{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T08:31:11Z","timestamp":1775291471185,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key-Area Research and Development Program of Guangdong Province, China","award":["2018B090905001"],"award-info":[{"award-number":["2018B090905001"]}]},{"name":"the Key Research and Development Program of Sichuan Province, China","award":["2020YFSY0054"],"award-info":[{"award-number":["2020YFSY0054"]}]},{"name":"the Key Research and Development Program of Hubei province, China","award":["2020BAB045"],"award-info":[{"award-number":["2020BAB045"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10845-021-01820-0","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T17:02:45Z","timestamp":1627923765000},"page":"683-693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["In-situ monitoring laser based directed energy deposition process with deep convolutional neural network"],"prefix":"10.1007","volume":"34","author":[{"given":"Jiqian","family":"Mi","sequence":"first","affiliation":[]},{"given":"Yikai","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4404-8845","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shengnan","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Yongqiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Changhui","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yucong","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Junwen","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Haibo","family":"Mai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,2]]},"reference":[{"key":"1820_CR1","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.addma.2018.10.045","volume":"24","author":"JL Bartlett","year":"2018","unstructured":"Bartlett, J. L., Heim, F. M., Murty, Y. V., & Li, X. (2018). In situ defect detection in selective laser melting via full-field infrared thermography. Additive Manufacturing, 24, 595\u2013605.","journal-title":"Additive Manufacturing"},{"key":"1820_CR2","unstructured":"Bouvrie, J. (2006). Notes on convolutional neural networks. In Practice, pp. 47\u201360."},{"issue":"5","key":"1820_CR3","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"YJ Cha","year":"2017","unstructured":"Cha, Y. J., Choi, M., & B\u00fcy\u00fck\u00f6zt\u00fcrk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361\u2013378.","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"93","key":"1820_CR4","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.compind.2017.08.002","volume":"92","author":"DR Eyers","year":"2017","unstructured":"Eyers, D. R., & Potter, A. T. (2017). Industrial additive manufacturing: A manufacturing systems perspective. Computers in Industry, 92(93), 208\u2013218.","journal-title":"Computers in Industry"},{"key":"1820_CR5","doi-asserted-by":"crossref","unstructured":"Fang, Q., Tan, Z., Li, H., Liu, S., Song, C., Zhou, X., Yang, Y., & Shen, S. (2020). In-situ capture of melt pool signature in selective laser melting using U-Net based convolutional neural network. Journal of Manufacturing Processes (submitted).","DOI":"10.1016\/j.jmapro.2021.05.052"},{"key":"1820_CR6","doi-asserted-by":"crossref","unstructured":"Fox, J. C., Moylan, S. P., & Lane, B. M. (2016). Effect of process parameters on the surface roughness of overhanging structures in laser powder bed fusion additive manufacturing. In 3rd CIRP conference on surface integrity (CIRP CSI), pp. 131\u2013134.","DOI":"10.1016\/j.procir.2016.02.347"},{"key":"1820_CR7","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.addma.2018.04.005","volume":"21","author":"C Gobert","year":"2018","unstructured":"Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., & Phoha, S. (2018). Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Additive Manufacturing, 21, 517\u2013528.","journal-title":"Additive Manufacturing"},{"key":"1820_CR8","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.rcim.2017.07.001","volume":"49","author":"M Grasso","year":"2018","unstructured":"Grasso, M., Demir, A. G., & Prevital, B. (2018). In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. The Journal Robotics and Computer-Integrated Manufacturing, 49, 229\u2013239.","journal-title":"The Journal Robotics and Computer-Integrated Manufacturing"},{"key":"1820_CR9","doi-asserted-by":"crossref","unstructured":"Jacobsm\u00fchlen, J., Kleszczynski, S., Witt, G., & Merhof, D. (2015). Detection of elevated regions in surface images from laser beam melting processes. In IECON 2015\u201441st annual conference of the IEEE industrial electronics society, pp. 1270\u20131275.","DOI":"10.1109\/IECON.2015.7392275"},{"issue":"3","key":"1820_CR10","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","volume":"29","author":"JN Kapur","year":"1985","unstructured":"Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics and Image Processing, 29(3), 273\u2013285.","journal-title":"Computer Vision Graphics and Image Processing"},{"key":"1820_CR11","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s10845-018-1451-6","volume":"31","author":"O Kwon","year":"2020","unstructured":"Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G. H., Cho, J. H., Kim, N., & Kim, K. (2020). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing, 31, 375\u2013386.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"4","key":"1820_CR12","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640\u2013651.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1820_CR13","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/j.promfg.2019.06.048","volume":"35","author":"AS Ngoveni","year":"2019","unstructured":"Ngoveni, A. S., Popoola, A. P. I., Arthur, N. K. K., & Pityana, S. L. (2019). Residual stress modelling and experimental analyses of Ti6Al4V ELI additive manufactured by laser engineered net shaping. Procedia Manufacturing., 35, 1001\u20131006.","journal-title":"Procedia Manufacturing."