The aging aircraft program sponsored by the FAA is keen on improving existing technologies and developing new techniques for the detection of cracks and corrosion in aircraft structures. This program is a part of the overall scheme of addressing the global aging infrastructure problem. The authors have been involved in the aging aircraft program through the NASA Langley Research Center (LaRC).</p

Fulton, J.

Nath, S.

Wincheski, B.

Digital Repository @ Iowa State University (ISU)

MAGNETO-OPTIC IMAGE CLASSIFICATION USING NEURAL NETWORKS S. Nath CNDE Johns Hopkins University 102 Maryland Hall Baltimore, MD 21218 J. P. Fulton and B. Wincheski Analytical Services and Materials, Inc. NASA Langley Research Center MS 231 Hampton, VA 23681 INTRODUCTION The aging aircraft program sponsored by the FAA is keen on improving existing technologies and developing new techniques for the detection of cracks and corrosion in aircraft structures. This program is a part of the overall scheme of addressing the global aging infrastructure problem. The authors have been involved in the aging aircraft program through the NASA Langley Research Center CLaRC). Magneto-optic imaging (MOl) is based on the principle of eddy current and imaging of the magnetic fields using a magneto-optic sensor. It is now an established technique used in the detection of cracks and corrosion in aircraft skins. The magneto-optic sensor with an area of 60 cm 2 provides rapid inspection of aircraft structures and with its built in optics, one can visualize the defects too [1]. Thus, MOl has the distinct advantage over conventional eddy current techniques in the operator being able to view the defect. The inverse problem in nondestructive evaluation (NDE) is to predict the defect dimensions from the transducer signal. Artificial intelligence tools such as neural networks are now readily available to help solve these problems. These tools can be used by any NDE operator to make decisions and reduce the burden on the operator. A neural network is 'trained' initially by feeding the network known data. The training process makes the network 'intelligent', when it is fed with new data, the neural net will predict the outcome. This research discusses the methodology used in predicting from the magneto-optic images, whether a crack exists near a rivet or not. The next few sections will briefly discuss the MOl unit, method of moments as the feature extraction scheme for the neural net and the results of the trained neural net on some realistic aircraft data. BACKGROUND Magneto-Optic Imaging Principles A schematic of the MOl instrument is shown in Figure 1. A thin copper film is excited by a time varying signal, which induces uniform, linear eddy currents in the aluminum skin located just below the foil. When these eddy current paths are perturbed by Review of Progress in Quantitative Nondestructive Evaluation. Vol. 15 Edited by D.O. Thompson and D.E. Chimenti, Plenum Press, New York, 1996 891 Light source • J .. Incident beam II Sample ~ -,..- Analyzer f~~~~ ySensor Current sheet Figure 1. Schematic of the MOl unit head. surface and subsurface defects, they generate normal magnetic fields. A thin film magneto~ optic sensor made of bismuth-doped iron gamet (Bi,Tm) 3 (Fe,Ga)sO 12 grown on a 75 mm diameter, 0.5 mm thick substrate of gadolinium gallium gamet senses these normal magnetic fields [2]. The three main properties of the gamet film are (1) they have an easy axis of magnetization normal to the sensor surface, (2) they have a memory effect and (3) posses a large Faraday rotation. A normally incident polarized light is transmitted through the sensor, and then reflected back through this same region. By properly viewing the reflected light, one can observe a good contrast between the background image and the defect image. A CCD camera at the analyzer end views the MOl images and provides the capability to download these images to a computer. Image Processing A simple Macintosh based imaging system has been developed by NASA LaRC for enhancing the MOl images. The image processing scheme consists of a background subtraction and thresholding to obtain a better contrast between the flawed and unflawed regions [3,4]. Figure 2 shows the overall image processing scheme for MOl images. Since this is done off-line, it allows the user to suppress noise and other spurious signals. From the threshold image an edge detected image and a solid silhouette image of the rivet is obtained and stored for classification. For any neural network scheme to perform well, it is essential that one has to choose an appropriate feature vector for a given image. These features should be unique, informative and invariant to translation, rotation and size of the image. Size invariance is essential for aircraft structures because the rivets to be classified could be of different sizes. It was decided that a few second and third order normalized moments of the edge