用声发射信号预测材料微区滑移事件，精度高且可迁移。
Deciphering Acoustic Emission with Machine Learning
- 基于机器学习，从声发射数据反推滑移事件的微观细节。
- 能精准预测滑移发生的时间和变形量，准确率高。
- 方法适用于不同尺寸样品，适合材料力学研究者使用。
声发射信号常伴随材料中雪崩式事件出现，如晶体中的位错雪崩、多孔材料空洞坍塌或铁电体畴壁运动。声发射数据信息丰富，但将其与触发雪崩的微观特征精确关联极具挑战。本文提出一种基于机器学习的方法，仅凭声发射数据即可推断微柱压缩实验中位错雪崩的微观细节。研究表明，该方法能准确预测力-时间响应，对雪崩发生时间的预测表现优异，并可预测单个变形事件的幅值。研究采用多种特征（包括频率相关与无关特征），并分析其在预测中的重要性。同时验证了该方法在不同样品尺寸间的可迁移性，并讨论了其在更通用场景中的应用潜力。
原文摘要 · Abstract (English)
Acoustic emission signals have been shown to accompany avalanche-like events in materials, such as dislocation avalanches in crystalline solids, collapse of voids in porous matter or domain wall movement in ferroics. The data provided by acoustic emission measurements is tremendously rich, but it is rather challenging to precisely connect it to the characteristics of the triggering avalanche. In our work we propose a machine learning based method with which one can infer microscopic details of dislocation avalanches in micropillar compression tests from merely acoustic emission data. As it is demonstrated in the paper, this approach is suitable for the prediction of the force-time response as it can provide outstanding prediction for the temporal location of avalanches and can also predict the magnitude of individual deformation events. Various descriptors (including frequency dependent and independent ones) are utilised in our machine learning approach and their importance in the prediction is analysed. The transferability of the method to other specimen sizes is also demonstrated and the possible application in more generic settings is discussed.
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