During the operation of a structure, damages may develop, which will cause a change in the strain/stress
state of the stru...
In this study, an integrated automated active damage detection method is developed for composite laminate
through theoreti...
1 . T .          1 .e           .
                                          Te = ∫ {u} ρ{u}dv = − {U }[ M UU ]{U e }      ...

Artificial neural networks can provide non-linear parameterized mapping between a set of inputs...
u ( x, y , z , t ) = u 0 ( x, t ) + zθ x0 ( x, t ),   v( x, y, z , t ) = 0 & w( x, y, z, t ) = w 0 ( x, t )...(18)
Where u...
for healthy structure. Thus there are eleven neural networks are experts in the corresponding layer for each
actuation fre...
8. Kleinke, D.K and Uras, H.M. A noncontacting magnetostrictive strain sensor, Rev. Sci.
      Instrum. 64, pp. 2361-2367,...
Figure 1: Artificial Neural Network

          Figure 2: Laminated Beam with Actuator, Sensor and Delamination.

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  1. 1. IDENTIFICATION OF DELAMINATION SIZE AND LOCATION OF COMPOSITE LAMINATE FROM TIME DOMAIN DATA OF MAGNETOSTRICTIVE SENSOR AND ACTUATOR USING ARTIFICIAL NEURAL NETWORK. Ghosh D. P. (a) and Gopalakrishnan S. (b) (a) Graduate student (b) Asst. professor, Dept of Aerospace Engineering, Indian Institute of Science, Bangalore 560012 ABSTRACT This paper presents an integrated method for damage detection of composite laminates using time domain data obtained from magnetostrictive sensors and actuators and artificial neural networks (ANN) identification. Magnetostrictive actuators are actuated through an actuation coil, which vibrates the composite laminate. The presence of delamination, due to induced magnetic field intensity, changes the stress response of the structure. This in turn changes the magnetic flux intensity of the magnetostrictive sensor. The changes in the flux density are sensed through a sensing coil as open circuit voltage. The ANN is applied to establish the mapping relationship between structural damage status (location and severity) and sensor open circuit voltage. The results of delamination damage detection for composite laminate show that the method developed in this paper can be applied to structural damage detection and health monitoring for various industrial structures. To demonstrate this approach, numerical simulations are carried out on a composite cantilever beam to identify size and location of delamination using the sensor data for a known actuation for a certain combination of sensor and actuator locations. KEYWORDS Magnetostrictive, Smart Materials, Composite, FEM, Structural Health Monitoring, ANN, Committee Machine, Inverse Problem, Terfenol-D. INTRODUCTION Composites have revolutionized structural construction. They are extensively used in aerospace, civil, mechanical and other industries. Present day aerospace vehicles have composites up to 60 % or more of the total material used. More recently, materials, which can give rise to mechanical response when subjected to non-mechanical loads such as PZTs, Terfenol-D, SMAs, have become available. Such materials may broadly refer to as functional materials. With the availability of functional materials and the feasibility of embedding them into or bonding them to composite structures, smart structural concepts are emerging to be attractive for potential high performance structural applications.1 A smart structure may be generally defined as one which has the ability to determine its current state, decides in a rational manner on a set of actions that would change its state to a more desirable state and carries out these actions in a controlled manner over a short period of time. With such features incorporated in a structure by embedding functional materials, it is feasible to achieve technological advances such as vibration and noise reduction, high pointing accuracy of antennae, damage detection, damage mitigation etc.2, 3
