Friction stir welding (FSW) is an exciting new solid-state welding process with the potential to advantageously impact many fabrication industries. Current take-up of the process by industry is hindered by lack of knowledge of suitable welding parameters for any particular alloy and sheet thickness. The FSW process parameters are usually chosen empirically and their success is evaluated via simple mechanical property testing. There are severe drawbacks with such methods of determining manufacturing conditions. These include indirect relationships between tensile and fatigue properties, particularly for welds, and a high probability of totally missing real optimized conditions. This research is therefore undertaken as a first step in providing information that will assist manufacturing industry to make sound decisions with respect to selecting FSW parameters for weldable structural alloys. Some of the key issues driving material selection for manufacturing are weld quality in terms of defects, fatigue strength and crack growth, and fracture toughness. Currently a very limited amount of data exists regarding these mechanical properties of FSW welds, and even less information exists regarding process parameter optimization. This is due to the mechanical microstructural complexity of the process and the relatively large number of process parameters (feed, speed, force and temperature) that could influence weld properties. In order to advance predictive understanding and modeling for FS welds, it is necessary to develop force and energy based models that reflect the underlying nature of the thermo-mechanical processes that the material experiences during welding. This project aims at determining the influence and effect of Friction Stir Welding process control parameters on the microstructure of the thermo-mechanically affected zone, the defect population in the weld nugget, hardness, residual stresses, tensile and fatigue performance of 6 mm plate of 5083-H321 aluminium alloy, which is known to be susceptible to planar defect formation. Welds were made with a variety of process parameters (that is feed rate and rotational speed) to create different rates of heat input. Forces on the FSW tool (horizontal and vertical), torque and tool temperature were measured continuously during welding from an instrumented FSW tool. Detailed information on fatigue performance, residual stress states, microstructure, defect occurrence, energy input and weld process conditions, were investigated using regression models and contour maps which offer a unique opportunity to gain fundamental insight into the process-structure-property relationships for FS welds. Weld residual strains have been extensively measured using synchrotron X-ray diffraction strain scanning to relate peak residual stresses and the widths of the peak profiles, taken from a single line scan from the mid depth of the FS welds, with the weld process conditions and energy input into the welds. Several residual stress maps were also investigated. The optical and scanning electron microscope were used to determine the type of intrinsic defects present in the FSW fatigue and tensile specimens. Vickers hardness measurements were taken from the mid depth of the welds and were compared with the weld input parameters. The main contribution of this thesis is as follow: (i) the relationship between input parameters and process parameters; (ii) the relationship between input weld parameters (that is feed rate and rotational speed) and process parameters (that is vertical downwards force Fz, tool temperature, tool torque and the force footprint data), energy input and tensile strength, fatigue life and residual stresses to obtain regions of optimum weld conditions; (iii) identification of the defects present in FSW, their relationship with process parameters and their effect on tensile strength and fatigue life; and (iv) the usefulness of the real time process parameter monitoring automated instrumented FSW tool to predict the mechanical properties of the welds.

Document Type


Publication Date