Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Discontinuity in welds is one of the causes of the quality of a connection in the material decreases function. Undamaged test with radiographic method is one of the tests to see the quality of a weld. The test results are radiograph images and evaluated by a radiographer. So this research is designed by optimizing a system to help the work of a radiography expert in identifying discontinuities by utilizing the Matlab Application. On this system uses the method of characteristic extraction and classification of neural networks (AAN). The system uses a characteristic extraction method with geometric invariant moment (GIM) algorithms and a gray level co-occurenece matrix (GLCM) as identification values used in the classification process. The calcification process uses a backpropagation-type multilayer Artificial Neural Network. The types of discontinuities used as data in this system are incompleted of penetration, crack, wormhole, and distributed porosity using a total of 800 datasets of radiograph imagery data.  This data sharing is organized using k fold cross validation. The study conducted 15 experiments in system testing to prove the truth in identifying. The results of the experiment resulted in the highest average performance score reaching 93.33%