Multi Objective Optimization of Turning Process Using Grey Relational Analysis and Simulated Annealing Algorithm

Abstract:

Multi objective optimizing of machining processes is used to simultaneously achieve several goals such as increased product quality, reduced production time and improved production efficiency. This article presents an approach that combines grey relational analysis and regression modeling to convert the values of multi responses obtained from Taguchi method design of experiments into a multi objective model. The proposed approach is implemented on turning process of St 50.2 Steel. After model development, Analysis of Variance (ANOVA) is performed to determine the adequacy of the proposed model. The developed multi objective model is then optimized by simulated annealing algorithm (SA) in order to determine the best set of parameter values. This study illustrates that regression analysis can be used for high precision modeling and estimation of process variables.

• Periodical: Applied Mechanics and Materials (Volumes 110-116)
• Pages: 2926-2932
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.110-116.2926
• Online since: October 2011
• Authors: Farhad Kolahan, Reza Golmezerji, Masoud Azadi Moghaddam
• Keywords: Grey Relational Analysis (GRA), Multi-Objective Optimization (MOP), Simulated Annealing Algorithm, Turning

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