Hybrid evolutionary multi-objective optimization and analysis of machining operations

Deb, Kalyanmoy ; Datta, Rituparna (2011) Hybrid evolutionary multi-objective optimization and analysis of machining operations Engineering Optimization, 44 (6). pp. 685-706. ISSN 0305-215X

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Official URL: http://www.tandfonline.com/doi/abs/10.1080/0305215...

Related URL: http://dx.doi.org/10.1080/0305215X.2011.604316


Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining process. In this article, the use of an EMO algorithm and a suitable local search procedure to optimize the machining parameters (cutting speed, feed and depth of cut) in turning operations is described. Thereafter, the efficiency of the proposed methodology is demonstrated through two case studies-one having two objectives and the other having three objectives. Then, EMO solutions are modified using a local search procedure to achieve a better convergence property. It has been demonstrated here that a proposed heuristics-based local search procedure in which the problem-specific heuristics are derived from an innovization study performed on the EMO solutions is a computationally faster approach than the original EMO procedure. The methodology adopted in this article can be used in other machining tasks or in other engineering design activities.

Item Type:Article
Source:Copyright of this article belongs to Taylor and Francis Group.
Keywords:Multi-objective Optimization; NSGA-II; ε-constraint Method; Local Search; Hybrid Algorithm; Machining Parameters; Innovative Design Principles
ID Code:80998
Deposited On:02 Feb 2012 15:04
Last Modified:18 Jun 2012 10:12

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