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Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures.
- Source :
-
Materials (1996-1944) . Jan2023, Vol. 16 Issue 1, p447. 22p. - Publication Year :
- 2023
-
Abstract
- A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material's mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels' yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961944
- Volume :
- 16
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Materials (1996-1944)
- Publication Type :
- Academic Journal
- Accession number :
- 161480638
- Full Text :
- https://doi.org/10.3390/ma16010447