Feedstock deformation during 3D printing of continuous fiber composites is a critical challenge in path planning and a main driver in the generation of manufacturing defects. The proposed work addressed the feedstock deformation during the deposition through several experimental and numerical pathways. The experimental setups and numerical simulations are used to identify the main driving phenomena in the deformation of feedstock through residual stress relief and drying, crystallization, and thermal stresses. A hybrid physics-based and data-driven modeling effort is performed, using Kelvin-Voigt viscoelastic modeling of the composite prepregs and a stabilized neural ODE for the modeling of drying and crystallization. The identified hybrid models from DMA and DSC experiments are used in robotic 3D printing to validate the deposition of a composite prepreg in real printing settings. The results show the ability of the model to reproduce the prepreg behavior far above the temperature used in the training, showcasing its robustness and generalization capability.

翻译：连续纤维复合材料3D打印过程中的丝材变形是路径规划中的关键挑战，也是制造缺陷产生的主要诱因。本研究通过多种实验与数值方法，探究了沉积过程中的丝材变形问题。利用实验装置和数值模拟，识别出残余应力释放与干燥、结晶及热应力等主导丝材变形的核心物理机制。采用基于物理机理与数据驱动的混合建模方法，通过Kelvin-Voigt粘弹性模型对复合预浸料进行建模，并采用稳定化神经常微分方程模拟干燥与结晶过程。基于动态力学分析（DMA）和差示扫描量热法（DSC）实验所辨识的混合模型被应用于机器人3D打印，在实际打印条件下验证了复合预浸料的沉积行为。结果表明，该模型能够重现远高于训练温度条件下的预浸料行为，展现了其鲁棒性与泛化能力。