<ul><li>This work proposes a Graph Neural Network (GNN) modeling approach for predicting the resulting surface in a particle-based fabrication process.</li><li>The GNN model uses robotic arm trajectory features, printing process parameters, and a particle representation of the wall domain and end effector.</li><li>It acts as a simulator of the printing process and aims to generate the robotic arm trajectory and optimize the printing parameters.</li><li>The proposed model outperforms an existing benchmark model, demonstrating improved performance and error scaling.</li></ul>

Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing

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