Manufacturing Technology 2023, 23(3):276-283 | DOI: 10.21062/mft.2023.036

Overall Equipment Effectiveness-related Assembly Pattern Catalogue based on Machine Learning

Péter Dobra ORCID...1, János Jósvai ORCID...2
1 Doctoral School of Multidisciplinary Engineering Science, Széchenyi István University, 9026 Győr, Egyetem tér 1, Hungary
2 Department of Vehicle Manufacturing, Széchenyi István University, 9026 Győr, Egyetem tér 1, Hungary

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the help of the revealed patterns, the assembly operations and processes can be further opti-mized, thus the profit achieved can be increased. This article attempts to explore the patterns related to the most used Key Performance Indicator (KPI) in manufacturing, the Overall Equipment Effec-tiveness (OEE) metric. The patterns and relationships discovered will be sorted into Assembly Pattern Catalogue (APC). Firstly, a literature review demonstrates scientific relevance. Secondly, it examines the circumstances and methods of samples in the Manufacturing Execution System (MES) data source and Enterprise Resource Planning (ERP) systems. In the third section, the detailed pattern catalogue is defined in the area of assembly. The novelty of the article is that beyond the generaliza-tion of patterns, it characterizes the pattern catalogue with mentioning practical industrial examples.

Keywords: Machine learning, Pattern catalogue, Assembly line, OEE
Grants and funding:

The APC was funded by Széchenyi István University, Győr, Hungary, Publication Support Program

Received: December 2, 2022; Revised: May 28, 2023; Accepted: May 29, 2023; Prepublished online: May 29, 2023; Published: July 5, 2023  Show citation

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Dobra P, Jósvai J. Overall Equipment Effectiveness-related Assembly Pattern Catalogue based on Machine Learning. Manufacturing Technology. 2023;23(3):276-283. doi: 10.21062/mft.2023.036.
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