Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction

Predicting the performance of mechanical properties is an important and current issue in the field of engineering and materials science, but traditional experiments and modeling calculations often consume large amounts of time and resources. Therefore, it is imperative to use appropriate methods to accelerate the process of material selection and design. The artificial intelligence method, particularly deep learning models, has been verified as an effective and efficient method for handling computer vision and neural language problems. In this paper, a deep learning surrogate model (DLS) is proposed for predicting the mechanical performance of materials, that is, the maximum stress value under complex working conditions. The DLS can reproduce the finite element analysis model results with 98.79% accuracy. The results show that deep learning has great potential. This research also provides a new approach for material screening in practical engineering. A deep learning surrogate model developed via artificial intelligence method is used to surrogate the traditional finite element analysis method for material screening and performance prediction.