detected and solid silhouette image be used as the feature space for training the neural network. The next section discusses in detail the method of moments for images. METHOD OF MOMENTS The central moments of any image is given by I1pq = ~~(u-uJ(v-vJ (1) where N is the number of pixels in the image, u and v are the mean values of the image coordinates u and v. The moments give information about the shape or contour, the second moment, known as the variance is a measure of the compactness, the third moment is 892 I Edge detection Rivet with a crack Figure 2. Schematic of the image processing scheme for the MOl images. related to the skewness and gives a measure of the symmetry, while the fourth moment is the kurtosis. Hu [5], in his classical paper of 1962 derived a set of moment functions which seem to be invariant to rotation and translation. A set of seven invariant functions based on the second and third order moments seem to satisfy the needs of a feature vector for this problem. These seven invariant functions [5] are MJ = (1120 + 1lo2) (2) M2 = (1120 - 1102)2 + 41121\ (3) M3 = (1120-31112)2 + (31121 - 1103)2 (4) M4 = (m30) + m12)2 + (m21 + m03)2 (5) M5 = (1130 - Ild(1130 + 1112) . [(1130 + 1112)23(1121 + 1lo3)2] (6) + (31121 - 1103)(1121 + 1lo3)' [3(1130 + 1112)2 - (1121 + 1103)2] M6 = (1120 - 1lo2)[1130 + 11)2)2 - (1121 + 1lo3)2] (7 ) + 41111(1130 + 1ln)(1121 + 1103) M7=(31121-1l03)(1l30+ 1112).[(1130+ 1l12)L3(1121 + 1103)2] (8) -(1130 - 31112)(1121 + 1103) [3(1130 + 1112)2 - (1121 + 1103)2] These functions have been used in the case of identifying aircraft [6], ships [7] and others. In the case of aircraft rivets, it is essential that these moments be size invariant too. Thus the above seven functions can be processed additionally to accomplish this. Thus for any function x and y, we have [Xy"] __ [an °a][Xy] Thus for moment invariants we have l1'pq = ap+Q+2 I1pq where a is a constant. (9) 893 Eliminating 'a' between the zeroth order relation, one obtains J.L'=a2J.L' Similitude moment invariants are Jlpq = Jlpq where p+q=2,3 .... Jl' (p + q) I 2 + I Jl' (p + q) I 2 + I (10) Once the seven invariant moments are obtained the additional step described above is performed. Thus, the moments are invariant to translation, rotation and sizing. IMAGE CLASSIFICATION Image enhancement includes subtracting a background image for eliminating artifacts due to uneven illumination, median filtering, smoothing and thresholding the resultant image to obtain a clean image of the rivet. The final step is to get two images of a rivet, an edge detected image and a solid silhouette image. To classify these images, the proposed task is to extract the invariant moments of the edge detected and the solid silhouette image as training data for a neural network. Artificial neural network model consists of several computational units i.e. nodes which are interconnected via weights that are iteratively adapted to improve performance [8]. Each simple node or neuron is a processing element. It calculates the weighted sum of the inputs and then transforms the sum to an output signal by a nonlinear function. In this application, a multilayer perceptron (MLP) network is being used. The network has an input layer, an output layer and a hidden layer between the input and output layer. The nonlinear function is a sigmoidal function at each node. Figure 3 shows a typical 2 layer MLP schematically. To "train" the neural network, one needs to supply the net known signals or patterns of interest and the desired output. A backpropagtion algorithm "trains" the net which generates an optimum set of weights between each layer. This algorithm typically minimizes the error between the model output and the desired output by using any of the number of schemes available, such as the least squares minimization etc. Input layer Figure 3. Schematic representation of a multilayer perceptron neural network. 894 The feature space for the training consists of the invariant function from both the images, the edge detected and solid silhouette image. The functions from the edge detected image is sensitive to the contours and outline of the image, while the moments of the solid silhouette image are sensitive to the bulk features. Thus by combining them, these moment functions represent all the features of the image. As mentioned earlier, the critical decision for any pattern recognition system is to select the appropriate features from the image for classification. After some extensive testing, it was decided to choose the first four invariant moments from each image and to combine them into a feature vector. This feature vector space was used to train the neural network for classifying the MOl images. RESULTS Initially the classification scheme was tested on manufactured samples at NASA LaRC. These consisted of Al 2024 lap joints with rivets in them. A few fatigue cracks were grown and covered with the rivets. The results obtained