This Grant Opportunities for Academic Liaison with Industry (GOALI) award provides funding for the investigation and development of a real-time defect detection approach for the material joining process known as friction stir welding. The defect detection approach will use physics-based process and defect dynamic modeling to filter or condition real-time process measurements to significantly improve the reliability of detection. The research project will develop and validate the physics-based process and defect disturbance models through a combination of process-level system identification, using both computational and experimental data, and system level validation, including verification of real-time in-situ defect detection capabilities. The project will investigate the limitations and applicability of the overall defect detection approach to include the evaluation of the sensitivity to variations in defect size and defect type and the evaluation of the robustness of the approach in the presence of non-defect related process disturbances.
If successful, real-time weld defect detection would substantially reduce the total cost of friction stir welding by reducing or eliminating the need for costly post-process inspection work. In addition, the development and validation of process and defect formation models will improve our understanding of the complex solid-state friction stir welding process. The resulting reduction in cost and increase in process understanding would help accelerate its adoption as a joining process - providing the substantial economic, environmental, and energy conservation advantages that wide-spread adoption of friction stir welding and friction stir processing would bring. Finally, the project will have a strong training and mentoring focus by providing an integrated educational and research environment for graduate and undergraduate student researchers - including those in underrepresented groups through established University fellowship programs.
