Neural network based thickness estimation from multiple radiographic images

Tansel I. N., Inanc F., Reen N., Chen P., Wang X., Kropas-Hughes C., ...More

JOURNAL OF NONDESTRUCTIVE EVALUATION, vol.25, no.2, pp.53-66, 2006 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 2
  • Publication Date: 2006
  • Doi Number: 10.1007/s10921-006-0011-8
  • Journal Name: JOURNAL OF NONDESTRUCTIVE EVALUATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.53-66
  • Istanbul Technical University Affiliated: No

Abstract

Back propagation (BP) type artificial neural networks (ANN) have been trained and used for thickness estimations from radiographic images. Test objects have been assembled from different materials and radiographic images of the test objects were obtained for thickness estimations. While some of the study has been based on the synthetic images formed through the radiographic simulation program XRSIM, the rest of the study has used actual radiographic images. The average estimation errors were 7% and 9% when two and three synthetic radiographic images obtained at different x-ray tube settings were used. With the actual images, the thickness of only one of the materials has been estimated and the material was identified. This has been due to the fact that scattering of x-rays by the test object results in a non uniform gray scale variation in the radiographic images even though the object thickness is uniform.