{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T18:58:50Z","timestamp":1784746730207,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52405550"],"award-info":[{"award-number":["52405550"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20241066"],"award-info":[{"award-number":["BK20241066"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["24KJD460004"],"award-info":[{"award-number":["24KJD460004"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["PA2025GDSK0089"],"award-info":[{"award-number":["PA2025GDSK0089"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Fund for High-Level Talents in Nanjing Institute of Technology","award":["YKJ202401"],"award-info":[{"award-number":["YKJ202401"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s10845-025-02663-9","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T19:26:17Z","timestamp":1754335577000},"page":"2767-2786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-task deep learning-empowered in-situ monitoring methodology for visible and invisible defects in laser melt injection: ceramic reinforced metal matrix composite"],"prefix":"10.1007","volume":"37","author":[{"given":"Hongmeng","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8237-2066","authenticated-orcid":false,"given":"Xixi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengchun","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanbo","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzheng","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"2663_CR1","doi-asserted-by":"publisher","first-page":"108522","DOI":"10.1016\/j.optlaseng.2024.108522","volume":"183","author":"L Cao","year":"2024","unstructured":"Cao, L., Guo, W., Li, J., Zhang, Y., Cai, W., Zhou, Q., et al. (2024). Transformer and cross-attention-based multi-sensor in-situ monitoring of molten pool stability and part quality in laser powder bed fusion. Optics and Lasers in Engineering, 183, 108522. https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108522","journal-title":"Optics and Lasers in Engineering"},{"key":"2663_CR3","doi-asserted-by":"publisher","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C. Y., & Rabinovich, A. (2018). GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1711.02257","DOI":"10.48550\/arXiv.1711.02257"},{"key":"2663_CR2","doi-asserted-by":"publisher","first-page":"102581","DOI":"10.1016\/j.rcim.2023.102581","volume":"84","author":"L Chen","year":"2023","unstructured":"Chen, L., Bi, G., Yao, X., Tan, C., Su, J., Ng, N. P. H., et al. (2023). Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition. Robotics and Computer-Integrated Manufacturing, 84, 102581. https:\/\/doi.org\/10.1016\/j.rcim.2023.102581","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"18","key":"2663_CR5","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.1177\/00219983231179092","volume":"57","author":"H Cheng","year":"2023","unstructured":"Cheng, H., Wang, H., Zhou, J., Guo, L., Wang, Q., & Tang, M. (2023). Performance analysis of recycled carbon fiber under recycling process parameters optimized using response surface methodology. Journal of Composite Materials, 57(18), 2857\u20132872. https:\/\/doi.org\/10.1177\/00219983231179092","journal-title":"Journal of Composite Materials"},{"key":"2663_CR4","doi-asserted-by":"publisher","first-page":"111122","DOI":"10.1016\/j.compositesb.2023.111122","volume":"270","author":"H Cheng","year":"2024","unstructured":"Cheng, H., Tang, M., Zhang, J., Wang, H., Zhou, J., Wang, Q., & Qian, Z. (2024). Effects of rCF attributes and FDM-3D printing parameters on the mechanical properties of rCFRP. Composites Part B: Engineering, 270, 111122. https:\/\/doi.org\/10.1016\/j.compositesb.2023.111122","journal-title":"Composites Part B: Engineering"},{"key":"2663_CR6","doi-asserted-by":"publisher","unstructured":"Cipolla, R., Gal, Y., & Kendall, A. (2018). Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7482\u20137491). Presented at the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA: IEEE. https:\/\/doi.org\/10.1109\/CVPR.2018.00781","DOI":"10.1109\/CVPR.2018.00781"},{"key":"2663_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-025-02614-4","author":"J Ferreira","year":"2025","unstructured":"Ferreira, J., Darabi, R., Sousa, A., Brueckner, F., Reis, L., Reis, A., Tavares, J., & Sousa, J. (2025). Gen-JEMA: Enhanced explainability using generative joint embedding multimodal alignment for monitoring directed energy deposition. