Volume 15, Issue 7 (9-2015)                   Modares Mechanical Engineering 2015, 15(7): 209-214 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Dadgar Asl Y, Tajdari M, Moslemi Naeini H, Davoodi B, Azizi Tafti R, Panahizadeh Rahimloo V. Prediction of Required Torque in Cold Roll Forming Process of a Channel Sections Using Artificial Neural Networks. Modares Mechanical Engineering 2015; 15 (7) :209-214
URL: http://mme.modares.ac.ir/article-15-7266-en.html
1- instuctor/shahid rajaee teacher training university
2- professor. Mechanical Engineering/ Department on Mechanical Engineering, Islamic Azad University, Arak Branch
3- professor. mechanical engineering/ tarbiat modares university
4- ASSOC. PROF-Iran university of science and technology
5- assistant professor/ yazd university
6- assistant professor/ shahid rajaee teacher training university
Abstract:   (5443 Views)
One of the most important issues in the review of cold roll forming process of metals is estimation of required torque. The optimum production line can be designed by determining the effective parameters on torque. Some of these parameters are sheet material and thickness, bending angle, lubrication conditions, rolls rotational speed and distance of the stands. The aim of this study is to predict amount of required torque considering the factors influencing torque, including thickness, yield strength, sheet width and forming angle using artificial neural network. So the forming process was 3D simulated in a finite element code. Simulation results showed that with increase of yield strength, thickness and forming angle, applied torque on rolls will increase. Also the increase in sheet width -assuming constant web length- will decrease the torque needed for forming. The effects of thickness and sheet width were experimentally investigated which verified the results obtained by finite element analysis. A feed-forward back-propagation neural network was created. The comparison between the experimental results and ANN results showed that the trained network could predict the required torque adequately.
Full-Text [PDF 599 kb]   (2831 Downloads)    
Article Type: Research Article | Subject: Metal Forming
Received: 2014/12/27 | Accepted: 2015/05/17 | Published: 2015/06/2

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.