In-Process Detection of Weld Defects Using Laser-Based Ultrasonic Lamb Waves

Kercel, S.W.

Oak Ridge National Laboratory

关键词: Ultrasound;    Pattern Recognition;    Monitoring;    Laser-Based Detection;    36 Materials Science;   

DOI  :  10.2172/814054
RP-ID  :  ORNL/TM-2000/346
RP-ID  :  AC05-00OR22725
RP-ID  :  814054

美国|英语

来源: UNT Digital Library

【 摘 要 】

Laser-based ultrasonic (LBU) measurement shows great promise for on-line monitoring of weld quality in tailor-welded blanks. Tailor-welded blanks are steel blanks made from plates of differing thickness and/or properties butt-welded together; they are used in automobile manufacturing to produce body, frame, and closure panels. LBU uses a pulsed laser to generate the ultrasound and a continuous wave (CW) laser interferometer to detect the ultrasound at the point of interrogation to perform ultrasonic inspection. LBU enables in-process measurements since there is no sensor contact or near-contact with the workpiece. The authors have used laser-generated plate (Lamb) waves to propagate from one plate into the weld nugget as a means of detecting defects. This report recounts an investigation of a number of inspection architectures based on processing of signals from selected plate waves, which are either reflected from or transmitted through the weld zone. Bayesian parameter estimation and wavelet analysis (both continuous and discrete) have shown that the LBU time-series signal is readily separable into components that provide distinguishing features, which describe weld quality. The authors anticipate that, in an on-line industrial application, these measurements can be implemented just downstream from the weld cell. Then the weld quality data can be fed back to control critical weld parameters or alert the operator of a problem requiring maintenance. Internal weld defects and deviations from the desired surface profile can then be corrected before defective parts are produced. The major conclusions of this study are as follows. Bayesian parameter estimation is able to separate entangled Lamb wave modes. Pattern recognition algorithms applied to Lamb mode features have produced robust features for distinguishing between several types of weld defects. In other words, the information is present in the output of the laser ultrasonic hardware, and it is feasible to extract it. Wavelet analysis produces results that are almost as good as Bayesian, but execute a thousand times faster. This study demonstrates the principle that it is feasible to construct a laser ultrasonic system to detect weld defects in thin metal parts on-line in real-time, and to classify the defects according to type.