@inproceedings{dd0aa29eb2f44acf82e7b8949db8fa6a,
title = "Ontology and rule-based reasoning for intelligent predictive manufacturing",
abstract = "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.",
keywords = "Ontology, Predictive Manufacturing, Rule-based Reasoning",
author = "Zhe Zhong and Salman Saeidlou and Mozafar Saadat and Ahmed Abukar",
year = "2018",
doi = "10.3233/978-1-61499-902-7-355",
language = "English",
series = "Advances in Transdisciplinary Engineering",
publisher = "IOS Press BV",
pages = "355--360",
editor = "Keith Case and Peter Thorvald",
booktitle = "Advances in Manufacturing Technology XXXII - Proceedings of the 16th International Conference on Manufacturing Research, ICMR 2018, incorporating the 33rd National Conference on Manufacturing Research",
note = "16th International Conference on Manufacturing Research, ICMR 2018 ; Conference date: 11-09-2018 Through 13-09-2018",
}