journal article·2026-06-23

A multi-modal deep regression framework for continuous process condition monitoring and closed-loop quality control in LPBF

Muhammad Shafiq, Hashim Farooq, Umar, Sumanth Ratna. Kandavalli, Ponduri Sivaprasad, K. B. V. Brahma Rao

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Fault Detection and Control Systems · Advanced Statistical Process Monitoring · Industrial Vision Systems and Defect Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Shandong Xiehe University

Baba Guru Nanak University

Teesside University

New York University

RVR&JC College of Engineering

Koneru Lakshmaiah Education Foundation

References (35)

Show source-provided references
1. DebRoy T, Wei HL, Zuback JS, et al. Additive manufacturing of metallic components–process, structure and properties. Prog Mater Sci. 2018;92:112–224.10.1016/j.pmatsci.2017.10.001 ↗
2. Yuhang YAO, Yicheng S. The application of laser forming technology in additive manufacturing and its quality monitoring. Global Acad Front. 2025;3(3):89–98.
3. 10.1063/1.493780910.1063/1.4937809 ↗
4. 10.36922/MSAM02522004210.36922/MSAM025220042 ↗
5. 10.1016/j.matdes.2016.01.09910.1016/j.matdes.2016.01.099 ↗
6. 10.1088/0957-0233/27/7/07200110.1088/0957-0233/27/7/072001 ↗
7. 10.1109/JESTPE.2024.338765210.1109/JESTPE.2024.3387652 ↗
8. 10.1016/j.addma.2017.11.00910.1016/j.addma.2017.11.009 ↗
9. Ye D, Hong GS, Zhang Y, et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int J Adv Manuf Technol. 2018;96(5):2791–2801.10.1007/s00170-018-1728-0 ↗
10. 10.1088/1361-6501/aa5c4f10.1088/1361-6501/aa5c4f ↗
11. 10.1007/s10845-025-02783-210.1007/s10845-025-02783-2 ↗
12. 10.1016/j.addma.2018.11.01510.1016/j.addma.2018.11.015 ↗
13. 10.1007/s11837-020-04155-y10.1007/s11837-020-04155-y ↗
14. 10.1016/j.jmatprotec.2023.11814410.1016/j.jmatprotec.2023.118144 ↗
15. 10.3389/fmech.2021.67807610.3389/fmech.2021.678076 ↗
16. 10.1016/j.ress.2025.11152010.1016/j.ress.2025.111520 ↗
17. 10.1016/j.phpro.2010.08.07810.1016/j.phpro.2010.08.078 ↗
18. 10.1016/j.actamat.2016.02.01410.1016/j.actamat.2016.02.014 ↗
19. 10.1115/1.402854010.1115/1.4028540 ↗
20. 10.1115/1.405318410.1115/1.4053184 ↗
21. 10.1016/j.addma.2017.05.00410.1016/j.addma.2017.05.004 ↗
22. 10.3390/machines1309082010.3390/machines13090820 ↗
23. 10.1109/TII.2019.291052410.1109/TII.2019.2910524 ↗
24. 10.1016/j.addma.2019.01.00610.1016/j.addma.2019.01.006 ↗
25. Fang Q, Tan Z, Li H, et al. In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network. J Manuf Processes. 2021;68:347–355.10.1016/j.jmapro.2021.05.052 ↗
26. 10.1007/s10845-023-02119-y10.1007/s10845-023-02119-y ↗
27. 10.1016/j.addma.2018.04.00510.1016/j.addma.2018.04.005 ↗
28. 10.1007/s40964-019-00108-310.1007/s40964-019-00108-3 ↗
29. Kwon O, Kim HG, Ham MJ, et al. A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf. 2020;31(2):375–386.10.1007/s10845-018-1451-6 ↗
30. 10.1016/j.addma.2024.10450210.1016/j.addma.2024.104502 ↗
31. 10.1016/j.amf.2024.20015310.1016/j.amf.2024.200153 ↗
32. 10.3390/s2223944610.3390/s22239446 ↗
33. 10.1007/s10845-023-02092-610.1007/s10845-023-02092-6 ↗
34. Kadhiravan D. Real-time prediction of process health and control adjustments in laser powder bed fusion (LPBF): multi-modal regression dataset [Data set]. GitHub. 2024. Available from: https://github.com/karankadhir86/Real-Time-Prediction-of-Process-Health-and-Control-Adjustments-in-LPBF
35. 10.1016/j.jmapro.2025.03.02410.1016/j.jmapro.2025.03.024 ↗