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-025-02614-4","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2663_CR8","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.jmatprotec.2018.12.018","volume":"267","author":"H Frei\u00dfe","year":"2019","unstructured":"Frei\u00dfe, H., Bohlen, A., & Seefeld, T. (2019). Determination of the particle content in laser melt injected tracks. Journal of Materials Processing Technology, 267, 177\u2013185. https:\/\/doi.org\/10.1016\/j.jmatprotec.2018.12.018","journal-title":"Journal of Materials Processing Technology"},{"key":"2663_CR9","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1016\/j.jmapro.2021.12.061","volume":"75","author":"Y Fu","year":"2022","unstructured":"Fu, Y., Downey, A. R. J., Yuan, L., Zhang, T., Pratt, A., & Balogun, Y. (2022). Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 75, 693\u2013710. https:\/\/doi.org\/10.1016\/j.jmapro.2021.12.061","journal-title":"Journal of Manufacturing Processes"},{"key":"2663_CR10","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.surfcoat.2019.01.104","volume":"362","author":"M Gopinath","year":"2019","unstructured":"Gopinath, M., Thota, P., & Nath, A. K. (2019). Role of molten pool thermo cycle in laser surface alloying of AISI 1020 steel with in-situ synthesized TiN. Surface and Coatings Technology, 362, 150\u2013166. https:\/\/doi.org\/10.1016\/j.surfcoat.2019.01.104","journal-title":"Surface and Coatings Technology"},{"issue":"1","key":"2663_CR12","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.cirp.2020.04.049","volume":"69","author":"W Guo","year":"2020","unstructured":"Guo, W., Grace, Tian, Q., Guo, S., & Guo, Y. (2020). A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. CIRP Annals, 69(1), 205\u2013208. https:\/\/doi.org\/10.1016\/j.cirp.2020.04.049","journal-title":"CIRP Annals"},{"key":"2663_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.jmsy.2021.11.003","volume":"62","author":"S Guo","year":"2022","unstructured":"Guo, S., Agarwal, M., Cooper, C., Tian, Q., Gao, R. X., Guo, W., & Guo, Y. B. (2022). Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm. Journal of Manufacturing Systems, 62, 145\u2013163. https:\/\/doi.org\/10.1016\/j.jmsy.2021.11.003","journal-title":"Journal of Manufacturing Systems"},{"key":"2663_CR13","doi-asserted-by":"publisher","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L. C., Tan, M. (2019). Searching for MobileNetV3. In 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) (pp. 1314\u20131324). Presented at the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South): IEEE. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2663_CR14","doi-asserted-by":"publisher","unstructured":"Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2017). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <\u20090.5\u00a0MB model size. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1602.07360","DOI":"10.48550\/arXiv.1602.07360"},{"key":"2663_CR15","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.jmapro.2022.05.013","volume":"79","author":"N Jamnikar","year":"2022","unstructured":"Jamnikar, N., Liu, S., Brice, C., & Zhang, X. (2022). In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing. Journal of Manufacturing Processes, 79, 803\u2013814. https:\/\/doi.org\/10.1016\/j.jmapro.2022.05.013","journal-title":"Journal of Manufacturing Processes"},{"key":"2663_CR16","doi-asserted-by":"publisher","first-page":"103684","DOI":"10.1016\/j.addma.2023.103684","volume":"73","author":"SA Khairallah","year":"2023","unstructured":"Khairallah, S. A., Chin, E. B., Juhasz, M. J., Dayton, A. L., Capps, A., Tsuji, P. H., et al. (2023). High fidelity model of directed energy deposition: Laser-powder-melt pool interaction and effect of laser beam profile on solidification microstructure. Additive Manufacturing, 73, 103684. https:\/\/doi.org\/10.1016\/j.addma.2023.103684","journal-title":"Additive