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Articles by V. Selladurai
Total Records ( 5 ) for V. Selladurai
  Sathyabalan P. , V. Selladurai and P. Sakthivel
  Problem statement: The reinforcements added to an alloy lead to variation in properties. The content and size of the reinforcement influences the properties of composites. Very little research has been carried out in hybrid composites. Work on hybrid LM6 aluminium alloy metal matrix composites (MMC) with flyash and SiC has been initiated here. The effect of the four parameters, size and weight of the reinforcements on the hardness and wear loss has been studied. Approach: Artificial neural networks, from the artificial intelligence family, is a type of information processing system, based on modeling the neural system of human brain. The effect of the parameters was investigated using ANN. Central composite rotatable method of design of experiments was used to arrive at the combination and the number of specimens. The specimens were prepared using the liquid metallurgy route and tested. Pin-on-disc apparatus was used for determining wear. Rockwell hardness on C scale was determined. The data from the experiments were used for training and testing the network. Results: The accuracy in ANN prediction was appreciable with the error estimated for wear loss and hardness being less than 2%. Conclusions/Recommendations: The ANN prediction is quick and economical way of estimating the properties.
  S. Nandhakumar , V. Selladurai and S. Sekar
 

Problem statement: An error minimization in robot arm dynamics improves operations andperformance of production systems. Many contributions have been made in area of robot dynamicssince the earliest study more than two decades, but, a number of researchers are still contributingvarious principles and new techniques for the best use of robots in reality, especially in the field ofindustry, as this field of study is inexhaustible. This study attempted to analyze the performance of anindustrial robot by comparing solutions obtained using RK method and Single-Term Haar WaveletSeries (STHWS) method. Exact solution of system of equations representing arm model of a robot hadbeen compared with corresponding discrete solution at different time intervals. Absolute error betweenexact and discrete solutions had also been determined to suggest the method of improving performanceof a robot.
Approach: Haar wavelet had been applied extensively for signal processing incommunications and proved to be a useful mathematical tool for dynamical systems. In this study,STWHS method had been used for solving differential equations. Result had been obtained andcompared with exact solutions.
Results: Error had been compared by exact solutions, RK and STHWSsolutions were reported for non-singular systems and estimated as almost zero. The validation hadbeen carried out with reference to earlier research output appeared in this field of study.
Conclusion/Recommendations:For robot arm model selected for study, solution obtained bySTHWS was found to be accurate from results.

  S. Venkatachalam , C. Arumugam , K. Raja and V. Selladurai
  Any manufacturing system has to attain key performance measures for its successful operation. Quality Function Deployment (QFD) is to convert the customer requirements into quality characteristics and develop a schedule for the jobs by systematically deploying the relationships between the due date and the completion time by adopting the just in time concept. Non-traditional optimization technique such as Neural Network (NN) technique provides a complete solution methodology for solving the shop floor scheduling problems. The problem considered in this study is to schedule different number of jobs on parallel machines with the objective of reducing the multiple objectives such as the earliness, the tardiness and the completion time of the jobs. All the objectives have been assigned with weights so that the priority of the objectives could be varied. It has been found that the proposed method simultaneously reduces all the performance measures considerably, thereby outperforming the existing heuristics.
  R. Sivaprakasam and V. Selladurai
  Cellular Manufacturing System (CMS) is an application of Group Technology (GT) in which similar parts and machines are grouped into part families and machine cells. In this study, a metaheuristic called Memetic Algorithm (MA) is introduced to solve the machine cell formation problem. This study is conducted to minimize the intercellular movement of parts known as exceptional elements. MA is incorporated using Genetic Algorithm (GA) and Tabu Search (TS) Algorithm. In the MA approach, local optimization (TS) is applied to each newly generated offspring at the end of genetic algorithm. The MA is tested on a number of problems of various sizes and its performance is evaluated. The results obtained by MA are highly comparable with an objective obtained by Metaheuristics GA, TS and there is a considerable reduction in computational effort.
  P.V. Senthiil , V. Selladurai and R. Rajesh
  This study introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.
 
 
 
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