Abstract
The utilisation of formal artificial intelligence (AI) tools has been implemented to produce a hybrid system for optical emission spectral analysis that combines a multilayer perceptron neural network with rule-based system techniques. Even though optical emission spectroscopy is extensively used as an in-situ diagnostic for ionised gas plasmas in manufacturing processes, ways of interpreting the spectra without prior knowledge or expertise from the user's stand-point has encouraged the use of Al techniques to automate the interpretation process. The hybrid approach presented here combines a modified network architecture with a simple rule-base in order to produce explicit models of the identifiable chemical species.KeywordsOptical EmissionOptical Emission SpectroscopyMixed PatternAtomic ArgonNeural Network ArchitectureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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