Automated structural repair based on continuous carbon fiber reinforced plastic 3D printing and online model reconstruction

Abstract

Additive manufacturing of continuous carbon fiber reinforced plastic (CCFRP) has gained increasing prominence in structural repair through material accumulation. However, the repairing process entails laborious preliminary tasks, such as positioning, model reconstruction, and data input. In this article, computer vision is integrated into a CCFRP 3D printing device and a printing path‐selecting strategy is proposed to automate the repair process with accuracy and efficiency. An irregularly damaged structure is placed arbitrarily on the printing platform. Subsequently, the damaged area is extracted by semantic segmentation to construct a 3D model. The printing paths including zig‐zag and contour offset infilling for an irregular damage area are selected, according to the prediction of flexural modulus based on the finite element method. Finally, an in‐situ composite printing is conducted based on coaxial extrusion principle. The results show that the intersection over union (IOU) of regular damaged areas by the applied pyramid scene parsing network (PSPNet) segmentation model is 0.81. Semantic segmentation is more robust than conventional feature extraction based on filtering methods. The appearance of the repaired structure is consistent with that of the surrounding structure. The combination of CV and additive manufacturing technology presents a new way to simplify the repair process and improve repair effects.Highlights An automated repairing system for irregular damaged areas is established. Deep learning for semantic segmentation is integrated into 3D printing. Finite element submodule is introduced to select optimal printing paths. The CCFRP 3D printing is introduced to surface defect repairing.