A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hybrid search for faster production and safer process conditions in friction stir welding
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermo-mechanical model implemented in the FE-code, ANSYS. This thermo-mechanical model is then used in the aforementioned constrained MOO case where the two objectives are conflicting. Following this, two reasonable design solutions among those multiple trade-off solutions have been selected based on the cost and the quality preferences.