Using Neural Networks to Describe Complex Phase Transformation Behavior
Final microstructures can often be the end result of a complex sequence of phase transformations. Fundamental analyses may be used to model various stages of the overall behavior but they are often impractical or cumbersome when considering multicomponent systems covering a wide range of compositions. Neural network analysis may be a useful alternative method of identifying and describing phase transformation beavior. A neural network model for ferrite prediction in stainless steel welds is described. It is shown that the neural network analysis provides valuable information that accounts for alloying element interactions. It is suggested that neural network analysis may be extremely useful for analysis when more fundamental approaches are unavailable or overly burdensome.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Oak Ridge, TN
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 6146
- Report Number(s):
- ORNL/CP-102650; KC 02 01 05 0; ON: DE00006146
- Resource Relation:
- Conference: Solid-Solid Phase Transformations '99, Kyoto, Japan, May 24-28, 1999
- Country of Publication:
- United States
- Language:
- English
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