Prediction of forming limit diagrams using machine learning

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD. Measured FLDs of various 5XXX and 6XXX aluminium alloys along with their chemical composition and thermomechanical processing parameters and the respective mechanical properties like n and r values were manually curated and used as training and validation datasets. A two-stage ML model was developed. In the first stage, the minimum and maximum points of the minor strain were predicted using the above features. In the second stage, the predicted minor strain in the first stage was used as input in addition to the same feature set to predict the major strain using gradient boost regression (GBR). The trained ML model successfully predicted FLDs with R2 value above 0.93. The current results show that ML can be a viable way for predicting FLDs.