Optimising Manufacturing Process with Bayesian Structure Learning and Knowledge Graphs

Tek Raj Chhetri*, Sareh Aghaei, Anna Fensel, Ulrich Göhner, Sebnem Gül-Ficici, Jorge Martinez-Gil

*Corresponding author for this work

Research output: Contribution to conferenceConference paperAcademicpeer-review

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.

Original languageEnglish
Pages594-602
Number of pages9
DOIs
Publication statusPublished - 10 Feb 2022
Event18th International Conference on Computer Aided Systems Theory, EUROCAST 2022 - Las Palmas de Gran Canaria, Spain
Duration: 20 Feb 202225 Feb 2022

Conference

Conference18th International Conference on Computer Aided Systems Theory, EUROCAST 2022
Country/TerritorySpain
CityLas Palmas de Gran Canaria
Period20/02/2225/02/22

Keywords

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

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