期刊
MATERIALS
卷 15, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/ma15155296
关键词
knowledge graph; high-throughput computing; microstructure design; crystal plasticity; Al alloys
类别
资金
- National Key Research and Development Program of China [2018YFB0704003]
- National Natural Science Foundation of China [51771234, 51820105001]
This study integrates multi-scale factors into a knowledge graph to investigate the evolution of microstructure and properties of age-strengthened aluminum alloys during heat treatment. It also establishes quantitative prediction models for industrial production. The constructed knowledge graph guides material design and provides clear microstructure-property relationships.
It is of great academic and engineering application to study the evolution of microstructure and properties of age-strengthened aluminum alloys during heat treatment and to establish quantitative prediction models that can be applied to industrial production. The main factors affecting the peak aging state strength of age-strengthened aluminum alloys are the precipitates, solid solution elements, grain size effects, and textures formed during the material processing. In this work, these multi-scale factors are integrated into the framework of the knowledge graph to assist the following crystal plasticity finite elements simulations. The constructed knowledge graph is divided into two parts: static data and dynamic data. Static data contains the basic properties of the material and the most basic property parameters. Dynamic data is designed to improve awareness of static data. High-throughput computing is performed to further obtain clear microstructure-property relationships by varying the parameters of materials properties and the characteristics of the structure models. The constructed knowledge graph can be used to guide material design for 6XXX Al-Mg-Si based alloys. The past experimental values are used to calibrate the phenomenological parameters and test the reliability of the analysis process.
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