Eddy current technique applied to the nondestructive evaluation of turbine blade wall thickness

被引:3
作者
Le Bihan, Y [1 ]
Joubert, PY [1 ]
Placko, D [1 ]
机构
[1] Ecole Normale Super Cachan, LESiR, F-94235 Cachan, France
来源
NONDESTRUCTIVE EVALUATION OF AGING AIRCRAFT, AIRPORTS, AND AEROSPACE HARDWARE IV | 2000年 / 3994卷
关键词
turbine blade; eddy current technique; thickness evaluation;
D O I
10.1117/12.385028
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The high pressure turbine blades of jet engines show internal channels designed for air cooling. These recesses define the internal walls (partitions) and external walls of the blade. The external wall thickness is a critical parameter which has to be systematically checked in order to ensure the blade strength. The thickness evaluation is usually lead by ultrasonic technique or by X-ray tomography. Nevertheless, both techniques present some drawbacks related to measurement speed and automation capability. These drawbacks are bypassed by the eddy current (EC) technique, well known for its robustness and reliability. However, the wall thickness evaluation is made difficult because of the complexity of the blade geometry. in particular, some disturbances appear in the thickness evaluation because of the partitions, which exclude the use of classical EC probes such as cup-core probe. In this paper, we show the main advantages of probes creating an uniformly oriented magnetic field in order to reduce the partition disturbances. Furthermore, we propose a measurement process allowing to separate the wall thickness parameter from the EC signals. Finally, we present some experimental results validating the proposed technique.
引用
收藏
页码:145 / 153
页数:9
相关论文
共 7 条
[1]  
ASHBY MF, 1981, ENG MAT
[2]  
BARRETT CS, 1957, STRUCTURE METALS
[3]   TRAINING FEEDFORWARD NETWORKS WITH THE MARQUARDT ALGORITHM [J].
HAGAN, MT ;
MENHAJ, MB .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (06) :989-993
[4]   MULTILAYER FEEDFORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS [J].
HORNIK, K ;
STINCHCOMBE, M ;
WHITE, H .
NEURAL NETWORKS, 1989, 2 (05) :359-366
[5]  
LIBBY HL, 1979, INTRO ELECTROMAGNETI
[6]  
MORISSEAU P, 1987, P 4 EUR C NDT LOND
[7]  
Rumelhart DE, 1986, PARALLEL DISTRIBUTED, V1, DOI DOI 10.7551/MITPRESS/5236.001.0001