journal article·2026-03

Study on Molten Pool Imaging and Weld Bead Formation Monitoring in the CMT-WAAM Process of Magnesium Alloy

Tianyang Zhang, Jingjing Cheng, Cheng Xu, Lyuyuan Wang, Kehong Wang

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OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Advanced Welding Techniques Analysis

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References (39)

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1. Meng M, Zhai Z, Lei G, Yan Z, Wu W, Wang Q, Zhang Z (2025) Precipitation characteristics and short-time aging treatment of Mg-Gd-Y-Zn-Zr alloy. J Magnes Alloys. https://doi.org/10.1016/j.jma.2025.05.00410.1016/j.jma.2025.05.004 ↗
2. Wang J, Khan MA, Dai S, Feng Y, Wang H, Afifi MA, Li J (2025) Effect of double-extrusion following by stepwise-hot-rolling on microstructures and mechanical properties of Mg-Gd-Y-Zn-Zr alloy. J Magnes Alloys. https://doi.org/10.1016/j.jma.2025.03.00710.1016/j.jma.2025.03.007 ↗
3. Li Y, Gao Z, Wang Q, Zhao X, Ban C, Zhang T, Wang F (2025) Influence of heat treatment on phase distribution and micro-galvanic corrosion in AZ91 and Mg-Gd-Y alloys: a comparative analysis. J Alloys Compd 1018:17928810.1016/j.jallcom.2025.179288 ↗
4. Yang Y, Li L, Bao J, Sha J, Qiao M, Tian J, Liu W, Zhao Z, Zhang Z (2024) Numerical simulation on metallurgical transport phenomena during DC casting of large-size Mg–Gd–Y alloy billet under various magnetic fields. Therm Sci Eng Progr 56:10308210.1016/j.tsep.2024.103082 ↗
5. Pei Y, Yuan M, Wei E, Zhang Z, Zhao M, Ma H, Teng B (2024) Cooperative achievement in grain refinement and texture weakening of Mg–Gd–Y–Sm–Zr alloy fabricated by asymmetric extrusion-shear process. Mater Sci Eng A 895:14626210.1016/j.msea.2024.146262 ↗
6. Pawanr S, Gupta K (2024) On dry machining of AZ31B magnesium alloy using textured cutting tool inserts. J Magnes Alloys 12:1608–161810.1016/j.jma.2024.04.007 ↗
7. Gain S, Veeman D (2025) A review on advances and challenges in wire arc additive manufacturing: process parameters, microstructural evolution and material performance across alloys. J Alloys Compd 1029:18073510.1016/j.jallcom.2025.180735 ↗
8. Srinivasan D, Sevvel P, Solomon IJ, Tanushkumaar P (2022) A review on cold metal transfer (CMT) technology of welding. Mater Today Proc 64:108–11510.1016/j.matpr.2022.04.016 ↗
9. Yi H, Wang Q, Cao H (2022) Wire-arc directed energy deposition of magnesium alloys: microstructure, properties and quality optimization strategies. J Mater Res Technol 20:627–64910.1016/j.jmrt.2022.07.083 ↗
10. Çam G, Günen A (2024) Challenges and opportunities in the production of magnesium parts by directed energy deposition processes. J Magnes Alloy 12(5):1663–168610.1016/j.jma.2024.05.004 ↗
11. Wang P, Zhang H, Zhu H, Li Q, Feng M (2021) Wire-arc additive manufacturing of AZ31 magnesium alloy fabricated by cold metal transfer heat source: processing, microstructure, and mechanical behavior. J Mater Process Technol 288:11689510.1016/j.jmatprotec.2020.116895 ↗
12. Wang H, Li Y, Yang B, Wang J, Lai R, Wang Z, Li Y (2025) Understanding the processing, microstructure, and deformation behavior of AZ31B Mg alloy fabricated by additive friction stir deposition. J Mater Process Technol. https://doi.org/10.1016/j.jmatprotec.2025.11878110.1016/j.jmatprotec.2025.118781 ↗
13. Snow Z, Reutzel EW, Petrich J (2022) Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning. J Mater Process Technol 302:11747610.1016/j.jmatprotec.2021.117476 ↗
14. Cho H-W, Shin S-J, Seo G-J, Kim DB, Lee D-H (2022) Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: molybdenum material. J Mater Process Technol 302:11749510.1016/j.jmatprotec.2022.117495 ↗
15. Pouranvari M (2021) Critical review on fusion welding of magnesium alloys: metallurgical challenges and opportunities. Sci Technol Weld Join 26:559–58010.1080/13621718.2021.1982339 ↗
16. Cai Y, Wang Y, Chen H, Xiong J (2024) Molten pool behaviors and forming characteristics in wire-laser directed energy deposition with beam oscillation. J Mater Process Technol 326:11832610.1016/j.jmatprotec.2024.118326 ↗
17. Gao S, Feng Y, Wang J, Qin M, Bodunde OP, Liao W-H, Guo P (2021) Molten pool characteristics of a nickel-titanium shape memory alloy for directed energy deposition. Opt Laser Technol 142:10721510.1016/j.optlastec.2021.107215 ↗
