Among various types of auxetic metamaterials, the perforated materials with peanut-shaped pores exhibit numerous advantages such as simple fabrication, high load-bearing capability, low stress-concentration level and flexibly tunable mechanical properties, and thus they have received much attention recently. However, one challenging is to make a high-efficient and reversible design of such metamaterials to meet diverse auxetic requirements, without the need to model them through conventional physics- or rule-based methods in time-consuming and case-by-case manner. In this study, a data-driven countermeasure is introduced by coupling back-propagation neural network (BPNN) and genetic algorithm (GA). Firstly, a dataset including microstructure-property pairs is prepared to train BPNN to determine the hidden logic mapping relationship from microstructural parameters to Poisson ratio. Then, GA is employed to optimize the mapping relationship to find the corresponding optimal solutions of microstructural parameters meeting the target Poisson’s ratio. The efficiency and accuracy of specific optimal designs is verified by the tensile experiment and finite element simulation. Subsequently, more optimal solutions corresponding to positive, zero or negative Poisson’s ratios are achieved under constrained/unconstrained conditions to accelerate the design of auxetic metamaterials by this interdisciplinary tool in which the auxetic characteristics and artificial intelligence are interconnected mutually.

The aim of this paper is to propose a novel computational technique of applying reliability-based design to thermoelastic structural topology optimization. Therefore, the optimization of thermoelastic structures' topology based on reliability-based design is considered by utilizing geometrical nonlinearity analysis. For purposes of introducing reliability-based optimization, the volume fraction parameter is viewed as a random variable with a normal distribution having a mean value and standard deviation. The Monte Carlo simulation approach for probabilistic designs is used to calculate the reliability index, which is used as a constraint related to the volume fraction constraint of the deterministic problem. A new bi-directional evolutionary structural optimization scheme is developed, in which a geometrically nonlinear thermoelastic model is applied in the sensitivity analysis. The impact of changing the constraint of a defined volume of the required design in deterministic problems is examined. Additionally, the impact of altering the reliability index in probabilistic problems is investigated. The effectiveness of the suggested approach is shown using a benchmark problem. Additionally, this research takes into account probabilistic thermoelastic topology optimization for a 2D L-shaped beam problem.

In this study, an iterative design window (DW) search using nonlinear dynamic simulation was proposed for polymer micromachined flapping-wing nano air vehicles (FWNAVs) that can satisfy both nonlinear and unsteady design requirements, which are contradictory to each other. The DW is defined as an existing area of satisfactory solutions in the design parameter space. The present FWNAVs have a complete 2.5-dimensional structure such that they can be fabricated using polymer micromachining. The micro-wing of our FWNAVs has been designed using morphological and kinematic parameters of an actual dipteran insect. Finally, using our method, we found the DW that allowed miniaturization of the design down to 10 mm while satisfying all the design requirements. Our findings demonstrate the possibility of further miniaturizing FWNAVs down to the size of small flying insects.

In this paper, a multiscale optimization approach for composite material design is presented. The objective is to find different material designs for a short fiber reinforced polymer (SFRP) with a desired effective (in general anisotropic) viscoelastic behavior. The paper extends the work of Staub et al. (2012) and proposes a combination of material homogenization, surrogate modeling, parameter optimization and robustness analysis. A variety of microstructure design parameters including the fiber volume fraction, the fiber orientation distribution, the linear elastic fiber properties, and the temperature dependent material behavior are considered. For the solution of the structural optimization problem, a surrogate-based optimization framework is developed. The individual steps of that framework consist of using design of experiments (DoE) for the sampling of the constraint material design space, numerical homogenization for the creation of a material property database, a surrogate modeling approach for the interpolation of the single effective viscoelastic parameters and the use of differential evolution (DE) for optimization. In the numerical homogenization step, creep simulations on virtually created representative volume elements (RVEs) are performed and a fast Fourier transform (FFT)-based homogenization is used to obtain the effective viscoelastic material parameters. For every identified optimal design, the robustness is evaluated. The considered Kriging surrogate models of Kriging type have a high prediction accuracy. Numerical examples demonstrate the efficiency of the proposed approach in determining SFRPs with target viscoelastic behavior. An experimental validation shows a good agreement of the homogenization method with corresponding measurements. During the manufacturing of composite parts, the results of such optimizations allow a consideration of the local microstructure in order to achieve the desired macroscopic viscoelastic behavior.

