A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

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.

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

“…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.…”