Sivakasi, India

Mepco Schlenk Engineering College is an autonomous engineering school located in Sivakasi, Tamil Nadu, India. It was founded on 17 October 1984. It is an ISO9001:2008 Certified Institution. It is sponsored by the Mepco Schlenk Charities, a social welfare organization of the Metal Powder Company Limited, Thirumangalam and its German collaborator, Schlenk. The college was affiliated with Madurai Kamaraj University, Madurai till 2002 and later with Anna University, Chennai, but since 2012 it has been affiliated with Anna University of Technology, Chennai. Wikipedia.

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Prabaharan T.,Mepco Schlenk Engineering College, Sivakasi | Yang X.S.,Middlesex University
IEEE Transactions on Evolutionary Computation | Year: 2014

Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature. © 1997-2012 IEEE.

Prabaharan T.,Mepco Schlenk Engineering College, Sivakasi | Yang X.S.,Middlesex University
Applied Soft Computing Journal | Year: 2014

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics. © 2014 Elsevier B.V.

Vettivel S.C.,PET Engineering College | Selvakumar N.,Mepco Schlenk Engineering College, Sivakasi | Leema N.,Anna University
Materials and Design | Year: 2013

High-energy mechanical milling was used to mix Cu and W powders. Cylindrical preforms with initial preform density of 85% were prepared using a die and punch assembly. The preforms were sintered in an electric muffle furnace at 750 °C, 800 °C, 850 °C, and subsequently furnace cooled and then the specimens are hot extruded to get 92% preform density. Scanning Electron Microscope and X-ray diffraction observations used to evaluate the characteristics. The pore size reduction during extrusion was studied using Auto CAD 2010. Neural networks are employed to study the tribological behavior of sintered Cu-W composites. The proposed neural network model has used the measured parameters namely the weight percentage of tungsten, sintering temperature, load and sliding distance to predict multiple material characteristics, hardness, specific wear rate, and coefficient of friction. The predicted values from the proposed networks coincide with the experimental values. In addition, a relative study between the regression analysis and the networks revealed that the artificial neural networks can predict the tribological characteristics of sintered Cu and W composites better than regression polynomials within a very few percent error. © 2012 Elsevier Ltd.

Vengatesh R.P.,Mepco Schlenk Engineering College, Sivakasi | Rajan S.E.,Mepco Schlenk Engineering College, Sivakasi
Solar Energy | Year: 2011

This paper focuses on a novel approach to the prediction of Voltage-Current (V-I) characteristics of a Photovoltaic panel under varying weather conditions and also the modelling of hourly cloudless solar radiation to provide the insolation on a PV module of any orientation, located at any site. The empirical model developed in this study uses standard specifications together with the actual solar radiation and cell temperature. This proposed work develops a Matlab-Simulink model to generate solar radiation at any location and for any time of the year. A new model for V-I characteristics and maximum power operation of a Photovoltaic (PV) module is also presented, which aims to model the effect on V-I and P-V curves of varying climatic conditions. Moreover, this model has been implemented using the Matlab-Simulink and is used to investigate the effect of meteorological conditions on the performance of a PV module generator. Thus the combined model of cloudless solar radiation and the photovoltaic module provides a tool that may be loaded in the library for analysis purpose. It is found that the predicted solar radiation strongly agrees with the experimental data. © 2011.

Selvakumar N.,Mepco Schlenk Engineering College, Sivakasi | Vettivel S.C.,PET Engineering College
Materials and Design | Year: 2013

High-energy mechanical milling was used to prepare Cu and W nanopowders. Cylindrical preforms with initial theoretical density of 86% were prepared using a die and punch assembly. The preforms were sintered in a muffle furnace and subsequently furnace cooled and then the hot specimens were extruded to attain 93% theoretical density. Differential Scanning Calorimetry and Thermal Gravimetric Analyzer, four point probe tester, Scanning Electron Microscope and pin on-disc system were used to evaluate the thermal, electrical conductivity, characterization and tribological property of Cu-W composite respectively and using curve fitting method the respective polynomial and power law model were developed. The results indicated that the wear rate decreased with increasing applied load and sliding distance. The composites were tested at high sliding speed which exhibited high value of coefficient of friction. © 2012 Elsevier Ltd.

Priyadharsini S.S.,Anna University | Rajan S.E.,Mepco Schlenk Engineering College, Sivakasi
Applied Soft Computing Journal | Year: 2012

Electroencephalography (EEG) is the recording of electrical activity of neurons within the brain and is used for the evaluation of brain disorders. But, EEG signals are contaminated with various artifacts which make interpretation of EEGs clinically difficult. In this research paper, we use a soft-computing technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) for the removal of EOG artifact, combined EOG and EMG artifact. Improvement in the output signal to noise ratio and minimum mean square error are used as the performance measures. The outputs of the proposed technique are compared with the outputs of techniques such as neural network, based on ADALINE (Adaptive Linear Neuron) and adaptive filtering method, which makes use of RLS (Recursive Least Squares) algorithm through wavelet transform (RLS-Wavelet). The obtained results show that the proposed method could significantly detect and suppress the artifacts. © 2011 Elsevier B.V. All rights reserved.

Ravi S.,Mepco Schlenk Engineering College, Sivakasi | Karthikeyan A.,Mepco Schlenk Engineering College, Sivakasi
Physics Procedia | Year: 2014

We report a modified route to synthesize La0.7Sr0.3MnO3 nanoparticle with oxalic acid as chelating agent and oleic acid as surfactant at different calcination temperatures. The synthesized nanoparticles were characterized using XRD, SEM, FTIR and VSM. XRD confirms the formation of phase pure perovskite structure with particle size of about 20 nm. SEM and HRSEM reveal a well refined structure with respect to calcination temperature. FTIR confirms the formation of perovskite with broad peak around 520 cm-1. Magnetic study reveals that these nanoparticle with irregular structure exhibit ferromagnetic nature with different value of magnetization except for 500°C, which shows paramagnetic nature. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

Kailasanathan C.,Sethu Institute of Technology | Selvakumar N.,Mepco Schlenk Engineering College, Sivakasi
Ceramics International | Year: 2012

Recently, nano bio-composites have emerged as an efficient strategy to upgrade the structural and functional properties of synthetic bone grafts. Bioinert ceramics have attracted wide attention because of their biocompatibility. Novel composites of nano-hydroxyapatite/GEL with incorporation of bioinert ceramics like Al 2O 3, TiO 2 and ZrO 2 for different composites as a reinforcing phase to increase its mechanical properties was prepared. The nHAp with the size of 10-50 nm in diameter and 50-100 nm in length was uniformly distributed into GEL matrix to form the composite. It was found that the composite with a high ceramic content has good homogeneity and mechanical strength, which are close to the cancellous bone. An interconnected porous material with porosity of at least 74% was achieved by phase inversion method. The formation reaction of the nHAp/GEL/bioinert ceramic nanocomposite was then investigated via FT-IR, XRD, TG/DTA and SEM. The organic-inorganic interaction between HAp nano crystallites and GEL molecules were confirmed from FT-IR and TG/DTA. The compressive strength of bioinert ceramic reinforced nanocomposites scaffolds could high up to 13.15 MPa while those of nHAp/GEL were 4.87 MPa. The nano indentation technique was used to find nano hardness and fracture toughness was evaluated by Vickers indentation. © 2012 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Neurocomputing | Year: 2014

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. © 2014 Elsevier B.V.

Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Applied Soft Computing Journal | Year: 2015

In this paper, speed control of Brushless DC motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (η), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless DC motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless DC motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions. ©2015 Elsevier B.V. All rights reserved.

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