Quantitative evaluation of the dependence of the Portevin-Le Chatelier effect on temperature and strain rate in an Al–Mg alloy (AA5083-H111): Insights from machine learning

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 effect strength based on stress–strain curves is presented, which can be used to evaluate the appearance of PLC effect serrations. The influence of different strain rates, temperatures, and holding times on PLC effect serrations is demonstrated using a 5083-H111 alloy. To classify PLC effect occurrence, unsupervised clustering was applied to stress–strain data. Additionally, machine learning models, including Gaussian process regression (GPR) and multilayer perceptron (MLP), were employed to predict the PLC effect based on experimental parameters. • Quantitative method to assess PLC effect strength from stress–strain data. • Strain rate and especially temperature strongly affect PLC serrations in Al–Mg alloy. • Holding time before deformation shows negligible influence on PLC behavior. • Unsupervised clustering validates the PLC strength quantification approach. • Machine learning models predict PLC strength from experimental parameters.