Defect extraction method for additive manufactured parts with improved learning-based image super-resolution and the Canny algorithm.

Abstract

Additive manufacturing (AM) is a highly competitive, low-cost, and high-degree-of-manufacturing technology. However, AM still has limitations because of some defects. Thus, defect detection technology is essential for quality enhancement in the AM process. Super-resolution (SR) technology can be utilized to improve defect image quality and enhance defect extraction performance. This study proposes a defect extraction method for additive manufactured parts with improved learning-based image SR and the Canny algorithm (LSRC), which is based on direct mapping methodology. The LSRC method is compared with the bicubic interpolation algorithm and the neighbor embedding (NE) algorithm in SR reconstruction quality and robustness. The experimental results show that the proposed LSRC method achieves satisfactory performance in terms of the averaged information entropy (E), standard deviation (SD), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), which are 7.259, 45.301, 27.723, and 0.822, respectively. The accordingly average improvement rates of the E, SD, PSNR, and SSIM, are 0.45%, 7.15%, 5.85%, and 6.35% in comparison with the bicubic interpolation algorithm, while the comparison data are 0.97%, 13.40%, 10.55%, and 15.35% in terms of the NE algorithm. This indicates that the LSRC method is significantly better than the comparison algorithm in reconstruction quality and robustness, which is of great significance for the extraction and analysis of key defect information of additive manufactured parts.