{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T17:43:00Z","timestamp":1781199780622,"version":"3.54.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100006502","name":"Defense Sciences Office, DARPA","doi-asserted-by":"publisher","award":["D22AP00140"],"award-info":[{"award-number":["D22AP00140"]}],"id":[{"id":"10.13039\/100006502","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Electron Beam Powder Bed Fusion (EB-PBF) is a pivotal additive manufacturing technology renowned for its ability to fabricate complex components with high precision and efficiency. However, the presence of defects such as porosity and powder spreading can compromise the mechanical properties and overall performance of the final products. Given the increasing use of EB-PBF for critical single-part production, maintaining quality standards through defect detection is crucial, yet conventional non-destructive evaluation methods are often costly or impractical, particularly for large, high-density components. This study presents a real-time monitoring system that uniquely leverages inherent electron emissions data for defect detection, eliminating the need for additional instrumentation while providing spatial defect distribution data. Unlike conventional approaches that rely on computationally intensive image or video analysis, our methodology utilizes time-series (profile) data from both in-situ and post-layer electron emissions, enhancing feasibility for real-time applications. We present a comprehensive comparative analysis of two deep learning models\u2014Convolutional Neural Networks (Conv1D) and Multi-Scale Temporal Autoencoders (MSTAE)\u2014to evaluate their effectiveness in detecting defects across varying severities, including low porosity, medium severity, severe porosity, and powder spreading defects. The models were assessed using reconstruction error metrics such as Mean Absolute Error (MAE), Huber Loss, Coefficient of Determination (R\n                    <jats:sup>2<\/jats:sup>\n                    ), and Structural Similarity Index (SSIM). Additionally, Exponentially Weighted Moving Average (EWMA) control charts were employed to monitor and analyze defect detection performance in real time.\n                  <\/jats:p>","DOI":"10.1007\/s10845-025-02598-1","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T08:34:57Z","timestamp":1744878897000},"page":"1297-1325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning-based real-time monitoring of electron beam powder bed fusion (EB-PBF) via electron emission"],"prefix":"10.1007","volume":"37","author":[{"given":"Temilola","family":"Gbadamosi-Adeniyi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Ferguson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-3743","authenticated-orcid":false,"given":"Tim","family":"Horn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"2598_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/adem.201901102","author":"C Arnold","year":"2020","unstructured":"Arnold, C., B\u00f6hm, J., & K\u00f6rner, C. (2020). In operando monitoring by analysis of backscattered electrons during electron beam melting. Advanced Engineering Materials. https:\/\/doi.org\/10.1002\/adem.201901102","journal-title":"Advanced Engineering Materials"},{"issue":"8","key":"2598_CR2","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1108\/RPJ-02-2018-0034","volume":"24","author":"C Arnold","year":"2018","unstructured":"Arnold, C., Pobel, C., Osmanlic, F., & K\u00f6rner, C. (2018). Layerwise monitoring of electron beam melting via backscatter electron detection. Rapid Prototyping Journal, 24(8), 1401\u20131406. https:\/\/doi.org\/10.1108\/RPJ-02-2018-0034","journal-title":"Rapid Prototyping Journal"},{"key":"2598_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.nme.2024.101626","author":"MJ Baldwin","year":"2024","unstructured":"Baldwin, M. J., Zhang, H., Zalo\u017enik, A., Patino, M. I., Simmonds, M. J., Nishijima, D., Carriere, P. R., Tynan, G. R., & Horn, T. (2024). D retention in e-beam powder-bed fused (3-D printed) tungsten exposed to high-flux deuterium plasma in PISCES-RF. Nuclear Materials and Energy. https:\/\/doi.org\/10.1016\/j.nme.2024.101626","journal-title":"Nuclear Materials and Energy"},{"issue":"3","key":"2598_CR4","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s40964-019-00108-3","volume":"5","author":"H