Open AccessIDENTIFICATION OF INCOMPLETE PENETRATION WELDING DEFECTS ON DIGITAL RADIOGRAPHIC FILM FIGURES WITH THE GEOMETRIC INVARIANT MOMENT
Author(s) -
Agus Probo Sutejo,
Haerul Ahmadi,
Tasih Mulyono
Publication year - 2021
Publication title -
jurnal forum nuklir/jurnal forum nuklir
Language(s) - English
Resource type - Journals
eISSN - 2580-9547
pISSN - 1978-8738
DOI - 10.17146/jfn.2021.15.2.6283
Subject(s) - artificial intelligence , computer vision , welding , computer science , identification (biology) , invariant (physics) , feature extraction , pattern recognition (psychology) , engineering drawing , mathematics , engineering , mechanical engineering , botany , mathematical physics , biology
The examination of defects in radiographic films necessitates specialized knowledge, as indicated by an expert radiographer (AR) degree, yet the subjectivity of AR in identifying defects is problematic. To overcome this subjectivity, an automatic welding defect identification is needed. This is executed by using Matlab to create artificial neural networks, which is beneficial for users with the graphical user interface (GUI) feature. One of the breakthroughs in the figure extraction into seven feature vector values is the geometric invariant moment theory. This prevents translation, rotation, and scaling from changing the figure's characteristics. Therefore, a welding defect identification system with a geometric invariant moment was created in the digital radiographic film figure to overcome the reading error by AR. The identification system obtained an accuracy rating of 89.9%.