EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction

• URL: http://arxiv.org/abs/2109.14248v1
• Date: Wed, 29 Sep 2021 07:48:20 GMT
• Title: EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction
• Authors: Chao Shu, Zhuoran Xin, Cheng Xie
• Abstract summary: The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance. This paper proposes a novel data-knowledge-driven organization representation and performance prediction method. The experimental results show that our method is superior to traditional machine learning and machine vision methods.
• Score: 2.049702429898688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials. However, tissue analysis methods of materials science, such as metallographic analysis, XRD analysis, and EBSD analysis, cannot directly establish a complete quantitative relationship between tissue structure and performance. Therefore, this paper proposes a novel data-knowledge-driven organization representation and performance prediction method to obtain a quantitative structure-performance relationship. First, a knowledge graph based on EBSD is constructed to describe the material's mesoscopic microstructure. Then a graph representation learning network based on graph attention is constructed, and the EBSD organizational knowledge graph is input into the network to obtain graph-level feature embedding. Finally, the graph-level feature embedding is input to a graph feature mapping network to obtain the material's mechanical properties. The experimental results show that our method is superior to traditional machine learning and machine vision methods.

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