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The most important geometric parameter in the friction stir welding (FSW) tool design is the shoulder diameter, which is currently estimated by trial and error. Here, we report a combined experimental and theoretical investigation on the influence of shoulder diameter on thermal cycles, peak temperatures, power requirements, and torque during FSW of AA7075-T6. An optimum tool shoulder diameter is identified using a three-dimensional, heat transfer and materials flow model. First, the predictive capability of the model is tested by comparing the computed values of peak temperature, spindle power, and torque requirements for various shoulder diameters against the corresponding experimental data. The change in the values of these variables with shoulder diameter is correctly predicted by the model. The model is then used to identify the optimum tool shoulder diameter that facilitates maximal use of the supplied torque in overcoming interfacial sticking. The tool with optimum shoulder diameter is shown to result in acceptable yield strength (YS) and ductility.

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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.

Friction stir welding is not used for hard alloys because of premature tool failure. A scheme is created that exploits the physical three-dimensional heat and mass flow models, and implements them into a fast calculation algorithm, which, when combined with damage accumulation models, enables the plotting of tool durability maps that define the domains of satisfactory tool life. It is shown that fatigue is an unlikely mechanism for tool failure, particularly for the welding of thin plates. Plate thickness, welding speed, tool rotational speed, shoulder, and pin diameters and pin length all affect the stresses and temperatures experienced by the tool. The large number of these variables makes the experimental determination of their effects on stresses and temperatures intractable and the use of a well-tested, efficient friction stir welding model a realistic undertaking. An artificial neural network that is trained and tested with results from a phenomenological model is used to generate tool durability maps that show the ratio of the shear strength of the tool material to the maximum shear stress on the tool pin for various combinations of welding variables. These maps show how the thicker plates and faster welding speeds adversely affect tool durability and how that can be optimized.