It has a positive impact on the machining accuracy to predict precisely the thermal error caused by the temperature change for the high-speed spindle-bearing system. In this paper, the dual reciprocity method (DRM) based on compactly supported radial basis functions (CSRBFs) and the line integration boundary element method (LIM-BEM) are presented for the thermal-deformation coupling calculation. The essential idea of this method is building the thermal-deformation coupling model only by the boundary information and obtaining results by line integrals. In this process, the boundary element model discretized by the discontinuous iso-parametric quadratic boundary element is established. Then, the transient temperature is calculated by the CSRBFs-DRM, and the thermo-elastic deformation is done by the LIM-BEM, under the exact calculation of the heat generation and the thermal contact resistance. To validate the effectiveness, thermal-deformation coupling experiments are conducted. The proposed method is compared with experimental data and the finite element method. The result shows that the proposed model is more appropriate for the thermal-deformation coupling calculation for the satisfactory universality and accuracy.

This paper analyzes the sliding frictional nanocontact of an exponentially graded layer perfectly bonded to a homogeneous half-plane substrate under the nanoindentation of a rigid cylinder. The punch is subjected to both normal and tangential loads, satisfying Coulomb’s friction law. The contact interface is modeled by the full version of Steigmann–Ogden surface mechanical theory, in which surface tension, surface membrane stiffness and surface flexural rigidity are all taken into account. The method of Fourier integral transforms was applied to convert the governing equations and nonclassical boundary conditions into a Fredholm integral equation. By separating a nonsingular term from the integrand of the kernel function of the integral equation, numerical integration of the kernel function can be significantly improved. After that, Gauss–Chebyshev numerical quadratures are further employed to discretize and collocate the integral equation and the indentation force equilibrium condition. An iterative algorithm is subsequently developed to solve the resultant nonlinear algebraic system regarding discretized contact pressures and the two asymmetric contact boundaries. Stresses and displacements in the layer/substrate structure are also determined for completeness. Extensive parametric studies clarify the relative importance among three surface parameters, all helping to partially carry on the indentation loading in addition to the conventional bulk portion of the layer/substrate system. The sliding frictional condition significantly affects the symmetry of the contact pressure distribution and contact zone. The property gradation of the layer is another important factor affecting contact properties. The results reported in the current work show a concrete means of tailoring nanocontact responses of graded layers in nanosized materials and devices.

An enhanced reduced-order model is proposed to analyze the stability of small- and mid-scale wind turbine blades. A series of linear and nonlinear analyses are presented conjointly to assess the effects of various design parameters on the blade’s stability, namely, the blade length, the manufacturing material, the additive manufacturing aspect, and the blade’s scaling approach among others. The effect of different nonlinearities, mainly geometric and inertial, on the aeroelastic system’s response is also explored for different stall coefficient scenarios. Furthermore, a more accurate determination of the stall characteristics from the lift curve data of various symmetric and nonsymmetric airfoils is discussed in details along with its incorporation in the suggested blade reduced-order model for an accurate and robust modeling that shall accurately portray different intricate fluid–structure interaction aspects between the blade and its surrounding flow. Additionally, the moments of inertia of the modeled blade are obtained via an exact airfoil cross-section solution and are compared to the rectangular approximation conventionally used in the literature. The results show the large impact of the blade’s material selection and the additively manufactured cross-section types on the stability of the blade, and therefore highlight the importance of conducting aeroelastic and stability analyses with accurate nonlinear reduced-order models even for smaller-scale turbine blades.

