{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:19:04Z","timestamp":1774552744352,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"crossref","award":["22-3721-A0001"],"award-info":[{"award-number":["22-3721-A0001"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10845-024-02355-w","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T18:02:01Z","timestamp":1711994521000},"page":"2409-2422","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spatiotemporal analysis of powder bed fusion melt pool monitoring videos using deep learning"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8328-3629","authenticated-orcid":false,"given":"Richard J.","family":"Williams","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3980-6605","authenticated-orcid":false,"given":"Swee Leong","family":"Sing","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"issue":"10","key":"2355_CR1","doi-asserted-by":"publisher","first-page":"3740","DOI":"10.3390\/s22103740","volume":"22","author":"B Booth","year":"2022","unstructured":"Booth, B., Heylen, R., Nourazar, M., Verhees, D., Philips, W., & Bey-Temsamani, A. (2022). Encoding stability into laser powder bed fusion monitoring using temporal features and pore density modelling. Sensors, 22(10), 3740.","journal-title":"Sensors"},{"key":"2355_CR2","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s10845-021-01787-y","volume":"33","author":"M Bugatti","year":"2022","unstructured":"Bugatti, M., & Colosimo, B. M. (2022). Towards real-time in-situ monitoring of hot-spot defects in L-PBF: A new classification-based method for fast video-imaging data analysis. Journal of Intelligent Manufacturing, 33, 293\u2013309.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"5","key":"2355_CR3","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s00170-014-6214-8","volume":"75","author":"S Clijsters","year":"2014","unstructured":"Clijsters, S., Craeghs, T., Buls, S., Kempen, K., & Kruth, J. P. (2014). In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system. The International Journal of Advanced Manufacturing Technology, 75(5), 1089\u20131101. https:\/\/doi.org\/10.1007\/s00170-014-6214-8","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2355_CR4","doi-asserted-by":"publisher","first-page":"102431","DOI":"10.1016\/j.addma.2021.102431","volume":"48","author":"HC de Winton","year":"2021","unstructured":"de Winton, H. C., Cegla, F., & Hooper, P. A. (2021). A method for objectively evaluating the defect detection performance of in-situ monitoring systems. Additive Manufacturing, 48, 102431. https:\/\/doi.org\/10.1016\/j.addma.2021.102431","journal-title":"Additive Manufacturing"},{"issue":"1","key":"2355_CR5","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1080\/10408436.2022.2041396","volume":"48","author":"J Elambasseril","year":"2023","unstructured":"Elambasseril, J., Rogers, J., Wallbrink, C., Munk, D., Leary, M., & Qian, M. (2023). Laser powder bed fusion additive manufacturing (LPBF-AM): The influence of design features and LPBF variables on surface topography and effect on fatigue properties. Critical Reviews in Solid State and Materials Sciences, 48(1), 132\u2013168. https:\/\/doi.org\/10.1080\/10408436.2022.2041396","journal-title":"Critical Reviews in Solid State and Materials Sciences"},{"issue":"6","key":"2355_CR6","doi-asserted-by":"publisher","first-page":"061003","DOI":"10.1115\/1.4054202","volume":"22","author":"SC Feng","year":"2022","unstructured":"Feng, S. C., Lu, Y., Jones, A. T., & Yang, Z. (2022). Additive manufacturing in situ and ex situ geometric data registration. Journal of Computing and Information Science in Engineering, 22(6), 061003. https:\/\/doi.org\/10.1115\/1.4054202","journal-title":"Journal of Computing and Information Science in Engineering"},{"key":"2355_CR7","doi-asserted-by":"publisher","first-page":"101336","DOI":"10.1016\/j.addma.2020.101336","volume":"35","author":"J-B Forien","year":"2020","unstructured":"Forien, J.-B., Calta, N. P., DePond, P. J., Guss, G. M., Roehling, T. T., & Matthews, M. J. (2020). Detecting keyhole pore defects and monitoring process signatures during laser powder bed fusion: A correlation between in situ pyrometry and ex situ x-ray radiography. Additive Manufacturing, 35, 101336. https:\/\/doi.org\/10.1016\/j.addma.2020.101336","journal-title":"Additive Manufacturing"},{"key":"2355_CR8","doi-asserted-by":"publisher","unstructured":"Fox, J. C., Lane, B. M., Yeung, & H. (2017). Measurement of process dynamics through coaxially aligned high speed near-infrared imaging in laser powder bed fusion additive manufacturing. In P.