Evaluating Material Failure with Synthetic Data and Machine Learning for Enhanced Predictive Accuracy

This study investigates the use of Random Forest and Gradient Boosting models to predict material failure based on stress and strain data. Both models achieved high accuracy, with Gradient Boosting slightly outperforming Random Forest in identifying failure cases, as demonstrated by a higher recall for the failure class. A decision boundary visualization reveals that the Random Forest model effectively separates failure and non-failure regions, though some misclassification persists. This comparative analysis, supported by confusion matrices and classification reports, highlights the strengths and limitations of each model in capturing failure patterns. Future work includes exploring additional features, advanced algorithms, interpretability methods, and real-time monitoring systems to improve prediction accuracy and industrial applicability.