## 1 Introduction  
In the recent manufacturing industry environment, with Digital Transformation (DT) [1] that combines digital technology and business models to create great value, manufacturing intelligence has become the biggest trend. At the same time, interest in Cyber-Physical Systems (CPS) is increasing [2]. In Industry 4.0, the development of information and communication is digitizing the industrial environment, improving the industrial system into an autonomous system that can make autonomous decisions through the CPS. As a result, industry can lower production costs by improving logistics and productivity [3]. As manufacturing systems become more complex to adapt to rapid changes in consumption trends, interest in CPS is increasing [4]. CPS, which also enables real-time prediction, monitoring, optimization, and control, is currently a key pillar of Industry 4.0 to turn the manufacturing industry into a fully automated smart factory [5]. Machine Learning (ML) and Artificial Intelligence (AI) can offer exciting new possibilities for manufacturing CPS [6]. A model can predict the quality of finished products usingAuto-Regressive Integrated Moving Average (ARIMA), which is machine learning [7]. A recent study has proven that Long Short-Term Memory (LSTM) is suitable for predicting inventory supply and demand in the manufacturing process [8]. There are also various attempts to use Bidirectional Long Short Term Memory (BI-LSTM) for intelligent manufacturing. Research results have shown that valid results can be obtained when BI-LSTM is applied to the life expectancy model of manufacturing equipment [9]. In addition, another study proved that BI-LSTM is suitable for predicting the damage tendency of bearings [10]. However, there are clear challenges to adopting these technological solutions. It is difficult to understand how AI works, which causes a lack of trust in AI; while AI can provide positive results for the manufacturing industry, its immediate application to industry is not easy. In particular, in the manufacturing environment, AI can show good performance in predicting manufacturing time based on real-time data [11], and we wanted to extend this model to an Explainable Artificial Intelligence (XAI) model. We want to provide a CPS-based AI collaboration framework through which AI and workers can collaborate by enabling operators and workers to understand AI judgments. In this paper, we devise a CPS model that predicts the remaining manufacturing time by analyzing the manufacturing machine operating time using process mining technology. We propose a possible cooperative architecture. Our study offers three main contributions:  
1. We propose a transition-based data preprocessing method that utilizes log data applied with process mining technology. This data processing technology can help interpret the manufacturing process data generated by manufacturing companies. 2. In order to apply AI-based CPS to actual processes, it is necessary to solve the black box characteristic, the biggest problem of AI. This paper can improve the reliability of AI by solving these black box characteristics. In addition, AI interpretation requires a lot of technology and labor costs, and in the case of small and medium-sized enterprises, it is difficult to continuously receive help from experts due to cost problems. 3. In this paper, using SHAP to solve this problem, we can try to analyze AI more simply and effectively, and through actual manufacturing environment data, we prove that the output of SHAP is helpful in solving the black box characteristics of AI. The rest of the thesis is structured as follows. Section 2 introduces process mining research applicable to the CPS. Section 3 describes the components of the approach proposed in the text, the main ideas of the study, the proposed transition system-based data preprocessing model, and the XAI method. Section 4 describes the experimental environment, the structure of the data set, and the experimental results.