Precision Blank Development for Hydro-Formed Aerospace Components via Inverse Finite Element Analysis
Source abstract
Abstract
The present article provides an abstract overview of the issue of optimal blank searching for integral parts utilized in complex engineering projects, including those pertaining to the fabrication of machine, ship, and aircraft components. The manufacturing process for these components is intricate and necessitates meticulous precision and strict adherence to the design model. Conventional blank calculation techniques are marred by substantial inaccuracies. The present research proposes and verifies an effective method based on the reverse solution of a mathematical problem. The focal point of this study is the aerodynamic curvature of aluminum alloys belonging to the Al–Mg–Mn family. The formation of the object is achieved through the employment of a hydroelastomer press of the QFC (Quintus Technologies) type. The forming process is simulated using PAM-STAMP software, developed by the French company ESI Group. The objective of the present study is to ascertain the optimal configuration of the blank by optimizing the discrepancy between the dynamic calculations and the design model using sweep contours. The resulting new shape of the part allows for the formation of parts with minimal deviation from their design contours.
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Research context
OpenAlex topics: Laser and Thermal Forming Techniques · Metal Forming Simulation Techniques · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Baikal Center for the Study of Artificial Intelligence and Digital Technologies (Baikal AI Center), Irkutsk National Research Technical University, 83 Lermontov St., Irkutsk 664074, Russia
Institute of Information Technology and Data Science, Irkutsk National Research Technical University, 83 Lermontov St., Irkutsk 664074, Russia
Federal State Budgetary Educational Institution of Higher Education “D.F.” Ustinov Baltic State Technical University “VOENMEH”, 1 1st Krasnoarmeyskaya Street, Saint Petersburg 190005, Russia
Innovation and Technology Center for Energy and Resource Conservation, A. P. Vinogradov Institute of Geochemistry of the Siberian Branch of the Russian Academy of Sciences, Irkutsk 664033, Russia
Stroytest Research and Testing Centre, Moscow State University of Civil Engineering, 26, Yaroslavskoye Shosse, Moscow 129337, Russia
References (57)
Show source-provided references
1. Choosing an efficient method for forming parts by means of an engineering analysis performed with the use of a CAE system10.1007/s11015-015-0039-z ↗
2. Study of deformations field in the working zone of vertical milling machine10.1016/j.proeng.2017.10.596 ↗
3. Dziubińska, A., Surdacki, P., and Majerski, K. (2021). The analysis of deformability, structure and properties of AZ61 cast magnesium alloy in a new hammer forging process for aircraft mounts. Materials, 14.10.3390/ma14102593 ↗
4. Mathematical modelling of flat and long hot rolling based on finite element methods (FEM)
5. Chumachenko, E., Aksenov, S., and Logashina, I. (2012, January 23–25). Optimization of superplastic forming technology. Proceedings of the METAL 2012—21st International Conference on Metallurgy and Materials, Brno, Czech Republic.
6. Guillaume, S. (2020, January 10–14). Titanium sheet hot forming in the aerospace industry. Proceedings of the 14th World Conference on Titanium, Nantes, France.
7. Simulation of rift element forming by magnetic-pulse deformation10.1088/1757-899X/919/2/022011 ↗
8. A review of metal additive manufacturing application and numerical simulation10.1088/1755-1315/252/2/022036 ↗
9. Numerical simulations of concrete processing: From standard formative casting to additive manufacturing10.1016/j.cemconres.2020.106075 ↗
10. Description of the heat exchanger unit construction, created in IRNITU
11. Accelerating sheet metal forming with AI: A case study in process optimization
12. Huang, H., and Julfikar, M. (2021). Stamping monitoring by using an adaptive 1D convolutional neural network. Sensors, 21.10.3390/s21010262 ↗
13. Deformation behavior and failure mechanism of AA7075 alloy during the cryogenic temperature-assisted incremental sheet forming process10.1016/j.tws.2024.112114 ↗
14. Attar, R., Lee, S., and Kim, J. (2022). Industrial designers which increases the component design development time. arXiv.
15. Additive manufacturing for visible nanophotonics: Fabrication of advanced systems
16. Long, T., Zhang, Y., and Zhang, H. (2024). Generative deep learning for the inverse design of materials. arXiv.
17. Wu, T., Maruyama, T., Wei, L., Zhang, T., Du, Y., Iaccarino, G., and Leskovec, J. (2025, August 08). Compositional generative inverse design: A modular approach. OpenReview 2024, ID: tOiQF. Available online: https://openreview.net/forum?id=wmX0CqFSd7.
18. Heidari Nobari, H. Generative adversarial networks for inverse design in engineering: Performance, constraints, and creativity. MIT Technical Report 2022, MIT-TR-2022-07.
