Improved ferrite number prediction in stainless steel arc welds using artificial neural networks -- Part 1: Neural network development
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:
- USDOE
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 20014306
- Journal Information:
- Welding Journal (Miami), Vol. 79, Issue 2; Other Information: PBD: Feb 2000; ISSN 0043-2296
- Country of Publication:
- United States
- Language:
- English
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