About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Symposium
|
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
Collision-Aware Topology Optimization (CATO) for Robotic-Enabled Additive Manufacturing |
| Author(s) |
Conner Petru, Ronnie Stone, Arya Haria, Zhenghui Sha |
| On-Site Speaker (Planned) |
|
| Abstract Scope |
Topology optimization (TO) is a method for generating designs that satisfy constraints while pursuing specific objectives (e.g., minimizing weight). Existing TO workflows typically focus on structural performance and manufacturability for traditional 3D printing, disregarding robot-enabled AM and assembly. As a result, many automated tasks still require human intervention to manage robot-environment interactions. One such scenario is robotic arm-enabled fused deposition modeling (FDM), which introduces complex kinematics and collision geometries generally not considered in TO. This requires a TO framework that not only adapts to the component’s fabrication process (e.g., additive), but also to its automation environment. This paper introduces Collision-Aware Topology Optimization (CATO) for robotic-enabled additive manufacturing, a new TO pipeline that incorporates a digital twin of the operating environment, providing collision feedback to ensure that the generated design can be feasibly manufactured and assembled using the robotic system. By analyzing the performance of this pipeline for a robotic-enabled fabrication task, we investigate how TO can leverage simulated feedback for future autonomous factories. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |