AUTOMATED DEFECT DETECTION IN FUSED FILAMENT FABRICATION COUPLING DEEP LEARNING AND COMPUTER VISION

Abstract

Fused filament fabrication (FFF) is an additive manufacturing technique, popular due to its versatility and cost-effectiveness. However, FFF machines, such as 3D printers, are prone to runtime errors, wasting time and material, while requiring constant human supervision. This paper presents a defect detection approach for FFF processes based on artificial intelligence, combining convolutional neural networks and computer vision. The defect detection approach is validated using 3D prints designed to mimic common FFF defects. The results demonstrate the capability of the proposed approach to automatically detect defects in FFF processes, thereby reducing time and material waste as well as the need for human supervision.