Numerical modeling and Digital Twins in Wire Arc Additive Manufacturing

Authors

DOI:

https://doi.org/10.20535/2521-1943.2025.9.3(106).338860

Keywords:

3D printing, additive manufacturing, WAAM, thermo-mechanical modelling, thermo-fluidic modelling, FEM, CFD, adaptive mesh refinement, digital twin, artificial intelligence, neural network, machine learning, process control, residual stress, distortions, microstructure, simulation, defect detection.

Abstract

Wire Arc Additive Manufacturing (WAAM) has become a valuable tool for cost-effective production of large metal structures with complex geometry using established arc-welding hardware and wire feedstock. However, the complexity of underlying physics makes it difficult to predict the geometry and stress state of final products. Heat accumulation, inter-pass temperature, and path planning influence bead shape and defect formation, while cyclic thermal loading induces residual stresses and distortion, which hinder repeatability and certification. High-fidelity numerical modelling, while being important for studying and optimization of WAAM process, remains unsuitable for real time simulation and control due to high computational cost. In order to overcome the limitations of pure physics-based models, the interest has shifted to hybrid workflows— combined physics-based and data-driven models calibrated by real-time sensing—embedded in Digital Twin architectures to support prediction, monitoring, and process control. The objective of this study is to systemise recent advances in multi-scale multi-physics numerical modelling for wire arc additive manufacturing (WAAM) and provide insight into Digital Twin (DT) architectures alongside data-driven approaches based on artificial intelligence (AI), machine learning (ML) and data management. The extensive literature review was performed to reveal advantages and limitations of these simulation method and how they could transition WAAM to intelligent manufacturing, driven by multiple data streams, with real-time monitoring, predictive analytics, and autonomous correction. The results of the review can be used in future studies to organize and assemble intelligent WAAM systems in laboratory experimental conditions with a perspective of industrial applications.

 