Manufacturing"},{"key":"2663_CR17","doi-asserted-by":"publisher","first-page":"112138","DOI":"10.1016\/j.measurement.2022.112138","volume":"204","author":"H Li","year":"2022","unstructured":"Li, H., Ren, H., Liu, Z., Huang, F., Xia, G., & Long, Y. (2022a). In-situ monitoring system for weld geometry of laser welding based on multi-task convolutional neural network model. Measurement, 204, 112138. https:\/\/doi.org\/10.1016\/j.measurement.2022.112138","journal-title":"Measurement"},{"key":"2663_CR18","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1016\/j.jmapro.2022.10.050","volume":"84","author":"J Li","year":"2022","unstructured":"Li, J., Zhang, X., Zhou, Q., Chan, F. T. S., & Hu, Z. (2022b). A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Processes, 84, 913\u2013926. https:\/\/doi.org\/10.1016\/j.jmapro.2022.10.050","journal-title":"Journal of Manufacturing Processes"},{"key":"2663_CR19","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.jmsy.2022.07.007","volume":"64","author":"J Li","year":"2022","unstructured":"Li, J., Zhou, Q., Cao, L., Wang, Y., & Hu, J. (2022c). A convolutional neural network-based multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Systems, 64, 429\u2013442. https:\/\/doi.org\/10.1016\/j.jmsy.2022.07.007","journal-title":"Journal of Manufacturing Systems"},{"key":"2663_CR20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2111.10603","author":"B Lin","year":"2022","unstructured":"Lin, B., Ye, F., Zhang, Y., & Tsang, I. W. (2022). Reasonable effectiveness of random weighting: A litmus test for multi-task learning. ArXiv. https:\/\/doi.org\/10.48550\/ArXiv.2111.10603","journal-title":"ArXiv"},{"key":"2663_CR21","doi-asserted-by":"publisher","unstructured":"Liu, S., Johns, E., & Davison, A. J. (2019). End-To-End Multi-Task Learning With Attention. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1871\u20131880). Presented at the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA: IEEE. https:\/\/doi.org\/10.1109\/CVPR.2019.00197","DOI":"10.1109\/CVPR.2019.00197"},{"key":"2663_CR22","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.jmatprotec.2019.04.026","volume":"271","author":"QY Lu","year":"2019","unstructured":"Lu, Q. Y., Nguyen, N. V., Hum, A. J. W., Tran, T., & Wong, C. H. (2019). Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density. Journal of Materials Processing Technology, 271, 520\u2013531. https:\/\/doi.org\/10.1016\/j.jmatprotec.2019.04.026","journal-title":"Journal of Materials Processing Technology"},{"key":"2663_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3432145","volume":"73","author":"Y Mao","year":"2024","unstructured":"Mao, Y., Lin, X., & Zhu, K. (2024). Selective laser melting monitoring based on the plume and its motion features. IEEE Transactions on Instrumentation and Measurement, 73, 1\u201314. https:\/\/doi.org\/10.1109\/TIM.2024.3432145","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2663_CR25","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.jallcom.2017.04.254","volume":"714","author":"G Muvvala","year":"2017","unstructured":"Muvvala, G., Patra Karmakar, D., & Nath, A. K. (2017). Monitoring and assessment of tungsten carbide wettability in laser cladded metal matrix composite coating using an IR pyrometer. Journal of Alloys and Compounds, 714, 514\u2013521. https:\/\/doi.org\/10.1016\/j.jallcom.2017.04.254","journal-title":"Journal of Alloys and Compounds"},{"key":"2663_CR24","doi-asserted-by":"publisher","first-page":"126100","DOI":"10.1016\/j.surfcoat.2020.126100","volume":"399","author":"G Muvvala","year":"2020","unstructured":"Muvvala, G., Mullick, S., & Nath, A. K. (2020). Development of process maps based on molten pool thermal history during laser cladding of inconel 718\/tic metal matrix composite coatings. Surface and Coatings Technology, 399, 126100. https:\/\/doi.org\/10.1016\/j.surfcoat.2020.126100","journal-title":"Surface and Coatings Technology"},{"key":"2663_CR26","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s10921-021-00786-6","volume":"40","author":"Z Qian","year":"2021","unstructured":"Qian, Z., & Huang, H. (2021). Monitoring of crack initiation at coating\/substrate interface by residual magnetic field measurement. Journal of Nondestructive