Abstract
Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.
Highlights
Model Based on a Transition SystemAs the complexity of manufacturing systems increases alongside rapid changes in recent consumption trends, forecasting to prevent future losses is becoming more important.A lot of research is being done on this related technology, and among them, digital twins technology is considered as an important core component of Industry 4.0
We propose a digital twin discovery model that can predict the remaining cycle time, which is an important indicator in manufacturing environments, using event log data
Research on predicting processes using event log data are a common research topic recently, but as far as we know, this is the first time that event log data have been used for digital twin modeling alongside transition based embedding
Summary
As the complexity of manufacturing systems increases alongside rapid changes in recent consumption trends, forecasting to prevent future losses is becoming more important. A lot of research is being done on this related technology, and among them, digital twins technology is considered as an important core component of Industry 4.0 This technology makes process predictions possible through digital data, not just physical implementations [1]. We propose a digital twin discovery model that can predict the remaining cycle time, which is an important indicator in manufacturing environments, using event log data. Process characteristics are extracted through embeddings based on transition technology of event log data with activity and Class Id. Extracted data are used to uncover digital twin models that predict the manufacturing remaining cycle times. We devised a digital twin model that predicts the remaining cycle time of a manufacturing system using event log data, and propose a cloud type architecture using this setup.
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