  2. 2. During the operation of a structure, damages may develop, which will cause a change in the strain/stress state of the structure and the vibration characteristics. By continuously monitoring one or more of these response quantities, it is possible to assess the condition of the structure for its structural integrity. Such a monitoring of the structure is called structural health monitoring. Health monitoring application has been receiving great deal of attention all over the world, due to possible significant impact on safety and longevity of the structure. To implement health-monitoring concept it is necessary to have a number of sensors to measure response parameters. These responses will then be post-processed to assess the condition of structure. Mark Lin and Fu-Kuo Chang 4 built such a system when they developed a built-in monitoring system for composite structures using SMART layer containing a network of actuators or sensors. Change of structural dynamic performance caused by structural damage that is less than 1% of the total structural size is unnoticeable. Yan and Yam5 pointed out that when the crack length in a composite plate equals 1% of the plate length, the relative variation of structural natural frequency is only about 0.01 to 0.1%. This was also shown by Nag et al.6 Therefore, using vibration modal parameters, e.g., natural frequencies, displacement or strain mode shapes, and modal damping are generally ineffective in identifying small and incipient structural damage. It has been theoretically and experimentally proved that local damage in a structure will cause the reduction of local structural stiffness, which leads to variation of dynamic performance of the whole structure. In industry, using the time domain measured structural vibration responses to identify and monitor structural damage is one of the important ways to ensure reliable operation and reduced maintenance cost for in-service structures. Magnetostrictive material such as Terfenol-D, hitherto considered as only actuator material, was shown to be used for sensing application in reference.7 In this work, the authors proved this capacity experimentally by passing a magnetic field on to an actuator magnetostrictive patch and measured the voltage across the sensing patch to infer the presence of damage. In this paper we take this approach not only to confirm its presence but also its location. Noncontact magnetostrictive strain sensor was explored by Kleinke, D. K. et al.8 and the study of magnetostrictive particulate actuator was done by Anjanappa, M. et al.9 Sensing of delamination in composite laminates using embedded magnetostrictive material was studied by Krishna Murty, A. V. et al. 10 In this paper we have developed a new finite element formulation for inbuilt magnetostrictive patches for performing numerical simulations. The mathematical relationships between sensor open circuit voltage and structural damage status (i.e., damage location and severity) are very complex. It is not only strongly non-linear, but also often has no analytical solution. Deduction from sensor output to practical damage status is mathematically classified as inverse problem, and is very hard to compute precise solution using mathematical analysis. If one takes the inherent law between sensor output and practical damage status as a black box, the mapping relationship between these two state spaces can be established using genetic algorithms (GAs) or artificial neural networks (ANN). Thus, one need not know explicitly the inherent law in structural damage detection. Moslem and Nafaspour11 and Chou and Ghaboussi12 reported some researches on structural damage detection using GAs, and they were successful in determining the severity and locations of structural damage. However, GAs-based structural damage detection requires repeatedly searching from numerous damage parameters so as to find the optimal solution of the objective function (measured data). The cost of computation limits the use of GA for damage detection applications. ANN has particular advantage in establishing accurate mapping relationships between sensor data and physical parameters of structural damage. When classifications and identification of structural damage needs to be carried out, the required task is only to train the ANN in advance using a set of known sensor data and damage physical parameters of the structures that needs detection. Hung and Kao 13 and Yun and Bahng14 reported their researches on structural damage detection using ANN, and their results showed that ANN is a highly effective tool for identifying structural damage.
