Determination of defect depth in industrial radiography imaging using MCNP code and SuperMC software

Author:

Khorshidi Abdollah,Khosrowpour Behzad,Hosseini S. Hamed

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference31 articles.

1. Multiclass defect detection and classification in weld radiographic images using geometric and texture features;Valavanis;Expert Syst. Appl.,2010

2. Standard Practice for Radiographic Examination of Weldments Using Industrial X-Ray Film;ASTM E1032-19,2019

3. Nacereddine N, Zelmat M, Belaïfa SS, Tridi M. Weld defect detection in industrial radiography based digital image processing. 3rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 27-31, 2005 – Tunisia.

4. A novel method for predicting pixel value distribution non-uniformity due to heel effect of X-ray tube in industrial digital radiography using artificial neural network;Nazemi;J. Nondestruct. Eval.,2019

5. A new technique to detect defect size and depth in composite structures using digital shearography and unconstrained optimization;De Angelis;NDT E Int.,2012

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