AccScience Publishing / ESAM / Volume 2 / Issue 4 / DOI: 10.36922/ESAM026360019
Cite this article
5
Download
110
Views
Related Info Links
More by Authors Links
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Vision-derived stability indicators from melt pool and wire dynamics in laser hot-wire directed energy deposition

Mario Rodriguez-Parra1,2* ,  Miguel Hoffmann2 ,  Mithulan Paramanathan2 ,  Thomas Feldhausen1,2 ,  Eric MacDonald2,3
Show Less
1 Aerospace and Mechanical Engineering, The University of Texas at El Paso, El Paso, Texas , United States of America
2 Manufacturing Science Division, Oak Ridge National Laboratory, Tennessee , United States of America
3 Industrial and Systems Engineering, University of Florida, Gainesville, Florida , United States of America
ESAM 2026, 2(4), 026360019 https://doi.org/10.36922/ESAM026360019
Received: 1 September 2026 | Revised: 2 October 2026 | Accepted: 15 October 2026 | Published online: 2 November 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Laser hot-wire directed energy deposition (LHW-DED) improves process performance by enhancing wire preheating. Reliable in situ assessment of process stability remains challenging because the melt-pool behavior and wire transfer are commonly evaluated separately. This study develops vision-derived stability indicators by jointly characterizing melt-pool geometry and wire dynamics during the deposition of stainless steel 316LSi under varying laser power and hot-wire power. Optical videos were analyzed using multi-Otsu segmentation for melt-pool area extraction and the Segment Anything Model 2 for temporal wire segmentation. Melt-pool area was used to characterize the energy input, while frame-to-frame Intersection over Union (IoU), IoU volatility, and angle variability were evaluated as indicators of wire stability. Low energy conditions produced smaller melt pools and greater wire flutter, whereas sufficient energy inputs produced larger melt pools and more consistent wire behavior. Parameter-level correlations showed that melt-pool area for single beads was related to penetration depth and total cross-sectional area measured by optical microscopy. Melt-pool area was also associated with wire stability during the single-bead laser-power sweep. These results establish melt-pool geometry and wire dynamics as complementary indicators of LHW-DED stability and support future process qualification and in situ feedback control.

Graphical abstract
Keywords
Laser hot-wire directed energy deposition
Computer vision
Additive manufacturing
In situ monitoring
Funding
This material is based on research sponsored by Air Force Research Laboratory under Agreement Number FA8650-20-2-5700. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. This research was supported in part by an appointment to the Oak Ridge National Laboratory GRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. This work was supported by the US Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Materials and Manufacturing Technologies Office under contract number DE-AC05-00OR22725.
Conflict of interest
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government.
References
  1. Hoffmann M, Heinrich L, Paramanathan M, Fillingim KB, Elwany A, Feldhausen T. Hybrid additive manufacturing of AISI 316L via asynchronous powder and hot-wire laser directed energy deposition. J Manuf Processes. 2024;127:446-456. doi: 10.1016/j.jmapro.2024.07.144
  2. F42 Committee. Terminology for Additive Manufacturing—General Principles—Terminology. doi: 10.1520/ISOASTM52900-15
  3. Pinkerton AJ. Laser direct metal deposition: theory and applications in manufacturing and maintenance. In: Advances in Laser Materials Processing. Elsevier; 2010:461-491. doi: 10.1533/9781845699819.6.461
  4. Stawovy MT. Comparison of LCAC and PM Mo deposited using Sciaky EBAMTM. Int J Refract Met Hard Mater. 2018;73:162-167. doi: 10.1016/j.ijrmhm.2018.02.009
  5. Duda T, Raghavan LV. 3D Metal Printing Technology. IFAC-PapersOnLine. 2016;49(29):103-110. doi: 10.1016/j.ifacol.2016.11.111
  6. Wu B, Pan Z, Ding D, et al. A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. J Manuf Processes. 2018;35:127-139. doi: 10.1016/j.jmapro.2018.08.001
