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|Title: ||The use of arc sound & on-line ultrasonic signal processing on computer technology in welding|
|Authors: ||McCardle, John|
|Editors: ||Lucas, WE|
|Issue Date: ||1996|
|Publisher: ||Abington Publishing for TWI|
|Citation: ||MCCARDLE, J. ...et al., 1996. The use of arc sound & on-line ultrasonic signal processing on computer technology in welding. IN: Lucas, W.E. (ed.) Proceedings of the 6th International Conference on Computer Technology in Welding, Lanaken, Belgium, June 9-12th., Paper 33.|
|Abstract: ||Monitoring the welding process on-line with ultrasound is problematic, but promises
great rewards. A fast classifier is required to exploit the redundancy available in
ultrasonic interrogation and ensure an adequate signal / noise ratio.
TARDIS is such a classifier, using logical neural network techniques and dedicated
hardware. The classification performance of TARDIS alone is noisy, but exceptionally
fast. This speed of operation can be used to offset the fuzziness of individual
classifications, using higher order correlations.
The expert manual welder is capable of simultaneously monitoring visual and acoustic
data and, coupled with a knowledge of the process and past experience, is able to
attempt an optimum weld. Observations of skilled manual welders has shown a
subconscious tendency to change the angle of the electrode and length of arc by listening
to adverse fluctuations in the process noise in addition to visual assessment. This has
resulted in much research into the analysis of airborne acoustic emissions (AEs) of
welding processes. It is evident that to artificially copy these skills requires a fast, robust
signal processing and pattern recognition technique similar the known architecture and
operation of the brain.
The Department of Design, Brunel University, has been researching the possibilities of
including the monitoring of airborne acoustic emissions as an additional correcting factor in
automated weld process control. Salient relationships between acoustic emissions and
process parameters using off-line statistical techniques has been established, however, real
time application remains problematic due to the computational intensity of such methods.
Statistical approaches to the interpretation of arc sounds relies on the direct correlation
observable between the acquired signal or its' various transforms and the monitored process
parameters. The method is a time consuming and often mathematically gruelling . Artificial
neural networks (ANNs) provide an alternative. By the construction of different architectures
and the application of various learning algorithms ANNs can provide a noise tolerant adaptive
knowledge acquisition system.
The work discussed in this paper illustrates the methods of signal preprocessing and
utilisation of artificial neural networks to interpret arc sounds. Techniques are used to filter
and compress high dimensional erratic data patterns to form classifiable representations of
the process state. Real time scenarios are discussed together with commercially viable
|Description: ||This is a conference paper.|
|Version: ||Accepted for publication|
|Appears in Collections:||Conference Papers and Presentations (Design School)|
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