Sathyabama University, Chennai , India 2. Indira Gandhi Centre for Atomic Research, Kalpakkam , India a

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1 More Info at Open Access Database Classification of Defects in Time of Flight Diffraction(TOFD) Images Using Artificial Neural Network C.F.Theresa Cenate 1,a, B.Sheela Rani 1,b B.Venkatraman 2,c and D.N.Sangeetha 2,d 1 Sathyabama University, Chennai , India 2 Indira Gandhi Centre for Atomic Research, Kalpakkam , India a th.cenate@gmail.com, b kavi_sheela@yahoo.com, c bvenkat@igcar.gov.in, d sangee@igcar.gov.in Keywords: TOFD, Statistical features, ANN, Classification Abstract. Conventional NDE techniques like radiography cannot be used for assessing the quality of thick walled welds due to problems of access. This is especially true in case of welds related to main vessel and safety vessel, because there is a difficulty in acquiring the data. In such critical situations Time-of-Flight Diffraction (TOFD), one of the advanced methods of ultrasonic inspection is the best suited NDE technique for assessing the quality of the welded structures. Most of work on these TOFD method focuses on aiding the manual interpretation process to determine defect characteristics. This paper highlights the successful application of image processing and neural networks (ANNs) to the task of semi automating the decision making process involved in the interpretation of TOFD scans. Weld pads with defined planar and volumetric defects have been fabricated with Austenitic stainless steel. The weldments are inspected by TOFD technique. In the acquired D-Scan ultrasonic images the Region of Interest is manually segmented. The Images thus obtained are with the noise components and are hence denoised using the multidimensional approach, the most promising signal conditioning techniques. By applying morphological processing on these denoised images, the defect areas are segmented from Region of Interest. The successful design of any image classification system is based on the selection of a good set of features. In this work statistical descriptors frame the feature vector that contains information on defect which is identified using the morphological processing and non-defect areas. These defect vectors are further classified using an ANN trained with the back-propagation algorithm. This paper presents in detail the experimental methodology adopted and also the results of the classification. Introduction: Austenitic stainless steel is the best choice of structural material in areas with high operating temperature corrosive environment, resistance to creep deformation and requires good toughness at low temperatures. For these wide spread reasons, austenitic stainless steel has found applications in strategic industries like nuclear, aerospace and process industries like petrochemical. The nuclear plant component contains a number of welded joints and its quality need to be assessed. Time of Flight diffraction (TOFD) is one of the advanced ultrasonic techniques used for Non Destructive testing (NDT) of welded structures, primarily used for its high speed data and probability of detection [1,2]. This technique employs two transducers, a transmitter and a receiver, in which diffracted energy characterizes the weld defects. The interpretation of weld defects is a crucial process and it requires skilled operators to achieve consistency and reliability [2]. However, error occurs in interpretation process when the volume of data increases. The latest trend is to automate the interpretation to increase the reproducibility and accuracy. This can be achieved by image processing and artificial intelligence techniques to differentiate between diverse regions in an image. The algorithm of neural networks can process a large amount of data, learn by training and can in turn be used for automatic classification [3]. Literature review revels that very few research has been undertaken on the application of ANN for classifying weld defects in TOFD images. Shawn Lawson describes, the application of image processing and neural networks (ANNs) to the task of completely automating the decision making process involved in the interpretation of TOFD scans. Shekhar,

