An improved fault diagnosis approach for FDM process with acoustic emission

The reliability and performance of additive manufacturing (AM) machines affect the product quality and manufacturing cost. Developing effective health monitoring and prognostics methods is critical to AM productivity. Yet limited work is done on machine health monitoring. Recently, the application of acoustic emission sensor (AE) to the fault diagnosis of material extrusion or fused deposition modeling process was demonstrated. One challenge in real-time process monitoring is processing the large amount of data collected by high-fidelity sensors for diagnostics and prognostics. In this paper, the efficiency of machine state identification from AE data is significantly improved with reduced feature space dimension. In the proposed method, features extracted in both time and frequency domains are combined and then reduced with the linear discriminant analysis. An unsupervised density based clustering method is applied to classify and recognize different machine states of the extruder. Experimental results show that the proposed approach can effectively identify machine states of the extruder even within a much smaller feature space.