\u00a0Bison, & D.\u00a0Burleigh (Eds.), Thermosense: Thermal infrared applications XXXIX (Vol. 10214, p. 1021407). International Society for Optics and Photonics, SPIE. https:\/\/doi.org\/10.1117\/12.2263863","DOI":"10.1117\/12.2263863"},{"key":"2355_CR9","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s10845-019-01495-8","volume":"31","author":"C Gonzales-Val","year":"2020","unstructured":"Gonzales-Val, C., Pallas, A., Panadeiro, V., & Rodriguez, A. (2020). A convolutional approach to quality monitoring for laser manufacturing. Journal of Intelligent Manufacturing, 31, 789\u2013795.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"11","key":"2355_CR10","doi-asserted-by":"publisher","first-page":"112001","DOI":"10.1088\/1361-6501\/ac0b6b","volume":"32","author":"M Grasso","year":"2021","unstructured":"Grasso, M., Remani, A., Dickins, A., Colosimo, B. M., & Leach, R. K. (2021). In-situ measurement and monitoring methods for metal powder bed fusion: An updated review. Measurement Science and Technology, 32(11), 112001. https:\/\/doi.org\/10.1088\/1361-6501\/ac0b6b","journal-title":"Measurement Science and Technology"},{"issue":"2","key":"2355_CR11","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1080\/00224065.2022.2106910","volume":"55","author":"M Gronle","year":"2023","unstructured":"Gronle, M., Grasso, M., Granito, E., Schaal, F., & Colosimo, B. M. (2023). Open data for open science in industry 4.0: In-situ monitoring of quality in additive manufacturing. Journal of Quality Technology, 55(2), 253\u2013265. https:\/\/doi.org\/10.1080\/00224065.2022.2106910","journal-title":"Journal of Quality Technology"},{"key":"2355_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/ma15031265","author":"J Harbig","year":"2022","unstructured":"Harbig, J., Wenzler, D. L., Baehr, S., Kick, M. K., Merschroth, H., Wimmer, A., Weigold, M., & Zaeh, M. F. (2022). Methodology to determine melt pool anomalies in powder bed fusion of metals using a laser beam by means of process monitoring and sensor data fusion. Materials. https:\/\/doi.org\/10.3390\/ma15031265","journal-title":"Materials"},{"key":"2355_CR13","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1016\/j.addma.2018.05.032","volume":"22","author":"PA Hooper","year":"2018","unstructured":"Hooper, P. A. (2018). Melt pool temperature and cooling rates in laser powder bed fusion. Additive Manufacturing, 22, 548\u2013559. https:\/\/doi.org\/10.1016\/j.addma.2018.05.032","journal-title":"Additive Manufacturing"},{"key":"2355_CR14","doi-asserted-by":"publisher","first-page":"23255","DOI":"10.1109\/ACCESS.2020.2970026","volume":"8","author":"O Kwon","year":"2020","unstructured":"Kwon, O., Kim, H. G., Kim, W., Kim, G.-H., & Kim, K. (2020). A convolutional neural network for prediction of laser power using melt-pool images in laser powder bed fusion. IEEE Access, 8, 23255\u201323263. https:\/\/doi.org\/10.1109\/ACCESS.2020.2970026","journal-title":"IEEE Access"},{"key":"2355_CR15","doi-asserted-by":"publisher","DOI":"10.18434\/M32233","author":"B Lane","year":"2020","unstructured":"Lane, B. (2020). Process monitoring dataset from the additive manufacturing metrology testbed (AMMT): Overhang part x4. National Institute of Standards and Technology. https:\/\/doi.org\/10.18434\/M32233","journal-title":"National Institute of Standards and Technology"},{"key":"2355_CR16","doi-asserted-by":"publisher","DOI":"10.18434\/mds2-2309","author":"B Lane","year":"2020","unstructured":"Lane, B., & Yeung, H. (2020a). Process monitoring dataset from the additive manufacturing metrology testbed (AMMT): Overhang part x16. National Institute of Standards and Technology. https:\/\/doi.org\/10.18434\/mds2-2309","journal-title":"National Institute of Standards and Technology"},{"key":"2355_CR17","doi-asserted-by":"publisher","DOI":"10.6028\/jres.125.027","author":"B Lane","year":"2020","unstructured":"Lane, B., & Yeung, H. (2020b). Process monitoring dataset from the additive manufacturing metrology testbed (AMMT): Overhang part x4. Journal of Research of the National Institute of Standards and Technology. https:\/\/doi.org\/10.6028\/jres.125.027","journal-title":"Journal of Research of the National Institute of Standards and Technology"},{"key":"2355_CR18","doi-asserted-by":"publisher","first-page":"102687","DOI":"10.1016\/j.addma.2022.102687","volume":"53","author":"S Lapointe","year":"2022","unstructured":"Lapointe, S., Guss, G., Reese, Z., Strantza, M., Matthews, M., & Druzgalski, C. (2022). Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries. Additive Manufacturing, 53, 102687. https:\/\/doi.org\/10.1016\/j.addma.2022.102687","journal-title":"Additive Manufacturing"},{"issue":"2","key":"2355_CR19","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s10845-021-01842-8","volume":"33","author":"S Larsen","year":"2022","unstructured":"Larsen, S., & Hooper, P. A. (2022). Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion. Journal of Intelligent Manufacturing, 33(2), 457\u2013471. https:\/\/doi.org\/10.1007\/s10845-021-01842-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2355_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110232","author":"J Li","year":"2022","unstructured":"Li, J., Cao, L., Xu, J., Wang, S., & Zhou, Q. (2022). In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2021.110232","journal-title":"Measurement"},{"key":"2355_CR21","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.addma.2019.05.033","volume":"28","author":"M Lowther","year":"2019","unstructured":"Lowther, M., Louth, S., Davey, A., Hussain, A., Ginestra, P., Carter, L., Eisenstein, N., Grover, L., & Cox, S. (2019). Clinical, industrial, and research perspectives on powder bed fusion additively manufactured metal implants. Additive Manufacturing, 28, 565\u2013584. https:\/\/doi.org\/10.1016\/j.addma.2019.05.033","journal-title":"Additive Manufacturing"},{"issue":"3","key":"2355_CR22","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s10845-020-01694-8","volume":"33","author":"V Mahato","year":"2022","unstructured":"Mahato, V., Obeidi, M. A., Brabazon, D., & Cunningham, P. (2022). Detecting voids in 3D printing using melt pool time series data. Journal of Intelligent Manufacturing, 33(3), 845\u2013852. https:\/\/doi.org\/10.1007\/s10845-020-01694-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2355_CR23","doi-asserted-by":"publisher","first-page":"102058","DOI":"10.1016\/j.addma.2021.102058","volume":"45","author":"R McCann","year":"2021","unstructured":"McCann, R., Obeidi, M. A., Hughes, C., McCarthy, \u00c9., Egan, D. S., Vijayaraghavan, R. K., Joshi, A. M., Acinas Garzon, V., Dowling, D. P., McNally, P. J., & Brabazon, D. (2021). In-situ sensing, process monitoring and machine control in laser powder bed fusion: A review. Additive Manufacturing, 45, 102058. https:\/\/doi.org\/10.1016\/j.addma.2021.102058","journal-title":"Additive Manufacturing"},{"issue":"2018","key":"2355_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.procir.2018.08.049","volume":"74","author":"T \u00d6zel","year":"2018","unstructured":"\u00d6zel, T., Shaurya, A., Altay, A., & Yang, L. (2018). Process monitoring of meltpool and spatter for temporal-spatial modeling of laser powder bed fusion process. Procedia CIRP, 74(2018), 102\u2013106. https:\/\/doi.org\/10.1016\/j.procir.2018.08.049","journal-title":"Procedia CIRP"},{"key":"2355_CR25","doi-asserted-by":"publisher","first-page":"125031","DOI":"10.6028\/jres.125.031","volume":"125","author":"M Praniewicz","year":"2020","unstructured":"Praniewicz, M., Lane, B., Kim, F., & Saldana, C. (2020). X-ray computed tomography data of additive manufacturing metrology testbed (ammt) parts: \u201cOverhang part x4\u2019\u2019. Journal of Research of the National Institute of Standards and Technology, 125, 125031.","journal-title":"Journal of Research of the National Institute of Standards and Technology"},{"key":"2355_CR26","doi-asserted-by":"publisher","unstructured":"Ren, Z., Gao, L., Clark, S. J., Fezzaa, K., Shevchenko, P., Choi, A., Everhart, W., Rollett, A. D., Chen, L., & Sun, T. (2023). Machine learning aided real-time detection of keyhole pore generation in laser powder bed fusion. Science, 379(6627), 