{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:22:28Z","timestamp":1782976948396,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075100"],"award-info":[{"award-number":["52075100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FC2020-006"],"award-info":[{"award-number":["FC2020-006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fudan University-CIOMP Joint Fund","award":["52075100"],"award-info":[{"award-number":["52075100"]}]},{"name":"Fudan University-CIOMP Joint Fund","award":["FC2020-006"],"award-info":[{"award-number":["FC2020-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22208023","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"8023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6177-3715","authenticated-orcid":false,"given":"Xing","family":"Peng","sequence":"first","affiliation":[{"name":"Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China"},{"name":"College of Intelligence Science, National University of Defense Technology, Changsha 410073, China"},{"name":"Key Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4522-2961","authenticated-orcid":false,"given":"Lingbao","family":"Kong","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China"},{"name":"Yiwu Research Institute, Fudan University, Chengbei Road, Yiwu City 322000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s40684-018-0006-9","article-title":"Influence of energy density on energy demand and porosity of 316L stainless steel fabricated by selective laser melting","volume":"5","author":"Peng","year":"2018","journal-title":"Int. J. Precis. Eng. Manuf. Green Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1007\/s00170-019-04753-4","article-title":"Review of defects in lattice structures manufactured by powder bed fusion","volume":"106","author":"Echeta","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jmatprotec.2010.09.019","article-title":"Selective laser melting of aluminium components","volume":"211","author":"Yang","year":"2011","journal-title":"J. Mater. Process. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100795","DOI":"10.1016\/j.pmatsci.2021.100795","article-title":"Emerging metallic systems for additive manufacturing: In-situ alloying and multi-metal processing in laser powder bed fusion","volume":"119","author":"Sing","year":"2021","journal-title":"Prog. Mater. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/17452759.2021.1928520","article-title":"Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion","volume":"16","author":"Wei","year":"2021","journal-title":"Virtual Phys. Prototyp."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"022057","DOI":"10.2351\/7.0000108","article-title":"Layer-wise control of selective laser melting by means of inline melt pool area measurements","volume":"32","author":"Vasileska","year":"2020","journal-title":"J. Laser Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6006","DOI":"10.1016\/j.actamat.2009.08.027","article-title":"A pragmatic model for selective laser melting with evaporation","volume":"57","author":"Verhaeghe","year":"2009","journal-title":"Acta Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100578","DOI":"10.1016\/j.pmatsci.2019.100578","article-title":"3D printing of Aluminium alloys: Additive Manufacturing of Aluminium alloys using selective laser melting","volume":"106","author":"Aboulkhair","year":"2019","journal-title":"Prog. Mater. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139227","DOI":"10.1016\/j.msea.2020.139227","article-title":"Study of the microstructure and mechanical performance of CX stainless steel processed by selective laser melting (SLM)","volume":"781","author":"Yan","year":"2020","journal-title":"Mater. Sci. Eng."},{"key":"ref_10","first-page":"68","article-title":"Machinability of SLM-produced Ti6Al4V titanium alloy parts","volume":"57","author":"Melotti","year":"2020","journal-title":"J. Manuf. Process."},{"key":"ref_11","first-page":"2839","article-title":"Review of on-line monitoring research on metal additive manufacturing process","volume":"33","author":"Chan","year":"2019","journal-title":"Mater. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"060801","DOI":"10.1115\/1.4028540","article-title":"A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing","volume":"136","author":"Tapia","year":"2014","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4028\/www.scientific.net\/SSP.278.1","article-title":"A Review of Metal Additive Manufacturing Technologies","volume":"278","author":"Yakout","year":"2018","journal-title":"Solid State Phenom."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zur Jacobsm\u00fchlen, J., Kleszczynski, S., Schneider, D., and Witt, G. (2013, January 6\u20139). High-resolution imaging for inspection of laser beam melting systems. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Minneapolis, MN, USA.","DOI":"10.1109\/I2MTC.2013.6555507"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1115\/1.4034715","article-title":"In-process monitoring of selective laser melting: Spatial detection of defects via image data analysis","volume":"139","author":"Grasso","year":"2017","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_16","first-page":"183","article-title":"Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion","volume":"24","author":"Caltanissetta","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.matdes.2018.07.002","article-title":"Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring","volume":"156","author":"Zhang","year":"2018","journal-title":"Mater. Des."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.phpro.2010.08.078","article-title":"Feedback control of Layerwise Laser Melting using optical sensors","volume":"5","author":"Craeghs","year":"2010","journal-title":"Phys. Procedia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.cirp.2018.04.097","article-title":"Experimental investigation of melt pool behaviour during selective laser melting by high-speed imaging","volume":"67","author":"Furumoto","year":"2018","journal-title":"CIRP Ann."