18. Jamnikar ND, Liu S, Brice C, Zhang X (2022) In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing. J Manuf Process 79:803–81410.1016/j.jmapro.2022.05.013 ↗
19. Fotovvati B, Wayne SF, Lewis G, Asadi E (2018) A review on melt-pool characteristics in laser welding of metals. Adv Mater Sci Eng 2018:492071810.1155/2018/4920718 ↗
20. Akbari P, Ogoke F, Kao N-Y, Meidani K, Yeh C-Y, Lee W, Farimani AB (2022) MeltpoolNet: melt pool characteristic prediction in Metal Additive Manufacturing using machine learning. Addit Manuf 55:102817
21. Cao L, Guo W, Li J, Zhang Y, Cai W, Zhou Q, Yu L, Li W (2024) Transformer and cross-attention-based multi-sensor in-situ monitoring of molten pool stability and part quality in laser powder bed fusion. Opt Lasers Eng 183:10852210.1016/j.optlaseng.2024.108522 ↗
22. Kim S, Jeon I, Sohn H (2023) Infrared thermographic imaging based real-time layer height estimation during directed energy deposition. Opt Lasers Eng 168:10766110.1016/j.optlaseng.2023.107661 ↗
23. Jalilvand MM, Saghafian H, Divandari M, Akbarifar M (2022) Surface oxidation study of molten Mg–Al alloys by oxide/metal/oxide sandwich method. J Magnes Alloys 10:1704–171710.1016/j.jma.2020.10.004 ↗
24. Li S, Yang X, Hou J, Du W (2020) A review on thermal conductivity of magnesium and its alloys. J Magnes Alloys 8:78–9010.1016/j.jma.2019.08.002 ↗
25. Scharowsky T, Osmanlic F, Singer R, Körner C (2014) Melt pool dynamics during selective electron beam melting. Appl Phys A 114:1303–130710.1007/s00339-013-7944-4 ↗
26. Liu J, Fan Z, Olsen SI, Christensen KH, Kristensen JK (2015) Boosting active contours for weld pool visual tracking in automatic arc welding. IEEE Trans Autom Sci Eng 14:1096–110810.1109/TASE.2015.2498929 ↗
27. Cheng Y, Wang Q, Jiao W, Xiao J, Chen S, Zhang Y (2021) Automated recognition of weld pool characteristics from active vision sensing. Welding J 100:183S-192S10.29391/2021.100.015 ↗
28. Wang Z (2024) The active visual sensing methods for robotic welding: review, tutorial and prospect. arXiv preprint arXiv:2405.0068510.1109/TIM.2024.3485460 ↗
29. Ogoke F, Pak P, Myers A, Quirarte G, Beuth J, Malen J, Farimani AB (2024) Deep learning for melt pool depth contour prediction from surface thermal images via vision transformers. Addit Manuf Lett 11:10024310.1016/j.addlet.2024.100243 ↗
30. Sun H, Xia L, Zhou Y, Qian W, Wang W, Zhang K (2024) Online detection and evaluation of weld surface defects based on lightweight network VGG16-UNet and laser scanning. J Manuf Process 129:292–30610.1016/j.jmapro.2024.08.037 ↗
31. Yang Z, Lu Y, Yeung H, Krishnamurty S (2019) Investigation of deep learning for real-time melt pool classification in additive manufacturing. In: 2019 IEEE 15th international conference on automation science and engineering (CASE). IEEE, pp 640–64710.1109/COASE.2019.8843291 ↗
32. Hemmasian A, Ogoke F, Akbari P, Malen J, Beuth J, Farimani AB (2023) Surrogate modeling of melt pool temperature field using deep learning. Addit Manuf Lett 5:10012310.1016/j.addlet.2023.100123 ↗
33. Xia C, Pan Z, Polden J, Li H, Xu Y, Chen S, Zhang Y (2020) A review on wire arc additive manufacturing: monitoring, control and a framework of automated system. J Manuf Syst 57:31–4510.1016/j.jmsy.2020.08.008 ↗
34. Zhang T, Xu C, Cheng J, Huang Y, Peng Y, Wang L, Wang K (2024) Study on droplet transfer behavior and spattering mechanism in Mg–Gd–Y–Zr alloy regulated short circuit (Fronius CMT) welding. J Manuf Process 126:35–4710.1016/j.jmapro.2024.07.075 ↗
35. Zhou W, Le Q, Shi Y, Liao Q, Yin Z, Jiang Y (2025) Numerical simulation of gas tungsten arc welding for ZW61 magnesium alloy thin plates. Mater Chem Phys 329:13013010.1016/j.matchemphys.2024.130130 ↗
36. Le Q, Zhou W, Liao Q, Zhao D, Shi Y (2024) Numerical simulation of GTAW for ZW61 magnesium alloy thin plates: coupling the finite element method with the cellular automata method. J Mater Res Technol 33:5692–570710.1016/j.jmrt.2024.10.209 ↗
37. Shen H, Yan J, Niu X (2020) Thermo-fluid-dynamic modeling of the melt pool during selective laser melting for AZ91D magnesium alloy. Materials (Basel) 13(18):1–1910.3390/ma13184157 ↗
38. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–77810.1109/CVPR.2016.90 ↗
39. Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 839–84710.1109/WACV.2018.00097 ↗