The extraordinary opto-electronic and mechanical properties of Graphene makes it a popular material for several applications. However, defects like: cracks, and voids are unavoidable during its production, which can lead to poor properties. Furthermore, the fracture properties degrades at higher temperatures. In this study, the fracture strength of Graphene is investigated as a function of temperature, considering the influence of lattice orientation, initial crack size and its orientation. As a first step, an analytical model is developed to estimate the fracture strength of Graphene with respect to temperature, considering the above parameters. Later on, molecular dynamics simulations are performed with an included initial edge crack in ten different sizes and four orientations, at three particular lattice orientations, and operating at thirteen different temperatures. Finally, a deep machine learning model is developed to estimate the fracture strength of defective Graphene. Results from molecular dynamics simulations are used to train the developed deep machine learning model. Furthermore, the training is enhanced using transfer learning, where the weights and biases for the data set considering \(0^\circ\) lattice orientation are adopted in training the networks for \(13.9^\circ\) and \(30^\circ\) lattice orientations. Results from the developed deep machine learning model are validated by comparing them with the results from the analytical and molecular dynamics models and a good agreement is observed. Thus, a deep machine learning model has been proposed here to estimate the fracture strength of defective Graphene. The developed model serves as a tool for quick estimation fracture strength of defective Graphene.

A dynamic model based on rotor dynamics was proposed to analyze the effect of workpiece-tool system vibration on the cutting accuracy of commercially pure titanium TA2. The proposed model considers the imbalance of mass and stiffness caused by material removal, regenerative chatter effect, the Johnson–Cook plastic material constitutive model of the workpiece, and coupled vibration between workpiece and tool. The differential equation of the rotating workpiece-tool system is derived by using D' Alembert's principle. The model's validity is verified by cutting tests and surface topography observation. Besides, the effects of different cutting parameters on cutting vibration characteristics and Processing quality are discussed. According to cutter-workpiece vibration response and the test results of surface roughness, chip morphology, and surface morphology, the causes of cutting vibration and the influence of cutting parameters on surface quality are revealed.

In this work, the phase-field framework coupled with J2 plasticity is expressed in the variational formulation to simulate the bimaterial interfacial problems. The quadratic energetic degradation function in conjunction with the AT2 model is employed for phase-field regularization. A load increment-independent and computationally efficient Staggered scheme is proposed to solve the phase field problems. The existing unconditionally stable quasi-Newton-based Monolithic scheme, which captures the cracking in brittle solids has been extended to capture the crack evolution in the elastoplastic solids using the return mapping algorithm. A Generalized user-defined element subroutine (UEL) is developed and implemented in the commercial software ABAQUS using the proposed Staggered and Monolithic schemes. The efficacy of the proposed algorithms was validated against existing literature and extended to study bimaterials with interfaces. Different geometry and loading configurations in the bimaterial and their interface are modeled using the phase-field framework and analyzed using proposed schemes. The contour plots of phase field for crack evolution, equivalent plastic strain, and reaction force are presented. The efficacy of proposed algorithms in terms of the total number of iterations and the computational CPU time is provided for all numerically simulated cases.

Thin film structures will be wrinkled due to buckling deformation under the influence of compressive stress. The wrinkle and tension states of the thin film can be changed by introducing microstructures. So we introduce rigid elements on the thin film to suppress the wrinkling behavior of the thin film, and propose a method to calculate the optimal distribution position of the rigid elements on the thin film. Using this method, the optimal distribution positions of the square rigid elements on the biaxially stretched square thin film were calculated, and the effectiveness of introducing rigid elements on the thin film to suppress the wrinkle was verified through numerical simulation and experimental research. The results show that the wrinkling behaviour of the film can be effectively suppressed by placing rigid elements at the optimal position obtained by the method proposed to this paper. Our findings could provide new design ideas for thin-film antenna structures in aerospace engineering.

