Development of prediction model for mechanical properties of batch annealed thin steel strip by using artificial neural network modeling

Journal of Materials Processing Technology

Volumn 60, Issue 1-4, 1996, Pages 399-404

  Myllykoski, P ^a   Larkiola, J ^a   Nylander, J ^b  

^a AALTO UNIVERSITY   (Finland)

^b Rautaruukki Corporation   (Finland)

Author keywords

Artificial neural networks; Mechanical properties; Modelling; Thin steel strip

Indexed keywords

ALLOYING ELEMENTS; ANNEALING; COMPUTER SIMULATION; DATA ACQUISITION; MECHANICAL PROPERTIES; ROLLING; STRIP METAL; TEMPERATURE MEASUREMENT; TENSILE TESTING;

ARTIFICIAL NEURAL NETWORK MODEL; PROCESS PARAMETERS; THIN STEEL STRIP;

NEURAL NETWORKS;

EID: 0030164326     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/0924-0136(96)02361-8     Document Type: Article

References (5)

1. Parlos, A., Chong, K., Atiya A., Application of Recurrent Multilayer Perceptron in Modeling Complex Process Dynamics, IEEE Transactions on Neural Networks, Vol. 5, No. 2, March 1994.
2. Boger, Z., Experience in Developing and Analyzing Models of Industrial Plants by Large Scale Artificial Neural Networks, Proceedings of ICANN '95, October 9 - 13, 1995, Paris, France.
3. Freeman, J., Skapura, D., Neural Network Algorithms, Applications and programming Techniques, Addison-Wesley Publishing Company Inc., USA, 1991.
4. Rumelhart, D., Hinton, G. and Williams, R., Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, MIT Press, 1986.
5. Cybenko, G., Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems 2, pp 303 - 314, 1989.