Learning acoustic emission signatures from a nanoindentation-based lithography process: Towards rapid microstructure characterization

We present an approach for rapid identification of the salient microstructural phases present on a metallic workpiece surface via nanoindentation-based lithography process. We employ a machine learning approach to connect the time-frequency patterns of the corresponding acoustic emission (AE) signals with the underlying microstructural phases. Results show that the AE frequencies in the range of 0.3–1 kHz and 30–50 kHz can discriminate between the microdynamics of the lithography process arising from different microstructural compositions and thereby predict these microstructural phases with accuracies exceeding 95%. We also draw physical interpretations of our “black-box” machine learning model and demonstrate that the physical insights into the underlying AE signals allow us to identify novel patterns and possible microstructural anomalies.