Performance Analysis of Pulse Generators on Residual Stress of Machined Silicon Steel Using the EDM Process

The cracks in the workpiece specimens can reduce the fatigue life of any machine components. Since the residual stress has a considerable amount of influence on determining crack formation over the machined surface, it is very essential to analyze the residual stress developed in any machining process. However, it is a very tedious process to compute the residual stress over the machined surface. In the present study, an endeavor has been made to measure and analyze the residual stress of machined silicon steel as a workpiece using the EDM process with different energy distribution. The nano-indentation method was used to compute the residual stress produced over the machined surface. From the experimental results, it was found that the uniform energy distribution has produced higher compressive residual stress owing to the tiny and uniform spark energy distribution. It has also been observed that the tool electrode has a considerable amount of influence on determining development of residual stress in the EDM process.     

Performance comparison of neural architectures for on-line flux estimation in sensor-less vector-controlled IM drives

The sensor-less vector-controlled induction motor drive requires accurate estimation of speed and flux. The speed estimation depends on the motor flux, which has to be measured or estimated. The flux measurement is difficult and expensive and hence generally estimated. Conventional voltage model equations for flux estimation encounter major drawbacks at low frequencies/speed. Neural network-based estimator provides an alternate solution for on-line flux estimation. The on-line flux estimator requires the neural network model to be accurate, simpler in design, structurally compact, and computationally less complex to ensure faster execution time in real-time implementation for effective control. This in turn, to a large extent, depends on the type of neural architecture. This paper investigates three types of neural architectures for on-line flux estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity, and execution time. The suitable neural architecture for on-line flux estimation is identified and the promising results obtained are presented.     

Thermal and electrical energy management in a PEMFC stack – An analytical approach

An analytical method has been developed to differentiate the electrical and thermal resistance of the PEM fuel cell assembly in the fuel cell operating conditions. The usefulness of this method lies in the determination of the electrical resistance based on the polarization curve and the thermal resistance from the mass balance. This method also paves way for the evaluation of cogeneration from a PEMFC power plant. Based on this approach, the increase in current and resistance due to unit change in temperature at a particular current density has been evaluated. It was observed that the internal resistance of the cell is dependent on the electrode fabrication process, which also play a major role in the thermal management of the fuel cell stack.     

Curing kinetics of medium reactive unsaturated polyester resin used for liquid composite molding process

The cure kinetics of medium reactivity unsaturated polyester resin formulated for Liquid Composite Molding process simulation was studied by Differential Scanning Calorimetry (DSC) under isothermal conditions over a specific range of temperature. For isothermal curing reactions performed at 100, 110, and 120°C, several influencing factors were evaluated using the heat evolution behavior of curing process. We propose two‐ and three‐parameter kinetic models to describe the cure kinetics of thermoset resins. Comparisons of the model solutions with our experimental data showed that the three‐parameter model was the lowest parameter model capable of capturing both the degree of cure and the curing rate qualitatively and quantitatively. The model parameters were evaluated by a non‐linear multiple regression method and the temperature dependence of the kinetic rate constants thus obtained has been determined by fitting to the Arrhenius equation. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning

Neural Computing and Applications - Machine learning (ML) and Deep learning (DL) methods are differently implemented with various decision-making abilities. Particularly, the usage of ML and DL...     

Lightweight Feistel structure based hybrid-crypto model for multimedia data security over uncertain cloud environment

The exponential rise in software computing and internet technologies have broadened the horizon of cloud computing applications serving numerous purposes like business processes, healthcare, finance, socialization, etc. In the last few years the increase is security breaches and unauthorized data access has forced industry to achieve computationally efficient and robust security system. The increase in multimedia data communication over different cloud applications too demands an efficient security model, which is expected to have low computational complexity, negligible quality-compromise and higher security robustness. Major conventional security-systems like cryptography and steganography undergo high computational overhead, thus limiting their potential towards cloud-communication where each data input used to be of large size and a gigantic amount of multimedia data is shared across the network. To alleviate above stated problems and enable a potential solution, in this paper a highly robust Lightweight Feistel Structure based Substitution Permutation Crypto Model is developed for multimedia data security over uncertain cloud environment. Our proposed model applies substitution permutation crypto concept with Feistel structure which performs substitution-permutation over five rounds to achieve higher confusion and diffusion. To retain higher security with low computation, we applied merely 64-bit block cipher and equal key-size. MATLAB based simulation revealed that the proposed lightweight security model achieves better attack-resilience even maintaining low entropy, high-correlation, and satisfactory computation time for multimedia data encryption. Such robustness enables our proposed security model to be applied for real-world cloud data security.

     

A novel NN based rotor flux MRAS to overcome low speed problems for rotor resistance estimation in vector controlled IM drives

This paper presents a new neural network based model reference adaptive system (MRAS) to solve low speed problems for estimating rotor resistance in vector control of induction motor (IM). The MRAS using rotor flux as the state variable with a two layer online trained neural network rotor flux estimator as the adaptive model (FLUX-MRAS) for rotor resistance estimation is popularly used in vector control. In this scheme, the reference model used is the flux estimator using voltage model equations. The voltage model encounters major drawbacks at low speeds, namely, integrator drift and stator resistance variation problems. These lead to a significant error in the estimation of rotor resistance at low speed. To address these problems, an offline trained NN with data incorporating stator resistance variation is proposed to estimate flux, and used instead of the voltage model. The offline trained NN, modeled using the cascade neural network, is used as a reference model instead of the voltage model to form a new scheme named as “NN-FLUXMRAS.” The NN-FLUX-MRAS uses two neural networks, namely, offline trained NN as the reference model and online trained NN as the adaptive model. The performance of the novel NN-FLUX-MRAS is compared with the FLUX-MRAS for low speed problems in terms of integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The proposed NN-FLUX-MRAS is shown to overcome the low speed problems in Matlab simulation.     

RF sputtering of ZnO films in Ar and Ar–H2 gas mixtures: Role of H incorporation in developing transparent conductive coatings

Fundamental and applied investigation of ZnO has been recently experiencing a renaissance due to its prospective use in various technological domains and, in particular, as transparent conductive oxide (TCO). In this respect, the present work aims to study the structural and physical properties of ZnO thin films deposited by RF sputtering in pure Ar and Ar–H₂ plasmas, at various concentrations (0–50%). The plasma chemical species were followed in function of the different gas mixture settings by optical emission spectroscopy (OES). X-ray photoelectron spectroscopy (XPS) and ATR-FTIR (Attenuated Total Reflection Fourier-Transformed Infrared) spectroscopy were used to study the bulk and surface chemical composition of the films, X-ray Diffraction (XRD) analysis allowed lattice structure and grain size determination while samples morphology was checked with a scanning electron microscope (SEM). The films were also characterized for their electrical and optical properties.The introduction of hydrogen in the plasma phase strongly affected the structural, chemical and physical properties of the films. In particular a pronounced change in the film electrical behavior was observed which become conductive when H is added in the gas mixture ([H₂] > 6%). The film transparency was on the other hand maintained. By combining XPS, ATR-FTIR and OES data we could correlate the established conductivity and its variations with intentional hydrogen incorporation in the crystal structure in the form of hydroxide species.     

Multi Criteria Decision Making of Abrasive Flow Oriented Process Parameters in Abrasive Water Jet Machining Using Taguchi–DEAR Methodology

Silicon - Owing to its ability of machining higher strength materials such as titanium alloy with less heat affected zone and higher material removal rate, abrasive water jet machining process is...