Unified architecture for user-adapted digital assistant for machine tool by using generative artificial intelligence

The reduction of on-site workers and the advancement of equipment lead to a lack of worker experience, resulting in human errors and decreased efficiency. To address this issue, this paper proposes a user-adapting training system using large language models. This system offers personalized assistance based on the initial background level of users. It updates the level in real time using feedback evaluated by the large language model. The proposed training system adjusts dynamically to the updated user level to provide appropriate assistance. Additionally, it identifies given materials, that need to be reinforced, through user feedback. These materials and user information are managed with a standardized asset administration shell, offering advantages in later adjustment and versatility. This study presents an effective digital assistant system for industrial sites, innovating through large language models that interact dynamically with users to provide personalized supporting materials. A partial implementation of the materials for machine tools was carried out, including educational content for three levels using virtual reality, augmented reality, and generative natural language models. Additionally, the efficiency and effectiveness of the proposed system were evaluated through a comparison with widely used rule-based chatbots.