References

  1. ISO/ASTM 52900:2021. Additive manufacturing – General principles – Fundamentals and vocabulary, 2021. Available: https://www.iso.org/standard/74514.html.
  2. T. Ron, A. Shirizly and E. Aghion, “Additive Manufacturing Technologies of High Entropy Alloys (HEA): Review and Prospects”, Materials, vol. 16, no. 6, p. 2454, 2023. DOI: https://doi.org/10.3390/ma16062454.
    | |
  3. Y. H. Kim, D. R. Gunasegaram, P. W. Cleary and A. B. Murphy, “Numerical modelling of wire arc additive manufacturing: methods, status, trends, and opportunities”, Journal of Physics D: Applied Physics, vol. 58, no. 14, p. 143001, 2025. DOI: https://doi.org/10.1088/1361-6463/adb3b0.
    |
  4. A. T. Silvestri, A. El Hassanin, G. de Alteriis, E. Viola, R. S. Lo Moriello and A. Squillace, "Advanced multi-sensor monitoring system in wire arc additive manufacturing for enhanced process and parts production", in Materials Research Proceedings, Paestum, Italy, 2025, vol. 54, pp. 264-273. DOI: https://doi.org/10.21741/9781644903599-29.
  5. A. Horbenko and C. Zvorykin, “Comprehensive analysis of arc methods of 3D printing of metal products: assessment of the efficiency and prospects of using TIG as a heat source”, Mechanics and Advanced Technologies, vol. 8, no. 3(102), pp. 296-301, 2024. DOI: https://doi.org/10.20535/2521-1943.2024.8.3(102).311225.
  6. M. Köhler, S. Fiebig, J. Hensel and K. Dilger, “Wire and Arc Additive Manufacturing of Aluminum Components”, Metals, vol. 9, no. 5, p. 608, 2019. DOI: https://doi.org/10.3390/met9050608.
    |
  7. T. A. Rodrigues, V. Duarte, R. M. Miranda, T. G. Santos and J. P. Oliveira, “Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)”, Materials, vol. 12, no. 7, p. 1121, 2019. DOI: https://doi.org/10.3390/ma12071121.
    | |
  8. F. Montevecchi, G. Venturini, A. Scippa and G. Campatelli, “Finite Element Modelling of Wire-arc-additive-manufacturing Process”, Procedia CIRP, vol. 55, pp. 109-114, 2016. DOI: https://doi.org/10.1016/j.procir.2016.08.024.
    |
  9. R. F. V. Sampaio, J. P. M. Pragana, I. M. F. Bragança, C. M. A. Silva, C. V. Nielsen and P. A. F. Martins, “Modelling of wire-arc additive manufacturing – A review”, Advances in Industrial and Manufacturing Engineering, vol. 6, p. 100121, 2023. DOI: https://doi.org/10.1016/j.aime.2023.100121.
    |
  10. M. Graf, A. Hälsig, K. Höfer, B. Awiszus and P. Mayr, “Thermo-Mechanical Modelling of Wire-Arc Additive Manufacturing (WAAM) of Semi-Finished Products”, Metals, vol. 8, no. 12, p. 1009, 2018. DOI: https://doi.org/10.3390/met8121009.
    |
  11. C. J. Kim, S. B. Han, B. W. Seo, I. Bae, D. Kim, S. Kim and Y. T. Cho, “Architecture Development of Digital Twin-Based Wire Arc Directed Energy Deposition”, International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 12, no. 3, pp. 885-904, 2025. DOI: https://doi.org/10.1007/s40684-025-00747-8.
    |
  12. X. Wang, S. Sridar, M. Klecka and W. Xiong, “Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing”, npj Advanced Manufacturing, vol. 2, p. 17, 2025. DOI: https://doi.org/10.1038/s44334-025-00028-x.
  13. A. Fernández-Zabalza, F. Veiga, A. Suárez, V. Uralde, X. Sandua and J. R. Alfaro, “Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)”, Symmetry, vol. 17, no. 3, p. 326, 2025. DOI: https://doi.org/10.3390/sym17030326.
    |
  14. X. F. Zhao, H. Panzer, A. Zapata, F. Riegger, S. Baehr and M. F. Zaeh, “Deposition sequence optimization for minimizing substrate plate distortion using the simplified WAAM simulation”, Journal of Manufacturing Systems, vol. 81, pp. 103-116, 2025. DOI: https://doi.org/10.1016/j.jmsy.2025.05.010.
    |
  15. S. Kumar, G. Singh, V. S. Sharma and S. Sekar, “Investigation of Process Parameters for Quality Deposition of AL5356 using Cold Metal Transfer Wire Arc Additive Manufacturing (CMT-WAAM)”, Research Square, 2024. [Preprint]. DOI: https://doi.org/10.21203/rs.3.rs-5475522/v1.
