A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling

Abstract

Additive manufacturing has been increasingly applied. As one of the most commonly used technologies, fused deposition modeling (FDM) still faces the challenge of instable performance. The appearance of the printed part is an important feature to assess its quality. As FDM processes usually take a long time, it is very important to timely identify the defects to avoid unnecessary waste of time and cost. At current stage, this identification work is usually done by the operators. However, it is difficult to realize continuous monitoring for multiple printers and identify surface defects shortly. With the advanced artificial intelligence techniques, a vision-based adaptive monitoring system is proposed in this article to achieve online monitoring with high efficiency and accuracy. The system design is introduced for common FDM printers that allows one camera to move to different angles and capture the images of the printing part. A heuristic algorithm is then proposed to achieve adaptive shooting position planning according to the part geometries. Furthermore, a convolutional neural network-based model is designed to achieve efficient defect classification with high accuracy. A series of experiments have been conducted to illustrate the effectiveness of the proposed system.