Related papers: Deciphering Acoustic Emission with Machine Learnin…
Wood is a multi-scale material exhibiting a complex viscoplastic response. We study avalanches in small wood samples in compression. "Woodquakes" measured by acoustic emission are surprisingly similar to earthquakes and crackling noise in…
Compression experiments on micron-scale specimens and acoustic emission (AE) measurements on bulk samples revealed that the dislocation motion resembles a stick-slip process - a series of unpredictable local strain bursts with a scale-free…
We present a statistical analysis of the acoustic emissions induced by dislocation motion during the creep of ice single crystals. The recorded acoustic waves provide an indirect measure of the inelastic energy dissipated during dislocation…
Plastic deformation of micron-scale crystalline materials differ considerably from bulk ones, because it is characterized by random strain bursts. To obtain a detailed picture about this stochastic phenomenon, micron sized pillars have been…
Machine learning models using seismic emissions can predict instantaneous fault characteristics such as displacement in laboratory experiments and slow slip in Earth. Here, we address whether the acoustic emission (AE) from laboratory…
Predicting the future behaviour of complex systems exhibiting critical-like dynamics is often considered to be an intrinsically hard task. Here, we study the predictability of the depinning dynamics of elastic interfaces in random media…
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…
Similarities between force-driven compression experiments of porous materials and earthquakes have been recently proposed. In this manuscript, we measure the acoustic emission during displacement-driven compression of a porous glass. The…
In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based…
Plastic deformation of micron-scale crystalline solids exhibits stress-strain curves with significant sample-to-sample variations. It is a pertinent question if this variability is purely random or to some extent predictable. Here we show,…
Forecasting fault failure is a fundamental but elusive goal in earthquake science. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with…
Single-particle traces of the diffusive motion of molecules, cells, or animals are by-now routinely measured, similar to stochastic records of stock prices or weather data. Deciphering the stochastic mechanism behind the recorded dynamics…
It has been recently observed that synthetic materials subjected to an external elastic stress give rise to scaling phenomena in the acoustic emission signal. Motivated by this experimental finding we develop a mesoscopic model in order to…
We consider the amount of energy dissipated during individual avalanches at the depinning transition of disordered and athermal elastic systems. Analytical progress is possible in the case of the Alessandro-Beatrice-Bertotti-Montorsi (ABBM)…
We study a one-dimensional model of a dislocation pileup driven by an external stress and interacting with random quenched disorder, focusing on predictability of the plastic deformation process. Upon quasistatically ramping up the…
We analyse some acoustic emission time series obtained from a lathe machining process. Considering the dynamic evolution of the process we apply two classes of well known stationary stochastic time series models. We apply a preliminary root…
One of the possible mechanisms for acoustic emission of growing micro-cracks under conditions of the material machining by com-pressed abrasive has been theoretically studied. Physical ground of this mechanism is the dislocation creep in…
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary…
We consider a modified Burridge-Knopoff model with a view to understand results of acoustic emission (AE) relevant to earthquakes by adding a dissipative term which mimics bursts of acoustic signals. Interestingly, we find a precursor…
There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first…