Learning and diagnosis in manufacturing processes through an executable Bayesian network

Authors:
M. A. Rodrigues;Y. Liu;L. Bottaci;D. I. Rigas
Affiliations:
-;-;-;-
Venue:
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Year:
2000

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Abstract

In this paper we present a novel approach to modelling a manufacturing process that allows one to learn about causal mechanisms of manufacturing defects through a Process Modelling and Executable Bayesian Network (PMEBN). The method combines probabilistic reasoning with time dependent parameters which are of crucial interest to quality control in manufacturing environments. We demonstrate the concept through a case study of a caravan manufacturing line using inspection data.