Bezier Curve-Based Shape Knowledge Acquisition and Fusion for Surrogate Model Construction

Peng An*, Wenbin Ye, Zizhao Wang, Hua Xiao, Yongsong Long, Jia Hao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Surrogate model technology is a key technology in the field of engineering design with limited data. Fusion of engineering knowledge into surrogate models is an effective method to improve the prediction accuracy. However, engineering knowledge in this field describes the complex relationship between variables, which makes it difficult to obtain quantitative knowledge. Therefore, the engineering knowledge acquisition and fusion technology based on Bezier Curve for complex equipment design was proposed, which covered the entire process from knowledge acquisition to filtering and fusion. Finally, through the verification of the Unmanned Vehicle Truss design case and test functions, the experimental results show that the technology can achieve the effective acquisition of complex curve knowledge and represent multi-knowledge information effectively.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2022, Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages328-342
Number of pages15
ISBN (Print)9783031184604
DOIs
Publication statusPublished - 2023
Event7th Future Technologies Conference, FTC 2022 - Vancouver, Canada
Duration: 20 Oct 202221 Oct 2022

Publication series

NameLecture Notes in Networks and Systems
Volume559 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference7th Future Technologies Conference, FTC 2022
Country/TerritoryCanada
CityVancouver
Period20/10/2221/10/22

Keywords

  • Complex equipment
  • Engineering knowledge
  • Knowledge representation
  • Surrogate models

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