Baumgartl","year":"2020","unstructured":"Baumgartl, H., Tomas, J., Buettner, R., & Merkel, M. (2020). A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Progress in Additive Manufacturing, 5(3), 277\u2013285. https:\/\/doi.org\/10.1007\/s40964-019-00108-3","journal-title":"Progress in Additive Manufacturing"},{"key":"2598_CR5","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.addma.2018.06.004","volume":"22","author":"N Boone","year":"2018","unstructured":"Boone, N., Zhu, C., Smith, C., Todd, I., & Willmott, J. R. (2018). Thermal near infrared monitoring system for electron beam melting with emissivity tracking. Additive Manufacturing, 22, 601\u2013605. https:\/\/doi.org\/10.1016\/j.addma.2018.06.004","journal-title":"Additive Manufacturing"},{"issue":"1","key":"2598_CR6","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(1), 293\u2013309. https:\/\/doi.org\/10.1007\/s10845-021-01787-y","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2598_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-024-02359-6","author":"D Cannizzaro","year":"2024","unstructured":"Cannizzaro, D., Antonioni, P., Ponzio, F., Galati, M., Patti, E., & Di Cataldo, S. (2024). Machine learning-enabled real-time anomaly detection for electron beam powder bed fusion additive manufacturing. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-024-02359-6","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3","key":"2598_CR8","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1089\/3dp.2018.0060","volume":"5","author":"A Du Plessis","year":"2018","unstructured":"Du Plessis, A., Yadroitsev, I., Yadroitsava, I., & Le Roux, S. G. (2018). X-ray microcomputed tomography in additive manufacturing: A review of the current technology and applications. 3D Printing and Additive Manufacturing, 5(3), 227\u2013247. https:\/\/doi.org\/10.1089\/3dp.2018.0060","journal-title":"3D Printing and Additive Manufacturing"},{"key":"2598_CR9","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/978-3-030-90633-7_58","volume-title":"Advanced intelligent systems for sustainable development (AI2SD\u20192020)","author":"L El-Bouny","year":"2022","unstructured":"El-Bouny, L., Khalil, M., & Adib, A. (2022). ECG heartbeats classification based on 1-D convolutional neural networks. In J. Kacprzyk, V. E. Balas, & M. Ezziyyani (Eds.), Advanced intelligent systems for sustainable development (AI2SD\u20192020) (pp. 697\u2013708). Springer International Publishing."},{"key":"2598_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnucmat.2021.153041","author":"EAI Ellis","year":"2021","unstructured":"Ellis, E. A. I., Sprayberry, M. A., Ledford, C., Hankwitz, J. P., Kirka, M. M., Rock, C. D., Horn, T. J., Katoh, Y., & Dehoff, R. R. (2021). Processing of tungsten through electron beam melting. Journal of Nuclear Materials. https:\/\/doi.org\/10.1016\/j.jnucmat.2021.153041","journal-title":"Journal of Nuclear Materials"},{"key":"2598_CR11","first-page":"431","volume-title":"Materials and design","author":"SK Everton","year":"2016","unstructured":"Everton, S. K., Hirsch, M., Stavroulakis, P. I., Leach, R. K., & Clare, A. T. (2016). Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials and design (Vol. 95, pp. 431\u2013445). Elsevier."},{"key":"2598_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-022-00808-5","author":"L Fang","year":"2022","unstructured":"Fang, L., Cheng, L., Glerum, J. A., Bennett, J., Cao, J., & Wagner, G. J. (2022). Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls. Npj Computational Materials. https:\/\/doi.org\/10.1038\/s41524-022-00808-5","journal-title":"Npj Computational Materials"},{"key":"2598_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2022.111115","author":"S Feng","year":"2022","unstructured":"Feng, S., Chen, Z., Bircher, B., Ji, Z., Nyborg, L., & Bigot, S. (2022). Predicting laser powder bed fusion defects through in-process monitoring data and machine learning. Materials and Design. https:\/\/doi.org\/10.1016\/j.matdes.2022.111115","journal-title":"Materials and Design"},{"key":"2598_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102138","author":"A Fernandez","year":"2021","unstructured":"Fernandez, A., Felice, R., Terrazas-N\u00e1jera, C. A., & Wicker, R. (2021). Implications for accurate surface temperature monitoring in powder bed fusion: Using multi-wavelength pyrometry to characterize spectral emissivity during processing. