Shape sensing and damage identification with iFEM on a composite structure subjected to impact damage and non-trivial boundary conditions

Abstract The inverse Finite Element Method (iFEM) has recently demonstrated to be a valuable tool, not only for shape sensing, but also for damage identification in a Structural Health Monitoring framework. The algorithm computes the displacement field, and consequently the strain field, based on discrete input strain measures in the structure without any a-priori knowledge of the material properties or the loading condition. In particular, the reconstruction is obtained by a least-squares minimization of an error functional defined by comparing the experimental strain measures with their numerical formulation, function of the displacements. Even though the formulation of the method is general for any arbitrary geometry and constraints, the definition of the correct boundary conditions is not trivial for structures subjected to non-ideal constraints. Thus, the work proposes a superimposition of the effects approach to weight the contribution of different basic models to simulate the real behavior of the structure. Once the component displacement and strain fields are correctly reconstructed, the structural assessment is performed by means of a load-independent anomaly index based on the comparison of the numerical strain reconstructions and their experimental counterparts. The discrepancies with respect to a load-independent baseline are highlighted by a Mahalanobis distance index. The procedure is experimentally verified on a CFRP reinforced panel subjected to impact damage and a compressive fatigue test.

[1]  Erkan Oterkus,et al.  Displacement and stress monitoring of a chemical tanker based on inverse finite element method , 2016 .

[2]  H. R. Riggs,et al.  A novel four‐node quadrilateral smoothing element for stress enhancement and error estimation , 1999 .

[3]  G. N. Labeas,et al.  Development and validation of a strain-based Structural Health Monitoring system , 2009 .

[4]  Umberto Papa,et al.  Health structure monitoring for the design of an innovative UAS fixed wing through inverse finite element method (iFEM) , 2017 .

[5]  K. Kuang,et al.  Use of conventional optical fibers and fiber Bragg gratings for damage detection in advanced composite structures: A review , 2003 .

[6]  Hong Bao,et al.  The Application Research of Inverse Finite Element Method for Frame Deformation Estimation , 2017 .

[7]  Keith Worden,et al.  A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring , 2018 .

[8]  Erkan Oterkus,et al.  Dent damage identification in stiffened cylindrical structures using inverse Finite Element Method , 2020 .

[9]  John E. Mottershead,et al.  Geometric Parameters for Finite Element Model Updating of Joints and Constraints , 1996 .

[10]  Claudio Sbarufatti,et al.  Definition of a load adaptive baseline by inverse finite element method for structural damage identification , 2019, Mechanical Systems and Signal Processing.

[11]  Z. Wan,et al.  Structural Health Monitoring (SHM) of Three-Dimensional Braided Composite Material using Carbon Nanotube Thread Sensors , 2013 .

[12]  Keith Worden,et al.  On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios , 2019, Mechanical Systems and Signal Processing.

[13]  Thomas J. R. Hughes,et al.  An improved treatment of transverse shear in the mindlin-type four-node quadrilateral element , 1983 .

[14]  Keith Worden,et al.  A multiresolution approach to cointegration for enhanced SHM of structures under varying conditions – An exploratory study , 2014 .

[15]  Marco Gherlone,et al.  Shape sensing of 3D frame structures using an inverse Finite Element Method , 2012 .

[16]  Amir Hossein Alavi,et al.  Fatigue cracking detection in steel bridge girders through a self-powered sensing concept , 2017 .

[17]  M. Gherlone,et al.  An inverse finite element method for beam shape sensing: theoretical framework and experimental validation , 2014 .

[18]  G. De Roeck,et al.  Vibration based Structural Health Monitoring using output-only measurements under changing environment , 2008 .

[19]  Erkan Oterkus,et al.  Displacement and stress monitoring of a Panamax containership using inverse finite element method , 2016 .

[20]  Marco Gherlone,et al.  Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads , 2015 .

[21]  Moshe Tur,et al.  Bearing fault detection and fault size estimation using fiber-optic sensors , 2019, Mechanical Systems and Signal Processing.

[22]  Keith Worden,et al.  Features for damage detection with insensitivity to environmental and operational variations , 2012, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Adnan Kefal,et al.  An efficient curved inverse-shell element for shape sensing and structural health monitoring of cylindrical marine structures , 2019, Ocean Engineering.

[24]  Bart Peeters,et al.  Modal Strain Energy Based Damage Detection Applied to a Full Scale Composite Helicopter Blade , 2013 .

[25]  D.E. Paris,et al.  Integrated Intelligent Vehicle Management Framework , 2008, 2008 IEEE Aerospace Conference.

[26]  Alexander Tessler,et al.  A Variational Principle for Reconstruction of Elastic Deformations in Shear Deformable Plates and Shells , 2003 .

[27]  Constantinos Soutis,et al.  Structural health monitoring techniques for aircraft composite structures , 2010 .

[28]  D. J. Gorman.  A general solution for the free vibration of rectangular plates resting on uniform elastic edge supports , 1990 .

[29]  H. T. Saliba,et al.  Free vibration analysis of rectangular cantilever plates with symmetrically distributed point supports along the edges , 1984 .

[30]  Robert D. Cook,et al.  Four-node ‘flat’ shell element: Drilling degrees of freedom, membrane-bending coupling, warped geometry, and behavior , 1994 .

[31]  Charles R. Farrar,et al.  Structural Health Monitoring: A Machine Learning Perspective , 2012 .

[32]  Alexander Tessler,et al.  A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells , 2005 .

[33]  A. Califano,et al.  Strain based method for monitoring the health state of composite structures , 2019, Composites Part B: Engineering.

[34]  Gabriel Alejandro,et al.  Statistical distances and probability metrics for multivariate data, ensembles and probability distributions , 2015 .

[35]  John E. Mottershead,et al.  Correction of Joint Stiffnesses and Constraints for Finite Element Models in Structural Dynamics , 1993 .

[36]  Hamid Ghorbani,et al.  MAHALANOBIS DISTANCE AND ITS APPLICATION FOR DETECTING MULTIVARIATE OUTLIERS , 2019, Facta Universitatis, Series: Mathematics and Informatics.

[37]  Marcias Martinez,et al.  iFEM benchmark problems for solid elements , 2019, Smart Materials and Structures.

[38]  Alexander Tessler,et al.  A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring , 2016 .

[39]  Alexander Tessler,et al.  Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles , 2007 .

[40]  Thomas Monnier,et al.  Lamb Waves-based Impact Damage Monitoring of a Stiffened Aircraft Panel using Piezoelectric Transducers , 2006 .

[41]  Marco Gherlone,et al.  A novel approach for displacement and stress monitoring of sandwich structures based on the inverse Finite Element Method , 2015 .