Improvement of defect detection system for powder metallurgy parts with digital radiography

Manufacturing of parts by powder metallurgy (PM) is one of the repeatable mass production methods in net-shape production, particularly, for automotive industry. Inspection of PM parts can remove defective components from the manufacturing lines and production cycle and increase efficiency. Radiography is a nondestructive testing (NDT) technique that allows the inspection of the outlet parts to increase the system performance and eliminate the defective parts, as much as possible. The detection of cracks and defects on radiography images can be improved with different image processing algorithms, as deblurring algorithms. The original radiography images are often blurred and defects or cracks are difficult to detect with the naked eye. The various images processing techniques can impressively detect the defects on digital radiographic images. In the digital radiography image, the defects are appeared as gradients. Therefore, the methods which are based on gradient can improve the defect detection such as Gaussian diffusion method. The method relies on a diffusion process formulated by parabolic partial differential equations as an isotropic diffusion equation. In this study, a set of PM automotive parts with different size and defects were made in Alamut Powder Technology Company in Qazvin, Iran. The initial radiography images of parts were provided by a computed radiography system. Then, the anisotropic diffusion method was used to improve visibility of the region of interests (ROI) in radiographs of parts. The results obtained with subtraction of smoothed image from the initial image. The reconstructed images show that the contrast of defect regions improved and interior designs were better specified.

Keywords:

Improvement of defect detection , Powder metallurgy , digital radiography , Gabor Filter , image processing