  3. 3. In this study, an integrated automated active damage detection method is developed for composite laminate through theoretical study and numerical simulation. This method requires the generation of excitation and structural response measurement using bonded magnetostrictive patch actuator and sensors; which is used in ANN framework for classification and identification of structural damage. CONSTITUTIVE MODEL FOR MAGNETOSTRICTIVE MATERIAL Application of magnetic field causes strain in the magnetostrictive material (Terfenol-D) and hence the stress, which changes permeability of that material.15 The three-dimensional constitutive relationship for magnetostrictive material is generally written as {ε } = [ S { H } ]{σ } + [d ]T {H } ....(1) {B} = [d ]{σ } + [ µ {σ } ]{H } ....(2) Where {ε} and {σ} are strain and stress respectively. [S (H)] represents elastic compliance measured at constant {H} and [µ{σ}] represents the permeability measured at constant stress {σ}. Here [d] is the magneto-mechanical coupling coefficient, which provides a measure of the coupling between the mechanical strain and magnetic field. In general, [S], [d] and [µ] are nonlinear as they depend upon {σ} and {H}. However, reasonable response estimation can be obtained by treating them as linear [15]. To work with displacement based finite element formulation, above equations can be rewritten as {σ } = [Q]{ε } − [e]T {H } ....(3) {B} = [e]{ε } + [ξ ]{H } ....(4) Where [Q] is Elasticity matrix, inverse of compliance matrix [S]. [e] and [ξ] are related to [Q] through [e] = [d ][Q] ...(5) [ξ ] = [ µ ] − [d ][Q][d ]T ....(6) For ordinary magnetic materials, where magnetostrictive coupling coefficients are zero, [ξ]=[µ], the permeability. FINITE ELEMENT FORMULATION Finite element formulation begins by writing the associated energy in term of nodal degrees of freedom by assuming the displacement and magnetic field variation over each element. These would lead to the associated stiffness, mass and coupling matrices on minimizing the total energy using Hamilton's Principle. The details of these are summarized below. Strain energy in magnetostrictive material is given by 1 T 1 T 1 e 1 e Ve = ∫ ε σdv = 2 ∫ ε {Qε − e H }dv = 2 {U }[ K UU ]{U } − 2 {U }[ KUH ]{H }...(7) T e e 2 Where {Ue} and {He} are nodal mechanical and magnetic degrees of freedom. KUU is stiffness matrix for mechanical-mechanical degrees of freedom and KUH is stiffness matrix for mechanical-magnetic degrees freedom. Kinetic energy in magnetostrictive material is
  4. 4. 1 . T . 1 .e . Te = ∫ {u} ρ{u}dv = − {U }[ M UU ]{U e } ...(8) 2 2 Where { u } is velocity vector, ρ is density of the magnetostrictive material, { U } are nodal velocity of . . e mechanical degree of freedom. MUU is mass matrix for mechanical-magnetic degrees freedom. Magnetic potential energy in magnetostrictive material is 1 1 1 e 1 Vm = ∫ B Hdv = 2 ∫ {eε + ξH } Hdv = 2 {U }[ KUH ]{H } + 2 {H }[ K HH ]{H }...(9) T T e e e 2 Where [KHH] is stiffness matrix of magnetic-magnetic degrees of freedom, {H e} is magnetic nodal degrees of freedom. Magnetic external work done for N turn coil with coil current I is Wm = IN ∫ B H dA =IN ∫ {ξH }T dA =In ∫ {ξH }T dv ={FH }T {H e } T ...(10) Where n is coil turn per unit length of patch and A is cross sectional area of the magnetostrictive material. Mechanical External Work done is We = ∫ b T udv + ∫ τ T udA ={R}T {U e } ...(11) Where b is body force, and τ is surface force, {R} is equivalent nodal load for external mechanical forces T of the magnetostrictive material. t2 ∫ Using Hamilton's Principle, ∂ ( [Te − Ve + Vm + Wm + We ]dt ) = 0 we get the following governing t1 equation and its associated force boundary conditions. 0 U e   K UU ..  M UU   − K UH  U e  − R   ..  +  =  ...(12)  0  0  H e   K HU    K HH   H e   FH   Expanding we get .. [ M UU ]{U e } + [ K UU ]{U e } − [ K UH ]{H e } = {− R} ...(13) And [ K UH ]T {U e } + [ K HH ]{H e } = {FH }T ⇒ {H e } = [ K HH ] −1{{FH } − [ K UH ]T {U e }}...(14) Substituting Equation (15) in Equation (14) we get .. [ M UU ]{U e } + [ K UU *]{U e } = {F * } ...(15) Where [ K UU *] = [[ K UU ] + [ K UH ][ K HH ] −1 [ K UH ]T ] & {F * } = [ K UH ][ K HH ] −1{FH } − {R}...(16) Here {FH} can be calculated from equation (11). The sensor circuit current is considered equal to zero. From Faraday's law open circuit voltage V in the sensing coil can be calculated. T T ∂B ∂B ∂ V = − N s ∫ H dA = −n s ∫ H dv = − n s {RH }T {H e } ∂t ∂t ∂t ∂ = n s [{R H }T [ K HH ] −1 [ K UH ]T ] {U e } ...(17) ∂t Where Ns are total coil turn and ns are coil turns per unit length of the sensing patch.
