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Optimising Manufacturing Process with Bayesian Stucture 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, withIn 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.show moreshow less

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Metadaten
Author:Tek Raj Chhetri, Sareh Aghaei, Anna Fensel, Ulrich GöhnerORCiD, Sebnem Gül - FiciciORCiD, Jorge Martinez-Gil
DOI:https://doi.org/10.1007/978-3-031-25312-6_70
Identifier:978-3-031-25311-9 OPAC HS OPAC extern
Identifier:978-3-031-25312-6 OPAC HS OPAC extern
Identifier:1611-3349 OPAC HS OPAC extern
Identifier:0302-9743 OPAC HS OPAC extern
Parent Title (English):Computer Aided Systems Theory – EUROCAST 2022: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers
Publisher:Springer
Place of publication:Berlin
Editor:Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Document Type:conference proceeding (article)
Conference:Computer Aided Systems Theor - EUROCAST 2022, February 20-25, 2022, Las Palmas de Gran Canaria, Spain
Language:English
Date of Publication (online):2023/02/10
Year of first Publication:2022
Number of pages:9 Seiten
First Page:594
Last Page:602
Institutes:IFA – Institut für Innovative Fahrzeugantriebe
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik
Research focus:FSP1: Energie
FSP2: Mobilität
FSP3: Produktion
Publication Lists:Göhner, Ulrich
Gül - Ficici, Sebnem
Publication reviewed:begutachtet
Release Date:2023/03/01
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