  7. Abranovic B, Chang-Davidson E, Beuth JL. Process mapping for laser hot wire additive manufacturing of Ti­6Al­4V. Rapid Prototyp J. 2025;31(6):1156-1174. doi: 10.1108/RPJ-05-2024-0216
  8. Kisielewicz A, Thalavai Pandian K, Sthen D, et al. Hot-Wire Laser-Directed Energy Deposition: Process Characteristics and Benefits of Resistive Pre-Heating of the Feedstock Wire. Metals. 2021;11(4):634. doi: 10.3390/met11040634
  9. Ghanadi N, Pasebani S. A Review on Wire-Laser Directed Energy Deposition: Parameter Control, Process Stability, and Future Research Paths. JMMP. 2024;8(2):84. doi: 10.3390/jmmp8020084
  10. DebRoy T, Wei HL, Zuback JS, et al. Additive manufacturing of metallic components – Process, structure and properties. Prog Mater Sci. 2018;92:112-224. doi: 10.1016/j.pmatsci.2017.10.001
  11. McLain B, Mathenia R, Sparks T, Liou F. Machine Vision to Provide Quantitative Analysis of Meltpool Stability for a Coaxial Wire Directed Energy Deposition Process. Materials. 2024;17(21):5311. doi: 10.3390/ma17215311
  12. Akbari M, Ding Y, Kovacevic R. Process Development for a Robotized Laser Wire Additive Manufacturing. In: Volume 2: Additive Manufacturing; Materials. American Society of Mechanical Engineers; 2017:V002T01A015. doi: 10.1115/MSEC2017-2951
  13. Heralić A, Christiansson AK, Lennartson B. Height control of laser metal-wire deposition based on iterative learning control and 3D scanning. Opt Lasers Eng. 2012;50(9):1230-1241. doi: 10.1016/j.optlaseng.2012.03.016
  14. Motta M, Demir AG, Previtali B. High-speed imaging and process characterization of coaxial laser metal wire deposition. Addit Manuf. 2018;22:497-507. doi: 10.1016/j.addma.2018.05.043
  15. Liu S, Liu W, Harooni M, Ma J, Kovacevic R. Real-time monitoring of laser hot-wire cladding of Inconel 625. Opt Laser Technol. 2014;62:124-134. doi: 10.1016/j.optlastec.2014.03.007
  16. Hagqvist P, Heralić A, Christiansson AK, Lennartson B. Resistance measurements for control of laser metal wire deposition. Opt Lasers Eng. 2014;54:62-67. doi: 10.1016/j.optlaseng.2013.10.010
  17. Ding D, Pan Z, Cuiuri D, Li H. Wire-feed additive manufacturing of metal components: technologies, developments and future interests. Int J Adv Manuf Technol. 2015;81(1-4):465-481. doi: 10.1007/s00170-015-7077-3
  18. Bernauer C, Merk T, Zapata A, Zaeh MF. Laser Metal Deposition with Coaxial Wire Feeding for the Automated and Reliable Build-Up of Solid Metal Parts. KEM. 2022;926:65-79. doi: 10.4028/p-ob7dg7
  19. Wei S, Wang G, Shin YC, Rong Y. Comprehensive modeling of transport phenomena in laser hot-wire deposition process. Int J Heat Mass Transf. 2018;125:1356-1368. doi: 10.1016/j.ijheatmasstransfer.2018.04.164
  20. Feng S, Wainwright J, Wang C, et al. Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model. Sensors. 2025;25(14):4346. doi: 10.3390/s25144346
  21. Sun Z, Guo W, Li L. In-process measurement of melt pool cross-sectional geometry and grain orientation in a laser directed energy deposition additive manufacturing process. Opt Laser Technol. 2020;129:106280. doi: 10.1016/j.optlastec.2020.106280
  22. Da Silva A, Frostevarg J, Kaplan AFH. Melt pool monitoring and process optimisation of directed energy deposition via coaxial thermal imaging. J Manuf Processes 2023;107:126-133. doi: 10.1016/j.jmapro.2023.10.021
  23. Gibson BT, Bandari YK, Richardson BS, et al. Melt pool monitoring for control and data analytics in large-scale metal additive manufacturing. In: Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference. Accessed May 1, 2026. https://utw10945.utweb.utexas.edu/sites/default/files/2019/122%20Melt%20Pool%20Monitoring%20for%20Control%20and%20Data%20Analytic.pdf
  24. Kledwig C, Perfahl H, Reisacher M, Brückner F, Bliedtner J, Leyens C. Analysis of Melt Pool Characteristics and Process Parameters Using a Coaxial Monitoring System during Directed Energy Deposition in Additive Manufacturing. Materials. 2019;12(2):308. doi: 10.3390/ma12020308
  25. Pandiyan V, Cui D, Richter RA, Parrilli A, Leparoux M. Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework. J Intell Manuf. 2025;36(2):909-933. doi: 10.1007/s10845-023-02279-x