from this limited data set provided the confidence to proceed with some actual samples. The FAA NOI Center at Sandia National Laboratories is a test bed for inspecting aircraft components using NOE. This facility is open to anybody interested in testing their NOE technique on some real samples. The NDI Center has a 737 with known areas of corrosion and cracks. It also has a series of panels with rivets, simulating lap joints. Some of these panels are painted, while some are not. These panels have several cracks in them emanating from the rivets. These panels had holes drilled in them, fatigued and then covered with rivets. Thus the lengths of the cracks were recorded optically before the rivets were placed in place. Another set of panels, called the Foster Miller panels were fatigued with the rivets in place. Thus only those having cracks visible beyond the rivet head were optically recorded. The goal of the neural network scheme is to develop an algorithm to classify rivets with and without cracks. The crack lengths inspected with the MOl and used as training data varied from 1.5 mm to 5 mm, as measured from the shank. The shank size is 4 mm with the rivet head diameter being 7 mm. A total of 178 rivets were inspected, with 81 of them having cracks emanating from them. These included data from the FAA NOI center as well as NASA LaRC samples. All of these rivets were manually tested, the images processed and stored for future analysis. A total of 85 rivet images were used for training the network, of which 40 had cracks emanating from them. For each of these rivets, an edge detected and solid silhouette image were obtained, moments obtained and then fed to the input layer of the neural network for training. An earlier study [4] compared the neural network behavior by training it with moments from the edge detected image only, moments from the solid silhouette image and a combination of the both. It was decided to use both the images and form a feature space with moments from each image. Thus the feature space contains eight moments, the first four normalized moments from each of the images. The neural net was trained in the laboratory. A number of different architecture were tried, before finalizing it. The final architecture consisted of the input layer with 8 nodes, a hidden layer with 7 nodes and the output layer with a single node. With proper choice of the different neural net parameters such as acceleration, threshold, the convergence of the training algorithm was rapid. Table I shows the classification results on the data obtained at the FAA NDI center. 895 Table I. Classification results of the neural network scheme. Rivets with cracks Rivets with no cracks Training data set 40 45 Rivets tested with 19 19 neural network Classification % 95 90 CONCLUSIONS The MOl unit is used in the detection of surface defects such as cracks under rivet heads in aircraft structures. The magnetic field due to the linear current source generates eddy current in the sample. Cracks perturb these eddy current paths, generate normal magnetic fields which are then sensed by the magneto-optic sensor. In a typical test environment an operator will view the image of the rivet and decide if any crack exists or not. For cracks that extend much beyond the rivet head, the POD is quite high. In this paper, a neural network scheme has been developed to automate the image classification scheme. A typical rivet image is processed, digitized, and then formed into two images - solid silhouette image and an edge detected image. A set of normalized moments of these images are obtained, which form the feature space for the neural network. A multilayer perceptron network was trained with data obtained at the FAA NDI center and tested on some of the unknown rivet images. The results indicate that this scheme can be an additional tool for the operator to help in making a decision if a crack exists or not. ACKNOWLEDGEMENTS This work would not have been possible without the guidance, support and encouragement of Dr. Min Namkung of the NASA Langley Research Center. REFERENCES 1. S. Simms, Mat. Eval. 51,529 (1993). 2. G.L. Fitzpatrick, et. aL Mat. Eval. 51, 1402 (1993). 3. B. Wincheski, D.R. Prabhu, M. Namkung and E.A.Birt, in Review of Progress in QNDE; Vol. 11, eds. D.O. Thompson and D.E. Chimenti (Plenum, New York, 1992), p. 871. 4. S. Nath, B. Wincheski, J.P. Fulton and M. Namkung, in ASNT Spring Conference digest, New Orleans, (1994). 5. Ming-Kuei Hu, IRE Trans Infonn Theory, 179 (1962). 6. S. Dudani, K. J. Breeding and R.F. McGhee, IEEE Trans. Comput. C-26, 39 (1977). 7. F.W. Smith, and M.H. Wright, IEEE Trans. Comput. C-20, 1089 (1971). 8. L. Udpa and S.S. Udpa, Mat. Eval. 48, 33 (1990). 896 

Magneto-Optic Image Classification Using Neural Networks

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Magneto-Optic Image Classification Using Neural Networks

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