  16. A. Love, O. A. Valdez Pastrana, S. Behseresht and Y. H. Park, “Advancing Metal Additive Manufacturing: A Review of Numerical Methods in DED, WAAM, and PBF”, Metrology, vol. 5, no. 2, p. 30, 2025. DOI: https://doi.org/10.3390/metrology5020030.
    |
  17. K. Šket, M. Brezočnik, T. Karner, R. Belšak, M. Ficko, T. Vuherer and J. Gotlih, “Predictive Modelling of Weld Bead Geometry in Wire Arc Additive Manufacturing”, Journal of Manufacturing and Materials Processing, vol. 9, no. 2, p. 67, 2025. DOI: https://doi.org/10.3390/jmmp9020067.
    |
  18. A. E. Davis, C. I. Breheny, J. Fellowes, U. Nwankpa, F. Martina, J. Ding et al., “Mechanical performance and microstructural characterisation of titanium alloy-alloy composites built by wire-arc additive manufacture”, Materials Science and Engineering: A, vol. 765, p. 138289, 2019. DOI: https://doi.org/10.1016/j.msea.2019.138289.
    |
  19. Y. Osama, M. Abdelwahed, A. Elsabbagh and M. A. Taha, “Experimental and numerical thermal monitoring of SS308L structures processed by wire arc additive manufacturing”, Progress in Additive Manufacturing, vol. 10, no. 10, pp. 7947-7962, 2025. DOI: https://doi.org/10.1007/s40964-025-01084-7.
    |
  20. C. Shen, Z. Pan, Y. Ma, D. Cuiuri and H. Li, “Fabrication of iron-rich Fe-Al intermetallics using the wire-arc additive manufacturing process”, Additive Manufacturing, vol. 7, pp. 20-26, 2015. DOI: https://doi.org/10.1016/j.addma.2015.06.001.
    |
  21. J. P. M. Pragana, L. G. Rosa, I. M. F. Bragança, C. M. A. Silva and P. A. F. Martins, “Expansion of Additive‐Manufactured Tubes: Deformation and Metallurgical Analysis”, Steel Research International, vol. 93, no. 1, p. 2100362, 2022. DOI: https://doi.org/10.1002/srin.202100362.
    |
  22. A. Spinasa, B. Weber, X. Meng, R. Zhang, M. Nitawaki and L. Gardner, “Influence of process parameters on the physical and material properties of WAAM steels”, Construction and Building Materials, vol. 479, p. 141078, 2025. DOI: https://doi.org/10.1016/j.conbuildmat.2025.141078.
    |
  23. V. Orlyanchik, J. Kimmell, Z. Snow, A. Ziabari and V. Paquit, “A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process”, Scientific Data, vol. 12, no. 1, p. 343, 2025. DOI: https://doi.org/10.1038/s41597-025-04638-0.
    | |
  24. Z. Chen, L. Yuan, Z. Pan, H. Zhu, N. Ma, D. Ding and H. Li, “A comprehensive review and future perspectives of simulation approaches in wire arc additive manufacturing (WAAM)”, International Journal of Extreme Manufacturing, vol. 7, no. 2, p. 022016, 2025. DOI: https://doi.org/10.1088/2631-7990/ada099.
    |
  25. Z. Jurišić and N. Krnić, “Influence of p-MIG Welding Parameters to Wall Geometry of Aluminium Alloy 2319 Fabricated Using WAAM”, Tehnički vjesnik, vol. 32, no. 3, pp. 830-840, 2025. DOI: https://doi.org/10.17559/TV-20240322001425.
    |
  26. M. J. Pailhes, “Modèle phénoménologique de la forme du cordon en Fabrication Additive arcs fils (WAAM) Phenomenological model of thermal effects on weld beads geometry produced by Wire and Arc Additive Manufacturing (WAAM)”, 2022, https://theses.hal.science/tel-03851334v1/file/MANOKRUANG_2022_archivage.pdf.
  27. P. Berce and R. Pǎcurar, “Challenges and Trends in Additive Manufacturing for Metallic Applications: Toward Optimized Processes and Performance”, Metals, vol. 15, no. 5, p. 525, 2025. DOI: https://doi.org/10.3390/met15050525.
    |
  28. A. Kumar, D. Das, N. D. Raj, S. Bag and V. C. Srivastava, “Comparison of Machine Learning Performance for the Prediction of Melting Efficiency and Bead Geometry in wire Arc Additive Manufacturing Process”, Archives of Metallurgy and Materials, vol. 70, no. 1, pp. 29-38, 2025. DOI: https://doi.org/10.24425/amm.2025.152503.
    |
