Machine learning-based efficient stress intensity factor calculation for aeroengine disk probabilistic risk assessment under polynomial stress fields

In probabilistic failure risk assessment, the accuracy and efficiency of the stress intensity factor calculation are important. The universal weight function method has been widely adopted for efficiency, but this method still has some debatable parts. For accurate and efficient stress intensity factor prediction, two approaches for machine learning techniques are specially designed. Three tests are conducted for the first approach where Gaussian process regression, tree-structure models, and artificial neural network are evaluated and compared for the ability of interpolation and extrapolation. Results show that the artificial neural network and extremely randomized trees perform better. Hybrid models in the second approach are also proposed, and results show that the accuracy of SIF calculation can be improved by 5–35% compared with the weight function. Real aeroengine disks are adopted, and the errors of machine learning methods are less than 20% in the disk life calculation.