The real time analysis of acoustic weld emissions using neural networks
K.L. Burge
T.J. Harris
Raymond Stroud
John McCardle
2134/26282
https://repository.lboro.ac.uk/articles/conference_contribution/The_real_time_analysis_of_acoustic_weld_emissions_using_neural_networks/9340859
Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool
in control scenarios where human expertise is so often required. The development of
software emulations and dedicated VLSI devices is proving successful in real world
applications where complex signal analysis, pattern recognition and discrimination are
important factors.
An established observation is that a skilled welder is able to monitor a manual arc
welding process by subconsciously changing the position of the electrode in response to
an adverse change in audible process noise. Expert systems applied to the analysis of
chaotic acoustic emissions have failed to establish any salient information due to the
inabilities of conventional architectures in processing vast quantities of erratic data at real
time speeds.
This paper describes the application of a hybrid ANN system, utilising a combination of
multiple ANN architectures and conventional techniques, to establish system parameter
acoustic signatures for subsequent on line control.
2017-08-31 10:31:45
untagged
Design Practice and Management not elsewhere classified