Estimating the Effective Fracture Toughness of a Variety of Materials using Several Machine Learning Models

Since conducting laboratory tests to obtain the fracture toughness of materials is time-consuming and costly, it is necessary to provide tools to estimate this property with high accuracy quickly and without the need for such a high cost. This study considered using machine learning (ML)-based models as a suitable option to address such problems. For this purpose, twelve ML-based models were presented using 1715 datasets generated from five experimental tests to estimate the adequate fracture toughness (Keff) of 44 different materials. The behavior of the ML models compared to the practice tests was investigated, and the correct and acceptable performance of each of them in estimating the Keff of different materials was confirmed. Among the twelve ML-based models, extreme tree regressor (ETR) and Gaussian process regression (GPR) models provided the highest and lowest accuracies in estimating the Keff of different materials, respectively. To further aid in the estimation of the Keff of different materials for engineering challenges, a graphical user interface (GUI) for the ML-based models was developed.