LAUSR

Abstract Currently additive manufacturing (AM) faces many challenges related to product quality, robustness, and reliability, which will hinder its industry-scale applications. Studies of the typical AM process failures and the corresponding effective sensor-based monitoring methods are needed to overcome these challenges. In this paper, a data-driven monitoring method for online AM process failure diagnosis based on acoustic emission (AE) is proposed, and its application to the fused filament fabrication (FFF) is demonstrated. Several typical FFF process failures are also investigated. Experimental study of a failed FFF printing process is benchmarked against the analysis of a normal printing process. AE signals from both normal and failed printing processes are recorded and processed. An unsupervised machine learning method, self-organizing map (SOM), is applied to formalize the diagnosis procedure of different failure modes. The experimental results show that it is feasible to use the proposed method to diagnose the typical process failures, including both detection and identification. This new method could serve as a non-intrusive diagnostic tool for FFF process monitoring, and has the potential to be applied to other AM processes.