Data-Driven Analysis of the Portevin–Le Chatelier Effect in an Al-Mg Alloy Across Temperatures and Strain Rates

Al-Mg alloys, among others, exhibit the Portevin-Le Chatelier (PLC) effect. In addition to serrations in the stress-strain curve, the PLC effect also manifests itself macroscopically as stretcher strain marks on the workpiece. Therefore, it is of particular interest to predict and quantify the appearance of the PLC effect. In this work, a simple method for calculating the PLC strength based on stress-strain curves is presented, which can be used to evaluate the appearance of PLC serrations. The influence of different strain rates, temperatures, and holding times on PLC serrations is demonstrated using a 5083-H111 alloy. To classify PLC occurrence, unsupervised clustering was applied to stress-strain data. Additionally, machine learning models, including support vector regression (SVR) and multilayer perceptron (MLP), were employed to predict the PLC effect based on experimental parameters.