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Abstract
When the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Furthermore, the calibration ability of a self-structuring fuzzy network (SSFN) is weak when few strain samples are used to train the SSFN. To solve this problem, a novel two-step calibration method for improving the reconstruction accuracy of the inverse FEM method is proposed in this paper. Initially, the errors derived from measured displacements and reconstructed displacements are distributed to the degrees of freedom (DOFs) of nodes. Then, the DOFs of nodes are used as knots, in order to produce non-uniform rational B-spline (NURBS) curves, such that the sample size employed to train the SSFN can be enriched. Next, the SSFN model is used to determine the relationship between the measured strain and the DOFs of the end nodes. A loading deformation experiment using a three-element structure demonstrates that the proposed algorithm can significantly improve the accuracy of reconstruction displacement.
Highlights
With the development of health monitoring and intelligent structures, structural deformation sensing technology based on strain measurement data has become increasingly important [1].The accurate deformation reconstruction of plates, beams, and other structures provides a basis for ensuring the safe operations of aircraft
The key to deformation reconstruction is constructing a relationship between the structural deformation and strain measurements
Where d represents the structural deformation displacement; d denotes the projected where d represents the structural deformation displacement; d denotes displacement, displacement, which is independent of strain measurement and relatedthe to projected rotation; and d is the which displacement is independentofofthe strain measurement related to only rotation; and d is Closer the elastic displacement elastic structure, which isand determined by the strain
Summary
With the development of health monitoring and intelligent structures, structural deformation sensing technology based on strain measurement data has become increasingly important [1]. The triangle element inverse FEM method was proposed to reconstruct the structural deformation in real-time by measuring the strain, based on the first-order shear deformation theory [6,7], and it solved the problem of the Ko and modal methods, in that they cannot adapt to complex topological structures and boundary conditions. Cerracchio et al improved the original inverse FEM formulation by adding the kinematic assumptions of the Refined Zigzag Theory [10] This method is suitable for the displacement monitoring of multi-layer composite materials and sandwich structures with high anisotropy and heterogeneity. In [17], a double objective optimization model was established, considering both accuracy and robustness in the reconstruction of beams by using the inverse finite element method.
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