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Title: Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys

Abstract

In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model’s ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improvedmore » material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.« less

Authors:
ORCiD logo;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1356480
Report Number(s):
PNNL-SA-120740
Journal ID: ISSN 0022-3115; 453040075
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Nuclear Materials; Journal Volume: 490
Country of Publication:
United States
Language:
English

Citation Formats

Johns, Jesse M., and Burkes, Douglas. Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys. United States: N. p., 2017. Web. doi:10.1016/j.jnucmat.2017.03.050.
Johns, Jesse M., & Burkes, Douglas. Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys. United States. doi:10.1016/j.jnucmat.2017.03.050.
Johns, Jesse M., and Burkes, Douglas. Sat . "Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys". United States. doi:10.1016/j.jnucmat.2017.03.050.
@article{osti_1356480,
title = {Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys},
author = {Johns, Jesse M. and Burkes, Douglas},
abstractNote = {In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model’s ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.},
doi = {10.1016/j.jnucmat.2017.03.050},
journal = {Journal of Nuclear Materials},
number = ,
volume = 490,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}