Developing weak textured ultra-fine grained magnesium (Mg) alloys by friction stir processing (FSP)

Mg alloys after conventional rolling, extrusion and forging normally have strong deformed and/or recrystallised textures, which decreases formability and introduces strong anisotropy behaviour. The strength and formability trade-off is more evident in Mg due to its hexagonal close packed (HCP) crystal structure. Grain size has been recognised as a critical factor influencing nearly all aspects of properties. Grain refinement, surface chemistry and texture are recognised to be three key factors to determine corrosion resistance behaviour. Severe plastic deformation(SPD) methods were developed to produce ultra-fine grained (UFG) alloys to improve strength, ductility and corrosion resistance. However, most current SPD methods cannot produce industry-scale components and need a large investment in tool design to endure repetitive high loads. Although some developed SPD methods (e.g., accumulative roll bonding) can manufacture large size work-pieces, they cannot avoid the formation of strong basal texture as with conventional processing in Mg alloys, resulting in anisotropic mechanical and corrosion behaviour. Hence there is a key need for the exploitation of novel cost-effective SPD routes to produce novel UFG Mg alloys with weak texture.

In general, the friction stir zone microstructure of Mg alloys is not homogeneous, resulting in different mechanical properties in different areas. For instance, texture distribution varies significantly in different areas of the stir zone. Our previous work proved the texture can be manipulated by adding the suitable alloying elements and optimising processing window to activate preferable recrystallisation nucleate site, induce solute segregation or concurrent precipitation to change the grain growth behaviour and texture evolution. The student will work with our industrial project partner TWI to address the texture issue in FSP. More specifically, controlling the recrystallisation behaviour and texture evolution by optimising the FSW parameters will be the particular focus of this project. At the same time, the student will develop machine learning models. The preliminary results will be fed into a valid machine learning model so that the experimental cost will be largely reduced for optimising FSP parameters. We will deeply investigate the synergy between alloy compositions in the new alloys (designed by a postdoc in our team) and processing parameters, thereby developing UFG Mg alloys with weak texture and produce industrial-scaled components.