  5. 5. ARTIFICIAL NEURAL NETWORK Artificial neural networks can provide non-linear parameterized mapping between a set of inputs and a set of outputs with unknown function relationship. A three-layer network (Figure-1) with the sigmoid activation functions can approximate any smooth mapping. A typical supervised feed-forward multi layer neural network is called as a back propagation (BP) neural network. The structure of a BP neural network mainly includes the input layer for receiving input data; the hidden layer for processing data; and the output layer to indicate the identified results. In this study, ability of identifying structural damage status for an ANN is acquired through training the neural network using the known samples. The training of a BP neural network is a two-step procedure. In the first step, the network propagates input through each layer until an output is generated. The error between the output and the target output is then computed. In the second step, the calculated error is transmitted backwards from the output layer and the weights are adjusted to minimize the error. The training process is terminated when the error is sufficiently small for all training samples. The data set is separated into two parts, one for training and the other for testing the network performance. The network parameters are determined, as is common practice, through experimentation. This includes the number of hidden nodes and the learning rates. Data obtained from the magnetostrictive sensors described above, is used to train conventional back propagation networks to identify the delamination size and location of the composite laminate. It is perhaps impossible to combine simplicity and accuracy in a single model of ANN. In this model a simple model that uses hard decisions to partition the input space and output space into a piecewise set of subspaces, with each subspace having its own expert. Single multi-layered perceptron (MLP) uses a black box approach to globally fit a single function into the data, thereby losing insight into the problem. Here computational simplicity is achieved by distributing the learning task among a number of experts, which in turn divides the input space and output space into a set of subspaces. The combination of experts is said to constitute a committee machine. Basically, it fuses knowledge acquired by predictors (experts) to arrive at an overall decision that is supposedly superior to that attainable by any one of them acting alone. NUMERICAL EXAMPLES In this paper a numerical study on 12 layered beam containing two patches, one acting as an actuator and the other as a sensor has been presented. In order to evaluate the influence of delamination location and extent on structural dynamic characteristics, the situation with only one delamination is considered in this study. In the finite element model, the delamination is modeled keeping two elements in the same location, and integrated bottom element from bottom layer to delamination layer and top element from delaminated layer to top layer. At delaminated zone, two nodes are created in the same places, one is connected with top elements and other is connected with bottom elements. Forward Problem In this example, a cantilever beam modeled with the formulated elements having cross coupling stiffness is used to demonstrate the concept of health monitoring. Mechanical displacement fields considered are as follows
  6. 6. u ( x, y , z , t ) = u 0 ( x, t ) + zθ x0 ( x, t ), v( x, y, z , t ) = 0 & w( x, y, z, t ) = w 0 ( x, t )...(18) Where u, v, w are the components of displacement in X, Y and Z direction at location (x, y, z). u0 and w0 are the displacement components in mid plane of the composite beam. θ0x is the angular rotation of the mid plane about X-axis. Magnetic fields are H x ( x, y, z, t ) = H x ( x, t ) + zQHX ( x, t ), H y ( x, y, z , t ) = 0 & H z ( x, y, z, t ) = 0 ...