2 developed a fully automated system using neural-fuzzy classifier for exhibiting high level of accuracy, consistency and reliability within reasonable computational time. This paper presents the findings in work undertaken to semi automate the interpretation of ultrasonic images acquired by TOFD method in austenitic stainless steel thick walled weldments. Multiresolution analysis is used for noise reduction whereas; Image processing techniques are used for segmentation. In the classification stage the number of hidden layers is varied. The classification of defects is performed by artificial neural network acquiring the signatures of different regions using the statistical descriptors. Fig 1 shows general processes flow of semi-automated TOFD weld defect recognition system. Data Acquisition Image Preprocessing Classification Fig 1 General process flow of semi-automated TOFD weld defect recognition system Semi-Automated TOFD Weld Defect Recognition System Data Acquisition Module. Material. A systematic experimental study has been undertaken on 5 austenitic stainless steel weld pads with thickness of 25mm and length and width of 200mm*200mm with volumetric and linear defects. These defects were introduced during the welding process. The weld that is used is of double V butt joint configuration and is made with shielded metal arc welding process. The type, position and size of the inserted defect were verified through radiographs. Ultrasonic testing system. Subsequent to ground flushing the weld faces, the examination of the specimen is carried out using the contact scanning Time of Flight Diffraction technique, µtofd of AEA technology equipment. The equipment stores the D-Scan Images from the region of weld being inspected. After the inspection of all the weldpads, the database was created with the images available. Image Preprocessing Module. After image acquisition, only the Region of Interest (ROI), i.e. the region between Lateral wave and Backwall echo is further processed. The raw TOFD data collected needs initial pre-processing to assist classification of flaws in weld. In this paper the author considers denoising (noise suppression) and segmentation as part of pre-processing. Image denoising. The aim of denoising is to reduce/remove the noise while retaining the original fundamental image. This is achieved conventionally by linear processing [5] in recent times non-linear techniques are being used. Author et al. [6] have proposed that the Symlet with six vanishing points in the fourth level of decomposition gives the maximum performance using universal threshold algorithm on the degraded TOFD Images, at ambient Temperature. All the images are denoised using the above algorithm. Segmentation. Segmentation subdivides an image into its constituent regions or objects. The level to which the subdivision is carried out depends on the problem being solved [7]. In this work, segmentation is achieved through morphological processing. Classification Module. As soon as the defects are segmented, descriptors can be extracted and then given as input to classifiers to detect possible defects and to identify eventually the exact defect type. This work focuses on feature extraction and classification methods.

3 Feature extraction. Several types of features have been used in literature. It includes geometric descriptors [7] wavelet features [7], texture features [8,1] and first order descriptors[4]. In this study, we use the first order descriptors like Mean, Standard Deviation, Skewness, Kurtosis and Energy. They are extracted from the Macro-Images of the segmented image and are fed as inputs to the neural network. Classification. Given the first order statistical descriptors, the next step concerns the classification. There are several multi-class classifiers that can be employed but the ones selected must be able to cope with non-linearity. Among the state of the art algorithms that best satisfy these requirements are the Artificial Neural Networks (ANN). The Artificial Neural Network (ANN) is considered as a feedforward ANN that has one input layer fed with a set of input variables, hidden layer of adjustable number of hidden neurons and output layer of one neuron [3]. The neurons pass the result from one layer to another through a transfer function - The tansig is used to activate functions in the input and hidden layer whereas purelin is used to activate neurons in the output layer, besides the ANN can be trained using a method such as backpropagation [3]. The training function selected for the network is trainrp. MATLAB NN toolbox is used for the design, implementation and simulation of the network with feed forward back propagation algorithm. In this work, different combination of layers and neurons were attempted. The developed neural network is trained several times until the network satisfies the goal of zero [10]. Table 1 Sample data for training the NN model Mean Std. deviation Skewness Kurtosis Energy No Defect No Defect Lack of Penetration Lack of Penetration Slag Slag Lack of Fusion Lack of Fusion The features extracted from each macro image are used as input to the artificial neural network using MATLAB. The neural network needs to be trained with the normalized data base representing different types of defects in order to classify defects reliably. A data base is created with 160 sets, consisting of 40 sets for each class. From these, 30 sets are used for training and 10 sets are used for testing the trained network. The sample data for training the NN model is given in Table 1. The network is trained with the training data after several iterations. After which, the test data are given to the network. This set of data is used for testing which are not considered from the training set. Results and Discussion The aim is to get a well-trained ANN capable of classifying various defects from their TOFD D-Scan images. The 40 sets of descriptor set is used for cross validation and for assessing the ability to generalize the previously trained ANN. Presently, a network having single neuron in the output layer is considered.

