Informatics Publishing Limited

N. Baskar ¹, A. D. Bharath ²

Affiliations
1 Sri Ramakrishna Mission Vidyalaya College of Arts and Science College Coimbatore, Tamilnadu, IN
2 Sri Ramakrishna Mission Vidyalaya College of Arts and Science College Coimbatore, Tamilnadu, IN

Abstract

In today’s life we are belongs to internet and its websites. It means websites are unique role in every department and which its help to gather information such as e-learning and many more. As the reason of many users the network traffic raised with short duration of time. As per reason traffic has to distribute equally. So we need valuable performance and better accessibility it wants more severs. On the role of load balancing concepts are help to develop high throughput and its algorithm help to control overloaded. By the learning of load balancing and its stages, which we are decided the concept of dynamic algorithm was consider better accessibility. Study of load balancing algorithm and its limitation we discover the dynamic technique was good accessibility but that is not in static technique. So on the whole dynamic load balancing gives better availability towards users. Here one valuable solution discover by using dynamic concepts and its algorithms.

Keywords

Dynamic Load Balancing, Load Balancing, Static Load Balancing.

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S. Bharathi Raja ¹, N. Baskar ²

Affiliations
1 School of Mechanical Engg, Shanmugha Arts, Science, Technology & Research Academy (SASTRA) University, Thanjavur, IN
2 Dept. of Mechanical Engg., M.A.M College of Engg., Tiruchirappalli, IN

Abstract

In this paper, the problem of determining optimum machining conditions for SiC grinding subjected to six different cutting constraints by Differential Evolution Technique (DET) is investigated. The optimum machining parameters namely feed and depth of cut are obtained to yield maximum maternal removal rate. The physical constraints are taken as feed, depth of cut, grain size of the grinding wheel, grain density of the grinding wheel, surface roughness and surface damage. In this work, the mathematical model proposed by Anne Venugopal et.al. is adopted and compared the result with the literature.

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T. Panneerselvam ¹, G. Karthikeyan ¹, N. Baskar ¹, R. Sivasankar ², P. Asokan ²

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy (SASTRA), Deemed University, Thanjavur-613402, Tamilnadu, IN
2 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, Tamilnadu, IN

Abstract

Machining parameters play a significant role for successful and efficient machining operations. Therefore, it is required to use the optimal parameters, especially for NC/CNC machines. Optimization of machining parameters can be defined as the appropriate selection of economical machining parameters in order to achieve maximum profit rate or minimum production cost or maximum production rate. The aim of this work is to maximize the profit rate by an optimization technique. Researchers have used many optimization techniques to solve the various engineering problems. This paper describes effectiveness and utilization of an optimization system, called Ants Colony Algorithm, for multi tool milling operations. The Ants Colony Algorithm based Optimization procedure evaluates the unit production cost, unit production time and maximum profit rate. The machining parameters such as optimal number of passes, cutting speed, feed, and depth of cut, are subjected to the constraints-maximum power, surface finish and cutting force. The Ants Colony System is a new kind of co-operative search procedure inspired by the foraging behavior of colonies of real ants. The problem has been solved by Ants Colony Algorithm with an example taken from the literature. The result is obtained through MATLAB software and it is compared with other methods.

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M. Sivakumar ¹, J. Alex Ananda Raj ¹, N. Baskar ¹

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy (SASTRA, Deemed University), Thanjavur, Tamil Nadu - 613 402, IN

Abstract

Optimization of machining parameters is an important step in machining optimization for operating CNC machines. A human process planner selects the proper machining parameters from the handbook or his own experience based on the part geometry, technological requirement, the machine tool, cutting tool material and work piece material. For optimization of a machining process, any of the following criteria may be used

• Maximum profit rate

• Minimum production time.

The success of the machining operation will depend on the selection of machining parameters. Some researchers optimized machining parameters by using only one variable without considering any constraints. However, it is very clear that we can obtain the real optimum values of machining parameters only after considering all the variables and constraints simultaneously. In this paper, a turning operation has been considered. The machining parameters such as depth of cut, speed and feed are obtained using a Genetic Algorithm (GA), to yield minimum total production cost which consider technological constraints such as allowable speed, feed, surface finish, tool wear, temperature, tolerance and work piece rigidity. The method proposed in the present work based on the Genetic Aigorithm(GA) always yields minimum production time and minimum production cost. This approach can be easily modified for other machining operation.

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N. Baskar ¹, P. Asokan ², R. Saravanan ³, G. Prabhaharan ²

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy, Thanjavur-613402, IN
2 Department of Production Engineering, Regional Engineering College, Tiruchirappalli-620015, IN
3 Department of Mechanical Engineering, J.J. College of Engineering & Technology, Tiruchirappalli-620009, IN

Abstract

In metal cutting process, cutting conditions have an influence on reducing the production time and cost. The variables affecting the economics of machining operations are numerous and include machine tool capacity, cutting conditions of velocity, feed rate and depth of cut. This paper describes a procedure to calculate the machining conditions for milling operations according to maximum profit rate as the objective function. In this work optimization procedures based on the Memetic Algorithm were developed for the optimization of machining parameters for multi-tool milling operation. An example has been presented at the end of the paper to give clear picture from the application of the system and its efficiency The results are compared and analyzed with Method of feasible direction and Handbook recommendations.

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K. Babu ¹, N. Baskar ¹, P. Asokan ², R. Saravanan ³

Affiliations
1 School of Mechanical Engineering, Shanmuga Arts Science Technology and Research Academy, Thanjavur-613402, IN
2 Department of Production Engineering, National Institute of Technology, Trichy, IN
3 Department of Mechanical Engineering, J.J College of Engineering and Technology, Trichy, IN

Abstract

A Simulated Annealing (SA) based optimization procedure has been developed to optimize grinding conditions viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using multi-objective function model with a weighted approach for surface grinding process. The procedure evaluates the production cost, production rate and surface finish for optimum grinding conditions, subjected to thermal damage, wheel-wear parameter, machine-tool stiffness and surface finish or production rate constraints. A computer program written in Visual C++ has been developed for optimization computations. The program prompts the user to input or modify all the constants related to the grinding operations. All the constants used here are default values, unless over-written by the user. User can also alter the specific Input values to perform sensitivity analysis of the relative contributions of grinding parameters to the weighted objective function. Simulated Annealing (SA) optimization is illustrated with an example and the optimum results are compared with Quadratic programming and Genetic Algorithm techniques.

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