Evaluation, 40, 53. https:\/\/doi.org\/10.1007\/s10921-021-00786-6","journal-title":"Journal of Nondestructive Evaluation"},{"issue":"5","key":"2663_CR28","doi-asserted-by":"publisher","first-page":"2101412","DOI":"10.1109\/TMAG.2022.3152860","volume":"58","author":"Z Qian","year":"2022","unstructured":"Qian, Z., Zeng, H., Liu, H., Ge, Y., Cheng, H., & Huang, H. (2022). Effect of hard particles on magnetic Barkhausen noise in metal matrix composite coatings: Modeling and application in hardness evaluation. IEEE Transactions on Magnetics, 58(5), 2101412. https:\/\/doi.org\/10.1109\/TMAG.2022.3152860","journal-title":"IEEE Transactions on Magnetics"},{"key":"2663_CR27","doi-asserted-by":"publisher","first-page":"114142","DOI":"10.1016\/j.measurement.2024.114142","volume":"226","author":"Z Qian","year":"2024","unstructured":"Qian, Z., Yang, C., Liu, H., Zhang, W., Chen, Z., Ge, Y., Cheng, H., & Huang, H. (2024). Visualization evaluation of damage degree on remanufacturing cores based on residual magnetic scanning measurement. Measurement, 226, 114142. https:\/\/doi.org\/10.1016\/j.measurement.2024.114142","journal-title":"Measurement"},{"issue":"2","key":"2663_CR29","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TII.2020.2978114","volume":"17","author":"W Ren","year":"2021","unstructured":"Ren, W., Wen, G., Xu, B., & Zhang, Z. (2021). A novel convolutional neural network based on time\u2013frequency spectrogram of Arc sound and its application on GTAW penetration classification. IEEE Transactions on Industrial Informatics, 17(2), 809\u2013819. https:\/\/doi.org\/10.1109\/TII.2020.2978114","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2663_CR30","doi-asserted-by":"publisher","first-page":"101413","DOI":"10.1016\/j.addma.2020.101413","volume":"35","author":"H Siva Prasad","year":"2020","unstructured":"Siva Prasad, H., Brueckner, F., & Kaplan, A. F. H. (2020). Powder incorporation and spatter formation in high deposition rate blown powder directed energy deposition. Additive Manufacturing, 35, 101413. https:\/\/doi.org\/10.1016\/j.addma.2020.101413","journal-title":"Additive Manufacturing"},{"key":"2663_CR31","doi-asserted-by":"publisher","unstructured":"Sun, T., Shao, Y., Li, X., Liu, P., Yan, H., Qiu, X., & Huang, X. (2019). Learning sparse sharing architectures for multiple tasks. ArXiv. https:\/\/doi.org\/10.48550\/ArXiv.1911.05034","DOI":"10.48550\/ArXiv.1911.05034"},{"key":"2663_CR32","doi-asserted-by":"publisher","first-page":"100904","DOI":"10.1016\/j.addma.2019.100904","volume":"31","author":"AT Sutton","year":"2020","unstructured":"Sutton, A. T., Kriewall, C. S., Leu, M. C., Newkirk, J. W., & Brown, B. (2020). Characterization of laser spatter and condensate generated during the selective laser melting of 304L stainless steel powder. Additive Manufacturing, 31, 100904. https:\/\/doi.org\/10.1016\/j.addma.2019.100904","journal-title":"Additive Manufacturing"},{"key":"2663_CR33","doi-asserted-by":"publisher","unstructured":"Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1905.11946","DOI":"10.48550\/arXiv.1905.11946"},{"key":"2663_CR34","doi-asserted-by":"publisher","unstructured":"Tang, Y., Han, K., Guo, J., Xu, C., Xu, C., & Wang, Y. (2022). GhostNetV2: Enhance Cheap Operation with Long-Range Attention. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2211.12905","DOI":"10.48550\/arXiv.2211.12905"},{"issue":"17","key":"2663_CR35","doi-asserted-by":"publisher","first-page":"4225","DOI":"10.1016\/S1359-6454(00)00278-0","volume":"48","author":"JA Vreeling","year":"2000","unstructured":"Vreeling, J. A., Ocel\u0131\u0301k, V., Pei, Y. T., Van Agterveld, D. T. L., De Hosson, J., & Th, M. (2000). Laser melt injection in aluminum alloys: On the role of the oxide skin. Acta Materialia, 48(17), 4225\u20134233. https:\/\/doi.org\/10.1016\/S1359-6454(00)00278-0","journal-title":"Acta Materialia"},{"key":"2663_CR36","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.jmsy.2020.10.002","volume":"57","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Jiao, W., & Zhang, Y. (2020). Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. Journal of Manufacturing Systems, 57, 429\u2013439. https:\/\/doi.org\/10.1016\/j.jmsy.2020.10.002","journal-title":"Journal of Manufacturing Systems"},{"key":"2663_CR37","doi-asserted-by":"publisher","first-page":"105598","DOI":"10.1016\/j.ijrmhm.2021.105598","volume":"100","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Huang, Y., Yang, L., & Sun, T. (2021). Microstructure and property of tungsten carbide particulate reinforced wear resistant coating by TIG cladding. International Journal of Refractory Metals and Hard Materials, 100, 105598. https:\/\/doi.org\/10.1016\/j.ijrmhm.2021.105598","journal-title":"International Journal of Refractory Metals and Hard Materials"},{"key":"2663_CR38","doi-asserted-by":"publisher","first-page":"127341","DOI":"10.1016\/j.surfcoat.2021.127341","volume":"420","author":"Q Xiao","year":"2021","unstructured":"Xiao, Q., Sun, W. L., Yang, K. X., Xing, X. F., Chen, Z. H., Zhou, H. N., & Lu, J. (2021). Wear mechanisms and micro-evaluation on WC particles investigation of WC-Fe composite coatings fabricated by laser cladding. Surface and Coatings Technology, 420, 127341. https:\/\/doi.org\/10.1016\/j.surfcoat.2021.127341","journal-title":"Surface and Coatings Technology"},{"issue":"2","key":"2663_CR39","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1016\/j.ceramint.2021.10.048","volume":"48","author":"H Xu","year":"2022","unstructured":"Xu, H., & Huang, H. (2022a). Plasma remelting and injection method for fabricating metal matrix composite coatings reinforced with tungsten carbide. Ceramics International, 48(2), 2645\u20132659. https:\/\/doi.org\/10.1016\/j.ceramint.2021.10.048","journal-title":"Ceramics International"},{"issue":"16","key":"2663_CR40","doi-asserted-by":"publisher","first-page":"22854","DOI":"10.1016\/j.ceramint.2022.04.189","volume":"48","author":"H Xu","year":"2022","unstructured":"Xu, H., & Huang, H. (2022b). Microstructure evolution and mechanical properties of thermally sprayed coating modified by laser remelting and injection with tungsten carbide. Ceramics International, 48(16), 22854\u201322868. https:\/\/doi.org\/10.1016\/j.ceramint.2022.04.189","journal-title":"Ceramics International"},{"issue":"3\u20134","key":"2663_CR41","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1007\/s00170-023-11977-y","volume":"128","author":"H Xu","year":"2023","unstructured":"Xu, H., & Huang, H. (2023a). Monitoring melted state of reinforced particle in metal matrix composite fabricated by laser melt injection using optical camera. The International Journal of Advanced Manufacturing Technology, 128(3\u20134), 1781\u20131800. https:\/\/doi.org\/10.1007\/s00170-023-11977-y","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2663_CR42","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.jmapro.2023.02.059","volume":"92","author":"H Xu","year":"2023","unstructured":"Xu, H., & Huang, H. (2023b). In situ monitoring in laser melt injection based on fusion of infrared thermal and high-speed camera images. Journal of Manufacturing Processes, 92, 466\u2013478. https:\/\/doi.org\/10.1016\/j.jmapro.2023.02.059","journal-title":"Journal of Manufacturing Processes"},{"issue":"8","key":"2663_CR43","doi-asserted-by":"publisher","first-page":"4181","DOI":"10.1007\/s10845-023-02207-z","volume":"35","author":"H Xu","year":"2024","unstructured":"Xu, H., & Huang, H. (2024). CNN architecture-based hybrid fusion model for in-situ monitoring to fabricate metal matrix composite by laser melt injection. Journal of Intelligent Manufacturing, 35(8), 4181\u20134200. https:\/\/doi.org\/10.1007\/s10845-023-02207-z","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2663_CR44","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.jmsy.2020.11.001","volume":"57","author":"R Yazdi","year":"2020","unstructured":"Yazdi, R., Imani, F., & Yang, H. (2020). A hybrid deep learning model of process-build interactions in additive manufacturing. Journal of Manufacturing Systems, 57, 460\u2013468. https:\/\/doi.org\/10.1016\/j.jmsy.2020.11.001","journal-title":"Journal of Manufacturing Systems"},{"key":"2663_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3406808","volume":"73","author":"M Yu","year":"2024","unstructured":"Yu, M., Zhu, L., Yang, Z., & Ning, J. (2024). In situ monitoring and innovative feature fusion neural network for