4 The output value (1,2,3,4), ±δ is considered as valid output 0,1,2,3,4 and all the other values are not valid output. The performance goal is set at 4.63e -8 by trial and error in this study, to make the error converge to zero and in turn achieve the valid output. Table 2 Selection of optimal BPN architecture for classification of four classes of defect No of neurons in different layer 5,20,10,5,1 5,10,5,1 5,5,1 Network N1 N2 N4 No Defect Lack of Penetration Slag Lack of Fusion Overall percentage The best three results based on highest percentage of total success rate for the classification of four classes of defect are shown in Table 2. Best BPN architecture is selected based on the best classification results. Best results are shown in network 2 for different number of neurons, with the same training algorithm and activation function throughout the research. Table 3: Table of success and errors training and testing Defect Training Testing Success rate Error rate Success rate Error rate (%) (%) (%) (%) No Defect Lack of Penetration Slag Lack of Fusion Total The results show that the ANN combined with first order descriptors gives classification accuracy at the rate of 65% (Table 3). First order descriptors are simple to calculate and thereby it reduces the training time considerably. Conclusion In this paper, a semi automatic weld defect classification system has been proposed. Initially denoising and segmentation were performed. Statistical descriptors are used for describing the defect features. Exemplars are created based on those features and are used as inputs for BPN based classifier. Performance of the network is determined for both trained and test dataset. It is found that the network provides 100% sensitivity of trained dataset and 65% sensitivity for the test dataset. Performance of the network can be improved by choosing powerful descriptors and simulated annealing based classifiers.

5 Acknowledgement This TOFD project work has been supported by the Board of Research in Nuclear Sciences (BRNS), Department of Atomic Energy, Government of India. Authors acknowledge the Management of Sathyabama University for their moral support, Head and Staff of QAD, RSD & NDT, Indira Gandhi Centre for Atomic Research(IGCAR) for their technical support extended for data acquisition. References 1. C Shekhar N Shitole, O Zahran and W Al-Nuaimy, Combining fuzzy logic and neural networks in classification of weld defects using ultrasonic time-of-flight diffraction, NDT 2006, the 45th Annual British Conference on NDT, Stratford-upon-Avon, UK, September Shyamal Mondal, T.Sattar, An overview TOFD method and its Mathematical Model, NDT.net April 2000 Vol. 05 No S W Lawson and G A Parker, Automatic Detection of Defects in Industrial Ultrasound Images Using a Neural Network, Proceedings of SPIE, Vol 2786, Pages 37-47, Reference to a book: 4. S. Haykin: Neural Networks, A Comprehensive Foundation, Macmillian College Publishing, Zhenjian Wang, Denoising Images using Wiener filter in directionalet domain (cimca-iawtic'06) 2006 IEEE. 6. C.F.Theresa Cenate, B.Sheela Rani, D.N.Sangeetha, B.Venkataraman, Baldev Raj, Noise reduction in Ultrasonic images using Wavelet algorithm, National Seminar on Non Destructive Evaluation, pp12, N.M.Nandhitha, N.Manoharan, B.Sheela Rani, B.Venkatraman, P.Kalyana Sundaram, Baldev Raj, A comparative study on the performance of the classical wavelet based edge detection for image de-noising in thermographs, 12th APCNDT 2006 Asia-Pacific Conference on NDT, Nov 2006, Auckland, New Zealand 8. Valavanis, I., & Kosmopoulos, D. Multiclass defect detection and classification in weld radiographic images using geometric and texture features, Expert Systems with Applications (2010), doi: /j.eswa Ahmed Kechida, Redouane Drai, Abderrezak Guessoum, Texture Analysis for Flaw Detection in Ultrasonic Images, Journal of Nondestructive Evaluation, June 2012, Volume 31, Issue 2, pp Information on http