of elastomers for applications that involve inhomogeneous deformation. The purpose of the proposed method is to identify the incompressible hyperelastic material constitutive relation coefficients using a single experiment under different loading cases. The method comprises sample surface 3D reconstruction and uses finite element simulations to replicate the experiments, and uses a hybrid optimisation technique to minimise the error between actual 3D deformations and FE simulation results. The proposed hybrid technique predicts the hyperelastic constitutive relation coefficients more accurately than other optimisation methods. This study introduces a novel approach by employing a subpixel image registration algorithm for 3D reconstruction. The method requires a single experiment with diverse loading cases to accurately determine the coefficients of hyperelastic constitutive relations. The setup is portable and can be accommodated in a small suitcase. For this purpose, an apparatus was constructed comprising a stereoscopic system with eight cameras and a six-degree-of-freedom force-torque sensor to measure the induced forces and torques during the experiments. We identified the constitutive relation coefficients of Ogden N1, Ogden N3, Yeoh, and Arruda-Boyce relations which are commonly used models for silicone materials, using a traditional uniaxial test, optical uniaxial test (experiments performed using a constructed optical system), and inhomogeneous deformations tests. The study demonstrated that the coefficients obtained from inhomogeneous deformation tests provided the most accurate FE predictions. It was also shown that hyperelastic constitutive relation coefficients obtained from traditional uniaxial tests are insufficient to describe the material behaviour when the material undergoes inhomogeneous deformations.