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2021.102138","journal-title":"Additive Manufacturing"},{"key":"2598_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2022.110919","author":"A Gaikwad","year":"2022","unstructured":"Gaikwad, A., Williams, R. J., de Winton, H., Bevans, B. D., Smoqi, Z., Rao, P., & Hooper, P. A. (2022). Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing. Materials and Design. https:\/\/doi.org\/10.1016\/j.matdes.2022.110919","journal-title":"Materials and Design"},{"key":"2598_CR16","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/978-3-030-56127-7_19","volume-title":"Additive manufacturing technologies","author":"I Gibson","year":"2021","unstructured":"Gibson, I., Rosen, D., Stucker, B., Khorasani, M., Gibson, I., Rosen, D., Stucker, B., & Khorasani, M. (2021). Design for additive manufacturing. Additive manufacturing technologies (pp. 555\u2013607). Springer."},{"key":"2598_CR17","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. https:\/\/doi.org\/10.1016\/j.addma.2018.04.005","journal-title":"Additive Manufacturing"},{"issue":"11","key":"2598_CR18","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":"4","key":"2598_CR19","doi-asserted-by":"publisher","first-page":"44005","DOI":"10.1088\/1361-6501\/aa5c4f","volume":"28","author":"M Grasso","year":"2017","unstructured":"Grasso, M., & Colosimo, B. M. (2017). Process defects and in situ monitoring methods in metal powder bed fusion: A review. Measurement Science and Technology, 28(4), 44005. https:\/\/doi.org\/10.1088\/1361-6501\/aa5c4f","journal-title":"Measurement Science and Technology"},{"key":"2598_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s40964-024-00576-2","author":"M Grasso","year":"2024","unstructured":"Grasso, M., & Colosimo, B. M. (2024). A review of the current state-of-the-art on in situ monitoring in electron beam powder bed fusion. Progress in Additive Manufacturing. https:\/\/doi.org\/10.1007\/s40964-024-00576-2","journal-title":"Progress in Additive Manufacturing"},{"key":"2598_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.mfglet.2020.03.011","volume":"24","author":"M Grasso","year":"2020","unstructured":"Grasso, M., Valsecchi, G., & Colosimo, B. M. (2020). Powder bed irregularity and hot-spot detection in electron beam melting by means of in-situ video imaging. Manufacturing Letters, 24, 47\u201351. https:\/\/doi.org\/10.1016\/j.mfglet.2020.03.011","journal-title":"Manufacturing Letters"},{"key":"2598_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fmech.2021.767444","author":"W Halsey","year":"2021","unstructured":"Halsey, W., Rose, D., Scime, L., Dehoff, R., & Paquit, V. (2021). Localized defect detection from spatially mapped, in-situ process data with machine learning. Frontiers in Mechanical Engineering. https:\/\/doi.org\/10.3389\/fmech.2021.767444","journal-title":"Frontiers in Mechanical Engineering"},{"key":"2598_CR23","doi-asserted-by":"publisher","DOI":"10.1115\/1.4051749","author":"X Huang","year":"2022","unstructured":"Huang, X., Xie, T., Wang, Z., Chen, L., Zhou, Q., & Hu, Z. (2022). A transfer learning-based multi-fidelity point-cloud neural network approach for melt pool modeling in additive manufacturing. ASCE\u2014ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering. https:\/\/doi.org\/10.1115\/1.4051749","journal-title":"ASCE\u2014ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering"},{"key":"2598_CR24","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. http:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"5","key":"2598_CR25","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1080\/09506608.2016.1176289","volume":"61","author":"C K\u00f6rner","year":"2016","unstructured":"K\u00f6rner, C. (2016). Additive manufacturing of metallic components by selective electron beam melting\u2014A review. International Materials Reviews, 61(5), 361\u2013377. https:\/\/doi.org\/10.1080\/09506608.2016.1176289","journal-title":"International Materials Reviews"},{"key":"2598_CR26","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1016\/j.promfg.2020.05.120","volume":"48","author":"C Ledford","year":"2020","unstructured":"Ledford, C., Rock, C., Tung, M., Wang, H., Schroth, J., & Horn, T. (2020a). Evaluation of electron beam powder bed fusion additive manufacturing of high purity copper for overhang structures using in-situ real time backscatter electron monitoring. Procedia Manufacturing, 48, 828\u2013838.","journal-title":"Procedia Manufacturing"},{"key":"2598_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101365","author":"C Ledford","year":"2020","unstructured":"Ledford, C., Tung, M., Rock, C., & Horn, T. (2020b). Real time monitoring of electron emissions during electron beam powder bed fusion for arbitrary geometries and toolpaths. