Defects detection technology is essential for monitoring and hence maintaining the product quality of additive manufacturing (AM) processes; however, traditional detection methods based on single sensor have great limitations such as low accuracy and scarce information. In this study, a multi-sensor defect detection system (MSDDS) was proposed and developed for defect detection with the fusion of visible, infrared, and polarization detection information. The assessment criteria for imaging quality of the MSDDS have been optimized and evaluated. Meanwhile, the feasibility of processing and assembly of each sensor module has been demonstrated with tolerance sensitivity and the Monte Carlo analysis. Moreover, multi-sensor image fusion processing, super-resolution reconstruction, and feature extraction of defects are applied. Simulation and experimental studies indicate that the developed MSDDS can obtain high contrast and clear key information, and high-quality detected images of AM defects such as cracking, scratches, and porosity can be effectively extracted. The research provides a helpful and potential solution for defect detection and processing parameter optimization in AM processes such as Selective Laser Melting.
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.
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