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TECHNICAL PAPERS

Optimization of Surface Grinding Operations Using Particle Swarm Optimization Technique

[+] Author and Article Information
P. Asokan, G. Prabhaharan

Department of Production Engineering,  National Institute of Technology, Trichy 620 015, India

N. Baskar1

School of Mechanical Engineering, Shanmugha, Arts Science Technology and Research Academy,  (SASTRA) Deemed University, Thanjavur 613 402, Indiabaskarnaresh@yahoo.co.in

K. Babu

School of Mechanical Engineering, Shanmugha, Arts Science Technology and Research Academy,  (SASTRA) Deemed University, Thanjavur 613 402, India

R. Saravanan

Department of Mechanical Engineering, J.J. College of Engineering and Technology, Trichy 620 009, India

1

To whom correspondence should be addressed.

J. Manuf. Sci. Eng 127(4), 885-892 (Jan 11, 2005) (8 pages) doi:10.1115/1.2037085 History: Received June 17, 2004; Revised January 11, 2005

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flow chart of the PSO algorithm

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Figure 2

Sample of the program

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Figure 3

Graphs showing the variations of CT, WRP (or) Ra, and COF w.r.t. iteration number

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Figure 4

Graphs showing the positions of the particles (randomly generated) at I iteration

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Figure 5

Graphs showing the positions of the particles at the 50th iteration

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Figure 6

Graphs showing the positions of the particles at 100th iterations

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