Deciphering Acoustic Emission with Machine Learning

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. Introduction It was shown that at the micron-scale and below crystalline materials (as well as many other heterogeneous materials) exhibit complex deformation behaviour including size-related hardening and significant sample-to-sample variation in the plastic response [1, 2, 3]. In addition, in this regime deformation becomes intermittent and constitutes of a series of random strain bursts that make the details of the deformation process unpredictable both in time and space [4, 5]. This intermittency and stochasticity originates from the sudden rearrangement events of the dislocation network, the so-called dislocation avalanches. In order to experimentally study the underlying physical process, that is, the source of the avalanche, it has to be connected to directly related proxies that are experimentally measurable with sufficient precision. This is usually a rather challenging task, since avalanches are fast and mainly occur inside the material below its surface.