enhanced Laser-Directed energy deposition track geometry prediction and control. IEEE Transactions on Instrumentation and Measurement, 73, 1\u201310. https:\/\/doi.org\/10.1109\/TIM.2024.3406808","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2663_CR46","doi-asserted-by":"publisher","first-page":"111146","DOI":"10.1016\/j.measurement.2022.111146","volume":"195","author":"J Yuan","year":"2022","unstructured":"Yuan, J., Liu, H., Liu, W., Wang, F., & Peng, S. (2022). A method for melt pool state monitoring in laser-based direct energy deposition based on densenet. Measurement, 195, 111146. https:\/\/doi.org\/10.1016\/j.measurement.2022.111146","journal-title":"Measurement"},{"key":"2663_CR48","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.jmsy.2019.02.004","volume":"51","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., You, D., Gao, X., Zhang, N., & Gao, P. P. (2019). Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of Thick plates. Journal of Manufacturing Systems, 51, 87\u201394. https:\/\/doi.org\/10.1016\/j.jmsy.2019.02.004","journal-title":"Journal of Manufacturing Systems"},{"issue":"9","key":"2663_CR50","doi-asserted-by":"publisher","first-page":"5769","DOI":"10.1109\/TII.2019.2956078","volume":"16","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Soon, H. G., Ye, D., Fuh, J. Y. H., & Zhu, K. (2020). Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks. IEEE Transactions on Industrial Informatics, 16(9), 5769\u20135779. https:\/\/doi.org\/10.1109\/TII.2019.2956078","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2663_CR47","doi-asserted-by":"publisher","first-page":"111475","DOI":"10.1016\/j.measurement.2022.111475","volume":"199","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Xu, F., & Jia, M. (2022). A centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and CNN. Measurement, 199, 111475. https:\/\/doi.org\/10.1016\/j.measurement.2022.111475","journal-title":"Measurement"},{"key":"2663_CR49","doi-asserted-by":"publisher","first-page":"108794","DOI":"10.1016\/j.optlaseng.2024.108794","volume":"186","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Fang, C., Zhang, J., Chen, G., Chen, Z., Du, H., et al. (2025). Multi-source data fusion monitoring system for super-elevation in laser powder bed fusion based on bi-stream cross-mode fusion network. Optics and Lasers in Engineering, 186, 108794. https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108794","journal-title":"Optics and Lasers in Engineering"},{"key":"2663_CR51","doi-asserted-by":"publisher","first-page":"117438","DOI":"10.1016\/j.jmatprotec.2021.117438","volume":"301","author":"S Zhao","year":"2022","unstructured":"Zhao, S., Xu, S., Yang, L., & Huang, Y. (2022). WC-Fe metal-matrix composite coatings fabricated by laser wire cladding. Journal of Materials Processing Technology, 301, 117438. https:\/\/doi.org\/10.1016\/j.jmatprotec.2021.117438","journal-title":"Journal of Materials Processing Technology"},{"key":"2663_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmsy.2023.02.016","volume":"68","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., Chang, B., Zou, H., Sun, L., Wang, L., & Du, D. (2023). Online visual monitoring method for liquid rocket engine nozzle welding based on a multi-task deep learning model. Journal of Manufacturing Systems, 68, 1\u201311. https:\/\/doi.org\/10.1016\/j.jmsy.2023.02.016","journal-title":"Journal of Manufacturing Systems"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02663-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-025-02663-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02663-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:58:11Z","timestamp":1782277091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-025-02663-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,4]]},"references-count":52,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["2663"],"URL":"https:\/\/doi.org\/10.1007\/s10845-025-02663-9","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,4]]},"assertion":[{"value":"1 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 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 authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}