通过声学信号分析，实现镍微柱塑性变形的实时建模与预测。
Data-Driven Plasticity Modeling via Acoustic Profiling
- 用莫莱特小波分解声发射信号，识别大中小尺度事件。
- 发现声发射能量与应变演化强相关，重大事件后应变速率上升。
- 机器学习提取关键特征，聚类揭示四种变形机制类型。
本文提出一种基于声发射（AE）分析的数据驱动框架，用于建模晶体金属中的塑性变形。基于镍微柱压缩实验数据，采用莫莱特小波变换在不同频段检测声发射事件，成功识别出大事件及以往被忽略的小尺度事件。事件检测结果与应力突降动态高度一致，揭示了声发射能量释放与应变演进之间的关系，包括重大事件后应变速率显著提升的现象。利用标记的事件-非事件数据集，应用机器学习方法表明，工程化的时间与频域特征显著优于原始信号分类器，并识别出均方根幅值、零交叉率和频谱质心等关键判别特征。聚类分析进一步发现四类具有不同变形机制特征的声发射事件，为从回溯分析转向基于声学信号的材料行为预测建模提供了可能。
原文摘要 · Abstract (English)
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.
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