Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding

Abstract

Tool and workpiece temperatures, torque, traverse force and stresses on the tools are affected by friction stir welding (FSW) variables such as plate thickness, welding speed, tool rotational speed, shoulder and pin diameters, pin length and tool material. Because of the large number of these welding variables, their effects cannot be realistically mapped by experiments. Here, we develop, test and make available a set of five neural networks to calculate the peak temperature, torque, traverse force and bending and equivalent stresses on the tool pin for the FSW of an aluminium alloy. The neural networks are trained and tested with the results from a well tested, comprehensive, three-dimensional heat and material flow model. The predictions of peak temperature and torque are also compared with appropriate experimental data for various values of shoulder radius and tool revolutions per minute. The models can be used even beyond the range of training with predictable levels of uncertainty.