Predicting Extrusion Process Parameters Using Neural Networks

The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Authors:

• Sachin Man Bajimaya
• SangChul Park
• Gi-Nam Wang

Keywords:

• Artificial Neural Network (ANN)
• Indirect Extrusion
• Finite Element Analysis
• MES.

References:

[1] Davis, J.R., Aluminum and Aluminum Alloys, ASM Specialty
Handbook, ASM International, 1996.
[2] Rao, P.N., Manufacturing Technology - Foundry, Forming and
Welding, Tata McGraw Hill, New Delhi, 1995.
[3] Sivaprasad, P.V., Venugopal, S, Davies, C.H.J., and Prasad, Y.V.R.K.,
"Identification of optimum process parameters for hot extrusion using
finite element simulation and processing maps.", Journal of Modeling
and Simulation in Materials Science and Engineering, Institute of
Physics Publishing, Feb. 2004.
[4] Tibbetts, B.R., and Ting-Yung, J. "Extrusion Process Control:
Modeling,Identification, and Optimization", IEEE transactions on
control systems technology, vol. 6, no. 2, March 1998.
[5] Hansson, S., "Simulation of Stainless Steel Tube Extrusion", Ph.D.
Thesis, Department of Applied Physics and Mechanical Engineering,
Division of Material Mechanics, Luleå University of Technology,
Sweden, 2006.
[6] Lertsiriyothin, W. and Kumtib, M., "Simulation of flour flow in
extrusion process by using computational fluid dynamics commercial
software", ANSCSE, July 2004.
[7] Salazar, C.A.G., "Process simulation and training: the case of plastics
extrusion", Journal of Modeling and Simulation in Materials science and
Engineering, 1994.
[8] Bajimaya, S.M., Park, C.M., Wang, G.N., "Simulation based
Optimization of Indirect Aluminum Extrusion Process Parameters",
Proceedings of ECMS2007, Prague, 2007.
[9] Delmia IGRIP Tutorial, Dassault Systems.
[10] Hagan, M.T., Demuth, H.B and Beale, M., Neural Network Design,
PWS Publishing Company, 1996.
[11] Demuth, H.B and Beale, M.,Neural Network Toolbox for use with
MATLAB, The MathWorks, Inc, 1998.
[12] Avitzur, B., Metal Forming Processes and Analysis, McGraw Hill, 1968.