چکیده مقاله
Neural networks can learn automatically complex relationships between data This feature makes this technique very useful in the modeling of processes which mathematical modeling is difficult or impossible In this study, Artificial Neural Network ANN is investigated as a tool for cold rolling’s mean force estimation and its performance is evaluated Two types of the feed forward networks, levenberg marquardt and bayesian, are considered for cold rolling force prediction of 1100 aluminium sheet Measurable variables and effective parameters on cold rolling force width sheet, back tension, front tension, initial and final sheet's thickness are five input parameters of the network Moreover, mean rolling force is used as the output parameter of the network Our experiments suggest that ANN is an effective method for cold rolling force prediction Particularly, the Bayesian network demonstrates high performance in this case
کلیدواژهها
نویسندگان
P. Kooche Baghi
Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
F. Djavanroodi
Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
S. Eskandari
Departments of Mining, Metallurgical and Petroleum Eng , Amirkabir University of Technology Tehran Polytechnic , Tehran, Iran
شیوه ارجاع
Kooche Baghi, P. and Djavanroodi, F. and Eskandari, S.,1390,Presentation of smart method for cold rolling force prediction using Artificial Neural Networks,12th Iranian Conference on Manufacturing Engineering (ICME 2010),Tehran
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