Data-Driven Plasticity Modeling via Acoustic Profiling

arXiv cs.LG / 3/30/2026

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Key Points

The paper proposes a data-driven framework to model plastic deformation in crystalline nickel micropillars using acoustic emission (AE) signals captured during compressive loading.
It uses wavelet-based Morlet transforms to detect AE events across multiple frequency bands, including small-scale events previously missed by conventional retrospective analysis.
Detected events are validated against mechanical stress-drop dynamics and show a consistent relationship between AE energy release and strain evolution, including changes in strain rate after major events.
Machine learning on engineered time/frequency-domain features (e.g., RMS amplitude, zero crossing rate, spectral centroid) is shown to outperform raw-signal classifiers using labeled event/non-event datasets.
Clustering reveals four distinct AE “event archetypes,” linking AE patterns to different deformation mechanisms and suggesting a path toward predictive material behavior modeling from acoustic signals.

Abstract

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.