Machine Learning-Based Online Monitoring and Closed-Loop Controlling for 3D Printing of Continuous Fiber-Reinforced Composites
Xinyun Chi, Jiacheng Xue, Lei Jia, Jiaqi Yao, Huihui Miao, Lingling Wu, Tengfei Liu, Xiaoyong Tian, Dichen Li
Abstract
Ensuring the consistent mechanical performance of three-dimensional (3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing . The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors, resulting in defects and inconsistent product quality, particularly in unmanned long-term operations or printing in extreme environments. To address these issues, we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process. Real-time printing image data were captured and analyzed using a well-trained neural network model, and a real-time control module-enabled closed-loop feedback control of the flow rate was developed. The neural network model, which was based on image processing and artificial intelligence, enabled the recognition of flow rate values with an accuracy of 94.70 %. The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites, with three to six times improvement in tensile strength and elastic modulus , demonstrating the effectiveness of the strategy. This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites, and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.