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A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artificial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to find out specific influence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating influence on the shear modulus of graphene nanostructure. [ABSTRACT FROM PUBLISHER]

A molecular dynamics (MD) based computational intelligence (CI) approach is proposed to investigate the Young modulus of two graphene sheets: Armchair and Zigzag. In this approach, the effect of aspect ratio, the temperature, the number of atomic planes and the vacancy defects on the Young modulus of two graphene sheets are first analyzed using the MD simulation. The data obtained using the MD simulation is then fed into the paradigm of a CI cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of Young modulus of two graphene structures. We find that the MD-based-CI model is able to model the Young modulus of two graphene structures very well, which compiles in good agreement with that of experimental results obtained from the literature. Additionally, we also conducted sensitivity and parametric analysis and found that the number of defects has the most dominating influence on the Young modulus of two graphene structures. [ABSTRACT FROM AUTHOR]

Automotive suspension system is an important part of car comfort and safety. In this article active suspension for 2DOF nonlinear coupled Passenger-Car model with force actuator is designed using PID control and H-infinity control. This paper is focused on comparison of those two controllers. Simulations on an exact nonlinear model of the suspension are performed for control validation. [ABSTRACT FROM AUTHOR]

A simple method for the preparation of magnetic nanocomposites consisting of cobalt ferrite (CF; CoFe2O4) nanoparticles, polybenzoxazine (PB), linear low-density polyethylene (LLDPE), and linear low-density polyethylene- g-maleic anhydride (LgM) is described. The composites were prepared by the formation of benzoxazine (BA)-CF nanopowders followed by melt blending with LLDPE and the thermal curing of BA. The composites were characterized by X-ray diffraction, thermogravimetric analysis, differential scanning calorimetry, scanning electron microscopy, universal testing machine measurement, and vibrating sample magnetometry. The composites consisting of LLDPE, PB, and LgM (47.5L-47.5PB-5LgM) exhibited a higher tensile strength (23.82 MPa) than pure LLDPE and a greater elongation at break (6.11%) than pure PB. The tensile strength of the composites decreased from 19.92 to 18.55 MPa with increasing CF loading (from 14.25 to 33.25 wt %). The saturation magnetization of the composites containing 33.25 wt % CF was 18.28 emu/g, and it decreased with decreasing amount of CF in the composite. The composite films exhibited mechanical flexibility and magnetic properties. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013 [ABSTRACT FROM AUTHOR]

In the current study an analytical model to determine fundamental frequency of perforated plate with circular perforation is formulated. Circular holes are replaced by equivalent square hole and non homogeneity in Young's modulus and density due to equivalent square perforation is expressed by using unit step functions. Analytical formulation is based on Rayleigh-Ritz method. In present study boundary condition considered is clamped at all edges. The deflected middle surface of the plate is approximated by a function which satisfies the boundary conditions. Proposed approach is validated by comparing numerical analysis results with Finite Element Method (FEM) modal analysis results. [ABSTRACT FROM AUTHOR]

In the current study an analytical model to determine fundamental frequency of perforated plate is formulated. Non homogeneity in Young's modulus and density due to perforation is expressed by using unit step function in Rayleigh's Quotient. In the present analysis the boundary condition considered is clamped at all edges. Perforated plate is considered as plate with uniformly distributed mass and holes are considered as non-homogeneous patches. The deflected middle surface of the plate is approximated by a function which satisfies the boundary conditions. The proposed approach is validated by comparing results with finite element method modal analysis. [ABSTRACT FROM AUTHOR]

This study aims to characterize and standardize the Sarasvati Veena of South India. The instrument is very unique in the way in which it is manufactured and played. This instrument is often tuned relative to the accompanying instruments. The aim of the study was to find out the optimum tuning frequency for a particular Veena. The natural frequency of the body of the Veena was determined by conducting an impact test. The strings of the Veena were tuned to the corresponding body frequency to obtain resonance and FFT plots were generated to see the frequency variation. The optimum tuning frequency for a given Veena was obtained such that the body undergoes conductive resonance when tuned to that particular frequency. Acoustic readings were taken using a sound level meter to confirm on the results obtained by impact test. [ABSTRACT FROM AUTHOR]

Present day requirements for enhanced reliability of rotating machinery have become critical for the manufacturing sector. Every rotating machine exhibits a unique characteristic vibration and acoustic signature. This can be used to identify the defective parts and estimate the present severity of the problem; most importantly, without opening the machine for inspection. Moreover, it aids in the reduction of unscheduled down time, turnaround time and existing noise levels. The paper deals with the vibro-acoustic condition monitoring of metal lathe and development of predictive models for the detection of probable faults using Machine Learning. Experiments were conducted to obtain vibration signatures using accelerometers and the data was further processed while the acoustic signatures were obtained using noise level meters. Results were obtained for idle running, turning and facing operations using a single point cutting tool for constant spindle speeds, feed and depth of cut. The vibro-acoustic signatures of six metal lathe machines were collected over a period of 5 months and the trends obtained were analyzed. The filtered acceleration (g-peak) signatures were compared with the General Machinery Vibration Severity Chart and based on the velocity classification results, the best machine was chosen for the development of predictive models. Vibration as well as acoustic signatures were isolated using filters, empirical relations and manufacturing data. Predictive models were made using machine learning algorithms to predict the failure of the lathe based on its historical data. These models can be used by industries to detect unhealthy trends and identify troublesome parts in the machine which can be then scheduled for maintenance thereby decreasing production downtimes. [ABSTRACT FROM AUTHOR]