Data-Driven Plasticity Modeling via Acoustic Profiling

TL;DR

This paper develops a data-driven acoustic emission analysis framework for modeling plastic deformation in metals, combining wavelet-based event detection, machine learning classification, and clustering to understand deformation mechanisms.

Contribution

It introduces a novel wavelet-based AE detection method, applies machine learning for event classification, and uncovers distinct deformation-related AE archetypes.

Findings

Wavelet-based method detects both large and small AE events.

Engineered features outperform raw signals in classification accuracy.

Four AE archetypes correspond to different deformation mechanisms.

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.