Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.