Premium
Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction
Author(s) -
Wang Tian,
Shao Mingqi,
Guo Rong,
Tao Fei,
Zhang Gang,
Snoussi Hichem,
Tang Xingling
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202006245
Subject(s) - surrogate model , artificial neural network , artificial intelligence , field (mathematics) , computer science , deep learning , machine learning , process (computing) , selection (genetic algorithm) , materials science , mathematics , pure mathematics , operating system
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.