Open Access
| Issue |
MATEC Web Conf.
Volume 414, 2025
9th Scientific and Technical Days in Mechanics and Materials: Innovative Materials and Processes for Industrial and Biomedical Applications (JSTMM 2024)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 13 | |
| Section | Additive Manufacturing & Advanced Materials | |
| DOI | https://doi.org/10.1051/matecconf/202541401002 | |
| Published online | 02 October 2025 | |
- J. Adamus, P. Lacki, Possibility of the increase in titanium sheets drawability. Key Eng. Mater. 2013, 1–8 (2013). [Google Scholar]
- K. Ahn, Plastic bending of sheet metal with tension/compression asymmetry. Int. J. Solids Struct. 202, 106–117 (2020). [Google Scholar]
- G. Purcek et al., Effect of commercial purity levels on the mechanical properties of ultrafine-grained titanium. Mater. Sci. Eng. A 528, 2303–2308 (2011). [Google Scholar]
- J. Noder et al., New methodologies for fracture detection of automotive steels in tight radius bending. Exp. Mech. 61, 543–554 (2021). [Google Scholar]
- I.A. Choudhury, V. Ghomi, Springback reduction of aluminum sheet in V-bending dies. Proc. Inst. Mech. Eng. 227, 879–888 (2013). [Google Scholar]
- D. Fei, P. Hodgson, Experimental and numerical studies of springback in air V-bending. Nucl. Eng. Des. 238, 1301–1307 (2006). [Google Scholar]
- K.M. Zhao, J.K. Lee, Finite element analysis of three-point bending of sheet metals. J. Mater. Process. Technol. 124, 190–195 (2002). [Google Scholar]
- K. Manabe et al., Artificial intelligence identification of process parameters for deepdrawing. J. Mater. Process. Technol. 80, 421–426 (1998). [Google Scholar]
- I. Karaagaç, Experimental investigation of springback in V-bending. Arab. J. Sci. Eng. 42, 3289–3298 (2017). [Google Scholar]
- D.K. Leu, Simplified approach for evaluating bendability and springback in anisotropic sheets. J. Mater. Process. Technol. 66, 9–16 (1995). [Google Scholar]
- M.A. Wahed et al., Parameter optimisation in V-bending of Ti-6Al-4V alloy. Adv. Mater. Process. Technol. 6, 456–468 (2020). [Google Scholar]
- M. Muller et al., Predicting plastic deformation during V-band formation. J. Mater. Process. Technol. 211, 1174–1183 (2011). [Google Scholar]
- M.V. Inamdar et al., Prediction of springback using artificial neural networks. J. Mater. Process. Technol. 98, 90–95 (2000). [Google Scholar]
- U. Seker et al., Determination of springback of stainless steel in V-dies. Mater. Des. 28, 1041–1047 (2007). [Google Scholar]
- W. Phanitwong, S. Thipprakmas, Process parameter design using Taguchi technique. Mater. Des. 32, 4430–4436 (2011). [Google Scholar]
- R.B. Osman, M.V. Swain, Critical review of dental implant materials. Materials 8, 932–958 (2015). [Google Scholar]
- S. Fahle et al., Systematic review on machine learning for manufacturing. Procedia CIRP 93, 413–418 (2020). [Google Scholar]
- H.I. Chew et al., Finite element analysis of springback in V-bending. J. Mater. Process. Technol. 145, 242–248 (2003). [Google Scholar]
- A.N. Hanoon et al., Hybrid AI model for CFST beam prediction. Eng. Comput. 36, 1677–1695 (2020). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.