Optimization of turning using evolutionary algorithms
AbstractAdvanced manufacturing requires a powerful tool for reliable modeling and solving complex machining optimization problems. A non-conventional approach using evolutionary algorithms inspired by Darwinian findings about the evolution of the biological species and the survival of the fittest organisms (i.e. natural selection) is proposed in this paper. It is illustrated with an experiment of rough longitudinal turning. Genetic programming (GP) is used to develop the models of the tool life, the tangential cutting force component and the surface roughness considering the cutting speed, and the feed and the depth of cut as predetermined cutting parameters. Finally, the genetic algorithm (GA) is applied for their optimization.
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