http://localhost:8080/xmlui/handle/123456789/2360 2026-06-14T05:26:00Z 2026-06-14T05:26:00Z Rao, R. V. Pawar, P. J. http://localhost:8080/xmlui/handle/123456789/2923 2020-12-31T09:51:11Z 2010-05-12T00:00:00Z

Title: Optimisation of process parameters of mechanical type advanced machining processes using a simulated annealing algorithm Authors: Rao, R. V.; Pawar, P. J. Abstract: The optimum selection of process parameters is essential for advanced machining processes as these processes incur high initial investment, tooling cost, operating and maintenance cost. This paper presents the results of optimisation of process parameters of mechanical type advanced machining processes using a simulated annealing algorithm. The results obtained are then compared with those obtained using a genetic algorithm. It is observed that simulated annealing algorithm has outperformed the genetic algorithm in the present work.

2010-05-12T00:00:00Z Pawar, P. J. Rao, R. V. Shankar, R. http://localhost:8080/xmlui/handle/123456789/2922 2020-12-31T09:43:14Z 2008-03-03T00:00:00Z

Title: Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm Authors: Pawar, P. J.; Rao, R. V.; Shankar, R. Abstract: The selection of optimum values of important process parameters of electrochemical machining processes such as the tool feed rate, electrolyte flow velocity, and applied voltage play a significant role in optimizing the measures of process performance. These performance measures generally include dimensional accuracy, tool life, material removal rate, and machining cost. In this paper, a particle swarm optimization algorithm is presented to find the optimal combination of process parameters for an electrochemical machining process. The objectives considered are dimensional accuracy, tool life, and the material removal rate subjected to the constraints of temperature, choking, and passivity. Both single- and multi-objective optimization aspects are considered. The results of the proposed algorithm are compared with the previously published results obtained by using other optimization techniques.

2008-03-03T00:00:00Z Pawar, P. J. Rao, R. V. Davim, J. P. http://localhost:8080/xmlui/handle/123456789/2921 2020-12-31T09:37:32Z 2009-06-01T00:00:00Z

Title: Multiobjective Optimization of Grinding Process Parameters Using Particle Swarm Optimization Algorithm Authors: Pawar, P. J.; Rao, R. V.; Davim, J. P. Abstract: Grinding is one of the very important machining operations in engineering industries. Optimization of grinding processes still remains as one of the most challenging problems because of its high complexity and non-linearity. This makes the application of traditional optimization algorithms quite limited. Hence, there is a need to apply most recent and powerful optimization techniques to get desired accuracy of optimum solution. In this paper, a recently developed nontraditional optimization technique, particle swarm optimization (PSO) algorithm is presented to find the optimal combination of process parameters of grinding process. The objectives considered in the present work are, production cost, production rate, and surface finish subjected to the constraints of thermal damage, wheel wear, and machine tool stiffness. The process variables considered for optimization are wheel speed, workpiece speed, depth of dressing, and lead of dressing. The results of the algorithm are compared with the previously published results obtained by using other traditional optimization techniques.

2009-06-01T00:00:00Z Pawar, P. J. Rao, R. V. http://localhost:8080/xmlui/handle/123456789/2914 2020-12-22T06:49:58Z 2009-08-16T00:00:00Z

Title: Grinding process parameter optimization using Authors: Pawar, P. J.; Rao, R. V. Abstract: Selection of machining parameters in any machining process significantly affects the production rate, quality, and cost of a component. This paper presents the multi-objective optimization of process parameters of a grinding process using various non-traditional optimization techniques such as artificial bee colony, harmony search, and simulated annealing algorithms. The objectives considered in the present work are production cost, production rate, and surface finish subjected to the constraints of thermal damage, wheel wear, and machine tool stiffness. The process variables considered for optimization are wheel speed, workpiece speed, depth of dressing, and lead of dressing. The results of the algorithms presented are compared with the previously published results obtained by using other optimization techniques

2009-08-16T00:00:00Z