Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models

Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. While effective for simple constitutive models like Hill′48, their performance declines with more complex models such as the Cazacu-Plunckett-Barlat yield criterion. This study examines the influence of training dataset size and dimensionality reduction via principal component analysis on predictive accuracy. Results show that increasing the training dataset size leads to only marginal improvements, with testing coefficient of determination value plateauing at about 0.50, despite a consistently high training value of about 0.99999, indicating overfitting. Similarly, applying principal component analysis to the baseline model provided no significant enhancement. These findings suggest that simply expanding the dataset or reducing dimensionality is insufficient to address the complexities of CPB′06. Instead, alternative approaches such as advanced feature selection, hybrid ML-physics-based models, or regularization techniques may be required to improve generalization. Future work should explore methods integrating domain knowledge and physics-based modelling to enhance predictive accuracy for complex constitutive models.