This work is focused on developing a stochastic model to study the effect of randomness in material properties of functionally graded material, of shell structure under the effect of thermal shock. A modified and more general form of power law is utilized to functionally grade the shells along their thickness. Upon consideration of the uncertainty in the constituent properties of functionally graded material the transient temperature distribution and therefore the development of the thermal stresses in the shells are obtained and analysed. The conventionally used Monte Carlo simulation is performed for validating computationally efficient Response Surface Method based Perturbation technique, which is subsequently applied to perform stochastic analysis of thermal stresses. For various distribution patterns of the constituent materials in the functionally graded material, the randomness of thermal stresses across the thickness of the shell structure due to uncertainty in input properties is analysed. Moreover with different level of uncertainty the maximum stochasticity of thermal stresses is predicted and plotted.

Knowledge of the temperature-dependent Young’s modulus (TDYM) is fundamentally important for it is a key index to access the deformation-resisting ability over a wide temperature range. In this article, considering the effects of temperature on fiber and matrix Young’s modulus, a TDYM model of short fiber reinforced metal matrix composites (SFRMMCs) is developed based on the classical shear-lag model. This model enables the prediction of Young’s modulus of SFRMMCs over a wide temperature range just requiring the material parameters at room temperature as inputs, which is convenient for engineering applications. Furthermore, the developed TDYM model can be conveniently applied to hybrid particle/short fiber metal matrix composites by taking the particle reinforced metal matrix composites as a new matrix. At the same time, the model enables the characterization of the hybrid effect at different temperatures effectively. In addition, good agreement between the two model predictions and available experimental values and finite element method results at different temperatures is achieved, verifying the rationality of the two models. Key influencing factors such as fiber geometry and matrix/short fiber Young’s modulus at different temperatures are analyzed in detail, and useful suggestions for improving the TDYM of composites are put forward.

The main purpose of this work is to understand the structural characterization of reinforced concrete slabs under near-field and contact explosions using the developed rate-sensitive damage model. The model is developed based on the experimental observation to include the effects of strain rate and damage rate. It is observed that with increasing strain rates there is a decrease in damage evolution due to artificial stiffening effects and the final level of damage is higher. This is achieved by using a power law model to relate the rate of damage to the equivalent plastic strain rate. The concrete undergoes pulverized damage because of the loss in cohesive strength at higher hydrostatic stress. Thus, the hydrostatic damage has to be considered along with tension and compression damage parameter. Strong volumetric deformation of the material that includes the hydrostatic and compaction damage is also accounted for in the model. The size of the yield surface increases with strain rate and is capped with an upper limiting value. The incremental effective stress–strain relationships are defined in terms of rate of damage, accumulated damage and viscosity parameters reflecting the inherent physical inertial, thermal and viscous mechanisms respectively. The results from the numerical analysis are found to match well with experimentally observed results.

In this work, novel four-unknown refined theories were used to evaluate the free vibration of rotating stiffened toroidal shell segments subjected to varying boundary conditions in thermal environments. The shell segments consist of a functionally graded graphene-platelet-reinforced composite (FG-GPLRC). The effective material properties of the composite were calculated using the modified Halpin–Tsai model and the mixture rule. The governing equations of motion for the shell were formulated within the novel four-unknown refined shell theory framework. The effects of centrifugal and Coriolis forces and the initial hoop tension resulting from rotation were all included. The Rayleigh–Ritz procedure and smeared stiffener technique were subsequently used to determine the natural frequencies of the shells with stiffeners. The advantages of the adopted shell theory result directly from the reduction of key unknowns without the need for the shear correction factor, and it can predict better results for FG-GPLRC structures. Finally, numerical examples were provided to validate the proposed solution and demonstrate the effects of four-unknown refined theories, material distribution patterns, boundary conditions, rotating speed, and temperature rise on the natural frequencies of toroidal shell segments.