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.phpro.2010.08.089","article-title":"Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies","volume":"5","author":"Berumen","year":"2010","journal-title":"Phys. Procedia"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11837-020-04291-5","article-title":"In Situ Analysis of Laser Powder Bed Fusion Using Simultaneous High-Speed Infrared and X-ray Imaging","volume":"73","author":"Gould","year":"2020","journal-title":"JOM"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s41871-020-00062-7","article-title":"Design of a Multi-sensor Monitoring System for Additive Manufacturing Process","volume":"3","author":"Peng","year":"2020","journal-title":"Nanomanuf. Metrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1007\/s11018-015-0810-3","article-title":"Means of Optical Diagnostics of Selective Laser Melting with Non-Gaussian Beams","volume":"58","author":"Gusarov","year":"2015","journal-title":"Meas. Technol."},{"key":"ref_24","first-page":"101659","article-title":"Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion\u2013 A single-track study","volume":"36","author":"Gaikwad","year":"2020","journal-title":"Addit. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106741","DOI":"10.1016\/j.optlastec.2020.106741","article-title":"In-situ monitoring and detection of spatter agglomeration and delamination during laser-based powder bed fusion of Invar 36","volume":"136","author":"Yakout","year":"2020","journal-title":"Opt Laser Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., and Feng, Y. (2018, January 7\u201310). Infrared and Visible Image Fusion Based on Compressive Sensing and OSS-ICA-Bases. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451015"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.inffus.2015.06.006","article-title":"Combining the spectral PCA and spatial PCA fusion methods by an optimal filter","volume":"27","author":"Shahdoosti","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1364\/JOT.86.000408","article-title":"Infrared and visible face fusion recognition based on extended sparse representation classification and local binary patterns for the single sample problem","volume":"86","author":"Xie","year":"2019","journal-title":"J. Opt. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"287789","DOI":"10.1117\/12.7977034","article-title":"Merging Thermal and Visual Images By A Contrast Pyramid","volume":"28","author":"Toet","year":"1989","journal-title":"Opt. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1743131","DOI":"10.1179\/1743131X15Y.0000000025","article-title":"Multifocus noisy image fusion using contourlet transform","volume":"63","author":"Srivastava","year":"2015","journal-title":"Imaging Sci. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"338","DOI":"10.3788\/COL20080605.0338","article-title":"Region-based fusion of infrared and visible images using nonsubsampled contourlet transform","volume":"2008","author":"Guo","year":"2008","journal-title":"Chin. Opt. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10851-005-2026-7","article-title":"Nonlinear Discrete Wavelet Transforms over Finite Sets and an Application to Binary Image Compression","volume":"23","author":"Kamstra","year":"2005","journal-title":"J. Math. Imaging Vis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/LGRS.2006.887056","article-title":"Investigation of the Dual-Tree Complex and Shift-Invariant Discrete Wavelet Transforms on Quickbird Image Fusion","volume":"4","author":"Ioannidou","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","first-page":"215","article-title":"Infrared and visible images fusion based on FDST and MSS","volume":"17","author":"Bai","year":"2017","journal-title":"Sci. Technol. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.infrared.2015.03.001","article-title":"Infrared and visual image fusion through feature extraction by morphological sequential toggle operator","volume":"71","author":"Bai","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11831-019-09381-5","article-title":"An Overview of Image Segmentation Based on Pulse-Coupled Neural Network","volume":"28","author":"Lian","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ijleo.2018.03.103","article-title":"A compendious study of super-resolution techniques by single image","volume":"166","author":"Garima","year":"2018","journal-title":"Optik"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.infrared.2014.11.015","article-title":"Morphological infrared image enhancement based on multi-scale sequential toggle operator using opening and closing as primitives","volume":"68","author":"Bai","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2846","DOI":"10.4028\/www.scientific.net\/AMR.860-863.2846","article-title":"Image Fusion Algorithm Based on Wavelet Transform and Laplacian Pyramid","volume":"2013","author":"Li","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0167-8655(89)90003-2","article-title":"Image fusion by a ratio of low pass pyramid Pattern Recogn","volume":"9","author":"Toet","year":"1989","journal-title":"Pattern Recogn. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2005.09.006","article-title":"Pixel- and region-based image fusion with complex wavelets","volume":"8","author":"Lewis","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_42","unstructured":"Hy\u00f6tyniemi, H. (2001). Multivariate Regression-Techniques and Tools, Helsinki University of Technology, Control Engineering Laboratory."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1260\/1748-3018.8.1.17","article-title":"PCNN Model Automatic Linking Strength Determination Based on Geometric Moments in Image Fusion","volume":"8","author":"Shu","year":"2014","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_44","unstructured":"Zhao, L.M. (2021). Research on Insulator Defect Detection Method Based on Image Fusion, Xi\u2019an Shiyou University."},{"key":"ref_45","first-page":"14","article-title":"Research on remote sensing image dehazing based on generative adversarial networks","volume":"41","author":"Liu","year":"2020","journal-title":"Spacecr. Recovery Remote Sens."},{"key":"ref_46","first-page":"77","article-title":"Low-light image enhancement algorithm based on improved retinex-net","volume":"34","author":"Ou","year":"2021","journal-title":"Int. J. Pattern Recog."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:19Z","timestamp":1760144299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":46,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208023"],"URL":"https:\/\/doi.org\/10.3390\/s22208023","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]}}}