## 1 Introduction  
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. In particular, this technology makes process predictions possible through digital data, not just physical implementations [1]. The output of these digital twins can provide important indicators for long term and short term decision-making in the manufacturing process.  
A lot of research on digital twins is being conducted, and recently, approaches using process mining have become popular. However, there are more specialized studies for business processes than manufacturing technology, and research using process mining technology to create a digital twin of the manufacturing industry is insufficient. It is necessary to be able to create digital twins using event log data to make appropriate decisions for manufacturing operations using the vast amounts of data generated in the manufacturing process. In this paper, 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. In this way, this study solves the problem of determining the time required to terminate the remaining processes based on the execution log data of the previously completed processes. We intend to solve this problem using the GRU(Gated Recurrent Unit) artificial intelligence model.  
In this paper, 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. There are three main contributions of our study.  
1. Transition system based embedding technology to easily extract process characteristics from event logs is proposed, and this technology can help interpret numerous log data accumulated by manufacturing companies.
2. Creating a digital twin model requires a lot of technology and is costly, and in most cases additional professional manpower and costs are required to reflect real data in the created twin. In this paper, we tried to implement this process through artificial intelligence learning, and it can satisfy the need for cost reduction.
3. By providing a cloud computing style digital twin architecture, our setup can help small companies with insufficient technical skills to realize digital twin methods.  
The rest of this thesis is structured as follows. Section 2 describes digital twins and introduces research that links process mining to digital twins. Section 3 provides a description of the components of the proposed approach and the main idea of this study, as well as describes the transition embedding digital twin model proposed in this paper. Section 4 describes the experimental environment, the structure of the data set, and the experimental results. Section 5 concludes the paper with a summary of the paper, evaluation results, and future research directions.