Compliant mechanisms have increasing popularity. However, very few studies are available on transmission characteristics of compliant mechanisms in the literature. For conventional rigid-body mechanisms, the transmission angle is used to define motion quality. On contrary, the classic transmission angle formula cannot easily be employed for compliant mechanisms, unlike rigid-body mechanisms. This paper, a generalized equation that defines the transmission angle of all types of compliant four-bar mechanisms is introduced. For all configurations of partially compliant four-bar mechanisms, the formula is altered. Four theorems in some special cases are devised. Deviation of the transmission angle of a fully compliant four-bar mechanism from its rigid-body counterpart is discussed. Finally, a prototype is built and the theoretical approaches are compared with experimental results. To the best of our knowledge, this is the first study on the transmission angle of a compliant four-bar mechanism.

Topology optimization of multi-material structures under dynamic loads is implemented to minimizing compliance on polygonal finite element meshes with multiple volume constraints. A multiresolution scheme is introduced to obtain high resolution de-signs for structural dynamics problems with less computational burden. This multiresolution scheme employs a coarse finite element mesh to fulfil the dynamic analysis, a refined density variable mesh for optimization and a density variable mesh overlapping with the density variable mesh for design configuration representation. To obtain the dynamic response, the HHT-α method is employed. A ZPR (Zhang-Paulino-Ramos Jr.) update scheme is used to update the design variables in association to multiple volume constraints by a sensitivity separation technique. Several numerical examples are presented to demonstrate the effectiveness of the method to solve the topology optimization problems for mul-ti-material structures under dynamic loads.

This research investigates the feasibility of mass sensing in piezoresistive MEMS devices based on catastrophic bifurcation and sensitivity enhancement due to the orientation adjustment of the device with respect to the crystallographic orientation of the silicon wafer. The model studied is a cantilever microbeam at the end of which an electrostatically actuated tip mass is attached. The piezoresistive layers are bonded to the vicinity of the clamped end of the cantilever and the device is set to operate in the resonance regime by means of harmonic electrostatic excitation. The nonlinearities due to curvature, shortening and electrostatic excitation have been considered in the modelling process. It is shown that once the mass is deposited on the tip mass, the system undergoes a cyclic fold bifurcation in the frequency domain, which yields a sudden jump in the output voltage of the piezoresistive layers; this bifurcation is attributed to the nonlinearities governing the dynamics of the response. The partial differential equations of the motion are derived and discretized to give a finite degree of freedom model based on the Galerkin method, and the limit cycles are captured in the frequency domain by using the shooting method. The effect of the orientation of the device with respect to the crystallographic coordinates of the silicon and the effect of the orientation of the piezoresistive layers with respect to the microbeam length on the sensitivity of the device is also investigated. Thanks to the nonlinearity and the orientation adjustment of the device and piezoresistive layers, a twofold sensitivity enhancement due to the added mass was achieved. This achievement is due to the combined amplification of the sensitivity in the vicinity of the bifurcation point, which is attributed to the nonlinearity and maximizing the sensitivity by orientation adjustment of the anisotropic piezoresistive coefficients.

Nowadays, research on the application of new materials with interesting electrical properties, such as high dielectric constant, on electrostatically-actuated microstructures has become one of the prominent research fields worldwide. One of the main disadvantages of these structures is the high required voltage. The main purpose of this paper is to demonstrate the ability of dielectric materials to reduce the required voltage of capacitive MEMS and also to intensify their softening behavior. So, a nonlinear model for a capacitive microstructure has been presented and HfO2 has been selected as the substrate material of the capacitor whose package is filled with high pressure & dielectric constant gas. It has been shown that both of these changed options together (or each of them) can significantly reduce the required actuating voltage. The physically gradient-descent-based learning method has been used to solve the governing nonlinear equation, allowing to obtain the primary and secondary resonances in the first harmony, as well as in higher harmonies of the response. It has been shown that, growing the thickness of the dielectric layer, as well as using a high coefficient dielectric gas in the package, intensifies the softening behavior.