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Novel correlations between process forces and void morphology for effective detection and minimization of voids during friction stir welding


 Sub-surface defects and material heterogeneities resulting from the friction stir welding (FSW) process have, to a certain degree, limited the wider industrial adoption of this solid-state welding process. However, in-process defect detection techniques for measuring welding quality have the potential to replace expensive and time-consuming post-process inspection of defects. Current in-process defect detection techniques rely on approaches that try to directly correlate the part-scale welding quality to defect formation, without a fundamental understanding of the underlying mechanics and materials physics that modulate defect evolution. In this work, we demonstrate an effective in-process numerical technique that uses process force signals to detect volumetric void formation and connect the variations in the force signals to interactions between the tool probe and the underlying material voids. Our approach relies on a high-fidelity FEA simulation of the FSW process, and on correlation of numerically obtained process force signals with the corresponding void structures. This correlation is obtained in the phase-space relating in-plane reaction forces on the tool to the tool rotation angle. We focus on the interactions of the tool geometry and tool motion with the surrounding material undergoing plastic deformation and deduce novel insights into various correlations of tool motion and void formation. Through this approach, we can identify tool-related process conditions that can be optimized to minimize void formation and demonstrate a force-based void monitoring method that links to the underlying plastic flow and defect structures during FSW.