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2020.101365","journal-title":"Additive Manufacturing"},{"key":"2598_CR28","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/j.addma.2018.04.038","volume":"22","author":"YS Lee","year":"2018","unstructured":"Lee, Y. S., Kirka, M. M., Dinwiddie, R. B., Raghavan, N., Turner, J., Dehoff, R. R., & Babu, S. S. (2018). Role of scan strategies on thermal gradient and solidification rate in electron beam powder bed fusion. Additive Manufacturing, 22, 516\u2013527. https:\/\/doi.org\/10.1016\/j.addma.2018.04.038","journal-title":"Additive Manufacturing"},{"key":"2598_CR102","doi-asserted-by":"publisher","unstructured":"Li, H., Hu, L., Ye, J., Wei, W., Gao, X., Qian, Z., & Long, Y. (2024). A high-precision in-situ monitoring system for laser directed energy deposition melt pool 3D morphology based on deep learning. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-024-02526-9","DOI":"10.1007\/s10845-024-02526-9"},{"key":"2598_CR29","unstructured":"Li, L., Jamieson, K., Rostamizadeh, A., & Talwalkar, A. (2018). Hyperband: A novel Bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research, 18. Retrieved from http:\/\/jmlr.org\/papers\/v18\/16-558.html."},{"key":"2598_CR30","unstructured":"Liu, Y., Zhang, Z., Blunt, L., Saunby, G., Dawes, J., Blackham, B., Rahman, H. A., Smith, C., Gao, F., & Jiang, X. (2019). Development of an in-situ inspection system for additive manufacturing based on phase measurement profilometry. Retrieved from www.euspen.eu"},{"key":"2598_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2019.100940","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Blunt, L., Zhang, Z., Rahman, H. A., Gao, F., & Jiang, X. (2020). In-situ areal inspection of powder bed for electron beam fusion system based on fringe projection profilometry. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2019.100940","journal-title":"Additive Manufacturing"},{"key":"2598_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110819","author":"J Liu","year":"2023","unstructured":"Liu, J., Xu, Y., Cao, M., Gao, F., He, J., & Lin, J. (2023). Fatigue crack size evaluation using acoustic emission signals for wire and arc additive manufactured material. Mechanical Systems and Signal Processing. https:\/\/doi.org\/10.1016\/j.ymssp.2023.110819","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2598_CR33","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.cirpj.2023.03.006","volume":"43","author":"G Maculotti","year":"2023","unstructured":"Maculotti, G., Ghibaudo, C., Genta, G., Ugues, D., & Galetto, M. (2023). An artificial intelligence classifier for electron beam powder bed fusion as-built surface topographies. CIRP Journal of Manufacturing Science and Technology, 43, 129\u2013142. https:\/\/doi.org\/10.1016\/j.cirpj.2023.03.006","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"key":"2598_CR101","doi-asserted-by":"publisher","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","DOI":"10.1007\/s10845-020-01694-8"},{"issue":"2","key":"2598_CR34","doi-asserted-by":"publisher","first-page":"661","DOI":"10.3390\/app14020661","volume":"14","author":"N Pan","year":"2024","unstructured":"Pan, N., Ye, X., Xia, P., & Zhang, G. (2024). The temperature field prediction and estimation of Ti-Al alloy twin-wire plasma arc additive manufacturing using a one-dimensional convolution neural network. Applied Sciences (Switzerland), 14(2), 661. https:\/\/doi.org\/10.3390\/app14020661","journal-title":"Applied Sciences (Switzerland)"},{"key":"2598_CR100","doi-asserted-by":"publisher","unstructured":"Peles, A., Paquit, V. C., & Dehoff, R. R. (2024). Deep-learning based artificial intelligence tool for melt pools and defect segmentation. