Structural Investigation of Catalytically Grown Carbon Nanotubes

Carbon nanotubes (CNTs) having diameters in the range of 30–50 nm and few micrometers in length were synthesized in one step through a chemical-reduction route under autogenous pressure of H₂/CO₂. The synthesized materials prepared under different experimental conditions were characterized using different techniques. Results showed that V₂O₅ used as a catalyst for the nucleation of CNTs become carburized to vanadium carbide (V₈C₇) and provides a site for growth of CNTs. At high temperature, carbide particles thus formed become encapsulated at the tip of nanotube followed by the growth of CNTs through the tip-growth mechanism. Thermogravimetric analysis results showed that the CNTs obtained after the longer reaction time are more stable at high temperatures. Raman analysis showed a well-ordered graphite structure.     

Rotary Ultrasonic Machining: A Review

Rotary ultrasonic machining (RUM) is a mechanical type of nontraditional hybrid machining process that has been utilized potentially to machine a wide range of latest and difficult-to-machine materials, including ductile, hard and brittle, ceramics, composites, etc. In RUM, the basic material removal phenomenon of ultrasonic machining (USM) and conventional diamond grinding amalgamates together and results in higher material removal rate (MRR), improved hole accuracy with superior surface finish. In the current article, several investigations carried out in the domain of RUM for enormous materials have been critically reviewed and reported. It also highlights several experimental and theoretical ensues of RUM to improve the process outcomes and it is reported that process performance can be substantially improved by making the right selection of machine, diamond tooling, material and operating parameters. In recent years, various investigators have explored umpteen ways to enhance the RUM process performance by probing the different factors that influence the quality attributes. Among the various accessible modifications in RUM as employed in industries, rotary ultrasonic drilling is more strongly established compared to other versions such as rotary ultrasonic side milling, face milling, grinding, surface texturing, etc. The micro machining applications of RUM have also been discussed briefly. The final section of this paper discusses RUM developments and outlines the aspects for future research.     

Determining the number of layers in graphene films synthesized by filtered cathodic vacuum arc technique

We have synthesized graphene film by the filtered cathodic vacuum arc (FCVA) technique and determined the number of layers in graphene films by various techniques. Amorphous carbon (a-C) films of different thicknesses (1, 2, 3, 6, 10 and 18 nm) were synthesized by the FCVA technique on Si/SiO₂/Ni substrate and then annealed in vacuum at 800°C and cooled down to room temperature naturally to obtain graphene. Prepared graphene films were transferred on different substrates and characterized by the Raman spectroscopy, UV-VIS-NIR spectroscopy, high-resolution transmission electron microscopy (HRTEM), optical microscopy, atomic force microscopy (AFM) and sheet resistance to determine the number of layers present in the graphene films. Raman spectra of the prepared graphene films exhibit that there is red shift in the position of D, G and 2 D peak. The value of I_2D/I_G varied from 0.18 to 0.51, I_D/I_G varied from 0.82 to 1.02 and full width at half maximum of 2 D peak varied from 101.2 to 128.0 cm^?1, for different thicknesses of graphene films, respectively. The value of transmittance decreases from 97 to 63.7% and that of sheet resistance increases from 460 to 1400 Ω/square with the increase in the thickness of the prepared graphene film. The HRTEM and AFM study revealed that the graphene synthesis from 1 nm thick a-C film possesses a single layer structure.     

Surface wave propagation in sandy layer overlying a liquid saturated porous half-space and lying under a uniform liquid layer

Dispersion of Rayleigh-type surface waves is studied in a sandy layer under a uniform layer of homogeneous liquid lying over liquid-saturated porous solid half-space. The frequency equation in the form of a ninth-order determinant is obtained and evaluated. Special cases have been deduced and dispersion curves for the phase velocity and wave number have been plotted for a particular model.     

Coexistence of Superconductivity and Spin Density Wave (SDW) in Ferropnictide Ba<Subscript>1−<Emphasis Type="Italic">x</Emphasis></Subscript>K<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>Fe<Subscript>2</Subscript>As<Subscript>2</Subscript>

This work focuses on the theoretical investigation of the coexistence of superconductivity and spin density wave (SDW) in ferropnictide Ba_1?x K _x Fe₂As₂. By developing a model Hamiltonian for the system and by using quantum field theory Green’s function formalism, we have obtained mathematical expressions for superconducting transition temperature (T _C), spin density wave transition temperature (T _sdw), superconductivity order parameter (Δ_Sc), and spin density wave order parameter (Δ_sdw). By employing the experimental and theoretical values of the parameters in the obtained expressions, phase diagrams of superconducting transition temperature (T _C) versus superconducting order parameter (Δ_Sc) and spin density wave transition temperature (T _sdw), versus spin density wave order parameter (Δ_sdw) have been plotted. By combining the two phase diagrams, we have demonstrated the possible coexistence of superconductivity and spin density wave (SDW) in ferropnictide Ba_1?x K _x Fe₂As₂.     

Fatigue-life estimation of functionally graded materials using XFEM

The present work deals with the fatigue crack growth simulation of alloy/ceramic functionally graded materials (FGMs) using extended finite element method (XFEM). Various cases of FGM containing multiple inhomogeneities/discontinuities along with either a major edge or a center crack are taken for the purpose of simulation. The fatigue life of the FGM plate is calculated using Paris law of fatigue crack growth under cyclic loading. The effect of multiple inhomogeneities/discontinuities (minor cracks, holes/voids, and inclusions) on the fatigue life of cracked FGM plate is studied in detail. These simulations show that the presence of inhomogeneities/discontinuities in the domain significantly influences the fatigue life of the components.     

Approximated fuzzy logic controlled shunt active power filter for improved power quality

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic‐based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4‐rule approximated fuzzy logic controller (AFLC) is compared with 25‐rule FLC. A three‐phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non‐linear loads. The simulation results presented under transient and steady‐state conditions confirm that the proposed 4‐rule AFLC efficiently approximates the 25‐rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.     

Systematic approach for automated determination of parting line for die-cast parts

The parting line decision for die-cast parts is a non-trivial task, which depends upon a number of factors related to the part geometry and the die-casting process requirements. This is a crucial decision which not only affects the design and manufacturing of the die-casting die but the part manufacturing as well. Normally, a die-casting die-design expert invests a lot of time, effort and resources to take this decision, which affects the part manufacturing lead time and cost. A systematic approach for automated determination of the parting line for die-cast parts is presented in this paper. Unlike the previous systems, which consider part geometric factors only, the system proposed in this paper also considers the die-casting process requirements to determine the parting line in a systematic and automated manner. Here we discuss classification of the die-cast part surfaces, identification of undercuts and protrusions, identification of parting line regions, and determination of the parting line. The system generates a number of feasible parting lines in a given parting direction after applying the die-casting process requirements. Finally, the most suitable parting line is determined from the feasible parting lines considering the industry best practices. The results obtained from the system are similar to those of the industry. The proposed system would prove to be a major step towards automation of the die-casting die design, leading to design–manufacturing integration of the die-casting process.     

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.