(19) 0 Where Hx is the magnetic field in X direction. H0x and QHX are X component of mid plane magnetic field and magnetic field gradient in thickness direction respectively. Numerical simulation is carried out by considering a unidirectional laminated composite beam of total thickness 1.8 mm as shown in Figure-2. Length and width of the beam is 500 mm and 50 mm respectively. The beam is made of 12 layers with thickness of each layer being 0.15 mm. Delamination is modeled as explained in the last section. Parametric studies are done by changing delamination size span wise of the cantilever beam for each layer. Position of sensor is fixed at 9th layer from bottom of the beam and near the support of the beam, while the position of actuator is fixed at 1st layer from bottom of the beam and 425 mm apart from support. Size of the actuator is 50 mm X 50 mm with 0.15 mm thickness and size of the sensor is 50 mm X 50 mm with 0.3 mm thickness. Elastic modulus of composite is assumed 181 GPa and 10.3 GPa in parallel (E1) and perpendicular (E2) direction of fiber. Poison ratio (ν), density (ρ) and shear modulus (G12) of composite are taken as 0.0, 1.6 gm/c.c. and 28 GPa respectively. Elastic modulus (Em), poison ratio (νm), shear modulus (Gm) and density (ρm) of magnetostrictive material are assumed 30 GPa, 0.0, 23 GPa and 9.25 gm/c.c. respectively. Magneto-mechanical coupling coefficient is 15E-09 m/amp. Direct transient dynamic analysis has been done with 500 time steps to calculate open circuit voltage of the sensor in each time steps. Relative permeability µr is the ratio of permeability of the material and permeability of air is assumed as 10 for magnetostrictive material. Permeability at vacuum or air is 400π nano-Henry/m. Number of coil turn in sensor (Ns) and actuator (N) is assumed 1000. Actuation current at actuator (I) is taken as 1.0 Amp at three different frequencies. Result and Discussion Numerical results have been simulated for a fixed position of sensor and actuator (x1 =25mm, y1 =0.45mm, w1 =50mm, d1 =0.3mm, x2 =425mm, y2 =-0.825mm, w2 =50mm, d2 =0.15mm) combination, for different locations of the delamination. Open circuit voltages in the sensor have been shown in 3-D plot. Figure-3 shows open circuit voltages in the sensor when the delamination is between 4th and 5th layer, between 8th and 9th layer with input frequency 5000Hz. Inverse Problem As structural damage information is distributed in different vibration modes, and vibration modes with high frequencies are generally more sensitive to small damage, three different frequencies (50, 500, 5000Hz) are considered to actuate the actuator. Thus input space is subdivided in three different subspaces. In this study, sinusoidal actuation current is considered in the actuation coil. Three sin wave excitation with different frequencies are exerted on the dynamic model of the composite laminate, and the vibration responses of 550 different cases are numerically simulated for each frequency. These 550 cases include the intact laminate, laminates with delamination damage at different layers and of 50 different delamination sizes (10 mm to 500 mm) at each layer. For all vibration responses of the given delamination length and location, each vibration response (open circuit voltage in magnetostrictive sensor) is preprocessed for dimension reduction in the input space of the artificial neural network. First fifteen optimum values of time integral of the sensor open circuit voltage are taken as the input space of the artificial neural network. In order to identify the delamination length at each layer, one BP neural network with 15 inputs and 1 output are designed. Two hidden layers of node strength 10 and 5 respectively are taken as the net architecture. Every layer nets are trained by 50 sample data. These samples are for delamination in the corresponding layer or
