PhD Thesis - Classification of Acoustic Signals Created During the Rapid Deformation of Microsamples by Machine Learning

The goal of this doctoral project is to investigate extreme strain rates, analyze experimental acoustic emission (AE) signals based on wave properties using machine learning (ML), and provide insights into rapid, small-scale deformation mechanisms. The project involves collaboration with ELTE Budapest and JUNIA Lille, utilizing a unique in situ setup that combines micromechanical testing and AE signal detection across a wide strain-rate range. The collected AE data will be processed using advanced ML algorithms and human perception tests for classification.