Improvements to processes and materials have led to increased additive manufacturing capabilities using the fused filament fabrication method in terms of speed, quality, and repeatability. However, there are significant challenges in guaranteeing the desired output quality due to uncertainties inherent to the printing process. These include uncertainties in the quality of raw materials across different batches, fabrication environment (e.g., humidity, temperature), and machine wearing. The widespread adoption of fused filament fabrication for industrial applications faces considerable challenges in reducing part-to-part variations and assuring the mechanical properties of a manufactured component. In this paper, an in situ fault detection platform that considers the structural properties of the printed part is proposed. The presented system uses the optical camera and a deep learning methodology to detect faults online using training sets developed offline. The performance of the system is quantified using a variety of metrics. Computational speed for inference computation, minimum fault-sized detection, and measurement noise in the system are examined in this work.
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