Surrogate-based Evolutionary Optimization for Friction Stir Welding (2016)
Cem C Tutum, Shaayaan Sayed and Risto Miikkulainen
Friction Stir Welding (FSW) is an innovative manu- facturing process, which is used to join two pieces of metal with frictional heating and plastic deformation due to stirring action. Melting is avoided during the process, therefore problems related to microstructure phase transformation (i.e., cooling from the liquid phase) are avoided. The temperature distribution in the weld zone, as a function of the heat generation, highly affects the evolution of the residual stresses in the work piece, hence the performance of the final product. Therefore, thermal models play a crucial role in detailed analysis and improvement of this process. In this study, a previously developed and validated three- dimensional steady state thermal model of FS welding of AA2024- T3 plates has been used for evaluating the quality of the candidate solutions. It should be noted that this is a computationally expen- sive model and closed form formulations (i.e. analytical equations) for the underlying physics are not available, which forces us to use them sparingly during the optimization procedure. A mathematical correlation model, a surrogate in other words, is iteratively constructed to replace the FSW simulations and guide the search towards feasible and promising regions. A new surrogate-based optimization algorithm named EICTS, Expected Improvement with Constrained Tournament Selection has been developed. The striking difference of EICTS from other surrogate based constrained optimization methodologies that it needs to construct only two surrogates, i.e. one for the objective function and another one to handle all constraint functions (i.e., instead of approximating each of them individually). EICTS is first tested on some well-known engineering problems with multiple constraints and finally on the FSW problem briefly mentioned above. Its runtime and convergence performances are compared with EIPF (Expected Improvement with Probability of Feasibility) method and found very promising.
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In Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2016), pp. 8 pages, Vancouver, BC, Canada, July 2016.
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Risto Miikkulainen Faculty risto [at] cs utexas edu
Cem C Tutum Research Scientist tutum [at] cs utexas edu