Neural network model for predicting ferrite number in stainless steel welds

Technical Report · Sun Nov 01 04:00:00 EST 1998

DOI:https://doi.org/10.2172/290929· OSTI ID:290929

Vitek, J M; Iskander, Y S; Oblow, E M; Babu, S S; David, S A

Predicting the ferrite content in stainless steel welds is desirable in order to assess an alloy`s susceptibility to hot cracking and to estimate the as-welding properties. Several methods have been used over the years to estimate the ferrite content as a function of the alloy composition. A new technique is described which uses a neural network analysis to determine the ferrite number. The network was trained on the same data set that was used to generate the WRC-1992 constitution diagram. The accuracy of the neural network predictions is compared to that for the WRC-1992 diagram as well as another recently proposed method. It was found that the neural network model was approximately 20% more accurate than either of the other two methods. In addition, it is suggested that further improvements to the neural network model, including the consideration of process variables, can be made which lead to even better accuracy.

• Research Organization: Oak Ridge National Lab., TN (United States)
• Sponsoring Organization: USDOE Office of Energy Research, Washington, DC (United States)
• DOE Contract Number: AC05-96OR22464
• OSTI ID: 290929
• Report Number(s): ORNL/CP--99107; CONF-980657--; ON: DE99000391; BR: KC0201050
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
FERRITE
MATHEMATICAL MODELS
MICROSTRUCTURE
NEURAL NETWORKS
PREDICTION EQUATIONS
STAINLESS STEELS
THEORETICAL DATA
VALIDATION
WELDED JOINTS