Datta, R. ; Deb, K. (2009) A classical-cum-evolutionary multi-objective optimization for optimal machining parameters Proceedings of International Conference on Nature and Biologically Inspired Computing (NaBIC) . pp. 607-612.
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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...
Related URL: http://dx.doi.org/10.1109/NABIC.2009.5393425
Abstract
Optimal machining parameters are very important for every machining process. This paper presents an Evolutionary Multi-objective Genetic Algorithm based optimization technique to optimize the machining parameters (cutting speed, feed and depth of cut) in a turning process. The effect of these parameters on production time, production cost and surface roughness (which are conflicting to each other) are mathematically formulated. The non-dominated sorting genetic algorithm (NSGA-II) is used to get a Pareto-optimal front of the machining problem. The Pareto-optimal points are checked using ¿-constraint single objective GA as well as using a classical optimization (SQP) method. An analysis of the obtained points is carried out to find the useful relation between the objective function and variable values.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Proceedings of International Conference on Nature and Biologically Inspired Computing (NaBIC). |
| ID Code: | 81031 |
| Deposited On: | 03 Feb 2012 11:45 |
| Last Modified: | 03 Feb 2012 11:45 |
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