Automatic classification of weld cracks using artificial intelligence and statistical methods Ryszard SIKORA, Piotr BANIUKIEWICZ, Marcin CARYK Szczecin University of Technology Department of Electrical and Computer Engineering ul. Sikorskiego 37, Szczecin POLAND

Szczecin, June OUTLINE  Digital radiography system  Automatic Defect Recognition algorithm  Introduction,  ADDIP,  Data base preparation,  Statistical analysis  Artificial neural network classifier  Conclusions

Szczecin, June DIGITAL RADIOGRAPHY SYSTEM 1.Portable X-Ray source (120KV, 1mA) 2.Phosphor-plate scanner (spatial resolution 50μm, digital resolution 16bit) 3.Personal computer ( Pentium D 3,2 GHz, 2GB RAM) 123 DR CP120

Szczecin, June AUTOMATIC DEFECT RECOGNITION ALGORITHM Radiograph acquisition ROI Selection, IQI detection and evaluation Contrast enhancement, normalization, noise reduction Image segmentation Defect detection & indexing Feature extraction Defect recognition Acceptance algorithm

Szczecin, June INTRODUCTION  The defects data base was prepared using ADDIP (developed by PS),  The classification is done in accordance with respective welding norm EN ISO  The statistical method PCA is applied in order to find redundant features,  The artificial neural network was used as a defect group classifier,  The real digital radiographs of welded parts of a ship were analyzed

Szczecin, June ADDIP Automatic Defect Detection and Identification Processor (ADDIP) is a collection of selected image processing algorithms dedicated for automatic radiograph analysis.  Automatic Defect Detection and Identification Processor (ADDIP) is a collection of selected image processing algorithms dedicated for automatic radiograph analysis.  The ADDIP was created as a programming environment for quick and easy testing of newly developed algorithms for defect identification and recognition.

Szczecin, June DATA BASE PREPARATION Algorithm of data base preparation using function implemented in ADDIP

Szczecin, June DATA BASE PREPARATION Acquired radiograph image with defects Image after rotation, crop and normalization operations

Szczecin, June DATA BASE PREPARATION ROI region detected Image cropped to ROI region and segmented Flaw 1Flaw 3 Flaw 2Flaw 4

Szczecin, June DATA BASE PREPARATION Flaw 1 Weld image Weld image - background Thresholded image Index image

Szczecin, June DATA BASE PREPARATION Flaw 2 Weld image Weld image - background Thresholded image Index image

Szczecin, June DATA BASE PREPARATION Flaw 3 Weld image - background Thresholded image Index image Weld image

Szczecin, June DATA BASE PREPARATION Flaw 4 Weld image Weld image - background Thresholded image Index image

Szczecin, June DATA BASE PREPARATION Type of defects analyzed (according to EN-ISO-6520_1) 101 (6,3%), 102 (2,0%) - Cracks Example image: 2011 (6,3%) - Porosity and gas pores, 2013 (15,0%) - Clustered porosity, 2015 (8,7%) - Elongated cavities, 2016 (12,8%) – wormholes Example image: 3011 (17,4%), 3012 (12,8%) - Slag inclusions Example image:

Szczecin, June DATA BASE PREPARATION Type of defects analyzed (according to EN-ISO-6520_1) 4011 (12,8%) - Lack of side wall fusion Example image: 5011 (6,3 %) - Continuous undercut Example image:

Szczecin, June STATISTICAL ANALYSIS Database:over 400 cracks in 20 pictures from Technic-Control Five groups according to EN-ISO 6520 norm - Group 1 – cracks - Group 2 – porosity and gas pores - Group 3 – slag and inclusions - Group 4 – lack of fusion, lack of penetration - Group 5 – continuous undercut Principal Components Analysis - a quantitatively rigorous method for achieving simplification of dimensionality of database. Dimensionality of features space: 21 First eight principal components (PC) explain almost 100% of the total variability in the standardized ratings. Cumulative sum

Szczecin, June STATISTICAL ANALYSIS Data separation for eight PC

Szczecin, June STATISTICAL ANALYSIS Factor analysis – finding redundant features 1 – Area 2 – Perimeter 3 – Center of gravity (x) 4 – Center of gravity (y) 5 – Center of gravity according to brightness (x) 6 – Center of gravity according to brightness (y) 7 – Longer diagonal of ellipse 8 – Second diagonal of ellipse 9 – Perpendicular diagonal to longer diagonal 10 – Angle 11 – Compactness 12 – Anisometry 13 – Elongation 14 – Lengthening 15 – Rectangularity 16 – Mean Brightness 17 – Max Dev of Brightness 18 – Ratio 19 – Heywood 20 – Surroundings 21 – Surroundings (mean brightness) Visualization of the principal component coefficients for each feature Features 4 and 6 are linearly depended Features 3 and 5 are linearly depended Features 2 and 7 are linearly depended Features 16, 20 and 21 are linearly depended

Szczecin, June ARTIFICAL NEURAL NETWORK CLASYFIER Feature Vector Neuron of hidden layer one hidden layer = 12 neurons two hidden layers = [15 10] neurons Neuron of output laye r Output layer = 5 neurons Two structures of neural networks were trained, with one hidden layer and with two hidden layer, Number of input corresponds to number of features, Number of inputs corresponds to number of defect group, The Levenberg-Marquardt optimization method was used as a network training function, The features database was randomly divided into three sets: 1) a training data set, 2) a validation data set and 3) testing data set. Artificial neural network structure

Szczecin, June ARTIFICAL NEURAL NETWORK CLASYFIER In order to evaluate effectiveness of neural network classifier the mean square error between output vector and target vector was calculated between output vector and target vector was calculated where Yi – Output vector Di – Target vector N – number of samples in each defect group i – number of output neurons = 5 Defect group 1Defect group 2Defect group 3Defect group 4Defect group 5 Training data Validation data Testing data MSE obtained for neural network with two hidden layers

Szczecin, June CONCLUSIONS Cracks (group 1) are most separated defects group so easiest to detect, The most difficult to distinguish is defect group 4, which can be confused with first, second and third defect group, Small error obtained for training data and validation data confirms that the structure of applied NN has been chosen correctly, The best results of NN have been achieved for second, third and fifth group of defects, which are porosity and gas pores, slag and inclusions, undercuts respectively, Having suitable big training set, it is possible to build semi- automatic system distinguishing among main groups of imperfections