| Abstract Scope |
Robotic directed energy deposition (DED) enables larger work volumes and faster material deposition than other additive manufacturing processes. Its productivity can be further enhanced by coordinating multiple robots. However, DED’s higher energy input causes non-uniform temperature distributions and part distortion, while nonprinting time, including interpass cooling and air travel, compromises the overall throughput. This work proposes a Scalable Multihead Assignment and Routing using Thermal- and Time-optimized (SMARTT) toolpath algorithm for dual-robot DED systems. The framework first develops a graph-based thermal simulation model to approximate the temperature distributions for optimization. Then, toolpath optimization, considering temperature uniformity, print time minimization, and collision avoidance, is applied to optimize the toolpath sequence of the two robotic print heads. Simulation shows that the proposed SMARTT toolpath can achieve superlinear speedup and improve temperature uniformity, compared to a single-robot benchmark, demonstrating its potential to enhance productivity and quality of robotic DED. |