Improved ferrite number prediction in stainless steel arc welds using artificial neural networks -- Part 1: Neural network development

Journal Article · Tue Feb 01 04:00:00 EST 2000 · Welding Journal (Miami)

OSTI ID:20014306

Vitek, J M; Iskander, Y S; Oblow, E M

Neural network modeling is a powerful nonlinear regression analysis method that is extremely useful in identifying behavioral trends. This methodology was applied to the problem of predicting Ferrite Number in arc welds as a function of composition. This paper describes the details of the development of the neural network model, named FNN-1999, including the identification of the optimum network architecture and network parameters. The model was trained on the same data as the WRC-1992 constitution diagram and covers a range of Ferrite Numbers from 0 to 117, with a corresponding wide range in composition. Results of the model are presented in Part 2. It is shown that the accuracy of the FNN-1999 model in predicting Ferrite Number is superior to the accuracy of other models that are currently available, including the WRC-1992 diagram.

• Research Organization: Oak Ridge National Lab., TN (US)
• Sponsoring Organization: US Department of Energy
• DOE Contract Number: AC05-96OR22464
• OSTI ID: 20014306
• Journal Information: Welding Journal (Miami), Journal Name: Welding Journal (Miami) Journal Issue: 2 Vol. 79; ISSN 0043-2296; ISSN WEJUA3
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
99 GENERAL AND MISCELLANEOUS
ARC WELDING
CHEMICAL COMPOSITION
FERRITE
MICROSTRUCTURE
NEURAL NETWORKS
STAINLESS STEELS
WELDED JOINTS