A data-sparse approach to in-situ fault detection and identification for metal additive manufacturing

With increasing adoption of metal additive manufacturing (AM) in manufacturing, detecting faults in the printing process has the potential to reduce waste from failed prints and streamline the production process. To increase robustness of anomaly detection, a statistical method of detecting faults from melt pool images is presented. This method uses parametric identification of 1D compression of melt pool images to build a nominal predictive model. Nominal melt pools result in residuals that are Gaussian white noise processes, whereas anomalous melt pools will not follow this distribution. Detection is performed through statistical comparison of incoming data with a nominal reference generated on sparse data. This approach successfully applies statistical time-series methods to detect anomalous melt pools in a metal AM process.