| Abstract Scope |
Additive manufacturing (AM) enables spatially selective material deposition, making it well suited for repairing damaged components. However, existing AM repair workflows remain heavily manual, often requiring human-guided segmentation and localization of damage from scanned geometry, as well as expert oversight to generate component-specific repair toolpaths. This lack of automation limits responsiveness to new geometries and constrains the scalability of AM-based repair. This work introduces a novel methodology that leverages in-situ geometric measurements to automatically generate conformal multi-axis repair toolpaths. Given the target final geometry and localized 3D scan data collected by a robot, the method can identify the compromised regions of a damaged component. Then, with the kinematic flexibility of multi-axis robotic AM deposition, material is selectively targeted to fix the damaged component. The robotic repair is validated by its use in scanning, identifying damage, generating and executing toolpath for a robotic Material Extrusion (MEX) AM processing system. |