Neural network model for predicting ferrite number in stainless steel welds

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Description

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 ... continued below

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6 p.

Creation Information

Vitek, J.M.; Iskander, Y.S.; Oblow, E.M.; Babu, S.S. & David, S.A. November 1, 1998.

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Description

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.

Physical Description

6 p.

Notes

OSTI as DE99000391

Source

  • 5. international conference on trends in welding research, Pine Mountain, GA (United States), 1-5 Jun 1998

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  • Other: DE99000391
  • Report No.: ORNL/CP--99107
  • Report No.: CONF-980657--
  • Grant Number: AC05-96OR22464
  • DOI: 10.2172/290929 | External Link
  • Office of Scientific & Technical Information Report Number: 290929
  • Archival Resource Key: ark:/67531/metadc688385

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • November 1, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • Jan. 22, 2016, 12:23 p.m.

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Vitek, J.M.; Iskander, Y.S.; Oblow, E.M.; Babu, S.S. & David, S.A. Neural network model for predicting ferrite number in stainless steel welds, report, November 1, 1998; Tennessee. (digital.library.unt.edu/ark:/67531/metadc688385/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.