🤖 AI Summary
This study addresses the deformation of continuous fiber-reinforced thermoplastic composites during robotic 3D printing, which arises from residual stress relaxation, drying, crystallization, and thermal stresses. To tackle this challenge, a hybrid modeling approach integrating physical mechanisms with data-driven techniques is proposed. The mechanical behavior of the prepreg is captured using a Kelvin–Voigt viscoelastic constitutive model, while a stabilized neural ordinary differential equation (Neural ODE) framework is introduced to describe the coupled drying and crystallization kinetics. Validated through dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC) experiments, and full-scale printing trials, the resulting model accurately reproduces real-world deformation phenomena and demonstrates strong generalization and robustness—even beyond the temperature ranges observed during training.
📝 Abstract
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.