Deep Learning Enabled Tool Compensation for Addressing Shape Distortion in Sheet Metal Stamping
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42093-1_5
Reference17 articles.
1. Zhou, J., Yang, X., Wang, B., Xiao, W.: Springback prediction of 7075 aluminum alloy V-shaped parts in cold and hot stamping. Int. J. Adv. Manuf. Technol. 119, 203–216 (2022). https://doi.org/10.1007/s00170-021-08204-x
2. Wang, A., Zhong, K., El Fakir, O., et al.: Springback analysis of AA5754 after hot stamping: experiments and FE modelling. Int. J. Adv. Manuf. Technol. 89, 1339–1352 (2017). https://doi.org/10.1007/s00170-016-9166-3
3. Szegda, D., Mohamed, M., Ziane, M.: On thermal compensation of Hot-Form-Quench stamping die. IOP Conf. Ser. Mater. Sci. Eng. 1157, 012087 (2021). https://doi.org/10.1088/1757-899X/1157/1/012087
4. ESI Group: PAM-STAMP User Guide (2012)
5. Attar, H.R., Zhou, H., Foster, A., Li, N.: Rapid feasibility assessment of components to be formed through hot stamping: a deep learning approach. J. Manuf. Process. 68, 1650–1671 (2021). https://doi.org/10.1016/j.jmapro.2021.06.011
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