Spot welds are used to join sheet steel in automobile bodies. To ensure vehicle integrity, these welds, must be tested. Ideally, non-destructive testing would be employed. However, spot weld quality in the automotive industry is currently assessed using destructive methods. Spot welds can be tested non-destructively with ultrasound. Operators place a single crystal ultrasonic probe on a weld and interpret the returning signal to estimate the quality of the weld. However, this ultrasonic method has not been widely accepted in the past, possibly due to difficulties in manually quantifying the information contained in the signals. In an attempt to make ultrasonic testing viable for automotive use, a system has been created which automatically interprets the ultrasonic signals and classifies welds as good or bad. There are two main aspects to the systems. Firstly, echoes occurring within the signal are identified by an algorithm. This was developed after discovering the sequence in which the critical intermediate echoes occur. The second aspect of the system is classification of the spot weld based upon certain features of the identified echoes. The strength of the intermediate echoes was found to be primary source of information on weld size. Extensive experimental studies were designed and conducted to identify other potential information sources. Notably, the attenuation rate of the back-wall echoes in the signal was investigated. Most published papers in the field report that signal attenuation may be used to estimate weld size. It has been generally believed that the grain structure of the welded steel significantly increases ultrasound scattering, leading to higher attenuation is not caused by weld grain structure. The evidence gathered strongly favours weld surface irregularities as the primary cause of ultrasound attenuation in spot welds.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:638690 |
| Date | January 2003 |
| Creators | Roberts, D. R. |
| Publisher | Swansea University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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