Data-Driven Plasticity Modeling via Acoustic Profiling

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.

Figures (13)

• Figure 1: Experimental setup for the original data acquisition (left) and acquired acoustic emission plot and its magnitude spectrum (right).
• Figure 2: Magnitude spectrum of the signal after apply a band-pass filter in the frequency range 2KHz to 60 KHz. The spectrum exhibits some prominent peaks after filtering in the vicinities of 8KHz, 16KHz, 25 KHz, and 44 KHz.
• Figure 3: Visual representation of identified events in each of the frequency bands. On the left the gray central plot is the filtered waverform. The blue dots on the blue line indicate times where the onset of an AE event in the 8KHz band is detected. The orange, green, and red dots similarly represent the same for the 16KHz, 25KHz, and 44KHz events respectively. On the right these are shown in histogram form as well.
• Figure 4: Average magnitude spectra for identified events in each of the frequency bands.
• Figure 5: Correlation of the AE events with drops in the stress curve. The AE events line up well with the stress drops.