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Abstract
This study presents an approach to model the shear layer in bobbin tool friction stir welding. The proposed CFD model treats the material in the weld zone as a highly viscous non-Newtonian shear thinning liquid. A customised parametric solver is used to solve the highly non-linear Navier–Stokes equations. The contact state between tool and workpiece is determined by coupling the torque within the CFD model to a thermal pseudomechanical model. An existing analytic shear layer model is calibrated using artificial neural networks trained with the predictions of the CFD model. Validation experiments have been carried out using 4 mm thick sheets of AA 2024. The results show that the predicted torque and the shear layer shape are accurate. The combination of numerical and analytical modelling can reduce the computational effort significantly. It allows use of the calibrated analytic model inside an iterative process optimisation procedure.