},{"key":"1820_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.optlastec.2015.09.025","volume":"78","author":"AJ Pinkerton","year":"2016","unstructured":"Pinkerton, A. J. (2016). Lasers in additive manufacturing. Optics & Laser Technology, 78, 25\u201332.","journal-title":"Optics & Laser Technology"},{"issue":"5","key":"1820_CR15","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1080\/09506608.2015.1116649","volume":"61","author":"WJ Sames","year":"2016","unstructured":"Sames, W. J., List, F. A., Pannala, S., Dehoff, R. R., & Babu, S. S. (2016). The metallurgy and processing science of metal additive manufacturing. International Materials Reviews, 61(5), 315\u2013360.","journal-title":"International Materials Reviews"},{"key":"1820_CR16","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.addma.2018.11.010","volume":"25","author":"L Scime","year":"2019","unstructured":"Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151\u2013165.","journal-title":"Additive Manufacturing"},{"key":"1820_CR17","doi-asserted-by":"publisher","first-page":"106222","DOI":"10.1016\/j.optlastec.2020.106222","volume":"128","author":"Z Shu","year":"2020","unstructured":"Shu, Z., Chen, Z., Wang, L., Wei, X., Li, W., & Zheng, Z. (2020). Microstructure evolution and formation mechanism of a crack-free nickel based superalloy fabricated by laser engineered net shaping. Optics & Laser Technology, 128, 106222.","journal-title":"Optics & Laser Technology"},{"key":"1820_CR18","doi-asserted-by":"crossref","unstructured":"Soukup, D., & Huber-M\u00f6rk, R. (2014). Convolutional neural networks for steel surface defect detection from photometric stereo images. In Advances in visual computing: 11th international symposium, ISVAdv visual computing, ISVC 2014, Lecture notes in computer science, Vol. 8887, pp. 668\u201377.","DOI":"10.1007\/978-3-319-14249-4_64"},{"key":"1820_CR19","doi-asserted-by":"publisher","first-page":"106347","DOI":"10.1016\/j.optlastec.2020.106347","volume":"130","author":"Z Tan","year":"2020","unstructured":"Tan, Z., Fang, Q., Li, H., Liu, S., Zhu, W., & Yang, D. (2020). Neural network based image segmentation for spatter extraction during selective laser melting processing. Optics & Laser Technology, 130, 106347.","journal-title":"Optics & Laser Technology"},{"key":"1820_CR20","doi-asserted-by":"crossref","unstructured":"Tola, E., Lepetit, V., & Fua, P. (2008). A fast local descriptor for dense matching. In 26th IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2008.4587673"},{"key":"1820_CR21","doi-asserted-by":"publisher","first-page":"106371","DOI":"10.1016\/j.optlastec.2020.106371","volume":"131","author":"D Yang","year":"2020","unstructured":"Yang, D., Li, H., Liu, S., Song, C., Yang, Y., Shen, S., Lu, J., Liu, Z., & Zhu, Y. (2020b). In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm. Optical Laser Technology, 131, 106371.","journal-title":"Optical Laser Technology"},{"key":"1820_CR22","doi-asserted-by":"publisher","first-page":"105925","DOI":"10.1016\/j.optlastec.2019.105925","volume":"123","author":"Q Yang","year":"2020","unstructured":"Yang, Q., Yuan, Z., Zhi, X., Yan, Z., Yang, Y., & Tian, H. (2020a). Real-time width control of molten pool in laser engineered net shaping based on dual-color image. Optics & Laser Technology, 123, 105925.","journal-title":"Optics & Laser Technology"},{"key":"1820_CR23","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.isatra.2018.07.021","volume":"81","author":"D Ye","year":"2018","unstructured":"Ye, D., Fuh, J. Y. H., Zhang, Y., Hong, G. S., & Zhu, K. (2018). In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Transactions, 81, 96\u2013104.","journal-title":"ISA Transactions"},{"key":"1820_CR24","doi-asserted-by":"publisher","first-page":"100778","DOI":"10.1016\/j.addma.2019.100778","volume":"29","author":"J Yin","year":"2019","unstructured":"Yin, J., Yang, L., Yang, X., Zhu, H., Wang, D., Ke, L., & Zeng, X. (2019). High-power laser-matter interaction during laser powder bed fusion. Additive Manufacturing, 29, 100778.","journal-title":"Additive Manufacturing"},{"key":"1820_CR25","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.apsusc.2013.05.081","volume":"280","author":"M Zhang","year":"2013","unstructured":"Zhang, M., Chen, G., Zhou, Y., Li, S., & Deng, H. (2013). Observation of spatter formation mechanisms in high-power fiber laser welding of thick plate. Applied Surface Science, 280, 868\u2013875.","journal-title":"Applied Surface Science"},{"key":"1820_CR26","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.addma.2018.10.020","volume":"25","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Fuh, J. Y. H., Ye, D., & Hong, G. S. (2019). In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches. Additive Manufacturing, 25, 263\u2013274.","journal-title":"Additive Manufacturing"},{"key":"1820_CR27","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.matdes.2018.07.002","volume":"156","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Hong, G. S., Ye, D., Zhu, K., & Fuh, J. Y. H. (2018). Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Materials and Design, 156, 458\u2013469.","journal-title":"Materials and Design"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01820-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01820-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01820-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T21:05:24Z","timestamp":1674162324000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01820-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,2]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1820"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01820-0","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,2]]},"assertion":[{"value":"28 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}