Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique

Abstract

Herein, a highly productive and defect‐free 3D‐printing system enforced by deep‐learning (DL)‐based anomaly detection and reinforcement‐learning (RL)‐based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image‐based anomaly detection implemented using a variational autoencoder DL model. Real‐time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics‐based engineering model. The RL‐simulated parameter set maximizes printing speed and minimizes deflection‐related failures throughout the 3D‐printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.

Keywords

• deep learning
• defect control
• reinforcement learning
• variational autoencoder; 3D printing