Neural computation analysis of alumina-titania wear resistance coating

被引：14
|
作者：
Guessasma, S
Bounazef, M
Nardin, P
机构：
[1] UTBM, LERMPS, F-90010 Belfort, France
[2] CREST, CNRS, UMR 6174, FEMTO ST, Belfort, France
来源：
INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS | 2006年 / 24卷 / 03期
关键词：
artificial neural network; alumina-titania coating; atmospheric plasma spraying; wear resistance; friction coefficient;
D O I：
10.1016/j.ijrmhm.2005.05.012
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Pin-on-disc tests were performed on alumina-13 wt.% titania coatings obtained under several APS conditions. Friction coefficient data were analysed using artificial neural network. This permitted to predict parameter ranges for which good wear resistance is possible when varying each of the process parameters individually with respect to a reference condition. In this case, results suggest that large parameter ranges did not permit to obtain a significant friction coefficient variation which was mainly between 0.51 and 0.61. In addition, injection parameters and total plasma gas flow rate were the control factors. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：240 / 246
页数：7
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