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Finite-element analysis case retrieval based on an ontology semantic tree

Published online by Cambridge University Press:  14 May 2024

Xuesong Xu ,
Zhenbo Cheng Open the ORCID record for Zhenbo Cheng [Opens in a new window] ,
Gang Xiao ,
Yuanming Zhang ,
Haoxin Zhang  and
Hangcheng Meng
Xuesong Xu
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Zhenbo Cheng*
Affiliation:
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Gang Xiao
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Yuanming Zhang
Affiliation:
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Haoxin Zhang
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Hangcheng Meng
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
*
Corresponding author: Zhenbo Cheng; Email: czb@zjut.edu.cn
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Abstract

The widespread use of finite-element analysis (FEA) in industry has led to a large accumulation of cases. Leveraging past FEA cases can improve accuracy and efficiency in analyzing new complex tasks. However, current engineering case retrieval methods struggle to measure semantic similarity between FEA cases. Therefore, this article proposed a method for measuring the similarity of FEA cases based on ontology semantic trees. FEA tasks are used as indexes for FEA cases, and an FEA case ontology is constructed. By using named entity recognition technology, pivotal entities are extracted from FEA tasks, enabling the instantiation of the FEA case ontology and the creation of a structured representation for FEA cases. Then, a multitree algorithm is used to calculate the semantic similarity of FEA cases. Finally, the correctness of this method was confirmed through an FEA case retrieval experiment on a pressure vessel. The experimental results clearly showed that the approach outlined in this article aligns more closely with expert ratings, providing strong validation for its effectiveness.

Keywords

finite element analysiscase retrievalontologysimilarity measureontology semantic tree

Information

Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e8
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Copyright
© Zhejiang University of Technology, 2024. Published by Cambridge University Press

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