Parametric design of ring billet for profile ring rolling process based on electric field method and feeding strategy design

2017 ◽  
Vol 93 (1-4) ◽  
pp. 1017-1027 ◽  
Author(s):  
Wujiao Xu ◽  
Fei Chen ◽  
Ziqian Guo ◽  
Qiaoli Wang ◽  
Xiaobing Yang
Keyword(s):  
Electric Field ◽  
Parametric Design ◽  
Feeding Strategy ◽  
Rolling Process ◽  
Field Method ◽  
Ring Rolling ◽  
Ring Rolling Process ◽  
Strategy Design ◽  
Profile Ring Rolling ◽  
Electric Field Method

Design method for intermediate roll in multi-stage profile ring rolling process: The case for excavator idler rim

2014 ◽  
Vol 15 (3) ◽  
pp. 503-512 ◽  
Author(s):  
Kyung-Hun Lee ◽  
Dae-Cheol Ko ◽  
Dong-Hwan Kim ◽  
Seon-Bong Lee ◽  
Nag-Mun Sung ◽  
...  
Keyword(s):  
Design Method ◽  
Rolling Process ◽  
Ring Rolling ◽  
Multi Stage ◽  
Ring Rolling Process ◽  
Profile Ring Rolling

scholarly journals A Mandrel Feeding Strategy in Conical Ring Rolling Process

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Wen Meng ◽  
Guoqun Zhao ◽  
Yanjin Guan
Keyword(s):  
Feed Rate ◽  
Feeding Strategy ◽  
Simulation Method ◽  
Rolling Process ◽  
Ring Rolling ◽  
Fe Model ◽  
Die Structure ◽  
Value Range ◽  
Ring Rolling Process

A mathematical model for radial conical ring rolling with a closed die structure on the top and bottom of driven roll, simplified as RCRRCDS, was established. The plastic penetration and biting-in conditions in RCRRCDS process were determined. A mandrel feeding strategy for RCRRCDS process was proposed. The mandrel feed rate and its reasonable value range were deduced. The coupled thermal-mechanical FE model of RCRRCDS process was established. The reasonable value range of the mandrel feed rate was verified by using numerical simulation method. The results indicate that the reasonable value range of the mandrel feed rate is feasible, the proposed mandrel feeding strategy can realize a steady RCRRCDS process, and the forming quality of conical ring rolled by using the proposed feeding strategy is better than that of conical ring rolled by using a constant mandrel feed rate.

Development of L-sectioned ring for construction machines by profile ring rolling process

2016 ◽  
Vol 17 (2) ◽  
pp. 233-240 ◽  
Author(s):  
Mincheol Park ◽  
Chanjoo Lee ◽  
Jungmin Lee ◽  
Inkyu Lee ◽  
Mansoo Joun ◽  
...  
Keyword(s):  
Rolling Process ◽  
Ring Rolling ◽  
Ring Rolling Process ◽  
Profile Ring Rolling

Process-induced defects in an L-shape profile ring rolling process

2018 ◽  
Vol 12 (4) ◽  
pp. 727-740
Author(s):  
Il Yeong Oh ◽  
Tae Woo Hwang ◽  
Young Yoon Woo ◽  
Hye Jeong Yun ◽  
Young Hoon Moon
Keyword(s):  
Rolling Process ◽  
Ring Rolling ◽  
Ring Rolling Process ◽  
Induced Defects ◽  
Profile Ring Rolling

Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling

2019 ◽  
Vol 233 (17) ◽  
pp. 5966-5976
Author(s):  
Ali Parvizi ◽  
Hamid Reza Rohani Raftar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Rolling Process ◽  
Ring Rolling ◽  
Section Profile ◽  
Artificial Neural ◽  
Ring Rolling Process ◽  
Profile Ring Rolling

Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.

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