Original scientific paper

https://doi.org/10.21278/TOF.451020920

Data-Driven Causal Modeling of the Manufacturing System

Gabriel-Radu Frumusanu orcid.org/0000-0003-3038-8758 ; Department of Manufacturing Engineering, ‘Dunarea de Jos’ University, Faculty of Engineering, Galati, Romania
Cezarina Afteni ; Department of Manufacturing Engineering, ‘Dunarea de Jos’ University, Faculty of Engineering, Galati, Romania
Alexandru Epureanu ; Department of Manufacturing Engineering, ‘Dunarea de Jos’ University, Faculty of Engineering, Galati, Romania

Abstract

In manufacturing system management, the decisions are currently made on the base of ‘what if’ analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.

Keywords

manufacturing system; causal modelling; ‘what-if’ analysis; instance-based learning

Hrčak ID:

257227

URI

https://hrcak.srce.hr/257227

Publication date:

19.5.2021.

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