Multiobjective Optimization of Grinding Process Parameters Using Particle Swarm Optimization Algorithm
- 28 May 2010
- journal article
- research article
- Published by Informa UK Limited in Materials and Manufacturing Processes
- Vol. 25 (6), 424-431
- https://doi.org/10.1080/10426910903124860
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.Keywords
This publication has 17 references indexed in Scilit:
- Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 SteelMaterials and Manufacturing Processes, 2009
- Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithmProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2008
- Identification of Constitutive Material Model Parameters for High-Strain Rate Metal Cutting Conditions Using Evolutionary Computational AlgorithmsMaterials and Manufacturing Processes, 2007
- RETRACTED: Optimization of surface grinding operations using a differential evolution approachJournal of the American Academy of Dermatology, 2007
- Comparison among five evolutionary-based optimization algorithmsAdvanced Engineering Informatics, 2005
- Combined Taguchi and dual response method for optimization of a centerless grinding operationJournal of the American Academy of Dermatology, 2003
- A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operationsInternational Journal of Machine Tools and Manufacture, 2002
- Applications of Artificial Intelligence in GrindingCIRP Annals, 1994
- Micro-computer-based optimization of the surface grinding processJournal of the American Academy of Dermatology, 1992
- Adaptive Control Optimization of GrindingJournal of Engineering for Industry, 1981