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Static neural network process models: Considerations and case studies
Ist Teil von
International journal of production research, 1998-11, Vol.36 (11), p.2953-2967
Ort / Verlag
Taylor & Francis Group
Erscheinungsjahr
1998
Quelle
EBSCOhost Business Source Ultimate
Beschreibungen/Notizen
Neural networks are beginning to be used for the modelling of complex manufacturing processes, usually for process and quality control. Often these models are used to identify optimal process settings. Since a neural network is an empirical model, it is highly dependent on the data used in construction and validation. Using data directly from production ensures availability and fidelity, however, the samples may not reflect the entire range of probable operation and, in particular, may not include the optimal process settings. Supplementing production data with observations gathered from designed experiments alleviates the problem of overly focused or incomplete production data sets. This paper considers practical aspects of building and validating neural network models of manufacturing processes, and illustrates the recommended approaches with two diverse case studies.