  26. Li J, Zhou Q, Huang X, Li M, Cao L. In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting. J Intell Manuf. 2023;34(2):853-867. doi: 10.1007/s10845-021-01829-5
  27. Hu D, Kovacevic R. Sensing, modeling and control for laser-based additive manufacturing. Int J Mach Tools Manuf. 2003;43(1):51-60. doi: 10.1016/S0890-6955(02)00163-3
  28. Zhang Y, Shen S, Li H, Hu Y. Review of in situ and real-time monitoring of metal additive manufacturing based on image processing. Int J Adv Manuf Technol. 2022;123(1-2):1-20. doi: 10.1007/s00170-022-10178-3
  29. Shah A, Weissbach R, Griggs DA, Hart AJ, Ertekin E, Tawfick S. Automated segmentation and analysis of microscopy images of laser powder bed fusion melt tracks. J Manuf Processes. 2025;154:61-70. doi: 10.1016/j.jmapro.2025.09.043
  30. Zhang K, Zhao T, Chen M, Zhang J, An Q. Improved U-net network with temporal adaptive Module for precise melt pool detection in LDED additive manufacturing. J Manuf Processes. 2025;153:650-664. doi: 10.1016/j.jmapro.2025.09.014
  31. De M, Dutta S, Mukherjee M. Enhanced in situ monitoring of metal deposition behaviour for pulsed wire arc directed energy deposition using integrated noncoaxial imaging and supervised deep learning framework. Sci Rep. 2025;15(1):42003. doi: 10.1038/s41598-025-26001-4
  32. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. arXiv. Preprint posted online 2017. doi: 10.48550/arXiv.1703.06870
  33. Asadi R, Queguineur A, Wiikinkoski O, et al. Process monitoring by deep neural networks in directed energy deposition: CNN-based detection, segmentation, and statistical analysis of melt pools. Robot Comput Integr Manuf. 2024;87:102710. doi: 10.1016/j.rcim.2023.102710
  34. Era IZ, Zhou F, Raihan AS, et al. In situ melt pool characterization via thermal imaging for defect detection in Directed Energy Deposition using Vision Transformers. J Manuf Processes. 2025;145:11-21. doi: 10.1016/j.jmapro.2025.03.123
  35. Kirillov A, Mintun E, Ravi N, et al. Segment Anything. arXiv. Preprint posted online 2023. doi: 10.48550/arXiv.2304.02643
  36. Ravi N, Gabeur V, Hu YT, et al. SAM 2: Segment Anything in Images and Videos. arXiv. Preprint posted online 2024. doi: 10.48550/arXiv.2408.00714
  37. Carion N, Gustafson L, Hu YT, et al. SAM 3: Segment Anything with Concepts. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2511.16719
  38. Liao PS, Chen TS, Chung PC. A fast algorithm for multilevel thresholding. J Inf Sci Eng. 2001;17(5):713-717. doi: 10.6688/JISE.2001.17.5.1
  39. Chen J, Li H, Shen S, et al. Universal light SAM algorithm for in situ melting pool monitoring of L-DED/L-PBF/PAM processes. J Comput Des Eng. 2025;12(7):35-48. doi: 10.1093/jcde/qwaf057
  40. Bernauer C, Leitner P, Zapata A, et al. Segmentation-based closed-loop layer height control for enhancing stability and dimensional accuracy in wire-based laser metal deposition. Robot Comput-Integr Manuf. 2024;86:102683. doi: 10.1016/j.rcim.2023.102683
  41. Asadi R, Queguineur A, Ituarte IF. CNN-Based Wire Monitoring in LW-DED: Correlation with Surface Metrology. IOP Conf Ser Mater Sci Eng. 2025;1332(1):012039. doi: 10.1088/1757-899X/1332/1/012039
  42. Zhao Y, Sun J, Zhang G, Sun F, Li H. Enhancing SAM2 for Industrial Defect Detection via Dual-Adapter Fine-Tuning. IEEE Trans Instrum Meas. 2026;75:5009413. doi: 10.1109/tim.2026.3693442
  43. Putz F, Beirami S, Schmidt MA, et al. The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning. Strahlenther Onkol. 2025;201(3):255-265. doi: 10.1007/s00066-024-02313-8
  44. Bambach M, Sizova I, Kies F, Haase C. Directed energy deposition of Inconel 718 powder, cold and hot wire using a six-beam direct diode laser set-up. Addit Manuf. 2021;47:102269. doi: 10.1016/j.addma.2021.102269
  45. Zhang K, Zhang XM, Liu WJ. Influences of Processing Parameters on Dilution Ratio of Laser Cladding Layer during Laser Metal Deposition Shaping. Adv Mater Res. 2012;549:785-789. doi: 10.4028/www.scientific.net/AMR.549.785
Share
Back to top
Engineering Science in Additive Manufacturing, Electronic ISSN: 3082-849X Published by AccScience Publishing