Optimising manufacturing process with Bayesian structure learning and knowledge graphs

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.

Bayesian structural learning, Knowledge graphs, Manufacturing product failure, Structure learning

10.1007/978-3-031-25312-6_70

0302-9743

594–602

Springer Cham

Chhetri, Tek Raj

c3431de5-4860-43e5-b09f-3dbb752c8490

Aghaei, Sareh

4f132d7b-e8c9-48ba-b6c5-6c24b5a94277

Fensel, Anna

6d0be8a7-8261-48f1-9214-fc5fc59c40d3

Göhner, Ulrich

a7801253-9e10-4583-a252-6ea85532c3d1

Gül-Ficici, Sebnem

34f8c8f4-8cd7-4e35-a887-e3d6b2e0a2c8

Martinez-Gil, Jorge

e30d0054-897f-48bb-9048-00aed89099fc

Moreno-Díaz, Roberto

Pichler, Franz

Quesada-Arencibia, Alexis

10 February 2023

Chhetri, Tek Raj

c3431de5-4860-43e5-b09f-3dbb752c8490

Aghaei, Sareh

4f132d7b-e8c9-48ba-b6c5-6c24b5a94277

Fensel, Anna

6d0be8a7-8261-48f1-9214-fc5fc59c40d3

Göhner, Ulrich

a7801253-9e10-4583-a252-6ea85532c3d1

Gül-Ficici, Sebnem

34f8c8f4-8cd7-4e35-a887-e3d6b2e0a2c8

Martinez-Gil, Jorge

e30d0054-897f-48bb-9048-00aed89099fc

Moreno-Díaz, Roberto

Pichler, Franz

Quesada-Arencibia, Alexis

Chhetri, Tek Raj, Aghaei, Sareh, Fensel, Anna, Göhner, Ulrich, Gül-Ficici, Sebnem and Martinez-Gil, Jorge (2023) Optimising manufacturing process with Bayesian structure learning and knowledge graphs. Moreno-Díaz, Roberto, Pichler, Franz and Quesada-Arencibia, Alexis (eds.) In Computer Aided Systems Theory – EUROCAST 2022. vol. 13789, Springer Cham. 594–602 . (doi:10.1007/978-3-031-25312-6_70).

Record type: Conference or Workshop Item (Paper)

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Published date: 10 February 2023

Additional Information: Funding Information: Acknowledgements. The research reported in this paper has been funded by European Interreg Austria-Bavaria project KI-Net3 (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). Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Venue - Dates: EUROCAST 2022: International Conference on Computer Aided Systems Theory, , Las Palmas de Gran Canaria, Spain, Spain, 2022-02-20 - 2022-02-25

Keywords: Bayesian structural learning, Knowledge graphs, Manufacturing product failure, Structure learning

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