Process physics-based quality criteria and online monitoring algorithm were developed in this research by considering in-situ force and torque signals which were further linked to the joint structure and strength behavior. Thin joints consisting of one Cu layer and three Al layers were manufactured by micro friction stir blind riveting (μFSBR) process, where a micro rivet rotating at high speed traveled through the thickness of stacked layers to form a point joint by frictional heat and mechanical interlocking. In this research, μFSBR joints were manufactured with different levels of process parameters (spindle speed, feed rate and stacking sequence of Cu layer), while real-time data of penetration force and torque was collected. The joint quality and strength behavior were evaluated through load-displacement curves, in which two joint integrity scenarios, “no initial load drop” and “initial load drop” were observed. The defined scenarios were distinguished quantitatively through proposed process-physics based quality indices, namely, force-torque synchronization parameter and stirring time ratio derived from the real-time data. It was demonstrated that the defined quality indices successfully diagnosed the joint integrity. Furthermore, an online monitoring algorithm based on the pattern recognition protocol of force signals was formulated, in which a “pseudo sinusoidal” pattern was typified for “initial load drop” scenarios contrary to the sinusoidal pattern of “no initial load drop” cases. The developed algorithm was validated through a cross-sectional analysis where defects were observed at the occurrence of a “pseudo sinusoidal” pattern. In addition, the contact conditions were dominant with three stages, i.e. sliding, stick-slip and sticking, which were demarcated by comparing the normalized force and torque curves.
All Science Journal Classification (ASJC) codes
- Ceramics and Composites
- Computer Science Applications
- Metals and Alloys
- Industrial and Manufacturing Engineering