Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800 °C for 3000 h

被引：16

作者：

Dudziak, T. ^{[1
]}

Gajewski, P. ^{[2
]}

Sniezynski, B. ^{[2
]}

Deodeshmukh, V. ^{[3
]}

Witkowska, M. ^{[4
]}

Ratuszek, W. ^{[4
]}

Chrusciel, K. ^{[4
]}

机构：

[1] Foundry Res Inst, Zakoplanska 73, PL-30418 Krakow, Poland

[2] AGH Univ Sci & Technol, Dept Comp Sci, Kawiory 21, PL-30055 Krakow, Poland

[3] Haynes Int, 1020 West Pk Ave, Kokomo, IN 46904 USA

[4] AGH Univ Sci & Technol, Fac Met Engn & Ind Comp Sci, Al A Mickiewicsa 30, PL-30059 Krakow, Poland

来源：

CORROSION SCIENCE | 2018年 / 133卷

关键词：

Steels; Ni based alloys; SEM; EDS; XRD; Steam oxidation; Neural network; OXIDATION BEHAVIOR; BREAKAWAY OXIDATION; WATER-VAPOR; FE-CR; GROWTH; SUPERALLOY; PREDICTION; CHROMIUM; STRENGTH; SCALES;

D O I：

10.1016/j.corsci.2018.01.013

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Two solid-solution strengthened alloys, (HAYNES degrees 230 degrees, 617 alloy), two gamma - prime (gamma') strengthened alloys, (263 and HAYNES degrees 282 degrees) and Cr rich steels (309S, 310S and HR3C) were tested under 1 bar pressure in 100% steam at 800 degrees C for 3000 h. The steels showed better resistance in terms of corrosion behaviour, where no internal corrosion occurred. The exposed samples were characterised using SEM, EDS and XRD. Artificial neural networking (ANN) was used to predict kinetic behaviour of the alloys after exposure by Time Delay Neural Networking (TDNN) and Non-linear Autoregressive Neural Networking (NARNN).

引用

页码：94 / 111

页数：18

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