Modelling high temperature oxidation behaviour of Ni-Cr-W-Mo alloys with Bayesian neural network

被引：24

作者：

Yun, Dae Won ^{[1
]}

Seo, S. M. ^{[1
]}

Jeong, H. W. ^{[1
]}

Kim, I. S. ^{[1
]}

Yoo, Y. S. ^{[1
]}

机构：

[1] Korea Inst Mat Sci, Chang Won 641831, Gyungnam, South Korea

来源：

JOURNAL OF ALLOYS AND COMPOUNDS | 2014年 / 587卷

关键词：

High-temperature alloys; Superalloys; Nickel; Oxidation; Modelling; SEM; BASE SUPERALLOY; RESISTANCE; ADDITIONS; ALUMINUM; SPECTROSCOPY; MOLYBDENUM; PREDICTION; ELEMENTS; SILICON; GROWTH;

D O I：

10.1016/j.jallcom.2013.10.138

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

High temperature oxidation property of Ni-Cr-W-Mo alloys is modelled as a function of alloy composition. A database has been constructed from cyclic oxidation experiments performed between 1150 degrees C and 400 degrees C of 27 alloys having various contents of Cr, W, and Mo. The Bayesian neural network technique was used for the modelling of cyclic oxidation experiments. With a 3-17-1 neural network architecture, the model shows a precise prediction of oxidation property of Ni-Cr-W-Mo alloys (R = 0.999). Automatic relevance determination (ARD) analysis reveals that the influence of alloying elements on the output is in the order of Cr, Mo, and W. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：105 / 112

页数：8

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