AI-driven data fusion modeling for enhanced prediction of mixed-mode I/ III fracture toughness

This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/ III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive testing methods, specifically using edge-notch disc bend specimens subjected to three-point bending tests. These established methods are not only expensive and time-consuming but also frequently limited by the availability of data. To address these challenges, this study introduces a data fusion source modeling approach. This approach integrates primary fracture toughness test data with secondary predictive data derived from the maximum tangential stress criterion and the local strain energy density criterion. By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. Additionally, a feature importance analysis using Shapley Additive exPlanations values reveals that the mode mixity parameter, specimen thickness, and radius are critical factors influencing fracture toughness. The study highlights that while mode mixity emerges as the most significant factor, a reduction in specimen thickness generally leads to decreased fracture toughness, whereas an increase in radius has a more complex, often negative, effect. The results of this study indicate that AI-powered models using data fusion can overcome limitations related to data scarcity, enabling more accurate predictions in fracture mechanics. Furthermore, this approach provides a pathway for utilizing AI in other engineering domains where data sets are limited.