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Over the past decade, the rapid growth of big data has led manufacturing intelligence to become one of the most popular topics in the area of advanced manufacturing. Although some of the current internet and computer network technologies enable collaborative enterprises to share manufacturing knowledge, they were unsuccessful in maximizing the potential predictive decision-making ability of using their historical data. The aim of this paper is to demonstrate the development of an intelligent predictive model, in order to predict the conformity of production orders. A manufacturing ontology was built, based on the historical data of a real industrial case study. The framework of the knowledge-based predictive model was drawn by a classification tree, which includes solutions to the predictive questions. The elements of the decision tree were transformed into SWRL rules to be input to Pellet reasoner, so that the intelligent machines can automatically infer knowledge from the ontology.
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