Abstract
This paper is aimed at identifying the success range of external inversion process of thin-walled tube to produce double-walled tubular parts. The inversion process tools were limited to a flat punch and conical die. Parametric study of inversion process was achieved by pressing the tube onto dies with different angles and fillet radii. Both finite element model and experimental device were performed. Axisymmetric finite element models were developed to estimate the adequate die geometry parameters leading to inversion forming. Experimental work was performed on copper industrial tubes. It was mainly utilized for supporting and validating numerical predictions. Different modes of tube deformation were generated such as inversion, local buckling, curling and flaring. The occurrence of secondary phenomena like stick-slip, wrinkling and tearing were also detected. Typical load-displacement curves and tube shapes after forming were presented for different die geometry parameters. It was shown that the best die dimension was the one that resulted the curling of the tube end and then achieved the inversion forming. A practical diagram was proposed to identify the contour boundaries of the region leading to the inversion of tube and then the prediction of the adequate geometry parameters of die ensuring a double-walled tubular parts.
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Acknowledgements
This work is carried out thanks to the support and funding allocated to the Unit of Mechanical Production Engineering and Materials (UGPMM/UR17ES43) by the Tunisian Ministry of Higher Education and Scientific Research. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Ketata, H., Essouli, I., Masmoudi, M., Krichen, A. (2024). Effects of Die Parameters on External Inversion of Thin-Walled Tube. In: Sai, L., Sghaier, R.B., Abdelkader, K., Saï, K., Bouzid Saï, W., Laribi, M.A. (eds) Proceedings of the 2nd International Conference on Innovative Materials, Manufacturing, and Advanced Technologies. IMMAT 2022. Mechanisms and Machine Science, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-42659-9_6
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