Acoustic Anomaly Detection in Additive Manufacturing with Long Short-Term Memory Neural Networks

The use of additive manufacturing is growing, especially in the small and medium sized enterprise sector. Still, the print process and its quality is prone to errors. Though there exist a variety of visual detection methods for additive manufacturing, acoustic ones are rare to find. This approach will serve as a method to detect acoustic cues and errors of a Fused Deposition Modeling printer. We propose a machine learning system detecting flaws and errors of a printer with varying difficulty. Regarding the first challenge, which is recording audio data, a microphone is attached close to the extruder of a printer. Since there is no public available data samples are recorded and annotated. To guarantee variety of the samples and more data different methods of data augmentation are applied. Mel-frequency cepstral coefficients and Mel filterbank energies are extracted from the recorded and augmented data to be used as features. A Long Short- Term Memory model was trained and validated with multiple classes of relevant sounds during 3d printing.