A rapid prediction method for geometric size in profiled ring rolling process
Author:
Funder
National Natural Science Foundation of China
the Key Research and Development Program of Heilongjiang
the Pre-research of Civil Aerospace Technology
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-13113-w.pdf
Reference31 articles.
1. Hua L, Han XH (2009) Precision forming technologies of ring rolling and rotary forging. J Net Form Eng 1(1):21–26. https://doi.org/10.3969/j.issn.1674-6457.2009.01.004
2. Huiping QI, Yongtang LI (2017) Research status and developing trends on the ring rolling process of profile ring parts. Procedia Eng 207:1260–1265. https://doi.org/10.1016/j.proeng.2017.10.880
3. Parvizi A, Rohani Raftar HR (2019) Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling[J]. P I MECH ENG C-J MEC 233(17):5966–5976. https://doi.org/10.1177/0954406219861989
4. Guo W, Yi Y, Huang S, Mao X, Fang J, Tong D, Luan Y (2020) Manufacturing large 2219 Al–Cu alloy rings by a cold rolling process. Mater Manuf Process 35(3):291–302. https://doi.org/10.1080/10426914.2020.1718696
5. Li X, Guo LG, Liang L, Yang W (2018) Motion control of guide rolls in intelligent simulation for profiled ring rolling process. Procedia Manuf 15:97–104. https://doi.org/10.1016/j.promfg.2018.07.175
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