期刊
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
卷 9, 期 6, 页码 14467-14477出版社
ELSEVIER
DOI: 10.1016/j.jmrt.2020.10.042
关键词
Powder metallurgy nickel base superalloys; gamma ' precipitation microstructure; Hardness; Machine learning; Deep neural network (DNN)
资金
- National Key Research and Development Program of China [2016YFB0700300]
- Key R&D Program of Guangdong Province, China [2019B010943001]
In precipitation hardening metallic materials, the size and volume fraction of precipitation phases are regarded as primary microstructural parameters to control the strength instead of others. Why? In this research, a supervised learning approach was developed to correlate gamma' precipitation microstructures with hardness based on experimentally observed 483 scanning electron microscope (SEM) images comprised with different gamma' precipitates. First, up to 23 descriptors were defined and extracted numerically as training inputs from SEM images by pattern recognition techniques. Then, 10 descriptors were further selected to reduce computational cost of deep neural network (DNN) with the assistance of shallow neural network (SNN). Furthermore, to improve the accuracy of DNN, new training sets were proposed to combine these 10 descriptors with two more descriptors: area distribution and one heat treatment parameter cooling rate. In conclusion, the supervised learning approach was proven to outperform the prediction of existing physics-based constitutive models. (C) 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
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