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Two-Step Calibration Method for Inverse Finite Element with Small Sample Features.

Libo Xu1, Feifei Zhao1, Jingli Du1

  • 1Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a two-step calibration method to enhance the accuracy of deformation field reconstruction using the inverse finite element method (FEM). The approach improves self-structuring fuzzy network (SSFN) calibration with limited strain data.

Keywords:
deformation reconstructionfuzzy networkinverse finite elementnon-uniform rational B-spline

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Area of Science:

  • Computational Mechanics
  • Structural Analysis
  • Data-Driven Engineering

Background:

  • Strain measurement errors degrade deformation field reconstruction accuracy in inverse finite element method (FEM).
  • Self-structuring fuzzy networks (SSFN) exhibit limited calibration ability with sparse strain data.

Purpose of the Study:

  • To propose a novel two-step calibration method to enhance the accuracy of inverse FEM deformation reconstruction.
  • To address the limitations of SSFN calibration when training data is scarce.

Main Methods:

  • Distributing displacement errors to nodal degrees of freedom (DOFs).
  • Utilizing DOFs as knots for Non-Uniform Rational B-Spline (NURBS) curve generation to enrich training samples.
  • Employing SSFN to establish relationships between measured strain and end-node DOFs.

Main Results:

  • The proposed method significantly improves the accuracy of displacement reconstruction.
  • Demonstrated effectiveness through a loading deformation experiment on a three-element structure.
  • Enhanced the calibration capability of SSFN with limited strain samples.

Conclusions:

  • The novel two-step calibration method effectively improves the accuracy of deformation field reconstruction in inverse FEM.
  • The integration of NURBS curves and SSFN offers a robust solution for handling strain measurement errors and limited data.
  • This approach provides a valuable tool for structural analysis and health monitoring applications.