  7. 7. for healthy structure. Thus there are eleven neural networks are experts in the corresponding layer for each actuation frequency to predict the size of the delamination. Similar to layer wise experts, the laminate is located span wise in 19 different overlapped zones. For each zone and for every actuation frequency one expert is trained to predict the delamination layer. Apart from these layer wise experts and length wise experts one actuator expert and one sensor expert are trained for every actuation frequency. Actuator nets are trained taking sample data, which are near the actuator, and similarly sensor nets are trained taking sample data, which are near the sensor to predict the size and layer location of the delamination. Figure-4 shows the training performance of different layer wise experts for input frequency 5000 Hz. In identification face, verification samples (time domain sensor output) for each frequency are feed into every experts (layer, length wise, sensor and actuator) to get there output and from this mutual information the location and size of the delamination is obtained. After getting the location and size of delamination it is checked that all experts, those are near the delaminated zone in output subspace for each input subspace agree with this information. CONCLUSIONS The study demonstrates the use of vibration response using magnetostrictive sensor and actuator of an in- service structure for health information of the structure. The study also shows the feasibility of online damage detection and health monitoring using ANN-based identification. This study is successful in classifying and identifying structural damage location and severity using the designed neural networks. The results show that ANN is a powerful tool for establishing the mapping relationships between open circuit voltages and the structural damage status, and demonstrate the ability of ANN for structural damage detection. REFERENCES 1. Loewy, R.G. Recent developments in smart structures with aeronautical applications, Smart Materials and Structures, 6, 1997, pp. R11-R42. 2. Gandhi, M.V. and Jhompson, B.S. Smart Materials and Systems, Chapman and Hall, London 1992. 3. Brain, C. Smart Structures and materials, Artech house, Boston 1996. 4. Mark Lin and Fu-Kuo Chang Composite structures with built-in diagnostics. Materials Today Volume 2 Issue 2 June 1999. 5. Y.J. Yan and L.H. Yam, Online detection of crack damage in composite plates using embedded piezoelectric actuators/sensors and wavelet analysis. Compos. Struct. 58 (2002), pp. 29-38. 6. Nag, A. Roy Mahapatra, D. and Gopalakrishnan, S. Identification of delamination in a composite beam using a damaged spectral element. Structural Health Monitoring. Vol-1(2). 7. Saidha E., Naik G N and Gopalakrishnan, S. An experimental investigation of a smart laminated composite beam with magnetostrictive patch for health monitoring applications. Structural Health Minitoring.
  8. 8. 8. Kleinke, D.K and Uras, H.M. A noncontacting magnetostrictive strain sensor, Rev. Sci. Instrum. 64, pp. 2361-2367, 1993. 9. Anjanappa, M., and Wu, Y. F. Magnetostrictive particulate actuators: configuration, modeling and charaterization, Smart Material and Structures, 6, 1997, pp. 393-402. 10. Krishna Murty, A. V., Anjanappa, M., Wang, Z. and Chen, X. Sensing of delaminations in composite laminates using embedded magnetostrictive particle layers, Journal of Intelligent Material Systems Structures, Vol-10 October 1999,pp 825-835 11. K. Moslem and R. Nafaspour, Structural damage detection by genetic algorithms. AIAA J. 40 7 (2002), pp. 1395-1401. 12. J.H. Chou and J. Ghaboussi, Genetic algorithm in structural damage detection. Comp. Struct. 79 (2001), pp. 1335-1353. 13. S.L. Hung and C.Y. Kao, Structural damage detection using the optimal weights of the approximating artificial neural networks. Earthquake Eng. Struct Dyn. 31 (2002), pp. 217-234. 14. C.B. Yun and E.Y. Bahng, Substructural identification using neural networks. Comp. Struct. 77 (2000), pp. 41-52 15. Butler, J.L, Application manual for the design of Terfenol-D magnetostrictive transducers, Edge Technologies Inc., Ames Iowa, 1988. 16. Kraus, John D. Electromagnetics, Fourth Edition, McGRAW-HILL International Editions, Electrical Engineering Series 17. Kumar, M., and Krishna Murty, A. V. Sensing of delaminations in smart composite laminates, J. Aero. Soc. Of India, Vol 51, p 7-9, February 1999. FIGURES
  9. 9. Figure 1: Artificial Neural Network Figure 2: Laminated Beam with Actuator, Sensor and Delamination. Figure 3: Actuation frequency 5000 Hz Figure 4: Learning performance of layer experts