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-024-02457-5","DOI":"10.1007\/s10845-024-02457-5"},{"key":"2598_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102364","author":"J Petrich","year":"2021","unstructured":"Petrich, J., Snow, Z., Corbin, D., & Reutzel, E. W. (2021). Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2021.102364","journal-title":"Additive Manufacturing"},{"key":"2598_CR36","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1016\/j.jmapro.2024.09.088","volume":"131","author":"D Peverall","year":"2024","unstructured":"Peverall, D., McDonald, T., Gbadamosi-Adeniyi, T., & Horn, T. (2024). Probability of detection of porosity defects for electron beam powder bed fusion additive manufacturing using total electron emissions. Journal of Manufacturing Processes, 131, 2294\u20132309. https:\/\/doi.org\/10.1016\/j.jmapro.2024.09.088","journal-title":"Journal of Manufacturing Processes"},{"key":"2598_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/app121910078","author":"TA Pham","year":"2022","unstructured":"Pham, T. A., Lee, J. H., & Park, C. S. (2022). MST-VAE: multi-scale temporal variational autoencoder for anomaly detection in multivariate time series. Applied Sciences (Switzerland). https:\/\/doi.org\/10.3390\/app121910078","journal-title":"Applied Sciences (Switzerland)"},{"key":"2598_CR38","doi-asserted-by":"crossref","unstructured":"Price, S., Lydon, J., Cooper, K., & Chou, K. (2014). Temperature measurements in powder-bed electron beam additive manufacturing. Retrieved from http:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings-pdf\/IMECE2014\/46438\/V02AT02A002\/4263213\/v02at02a002-imece2014-36661.pdf","DOI":"10.1115\/IMECE2014-36661"},{"key":"2598_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/srep43554","author":"J Raplee","year":"2017","unstructured":"Raplee, J., Plotkowski, A., Kirka, M. M., Dinwiddie, R., Okello, A., Dehoff, R. R., & Babu, S. S. (2017). Thermographic microstructure monitoring in electron beam additive manufacturing. Scientific Reports. https:\/\/doi.org\/10.1038\/srep43554","journal-title":"Scientific Reports"},{"key":"2598_CR40","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-38967-5","volume-title":"Scanning electron microscopy","author":"L Reimer","year":"1998","unstructured":"Reimer, L. (1998). Scanning electron microscopy (Vol. 45). Springer."},{"issue":"11","key":"2598_CR41","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1080\/10426914.2021.1906891","volume":"37","author":"W Ren","year":"2022","unstructured":"Ren, W., Wen, G., Zhang, Z., & Mazumder, J. (2022). Quality monitoring in additive manufacturing using emission spectroscopy and unsupervised deep learning. Materials and Manufacturing Processes, 37(11), 1339\u20131346. https:\/\/doi.org\/10.1080\/10426914.2021.1906891","journal-title":"Materials and Manufacturing Processes"},{"key":"2598_CR42","doi-asserted-by":"publisher","first-page":"103172","DOI":"10.1016\/j.addma.2022.103172","volume":"60","author":"J Renner","year":"2022","unstructured":"Renner, J., Breuning, C., Markl, M., & K\u00f6rner, C. (2022). Surface topographies from electron optical images in electron beam powder bed fusion for process monitoring and control. Additive Manufacturing, 60, 103172. https:\/\/doi.org\/10.1016\/j.addma.2022.103172","journal-title":"Additive Manufacturing"},{"key":"2598_CR43","doi-asserted-by":"crossref","unstructured":"Rescsanski, S., Yadollahi, A., Khanzadeh, M., & Imani, F. (2023). Anomaly detection of laser-based metal additive manufacturing using neural-variational auto-encoder. Retrieved from http:\/\/asmedigitalcollection.asme.org\/MSEC\/proceedings-pdf\/MSEC2023\/87233\/V001T01A026\/7046431\/v001t01a026-msec2023-105156.pdf","DOI":"10.1115\/MSEC2023-105156"},{"key":"2598_CR44","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.procir.2021.03.050","volume":"99","author":"LL Roux","year":"2021","unstructured":"Roux, L. L., Liu, C., Ji, Z., Kerfriden, P., Gage, D., Feyer, F., K\u00f6rner, C., & Bigot, S. (2021). Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning. Procedia CIRP, 99, 342\u2013347. https:\/\/doi.org\/10.1016\/j.procir.2021.03.050","journal-title":"Procedia CIRP"},{"issue":"4","key":"2598_CR45","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s00339-013-7944-4","volume":"114","author":"T Scharowsky","year":"2014","unstructured":"Scharowsky, T., Osmanlic, F., Singer, R. F., & K\u00f6rner, C. (2014). Melt pool dynamics during selective electron beam melting. Applied Physics a: Materials Science and Processing, 114(4), 1303\u20131307. https:\/\/doi.org\/10.1007\/s00339-013-7944-4","journal-title":"Applied Physics a: Materials Science and Processing"},{"issue":"4","key":"2598_CR46","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1108\/13552541211231572","volume":"18","author":"J Schwerdtfeger","year":"2012","unstructured":"Schwerdtfeger, J., Singer, R. F., & K\u00f6rner, C. (2012). In situ flaw detection by IR-imaging during electron beam melting. Rapid Prototyping Journal, 18(4), 259\u2013263. https:\/\/doi.org\/10.1108\/13552541211231572","journal-title":"Rapid Prototyping Journal"},{"key":"2598_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101453","author":"L Scime","year":"2020","unstructured":"Scime, L., Siddel, D., Baird, S., & Paquit, V. (2020). Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2020.101453","journal-title":"Additive Manufacturing"},{"key":"2598_CR48","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. https:\/\/doi.org\/10.1016\/j.addma.2018.11.010","journal-title":"Additive Manufacturing"},{"key":"2598_CR107","unstructured":"Selesnick, I. (2012). Total variation denoising (an MM algorithm). NYU Polytechnic School of Engineering Lecture Notes, 32."},{"key":"2598_CR104","doi-asserted-by":"publisher","unstructured":"Shi, Z., Mamun, A. Al, Kan, C., Tian, W., & Liu, C. (2023). An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing. Journal of Intelligent Manufacturing, 34(4), 1815\u20131831. https:\/\/doi.org\/10.1007\/s10845-021-01879-9","DOI":"10.1007\/s10845-021-01879-9"},{"key":"2598_CR49","doi-asserted-by":"publisher","unstructured":"Slotwinski, J. A. (2014). Additive manufacturing: Overview and NDE challenges. 40th annual review of progress in quantitative nondestructive evaluation (pp. 1173\u20131177). https:\/\/doi.org\/10.1063\/1.4864953","DOI":"10.1063\/1.4864953"},{"key":"2598_CR50","doi-asserted-by":"publisher","unstructured":"Tan, Y., Jin, B., Nettekoven, A., Chen, Y., Yue, Y., Topcu, U., & Sangiovanni-Vincentelli, A. (2019). An encoder-decoder based approach for anomaly detection with application in additive manufacturing. Proceedings\u201418th IEEE international conference on machine learning and applications, ICMLA 2019 (pp. 1008\u20131015). https:\/\/doi.org\/10.1109\/ICMLA.2019.00171","DOI":"10.1109\/ICMLA.2019.00171"},{"key":"2598_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2022.102735","author":"JR Tempelman","year":"2022","unstructured":"Tempelman, J. R., Wachtor, A. J., Flynn, E. B., Depond, P. J., Forien, J. B., Guss, G. M., Calta, N. P., & Matthews, M. J. (2022). Detection of keyhole pore formations in laser powder-bed fusion using acoustic process monitoring measurements. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2022.102735","journal-title":"Additive Manufacturing"},{"key":"2598_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2023.103404","author":"CA Terrazas-N\u00e1jera","year":"2023","unstructured":"Terrazas-N\u00e1jera, C. A., Romero, A., Felice, R., & Wicker, R. (2023). Multi-wavelength pyrometry as an in situ diagnostic tool in metal additive manufacturing: Detecting sintering and liquid phase transitions in electron beam powder bed fusion. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2023.103404","journal-title":"Additive Manufacturing"},{"key":"2598_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107751","author":"M Thill","year":"2021","unstructured":"Thill, M., Konen, W., Wang, H., & B\u00e4ck, T. (2021). Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing. https:\/\/doi.org\/10.1016\/j.asoc.2021.107751","journal-title":"Applied Soft Computing"},{"issue":"2","key":"2598_CR54","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.cirp.2016.05.004","volume":"65","author":"MK Thompson","year":"2016","unstructured":"Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., Bernard, A., Schulz, J., Graf, P., Ahuja, B., & Martina, F. