This work proposes a Graph Neural Network (GNN) modeling approach to predict the resulting surface from a particle based fabrication process. The latter consists of spray-based printing of cementitious plaster on a wall and is facilitated with the use of a robotic arm. The predictions are computed using the robotic arm trajectory features, such as position, velocity and direction, as well as the printing process parameters. The proposed approach, based on a particle representation of the wall domain and the end effector, allows for the adoption of a graph-based solution. The GNN model consists of an encoder-processor-decoder architecture and is trained using data from laboratory tests, while the hyperparameters are optimized by means of a Bayesian scheme. The aim of this model is to act as a simulator of the printing process, and ultimately used for the generation of the robotic arm trajectory and the optimization of the printing parameters, towards the materialization of an autonomous plastering process. The performance of the proposed model is assessed in terms of the prediction error against unseen ground truth data, which shows its generality in varied scenarios, as well as in comparison with the performance of an existing benchmark model. The results demonstrate a significant improvement over the benchmark model, with notably better performance and enhanced error scaling across prediction steps.

翻译：本研究提出了一种基于图神经网络（GNN）的建模方法，用于预测基于颗粒的制造工艺所生成的表面。该工艺涉及利用机械臂在墙面上进行基于喷涂的水泥基抹灰打印。预测计算利用了机械臂轨迹特征（如位置、速度和方向）以及打印工艺参数。所提出的方法基于墙面域和末端执行器的颗粒表示，从而允许采用基于图的解决方案。该GNN模型采用编码器-处理器-解码器架构，并利用实验室测试数据进行训练，其超参数通过贝叶斯方案进行优化。该模型旨在作为打印过程的模拟器，并最终用于生成机械臂轨迹和优化打印参数，以实现自主抹灰工艺。所提出模型的性能通过对比未见地面真实数据的预测误差进行评估，结果显示其在多种场景下的泛化能力，并与现有基准模型的性能进行了比较。结果表明，相较于基准模型，该模型取得了显著改进，在预测步长上表现出更优的性能和更强的误差缩放能力。