Learning and Diagnosis in Manufacturing Processes through an Executable Bayesian Network
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.
KeywordsBayesian Network Joint Probability Distribution Representation Framework Component Code Time Dependent Parameter
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