  29. N. Ethiraj, T. Sivabalan, J. Sofia, et al., “A comprehensive review on application of machine intelligence in additive manufactur-ing,” Turkish Journal of Engineering, Vol. 9, No. 1, pp. 37–46, Jan. 2025, doi: https://doi.org/10.31127/tuje.1502587.
  30. M. M. Mahdi, M. S. Bajestani, S. D. Noh, and D. B. Kim, “Digital twin-based architecture for wire arc additive manufacturing using OPC UA,” Robotics and Computer-Integrated Manufacturing, Vol. 94, p. 102944, Aug. 2025, doi: https://doi.org/10.1016/j.rcim.2024.102944.
  31. D. Svetlizky, M. Das, B. Zheng, “Directed energy deposition (DED) additive manufacturing: Physical characteristics, defects, challenges and applications,” Materials Today, Vol. 49, pp. 271–295, Oct. 2021, doi: https://doi.org/10.1016/j.mattod.2021.03.020.
  32. K. Kelly, A. Thien, Dr. K. Saleeby, and Dr. C. Saldana, “A novel approach to path planning related to the intersections of alumi-num WAAM,” Int. J. Adv. Manuf. Technol., Vol. 137, No. 5–6, pp. 2579–2593, Mar. 2025, doi: https://doi.org/10.1007/s00170-025-15285-5.
  33. T. Fritschle, M. Kaess, S. Weihe, and M. Werz, “Investigation of the Thermo-Mechanical Modeling of the Manufacturing of Large-Scale Wire Arc Additive Manufacturing Components with an Outlook Towards Industrial Applications,” JMMP, Vol. 9, No. 5, p. 166, May 2025, doi: https://doi.org/10.3390/jmmp9050166.
  34. B. Liu et al., “The Effects of Processing Parameters during the Wire Arc Additive Manufacturing of 308L Stainless Steel on the Formation of a Thin-Walled Structure,” Materials, Vol. 17, No. 6, p. 1337, Mar. 2024, doi: https://doi.org/10.3390/ma17061337.
  35. W. Huang, Q. Wang, N. Ma, and H. Kitano, “Distribution characteristics of residual stresses in typical wall and pipe components built by wire arc additive manufacturing,” Journal of Manufacturing Processes, Vol. 82, pp. 434–447, Oct. 2022, doi: https://doi.org/10.1016/j.jmapro.2022.08.010.
  36. A. Ikram and H. Chung, “Computational analysis of metal transfer mode, dynamics, and heat transfer under different pulsating frequencies in pulsed wire-arc additive manufacturing,” Journal of Computational Design and Engineering, Vol. 9, No. 3, pp. 1045–1063, Jun. 2022, doi: https://doi.org/10.1093/jcde/qwac043.
  37. S. Ben Amor, N. Elloumi, A. Eltaief, et al., “Digital Twin Implementation in Additive Manufacturing: A Comprehensive Re-view,” Processes, Vol. 12, No. 6, p. 1062, May 2024, doi: https://doi.org/10.3390/pr12061062.
  38. H. Huang and H. Murakawa, “A Selective Integration-Based Adaptive Mesh Refinement Approach for Accurate and Efficient Welding Process Simulation,” JMMP, Vol. 7, No. 6, p. 206, Nov. 2023, doi: https://doi.org/10.3390/jmmp7060206.
  39. C. A. Moreira, M. A. Caicedo, M. Cervera, et al., “A multi-criteria h-adaptive finite-element framework for industrial part-scale thermal analysis in additive manufacturing processes,” Engineering with Computers, Vol. 38, No. 6, pp. 4791–4813, Dec. 2022, doi: https://doi.org/10.1007/s00366-022-01655-0.
  40. S. Jayanath and A. Achuthan, “A Computationally Efficient Finite Element Framework to Simulate Additive Manufacturing Processes,” Journal of Manufacturing Science and Engineering, Vol. 140, No. 4, Apr. 2018, doi: https://doi.org/10.1115/1.4039092.
  41. J. Goldak, A. Chakravarti, and M. Bibby, “A new finite element model for welding heat sources,” Metallurgical Transactions B, Vol. 15, No. 2, pp. 299–305, Jun. 1984, doi: https://doi.org/10.1007/BF02667333.
  42. D. Ding, S. Zhang, Q. Lu, et al., “The well-distributed volumetric heat source model for numerical simulation of wire arc addi-tive manufacturing process,” Materials Today Communications, Vol. 27, p. 102430, Jun. 2021, doi: https://doi.org/10.1016/j.mtcomm.2021.102430.
  43. D. Strobl, J. F. Unger, C. Ghnatios, and A. Robens‐Radermacher, “PGD in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency,” Engineering Reports, Vol. 6, No. 11, p. e12887, Nov. 2024, doi: https://doi.org/10.1002/eng2.12887.