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals\u2014Manufacturing Technology, 65(2), 737\u2013760. https:\/\/doi.org\/10.1016\/j.cirp.2016.05.004","journal-title":"CIRP Annals\u2014Manufacturing Technology"},{"key":"2598_CR55","doi-asserted-by":"publisher","unstructured":"Visca, M., Bouton, A., Powell, R., Gao, Y., & Fallah, S. (2021). Conv1D energy-aware path planner for mobile robots in unstructured environments. Proceedings\u2014IEEE international conference on robotics and automation, 2021-May (pp. 2279\u20132285). https:\/\/doi.org\/10.1109\/ICRA48506.2021.9560771","DOI":"10.1109\/ICRA48506.2021.9560771"},{"key":"2598_CR56","doi-asserted-by":"publisher","DOI":"10.1115\/1.4065445","author":"J Wang","year":"2025","unstructured":"Wang, J., Ye, Y., Wu, M., Zhang, F., Cao, Y., Zhang, Z., Chen, M., & Tang, J. (2025). Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework. Journal of Electrochemical Energy Conversion and Storage. https:\/\/doi.org\/10.1115\/1.4065445","journal-title":"Journal of Electrochemical Energy Conversion and Storage"},{"key":"2598_CR57","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00170-018-2702-6","volume":"100","author":"H Wong","year":"2019","unstructured":"Wong, H., Neary, D., Jones, E., Fox, P., & Sutcliffe, C. (2019). Pilot capability evaluation of a feedback electronic imaging system prototype for in-process monitoring in electron beam additive manufacturing. The International Journal of Advanced Manufacturing Technology, 100, 707\u2013720.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2598_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113379","author":"J Xu","year":"2020","unstructured":"Xu, J., & Duraisamy, K. (2020). Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics. Computer Methods in Applied Mechanics and Engineering. https:\/\/doi.org\/10.1016\/j.cma.2020.113379","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"2598_CR103","doi-asserted-by":"publisher","unstructured":"Yun, H., Kim, H., Jeong, Y. H., & Jun, M. B. G. (2023). Autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor. Journal of Intelligent Manufacturing, 34(3), 1427\u20131444.\nhttps:\/\/doi.org\/10.1007\/s10845-021-01862-4","DOI":"10.1007\/s10845-021-01862-4"},{"issue":"10","key":"2598_CR59","doi-asserted-by":"publisher","first-page":"4094","DOI":"10.1007\/s11837-023-06045-5","volume":"75","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Carriere, P. R., Amoako, E. D., Rock, C. D., Thielk, S. U., Fletcher, C. G., & Horn, T. J. (2023a). Microstructure and elevated temperature flexure testing of tungsten produced by electron beam additive manufacturing. JOM Journal of the Minerals Metals and Materials Society, 75(10), 4094\u20134107. https:\/\/doi.org\/10.1007\/s11837-023-06045-5","journal-title":"JOM Journal of the Minerals Metals and Materials Society"},{"key":"2598_CR106","doi-asserted-by":"publisher","unstructured":"Zhang, S., Wang, Y., He, S., & Jiang, Z. (2016). Bearing fault diagnosis based on variational mode decomposition and total variation denoising. Measurement Science and Technology, 27(7), 075101. https:\/\/doi.org\/10.1088\/0957-0233\/27\/7\/075101","DOI":"10.1088\/0957-0233\/27\/7\/075101"},{"issue":"2","key":"2598_CR60","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1109\/TKDE.2021.3102110","volume":"35","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Chen, Y., Wang, J., & Pan, Z. (2023b). Unsupervised deep anomaly detection for multi-sensor time-series signals. IEEE Transactions on Knowledge and Data Engineering, 35(2), 2118\u20132132. https:\/\/doi.org\/10.1109\/TKDE.2021.3102110","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02598-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-025-02598-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02598-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:48:48Z","timestamp":1771922928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-025-02598-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,17]]},"references-count":67,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2598"],"URL":"https:\/\/doi.org\/10.1007\/s10845-025-02598-1","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,17]]},"assertion":[{"value":"4 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2025","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 author have no competing interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}