Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion
Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/8023 |
| _version_ | 1827647936312377344 |
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| author | Xing Peng Lingbao Kong Wei Han Shixiang Wang |
| author_facet | Xing Peng Lingbao Kong Wei Han Shixiang Wang |
| author_sort | Xing Peng |
| collection | DOAJ |
| description | Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process. |
| first_indexed | 2024-03-09T19:29:24Z |
| format | Article |
| id | doaj.art-13333067c40a40c2aa8dcca36f6c502d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:29:24Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-13333067c40a40c2aa8dcca36f6c502d2023-11-24T02:30:27ZengMDPI AGSensors1424-82202022-10-012220802310.3390/s22208023Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed FusionXing Peng0Lingbao Kong1Wei Han2Shixiang Wang3Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, ChinaShanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, ChinaShanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, ChinaShanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, ChinaMulti-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.https://www.mdpi.com/1424-8220/22/20/8023powder bed fusionmulti-sensor image fusiondefect detectionvisible imaginginfrared imaging |
| spellingShingle | Xing Peng Lingbao Kong Wei Han Shixiang Wang Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion Sensors powder bed fusion multi-sensor image fusion defect detection visible imaging infrared imaging |
| title | Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion |
| title_full | Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion |
| title_fullStr | Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion |
| title_full_unstemmed | Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion |
| title_short | Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion |
| title_sort | multi sensor image fusion method for defect detection in powder bed fusion |
| topic | powder bed fusion multi-sensor image fusion defect detection visible imaging infrared imaging |
| url | https://www.mdpi.com/1424-8220/22/20/8023 |
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