19. Abio, M., García, L., and Fernández, A. (2022). Machine learning-based surrogate model for press hardening process of 22MnB5 sheet steel. Materials, 15.10.3390/ma15103647 ↗
20. The development of a test stand for developing technological operation “flotation and separation of MD2. The deposition of nanostructures MD1” produce nanostructures with desired properties
21. Xiao, Y., Yan, W., Wang, R., Jiang, Z., and Liu, Y. (2021). Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model. Sustainability, 13.10.3390/su13041929 ↗
22. Sevšek, L., and Pepelnjak, T. (2024). Optimisation of Flexible Forming Processes Using Multilayer Perceptron Artificial Neural Networks and Genetic Algorithms: A Generalised Approach for Advanced High-Strength Steels. Materials, 17.10.3390/ma17225459 ↗
23. Prediction of forming accuracy in incremental sheet forming using artificial neural networks10.1007/s00170-024-14023-7 ↗
24. A comprehensive benchmark dataset for sheet metal forming
25. Generative design of sheet metal structures10.1145/3592444 ↗
26. Editorial Board (2022). Journal of Manufacturing Processes, SciSpace. Available online: https://scispace.com/journals/journal-of-manufacturing-processes-3owohb6q.
27. COBISS Plus Team (2022). Impact factor for Journal of Manufacturing Processes in 2022, COBISS Plus Team. COBISS Plus 2022, Report ID: JMP-2022-IF.
28. Resurchify Team (2025, August 08). International Journal of Advanced Manufacturing Technology: Metrics 2024. Available online: http://Resurchify.com.
29. Editorial Board (2025, August 08). Editorial Board. The International Journal of Advanced Manufacturing Technology. 2024, Volume 135, pp. 1–500. Available online: http://eJournal.net.10.1016/S1526-6125(25)00103-3 ↗
30. LetPub Team (2025, August 08). Scientific Journal Selector: Engineering and Manufacturing. 2024, Report ID: SEL-2024-ENG. Available online: http://LetPub.com.
31. CIRP Editorial Board (2025, August 08). CIRP Annals-Manufacturing Technology: 2025, Volume 74, pp. 1–300. Available online: http://CIRP.net.
32. A physics-informed and data-driven framework for robotic welding in complex geometries10.1038/s41467-025-60164-y ↗
33. CIRP Secretariat (2025, August 08). Newsletter No. 67–August 2024. CIRP.net 2024, pp. 1–20. Available online: https://www.cirp.net/images/cirpfichiers/publicfiles/Newsletter/news_67_spring_2024.pdf.
34. Improving the efficiency of aluminium production by application of composite chrome plating on the anode pins
35. Composite hard-alloy end mills10.3103/S1068798X16120108 ↗
36. Smart process combination for aluminum/plastic hybrid components
37. Evaluation of residual stresses in lock valve elements of petrochemical productions10.1051/matecconf/201712906006 ↗
38. Effect of heat treatment conditions on structure and properties of high-speed steel10.1088/1757-899X/177/1/012113 ↗
39. Quality of anode. Overview of problems and some methods of their solution part 2. Improving the quality of the anode
40. Hydraulic model and algorithm for branched hydraulic systems parameters optimization10.1016/j.proeng.2017.10.672 ↗
41. Evaluation of residual stresses in high-pressure valve seat surfacing10.1007/s10556-017-0363-1 ↗
42. ESI Group (2023). PAM-STAMP 2G Reference Manual: Shell Elements, ESI Group.
43. A simple error estimator and adaptive procedure for practical engineering analysis10.1002/nme.1620240206 ↗
44. Dörfler, W. (1996). A convergent adaptive algorithm for Poisson’s equation. SIAM J. Numer. Anal., 1106–1124.10.1137/0733054 ↗
45. Adaptive mesh refinement with constraints for metal forming simulations
46. Johnson, C. (2009). Numerical Solution of Partial Differential Equations by the Finite Element Method, Dover Publications.
47. Belytschko, T., Liu, W.K., and Moran, B. (2000). Nonlinear Finite Elements for Continua and Structures, Wiley.
48. Shewchuk, J.R. (2002, January 15–18). What is a good linear finite element? Interpolation, conditioning, anisotropy, and quality measures. Proceedings of the 11th International Meshing Roundtable, Ithaca, NY, USA.
49. Tailored fiber placement in thermoplastic composites
50. Nocedal, J., and Wright, S. (2006). Numerical Optimization, Springer.
51. Bazilevs, Y. (2009). Isogeometric Analysis: Toward Integration of CAD and FEA, Wiley.
52. Error estimates for adaptive finite element computations10.1137/0715049 ↗
53. Minimization of functions having Lipschitz continuous first partial derivatives10.2140/pjm.1966.16.1 ↗
54. Zhou, B., Liu, B., and Zhang, S. (2021). The advancement of 7xxx series aluminum alloys for aircraft structures: A review. Metals, 11.10.3390/met11050718 ↗
55. Recycling of electrolyzer spent carbon-graphite lining with aluminum fluoride regeneration10.1007/s11015-016-0333-4 ↗
56. A comparative study of surrogate models for predicting process failures in sheet metal forming
57. Karlina, A.I., Karlina, Y.I., Kondratiev, V.V., Kononenko, R.V., and Breki, A.D. (2023). Study of Wear of an Alloyed Layer with Chromium Carbide Particles after Plasma Melting. Crystals, 13.10.3390/cryst13121696 ↗