Real-time modelling of incremental multi-pass flow forming by a hybrid, data-based model
Lukas Kersting, Sharin Kumar Gunasagran, Bahman Arian, Julian Rozo Vasquez, Ansgar Trächtler, Werner Homberg, Frank Walther
Abstract. The incremental flow forming process features a large number of process parameter combinations that can be varied from pass to pass or during a pass. In the future however, a more efficient utilization of this large number of process parameter combinations and a compensation of process disturbances could be required. This is due to a rising demand for increasing the part complexity, e.g. by graded property structures or a more complex geometry. In this context, innovative approaches like closed-loop property control and optimal control are advantageous, but require fast process models of flow forming that are not state of the art. This paper thus proposes a new modelling approach of multi-pass flow forming especially taking the transfer behavior between process parameters and wall thickness evolution from pass to pass into focus. A hybrid modelling approach is developed that combines knowledge about the incremental process character with empirical data regression to a basic analytic relation. The basic relation is further extended by a multi-layer neural network to enhance the overall model accuracy. This hybrid modelling approach is finally validated using experimental data. Thus, it is shown that a suitable model structure was found in context of a future closed-loop control or optimal control for multi-pass flow forming.
Keywords
Flow Forming, Real-Time Model, Neural Network, Closed-Loop Control
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Lukas Kersting, Sharin Kumar Gunasagran, Bahman Arian, Julian Rozo Vasquez, Ansgar Trächtler, Werner Homberg, Frank Walther, Real-time modelling of incremental multi-pass flow forming by a hybrid, data-based model, Materials Research Proceedings, Vol. 54, pp 1287-1296, 2025
DOI: https://doi.org/10.21741/9781644903599-140
The article was published as article 140 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] M. Houillon, E. Massoni, E. Ramel, R. Logé, 3D FEM Simulation of the Flow Forming Process Using Lagrangian and ALE Methods, AIP Conf. Proc 908 (2007) 257–262. https://doi.org/10.1063/1.2740821
[2] P. Banerjee, N.B. Hui, M.K. Dikshit, S. Som, FE modelling of three dimensional staggered backward metal flow forming process, IJMMT XIII (2021) 15–25.
[3] N.M.R. Kauta, P.-O. Bouchard, K. Mocellin, Relevance of Symmetry Assumptions in the 3D Numerical Modeling of a Flowformability Test, KEM 926 (2022) 776–783. https://doi.org/10.4028/p-07aps3
[4] B. Podder, P. Banerjee, K.R. Kumar, N.B. Hui, Study of the influences of process parameters on cold flow forming of AL-tubes, IJMMT XI (2019) 95–106.
[5] A.R.F. Nahrekhalaji, M. Ghoreishi, E.S. Tashnizi, Modeling and Investigation of the Wall Thickness Changes and Process Time in Thermo-Mechanical Tube Spinning Process Using Design of Experiments, ENG 02 (2010) 141–148. https://doi.org/10.4236/eng.2010.23020
[6] T. Mahender, B. Venkatesh, Optimization of flow forming process parameters of al-8014 using genetic algorithm, IJET 7 (2018) 868–873. https://doi.org/10.14419/ijet.v7i2.10852
[7] M.J. Davidson, K. Balasubramanian, G. Tagore, Surface roughness prediction of flow-formed AA6061 alloy by design of experiments, JMPT 202 (2008) 41–46. https://doi.org/10.1016/j.jmatprotec.2007.08.065
[8] L. Kersting, B. Arian, J. Rozo Vasquez, A. Trächtler, W. Homberg, F. Walther, Innovative online measurement and modelling approach for property-controlled flow forming processes, KEM 926 (2022) 862–874. https://doi.org/10.4028/p-yp2hj3
[9] L. Kersting, B. Arian, J. Rozo Vasquez, A. Trächtler, W. Homberg, F. Walther, Echtzeitfähige Modellierung eines innovativen Drückwalzprozesses für die eigenschaftsgeregelte Bauteilfertigung, at – Automatisierungstechnik 71 (2023) 68–81. https://doi.org/10.1515/auto-2022-0106
[10] B. Arian, W. Homberg, J. Rozo Vasquez, F. Walther, M. Riepold, A. Trächtler, Forming of metastable austenitic stainless steel tubes with axially graded martensite content by flow-forming, ESAFORM 2021 (2021). https://doi.org/10.25518/esaform21.2759
[11] Deutsches Institut für Normung (DIN), DIN EN ISO 1127:2019-03, Stainless steel tubes – Dimensions, tolerances and conventional masses per unit length (ISO 1127:1992), DIN Media, Berlin.
[12] M. Liewald, T. Bergs, P. Groche, B.-A. Behrens, D. Briesenick, M. Müller, P. Niemietz, C. Kubik, F. Müller, Perspectives on data-driven models and its potentials in metal forming and blanking technologies, Prod. Eng. Res. Devel. 16 (2022) 607–625. https://doi.org/10.1007/s11740-022-01115-0
[13] M.A. Bansal, R. Sharma, M. Kathuria, A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications, ACM Comput. Surv. 54 (2022) 1–29. https://doi.org/10.1145/3502287
[14] A. Angermann, M. Beuschel, M. Rau, U. Wohlfahrt, MATLAB – Simulink – Stateflow: Grundlagen, Toolboxen, Beispiele, Oldenbourg, Munich, 2009.
[15] D. Sarkar, Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems, Apress L. P, Berkeley, 2017.
