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.     

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...     

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.     

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...     

Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization

This paper presents a hardware implementation of multilayer feedforward neural networks (NN) using reconfigurable field-programmable gate arrays (FPGAs). Despite improvements in FPGA densities, the numerous multipliers in an NN limit the size of the network that can be implemented using a single FPGA, thus making NN applications not viable commercially. The proposed implementation is aimed at reducing resource requirement, without much compromise on the speed, so that a larger NN can be realized on a single chip at a lower cost. The sequential processing of the layers in an NN has been exploited in this paper to implement large NNs using a method of layer multiplexing. Instead of realizing a complete network, only the single largest layer is implemented. The same layer behaves as different layers with the help of a control block. The control block ensures proper functioning by assigning the appropriate inputs, weights, biases, and excitation function of the layer that is currently being computed. Multilayer networks have been implemented using Xilinx FPGA "XCV400hq240." The concept used is shown to be very effective in reducing resource requirements at the cost of a moderate overhead on speed. This implementation is proposed to make NN applications viable in terms of cost and speed for online applications. An NN-based flux estimator is implemented in FPGA and the results obtained are presented     

Experimental Investigation of White Layer Thickness on EDM Processed Silicon Steel Using ANFIS Approach

Since the white layer thickness influences the surface quality of the machined specimens using electrical discharge machining process, the prediction of su     

Photoreduction of Cr(VI) in water using Bi2O3–ZrO2 nanocomposite under visible light irradiation

Chromium(VI) is a common heavy metal pollutant and extensively used in variety of industrial processes. In the present study, bismuth oxide–zirconium oxide nanocomposite (Bi₂O₃–ZrO₂) was synthesized to improve photoreduction of Cr(VI) under visible light irradiation. The synthesized photocatalyst was characterized by UV-visible-diffuse reflectance spectroscopy (UV-vis-DRS), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), Brunauer–Emmett–Teller (B.E.T) surface area analysis and photoluminescence spectroscopy (PL). Bi₂O₃–ZrO₂ was found to be more photoactive than Bi₂O₃, ZrO₂, TiO₂ and ZnO for the reduction of Cr (VI). The influences of various reaction parameters like the effect of catalyst concentration, initial Cr(VI) concentration and addition of inorganic salts on the photocatalytic activity have been investigated in detail. Meanwhile, the stability of Bi₂O₃–ZrO₂ was investigated by repeatedly performing Cr(VI) photoreducing experiments.     

Different commercial grades of aluminium as galvanic anodes in alkaline zincate solutions

The self corrosion and electrochemical properties, such as open circuit potentials, polarisation characteristics and anode efficiencies, of different grades of aluminiumviz., 2S, 3S, 26S and 57S were examined in 4 M NaOH, containing 0.01 to 0.6 M zinc oxide. From these studies, 3S and 57S aluminiums were found to be the most suitable anode materials, among the different grades of aluminium and 4 M NaOH solution containing 0.6 M zinc oxide was found to be the best electrolyte.     

Experimental Investigation of White Layer Formation on Machining Silicon Steel in PMEDM Process

Silicon - The silicon steel can be easily machined using electrical discharge machining process due to its higher strength. Since the white layer thickness and propoerties can affect the surface...