Data-Driven Plasticity Modeling via Acoustic Profiling
This work addresses material science researchers by enabling more accurate detection and classification of deformation events in metals, though it is incremental as it builds on existing acoustic emission analysis methods.
The paper tackles modeling plastic deformation in crystalline metals by analyzing acoustic emission signals, showing that engineered features from wavelet-based detection significantly outperform raw signal classifiers and identifying four distinct event archetypes corresponding to different deformation mechanisms.
This paper presents a data-driven framework for modeling plastic deformation in crystalline metals through acoustic emission (AE) analysis. Building on experimental data from compressive loading of nickel micropillars, the study introduces a wavelet-based method using Morlet transforms to detect AE events across distinct frequency bands, enabling identification of both large and previously overlooked small-scale events. The detected events are validated against stress-drop dynamics, demonstrating strong physical consistency and revealing a relationship between AE energy release and strain evolution, including the onset of increased strain rate following major events. Leveraging labeled datasets of events and non-events, the work applies machine learning techniques, showing that engineered time and frequency domain features significantly outperform raw signal classifiers, and identifies key discriminative features such as RMS amplitude, zero crossing rate, and spectral centroid. Finally, clustering analysis uncovers four distinct AE event archetypes corresponding to different deformation mechanisms, highlighting the potential for transitioning from retrospective analysis to predictive modeling of material behavior using acoustic signals.