Parts of complex surface are widely used now in many fields, and their detection has caused much concern. In China many manufactories still carry on the traditional way of manual detection, which requires highly skilled personnel and efficiency is low. Some large manufactories have imported auto-detecting equipments, which require CAD data on the parts, or just divide the surface into several approximate planes for automatic detection. Phased-array system is seldom used, and the cost is high. Besides, most of the systems have not considered the automatic sensitivity compensation of parts with varying thickness. To improve the detection quality and efficiency of nondestructive test (NDT) of parts of complex surface, this paper puts forward an integrated ultrasonic NDT system characterized by: (1) Use of ultrasonic measurement and reverse of curved surface to solve the CAD data problem; (2) Use of an automatic sensitivity compensation algorithm (based on the part’s modelling information obtained in surface reverse) to fit the variety of the thickness; (3) Use of template matching and pseudo-color imaging to improve the quality of detection results. The system features integration of low cost mature technologies, and is suitable for detection of various parts of different complex surfaces in medium-and-small enterprises. The test results showed that the system can automatically detect parts of complex surface successfully, and that the inspection result is good and reliable.
[1]
Olivier Roy,et al.
An adaptive system for advanced NDT applications using phased arrays
,
1998
.
[2]
Zhou Xiaojun.
Automatic Sensitivity Compensation Technology on Ultrasonic Inspection of Curved Surface
,
2004
.
[3]
Shun'ichi Kaneko,et al.
Using orientation codes for rotation-invariant template matching
,
2004,
Pattern Recognit..
[4]
J. Wei,et al.
Research on an ultrasonic NDT system for complex surface parts
,
2002
.
[5]
Hee-Won Jeong,et al.
High-efficiency fixed abrasive polishing method for quartz crystal blanks
,
2004
.
[6]
Milan Sonka,et al.
Image Processing, Analysis and Machine Vision
,
1993,
Springer US.