Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

Abstract

Today, artificial intelligence plays a huge role in the mechanical engineering field for solving many complex problems and the problem with fracture mechanics is one of them. In fracture mechanics, artificial intelligence is used to predict crack behavior under various conditions such as mixed-mode loading. Many parameters are used for explaining the crack behavior under various conditions, but those parameters are obtained from destructive testing, in which usually, only one data point is obtained from each test. An artificial problem method requires a large amount of data to train the model to be able to learn crack behavior, which is a disadvantage of applying this method to fracture mechanics. To eliminate the disadvantage of the large amount of experiment data required for modeling, in this study, the small data obtained from the experiment along with data obtained from fracture criteria that were used for elementary prediction of mixed mode fracture toughness were used to create an artificial intelligence model. Data from the experiment was combined with fracture criteria data using the multi-fidelity surrogate model that is described in this study. The mixed mode I/II fracture toughness of the PMMA material was tested in order to primarily propose the data combination technique. After the modeling process, the prediction results indicated that the performance of a model in which the actual test data was combined with the data from the fracture criteria (multi-fidelity surrogate model) was more predictively effective compared to only actual data-based modeling.

Design Optimization of Alloy Wheels Based on a Dynamic Cornering Fatigue Test Using Finite Element Analysis and Multi-Additional Sampling of Efficient Global Optimization

“…That point is the next additional sampling point for evaluation to increase the accuracy of the surrogate model by adding the next sampling point into the model [10]. The optimization method with a surrogate model is famous for being utilized in various fields such as chemical engineering design [11,12], mechanical engineering design [13,14], material engineering analysis [15,16], biomedical engineering [17] and especially in aerospace engineering design [18,19] because of high computation time or cost experiment in this field. Furthermore, the original EGO was designed for exploring only a Single Additional sampling (Sas) point per iteration that previous research performed on the design shape of airfoil [20] and blade of the helicopter [21] with multi-objective and multi-level fidelity of valuation.…”