A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

Deep learning is a particular kind of machine learning, which achieves great power and flexibility by a nested hierarchy of concepts. A general life prediction method for components under creep, fatigue and creep-fatigue conditions is proposed. Fatigue, creep and creep-fatigue data of a typical austenitic stainless steel (i.e., 316) are integrated. Conventional machine learning models (e.g., support vector machine, random forest, Gaussian process regression, shallow neural network) and deep learning model (e.g., deep neural network) are applied for life predictions. Results show that deep learning model exhibits better prediction accuracy and generalization ability than conventional machine learning model.

Automated summary

The study introduces a deep learning method for predicting the life of austenitic stainless steel under creep, fatigue, and creep-fatigue conditions. Results demonstrate that the deep learning model outperforms conventional machine learning models in terms of prediction accuracy and generalization ability.