3D Concrete Printing (3DCP) has been gaining popularity in the past few years. Due to the nature of line-by-line printing and the slump of the material deposition in each extruded line, 3D printed structures exhibit obvious lines or marks at the layer interface, which affects surface finish quality and potentially affect bonding strength between layers. This makes it necessary to control the extrudate formation in 3DCP. However, it is difficult to directly analyse the extrudate formation process because the extrudate shape depends on many parameters. In this paper, a machine learning technique is applied to correlate the formation of the extrudate to the printing parameters using an Artificial Neural Network model. The training data for the model development was obtained from extrudates printed in 3DCP experiments. The performance of the trained model was experimentally validated and the predicted extrudate geometry resulting from the developed model showed good agreement to the actual extrudate geometry. Subsequently, the developed model was used to find proper nozzle shapes to produce designated extrudate geometries. Significant improvement on the printing quality was demonstrated using nozzle shapes generated from the model on 3D printed objects consisting a vertical wall, an inclined wall and a curved part.

在过去的几年中，3D混凝土印刷（3DCP）越来越受欢迎。由于逐行打印的性质以及每条挤出线中材料沉积的坍落度，3D打印的结构在层界面处显示明显的线或痕迹，这会影响表面光洁度质量并可能影响层之间的粘合强度。这使得必须控制3DCP中的挤出物形成。然而，由于挤出物的形状取决于许多参数，因此难以直接分析挤出物的形成过程。在本文中，使用了一种机器学习技术，使用人工神经网络模型将挤出物的形成与印刷参数相关联。从3DCP实验中印刷的挤出物中获得用于模型开发的训练数据。通过实验验证了训练模型的性能，并且从开发的模型得出的预测挤出物几何形状与实际挤出物几何形状具有很好的一致性。随后，使用开发的模型来找到合适的喷嘴形状以生产指定的挤出物几何形状。使用从模型生成的3D打印对象（包括垂直壁，倾斜壁和弯曲部分）生成的喷嘴形状，可以证明打印质量得到了显着改善。