Abstract
In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to reduce the occurrence of this failure and to improve the manufacturing process. While these data-driven approaches enable pattern recognition, they lack the advantages associated with knowledge-driven approaches, such as knowledge representation and deductive reasoning. Similarly, knowledge-driven approaches lack the pattern-learning capabilities inherent in data-driven approaches such as machine learning. Therefore, in this paper, leveraging the advantages of both data-driven and knowledge-driven approaches, we present a strategy with a prototype implementation to reduce manufacturing product failure. The proposed strategy combines a data-driven technique, Bayesian structural learning, with a knowledge-based technique, knowledge graphs.
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Acknowledgements
The research reported in this paper has been funded by European Interreg Austria-Bavaria project KI-Net\(^{3}\) (grant number: AB292). We would also like to thank Oleksandra Roche-Newton for her assistance in the manuscript preparation and Simon Außerlechner, system engineer at STI Innsbruck, for facilitating servers for experimentation(\(^{3}\) https://ki-net.eu).
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Chhetri, T.R., Aghaei, S., Fensel, A., Göhner, U., Gül-Ficici, S., Martinez-Gil, J. (2022). Optimising Manufacturing Process with Bayesian Structure Learning and Knowledge Graphs. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_70
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