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Computational Intelligence in Optimization of Process Parameters in Turning Metals and Composites – A Review

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Abstract:

In the modern competitive scenario in manufacturing industries, producing products with low cost, less time and good quality are the ultimate goal of any manufacturer. To achieve the goal, several optimization tools are developed to optimize the process parameters of the machining process. Turning is one of the machining processes that cannot be avoided in any manufacturing industries. In this review, optimization of process parameters in turning process by computational intelligence (CI) paradigms for the past ten years is studied. Optimization by CI paradigms such as Fuzzy System (FS), Evolutionary Computation techniques Genetic Algorithm (GA), Swarm Intelligence including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN) etc., is considered. In turning process, surface roughness, tool wear, production time and cost are optimized.

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Periodical:

Applied Mechanics and Materials (Volumes 766-767)

Pages:

914-920

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.766-767.914

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Cite this paper

Online since:

June 2015

Authors:

Sivaraman V., S. Prakash

Keywords:

Ant Colony Optimization, Artificial Neural Network, Fuzzy System, Genetic Algorithm, Particle Swarm Optimization, Turning

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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