Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

Abstract

The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.