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

Data associated with the research paper:Data-Driven Analysis of the Portevin–Le Chatelier Effect in an Al-Mg Alloy Across Temperatures and Strain RatesPaper Abstract:Al-Mg alloys, among others, exhibit the Portevin-Le Chatelier (PLC) effect. In addition toserrations in the stress-strain curve, the PLC effect also manifests itself macroscopically asstretcher strain marks on the workpiece. Therefore, it is of particular interest to predict andquantify the appearance of the PLC effect. In this work, a simple method for calculating the PLCstrength based on stress-strain curves is presented, which can be used to evaluate the appearanceof PLC serrations. The influence of different strain rates, temperatures, and holding times on PLCserrations is demonstrated using a 5083-H111 alloy. To classify PLC occurrence, unsupervisedclustering was applied to stress-strain data. Additionally, machine learning models, includingsupport vector regression (SVR) and multilayer perceptron (MLP), were employed to predict thePLC effect based on experimental parameters.Description of the files:

Rohdaten.xlsx: This file holds the original, unprocessed data collected from your experiment. It is an Excel file and represents the raw information before any feature analysis or processing was performed.

data_exploded.csv: This file contains the processed results of your feature analysis. It's structured as a table (Pandas DataFrame) and saved as a CSV file. The data includes information about curves and newly generated features, such as cluster IDs, which were likely created during your analysis. The GIT repository is linked in our file upload.