Showing papers by "Sudip Dey published in 2020"

Radial Basis Function-Based Stochastic Natural Frequencies Analysis of Functionally Graded Plates

Abstract: This paper deals with portraying the stochastic natural frequencies of cantilever plates made up of functionally graded materials (FGMs) by employing the radial basis function (RBF)-based finite el...

Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates

Abstract: The initiation and propagation of uncertainties in structural behavior of complex anisotropic sandwich structures have significant computational challenges. Owing to limitations of experimental data, probabilistic descriptions of uncertain parameters are not practically feasible to expedite. This chapter presents the prediction capability of a surrogate model (polynomial neural network [PNN]) to estimate the stochastic buckling behavior of sandwich plates. The PNN is used to construct the surrogate. The computational time and cost is significantly reduced by using the proposed model in conjunction to finite element model using higher order zigzag theory. Both material and geometric uncertainties are considered to obtain the statistical quantity of interest. The computational efficacy of PNN is validated by means of scatter plot and probability density function plot. The constructed PNN model is found to be convergent with the results obtained by direct Monte Carlo simulation techniques. The proposed model can be used for more complex structures in future.

Comparative Study of Various Defects in Monolayer Graphene Using Molecular Dynamics Simulation

Abstract: The present study uses the molecular dynamics approach to study the various defects available in graphene sheet and also to record its effect on the strength and stiffness of graphene. The graphene sheet is uniaxially deformed in its armchair and zigzag direction. In order to examine the fracture behaviour of defective graphene, molecular dynamics (MD) simulations based on AIREBO interatomic potential field and Nose-Hoover thermostat and barostat techniques are implemented. The present study shows that with the introduction of the defects, the fracture/yield strength of graphene reduces up to some extent in both of its direction. However, the presence of crack reduces the strength of graphene significantly more. Further, the study also concludes that the graphene withholds much higher stress when loaded in its zigzag direction in comparison with loading it in armchair direction.

Effect of Skewness on Random Frequency Responses of Sandwich Plates

Abstract: This study presents the effect of skewness in natural frequency responses of sandwich plates. The free vibration analysis is carried out by using higher order zigzag theory (HOZT) considering random input parameters. It satisfies the transverse shear stress continuity condition and the transverse flexibility effect. The in-plane displacement throughout the thickness is assumed to vary cubically while transverse displacement is considered to vary quadratically within the core and constant at top and bottom plates. An efficient \( C_{0} \) stochastic finite element approach is developed for the implementation of proposed plate theory in the random variable surrounding. Compound stochastic effect of all input parameters is presented for the different degrees of skewness in sandwich plates. Intensive Monte Carlo simulation (MCS) is employed for solving the stochastic-free vibration equations and statistical analysis is conducted for illustration of the results. The present algorithm for sandwich plate is validated with previous literatures and it is found to be in good agreement.

Effect of spatial distribution of nanopores on mechanical properties of mono layer graphene

Abstract: Since the discovery of graphene, it has immense popularity among scientists and researchers due to its superior mechanical and electrical properties In the present study, the effects of the spatial distribution of nanopore defects on the mechanical properties of the single-layer graphene sheet (SLGS) are investigated Based on Tersoff potential functions, molecular dynamics (MD) simulations are conducted to perform the uniaxial deformation of defected graphene The nanopore defects are induced intentionally at various spatial locations on a pristine graphene sheet for studying the variation in its mechanical properties such as fracture strength, Young's modulus and failure strain The results illustrate that the mechanical properties are predominantly dependent on the spatial locations of the defects It is also observed that the mechanical properties are slightly higher in case of zigzag direction than armchair direction but it decreases with the presence of defects in both the cases In the consequence, it is suggested to consider spatial locations of defects while fabricating nanodevices with graphene

A Stochastic Investigation of Effect of Temperature on Natural Frequencies of Functionally Graded Plates

Abstract: The present paper deals with thermal uncertainty quantification in the free vibration of functionally graded materials (FGMs) cantilever plate by using the finite element method coupled with multivariate adaptive regression splines surrogate (MARS) model. The combined effects of uncertainty in material properties on the natural frequency are examined. The power law is employed for gradation of material properties across the depth of FGM plate, while the Touloukian model is used to evaluate temperature effects on the material properties. In finite element analysis (FEA), eight noded iso-parametric elements are considered with each element having five degrees of freedoms. In MARS, Sobol sampling is employed to train the model, which results in better convergence and accuracy. The results of MARS model are validated with Monte Carlo simulation results. The results reveal that MARS model can achieve a significant level of accuracy without compromising the accuracy of results.

Stochastic Free Vibration Analysis of Sandwich Plates: A Radial Basis Function Approach

Abstract: In this paper, stochastic free vibration analysis of sandwich plate using a radial basis function (RBF) approach is carried out. Due to uncertain material anisotropy and inherent manufacturing inaccuracies, such types of structures are subject to variability. It is therefore required to consider the randomness in input parameters to assess the stochastic free vibration behaviour of sandwich plates which have significant computational challenges. The mathematical formulation is developed based on the C0 stochastic finite element method (SFEM) coupled with higher-order zigzag theory (HOZT). Natural frequency analysis is conducted for stochasticity in ply orientation angle, face sheet thickness, core thickness, face sheet material properties, core material properties and skew angle. The cost-effective and computationally efficient RBF model is utilized as the surrogate to obtain the uncertain first five natural frequencies. In a single array, the global stiffness matrix is stored by using skyline techniques, while it is solved by subspace iteration.

Artificial-Neural-Network-Based Uncertain Material Removal Rate by Turning

Abstract: The present paper deals with the uncertain material removal rate (UMRR) of cylindrical turning of AISI 52100 steel (as workpiece) being turned by cubic boron nitride (CBN) (as single-point cutting tool insert). During machining operations such as cylindrical turning, assessment of material removal rate is often inharmonious, non-uniform and unpredictable due to variabilities in rotational speed of workpiece, feed and depth of cut provided by the cutting tool. It occurs due to unforeseen operational and manufacturing uncertainties. The present work is aimed to develop a computational model in conjunction with artificial neural network (ANN) approach. The constructed computational model is validated with the previous published experimental results. The traditional Monte Carlo simulation (MCS) is employed to compare the efficacy and accuracy of the constructed artificial neural network (ANN)-based surrogate model. The effect of both individual and combined variations of input parameters such as cutting speed, feed and depth of cut on the material removal rate is portrayed. The surrogate model is validated with the original Monte Carlo simulation (MCS), and the intensity of variation of output quantity of interest (QoI) is presented by the probability density function plots. The statistical analyses are carried out based on parametric studies, and the subsequent results are illustrated. The effect of depth of cut is observed to be maximum sensitive to influence the uncertain material removal rate, followed by feed and cutting speed.