89\u201394. https:\/\/doi.org\/10.1126\/science.add4667","DOI":"10.1126\/science.add4667"},{"key":"2355_CR27","doi-asserted-by":"publisher","first-page":"100803","DOI":"10.1016\/j.rineng.2022.100803","volume":"17","author":"T Sahar","year":"2023","unstructured":"Sahar, T., Rauf, M., Murtaza, A., Khan, L. A., Ayub, H., Jameel, S. M., & Ahad, I. U. (2023). Anomaly detection in laser powder bed fusion using machine learning: A review. Results in Engineering, 17, 100803. https:\/\/doi.org\/10.1016\/j.rineng.2022.100803","journal-title":"Results in Engineering"},{"issue":"7","key":"2355_CR28","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nmeth.2019","volume":"9","author":"J Schindelin","year":"2012","unstructured":"Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open-source platform for biological-image analysis. Nature Methods, 9(7), 676\u2013682. https:\/\/doi.org\/10.1038\/nmeth.2019","journal-title":"Nature Methods"},{"issue":"3","key":"2355_CR29","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1080\/17452759.2021.1944229","volume":"16","author":"SL Sing","year":"2021","unstructured":"Sing, S. L., Kuo, C. N., Shih, C. T., Ho, C. C., & Chua, C. K. (2021). Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing. Virtual and Physical Prototyping, 16(3), 372\u2013386. https:\/\/doi.org\/10.1080\/17452759.2021.1944229","journal-title":"Virtual and Physical Prototyping"},{"key":"2355_CR30","doi-asserted-by":"publisher","first-page":"117550","DOI":"10.1016\/j.jmatprotec.2022.117550","volume":"304","author":"Z Smoqi","year":"2022","unstructured":"Smoqi, Z., Gaikwad, A., Bevans, B., Kobir, M. H., Craig, J., Abul-Haj, A., Peralta, A., & Rao, P. (2022). Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning. Journal of Materials Processing Technology, 304, 117550. https:\/\/doi.org\/10.1016\/j.jmatprotec.2022.117550","journal-title":"Journal of Materials Processing Technology"},{"issue":"1","key":"2355_CR31","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1089\/3dp.2021.0060","volume":"10","author":"CKP Vallabh","year":"2023","unstructured":"Vallabh, C. K. P., & Zhao, X. (2023). Continuous comprehensive monitoring of melt pool morphology under realistic printing scenarios with laser powder bed fusion. 3D Printing and Additive Manufacturing, 10(1), 101\u2013110. https:\/\/doi.org\/10.1089\/3dp.2021.0060","journal-title":"3D Printing and Additive Manufacturing"},{"key":"2355_CR32","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4255024","author":"RJ Williams","year":"2022","unstructured":"Williams, R. J., De Winton, H., Fernandez, V., & Hooper, P. A. (2022). Localised porosity detection in laser powder bed fusion using in-situ monitoring. SSRN. https:\/\/doi.org\/10.2139\/ssrn.4255024","journal-title":"SSRN"},{"key":"2355_CR33","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.jmapro.2021.12.030","volume":"74","author":"W Xing","year":"2022","unstructured":"Xing, W., Chu, X., Lyu, T., Lee, C.-G., Zou, Y., & Rong, Y. (2022). Using convolutional neural networks to classify melt pools in a pulsed selective laser melting process. Journal of Manufacturing Processes, 74, 486\u2013499. https:\/\/doi.org\/10.1016\/j.jmapro.2021.12.030","journal-title":"Journal of Manufacturing Processes"},{"key":"2355_CR34","doi-asserted-by":"crossref","unstructured":"Yue-Hei\u00a0Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015). Beyond short snippets: Deep networks for video classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"2355_CR35","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. (2018). Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion am process monitoring. Materials & Design, 156, 458\u2013469. https:\/\/doi.org\/10.1016\/j.matdes.2018.07.002","journal-title":"Materials & Design"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02355-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02355-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02355-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T20:19:43Z","timestamp":1744316383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02355-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2355"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02355-w","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,1]]},"assertion":[{"value":"12 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2024","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}