  44. B. Denkena, M. Wichmann, V. Böß, and T. Malek, “Technological simulation of the resulting bead geometry in the WAAM process using a machine learning model,” Procedia CIRP, Vol. 126, pp. 627–632, 2024, doi: https://doi.org/10.1016/j.procir.2024.08.269.
  45. M. R. Enders and N. Hoßbach, “Dimensions of Digital Twin Applications - A Literature Review”. https://www.researchgate.net/publication/359715537_Dimensions_of_Digital_Twin_Applications_-_A_Literature_Review.
  46. D. R. Gunasegaram, A. B. Murphy, M. J. Matthews, and T. DebRoy, “The case for digital twins in metal additive manufactur-ing,” J. Phys. Mater., Vol. 4, No. 4, p. 040401, Oct. 2021, doi: https://doi.org/10.1088/2515-7639/ac09fb.
  47. Z. Chen et al., “Service oriented digital twin for additive manufacturing process,” Journal of Manufacturing Systems, Vol. 74, pp. 762–776, Jun. 2024, doi: https://doi.org/10.1016/j.jmsy.2024.04.015.
  48. R. Jain, N. Bharat, and P. S. C. Bose, “Digital Twins of Hybrid Additive and Subtractive Manufacturing Systems–A Review,” in Machining and Additive Manufacturing, V. S. Sharma, U. S. Dixit, A. Gupta, R. Verma, and V. Sharma, Eds., in Lecture Notes in Mechanical Engineering, Singapore: Springer Nature Singapore, 2024, pp. 173–183. doi: https://doi.org/10.1007/978-981-99-6094-1_18.
  49. D. M. Botín-Sanabria, A.-S. Mihaita, R. E. Peimbert-García, et al., “Digital Twin Technology Challenges and Applications: A Comprehensive Review,” Remote Sensing, Vol. 14, No. 6, p. 1335, Mar. 2022, doi: https://doi.org/10.3390/rs14061335.
  50. H. Mu, “Digital Twin of Wire Arc Additive Manufacturing,” p. 6629040 Bytes, 2025, doi: https://doi.org/10.71747/UOW-R3GK326M.28216976.V1.
  51. L. Nele, G. Mattera, E. W. Yap, M. Vozza, and S. Vespoli, “Towards the application of machine learning in digital twin technol-ogy: a multi-scale review,” Discov. Appl. Sci., Vol. 6, No. 10, p. 502, Sep. 2024, doi: https://doi.org/10.1007/s42452-024-06206-4.
  52. R. T. Reisch, L. Janisch, J. Tresselt, T. Kamps, and A. Knoll, “Prescriptive Analytics - A Smart Manufacturing System for First-Time-Right Printing in Wire Arc Additive Manufacturing using a Digital Twin,” Procedia CIRP, Vol. 118, pp. 759–764, 2023, doi: https://doi.org/10.1016/j.procir.2023.06.130.
  53. M.-S. Kang, D.-H. Lee, M. S. Bajestani, et al., “Edge Computing-Based Digital Twin Framework Based on ISO 23247 for Enhancing Data Processing Capabilities,” Machines, Vol. 13, No. 1, p. 19, Dec. 2024, doi: https://doi.org/10.3390/machines13010019.
  54. ISO 23247-1:2021. Automation systems and integration – Digital twin framework for manufacturing – Part 1: Overview and general principles., Oct. 2021. [Online]. Available: https://www.iso.org/standard/75066.html.
  55. F. Hajializadeh and A. Ince, “A High-Performance Modeling Approach Of Artificial Neural Network And Finite Element Anal-ysis For Residual Stress Prediction Of Direct Metal Deposition Process,” in Progress in Canadian Mechanical Engineering. Vol. 4, University of Prince Edward Island. Robertson Library, Jun. 2021. doi: https://doi.org/10.32393/csme.2021.11.
  56. H. Mu, F. He, L. Yuan, H. Hatamian, et al., “Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing,” Journal of Industrial Information Integration, Vol. 38, p. 100563, Mar. 2024, doi: https://doi.org/10.1016/j.jii.2024.100563.

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Published

2025-09-29

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[1]
R. Tryhubov and M. Kryshchuk, “Numerical modeling and Digital Twins in Wire Arc Additive Manufacturing”, Mech. Adv. Technol., vol. 9, no. 3(106), pp. 357–371, Sep. 2025.

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Vol. 9 No. 3(106) (2025)

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Advanced Mechanical Engineering and Manufacturing Technologies

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