Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2022
Abstract
The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-based data gathering for every layer printed. Current crystal plasticity finite element (CPFE) modelling is capable of predicting the associated strength based on a microstructural image and material data but is computationally expensive. This work utilizes a large database of input–output samples from CPFE modelling to develop a trained deep neural network (DNN) model which instantly estimates the output (strength prediction) associated with a given input (microstructure) of multi-phase additive manufactured stainless steels. The DNN model successfully recognizes phase regions and the associated unique crystallographic orientation variations. It also captures differences in macroscopic stress response due to the varying microstructure. However, it is less reliable in terms of fatigue life predictions. The DNN model exhibits high accuracy for the structure–property relationship as a surrogate prediction tool compared to CPFE while significantly reducing the computational cost to just a few seconds.
Keywords
Crystal plasticity, Deep neural network, 17-4PH stainless steel, Additive manufacturing, Micromechanics
Discipline
Numerical Analysis and Scientific Computing | Operations Research, Systems Engineering and Industrial Engineering
Publication
Materials and Design
Volume
213
First Page
1
Last Page
20
ISSN
0261-3069
Identifier
10.1016/j.matdes.2021.110345
Publisher
Elsevier
Citation
TU, Yuhui; LIU, Zhongzhou; Carneiro, Luiz; Ryan, Caitriona M.; Parnell, Andrew C.; and Leen, Sean B..
Towards an instant structure-property prediction quality control tool for additive manufactured steel using a crystal plasticity trained deep learning surrogate. (2022). Materials and Design. 213, 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/6953
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1016/j.matdes.2021.110345
Included in
Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons