Research Progress of Friction Stir Welding Process

Dear Colleagues,

Over the past three decades, since its inception, friction stir welding has emerged as one of the novel welding techniques and has shown enormous potential when it comes to joining similar and dissimilar materials. A significant amount of research has been carried out, and this has nearly perfected almost every major lightweight and heavier alloy system including, but not limited to, steel, aluminum, magnesium and titanium. The goal of the majority of the research, and rightly so, has been to perfect the processing conditions of the FSW process to render superior mechanical and metallurgical properties of the metal alloy systems. However, considering the major shift in the economy and immediate need to tackle the effects of global warming and climate change, two of the major manufacturing industries, automobile and aerospace, must shift their focus to lighter weight structures to reduce fuel consumption. In practice, FSW is still widely used for smaller structures and predominantly for lab testing specimens only. Given the dominance of ever-changing and self-improving artificial intelligence (AI) and several other emerging technologies, it is about time we take a step further into the realm of smarter and more efficient manufacturing.

The goal of this Special Issue is to encourage scientists to utilize AI and focus on predicting and/or possibly adapting better processing conditions to develop microstructure and processing conditions on a rather larger scale. Scientists are encouraged to submit their outstanding work towards addressing this issue, focusing on (but not limited to) the topics listed below:

• Adaptation and self-improvement of processing conditions in FSW;
• Utilizing the capabilities of calculation of phase diagram (CALPHAD) and phase field modelling (PFM) to predict the mechanical and metallurgical properties of the FSWed materials (similar and dissimilar alloys);
• Feasibility of FSW for monolithic structures;
• Properties-based material knowledge systems (MKS) to correlate FSW processes and microstructural evolution.

Dr. Kapil Gangwar
Guest Editor

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