Bending Process Parameters Optimization of the 316L Tube with Small R/D Ratio: A Physical Prior Knowledge Based Bayesian Optimization Approach
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40070-4_23
Reference21 articles.
1. Zhang, S., Fu, M., Wang, Z., Lin, Y., He, C.: Spring-back prediction of the Bi-layered metallic tube under CNC bending considering neutral layer shifting extraction. Appl. Sci. 10(14), 4978 (2020). https://doi.org/10.3390/app10144978
2. Wang, Z., Lin, Y., Qiu, L., Zhang, S., Fang, D., He, C., Wang, L.: Spatial variable curvature metallic tube bending springback numerical approximation prediction and compensation method considering cross-section distortion defect. Int J Adv Manuf Technol 112(1), 1–15 (2021). https://doi.org/10.1007/s00170-021-08051-wZH1
3. Zhang, S., Fu, M., Wang*, Z., Fang, D., Lin, W., Zhou, H.: Springback prediction model and its compensation method for the variable curvature metal tube bending forming. Int. J. Adv. Manuf. Technol 112(11), 3151–3165 (2021)
4. Zhou, H., Zhang, S., Qiu, L., Wang, Z.: Springback angle prediction of circular metal tube considering the interference of cross-sectional distortion in mandrel-less rotary draw bending. Sci. Prog. 104(1), 003685042098430 (2021)
5. Fu, M., Wang, Z., Zhang, S., Liu, X., Lin, Y., Wang, L.: Full-cross-section deformation characterization of Cu/Al bimetallic tubes under Rotary-Draw-Bending based on physics-driven B-spline curves fitting. Mater. Des. 215, 110493 (2022). https://doi.org/10.1016/j.matdes.2022.110493
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