Research on composition-process-property prediction of die casting Al alloys via combining feature creation and attention mechanisms

Abstract

In industrial applications, the mechanical properties of die casting Al alloy parts are influenced by a combination of material composition and forming process. This study establishes a performance data-driven framework that takes into account balanced weights of alloy compositions and process parameters. First, a compositional coefficient normalization method is proposed, in which the electronegativity, thermal, and physical descriptors are dot-multiplied into the coefficient matrix to build alloy factors for coupled compositional information. Higher prediction accuracy and lower risk of overfitting validate the feasibility of the method. Subsequently, to improve the performance of machine learning (ML) model for prediction of mechanical properties, a feature engineering method embedded in attention mechanism is used. The experimental results with high accuracy confirm the ability of the ML model to reproduce the relationships between alloy properties and balance their sensitivities. Finally, contour plots of the characterized parameters are constructed for predicting mechanical property intervals and guiding parameter design. This study innovatively applies the normalization method to the field of die casting aluminum alloy feature optimization. And combined with the attention mechanism to improve the prediction accuracy between material features and target properties, which may also be extended to other alloys.

Keywords

• Die cast Al alloy
• Machine learning
• Mechanical properties
• Feature engineering
• Data-driven