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

Abstract

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 fedmore » 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.« less

Authors:
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
814054
Report Number(s):
ORNL/TM-2000/346
TRN: US200316%%407
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 4 Jan 2001
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; ALGORITHMS; AUTOMOBILES; CLOSURES; DEFECTS; DETECTION; INTERFEROMETERS; LASERS; MAINTENANCE; MANUFACTURING; MONITORING; PATTERN RECOGNITION; STEELS; THICKNESS; ULTRASONIC WAVES; WELDMENTS; WELD DEFECTS; ULTRASOUND; LASER-BASED DETECTION

Citation Formats

Kercel, S W. In-Process Detection of Weld Defects Using Laser-Based Ultrasonic Lamb Waves. United States: N. p., 2001. Web. doi:10.2172/814054.
Kercel, S W. In-Process Detection of Weld Defects Using Laser-Based Ultrasonic Lamb Waves. United States. https://doi.org/10.2172/814054
Kercel, S W. 2001. "In-Process Detection of Weld Defects Using Laser-Based Ultrasonic Lamb Waves". United States. https://doi.org/10.2172/814054. https://www.osti.gov/servlets/purl/814054.
@article{osti_814054,
title = {In-Process Detection of Weld Defects Using Laser-Based Ultrasonic Lamb Waves},
author = {Kercel, S W},
abstractNote = {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.},
doi = {10.2172/814054},
url = {https://www.osti.gov/biblio/814054}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2001},
month = {1}
}