Smart and user-centric manufacturing information recommendation using multimodal learning to support human-robot collaboration in mixed reality environments

The future manufacturing system must be capable of supporting customized mass production while reducing cost and must be flexible enough to accommodate market demands. Additionally, workers must possess the knowledge and skills to adapt to the evolving manufacturing environment. Previous studies have been conducted to provide customized manufacturing information to the worker. However, most have not considered the worker's situation or region of interest (ROI), so they had difficulty providing information tailored to the worker. Thus, a manufacturing information recommendation system should utilize not only manufacturing data but also the worker's situational information and intent to assist the worker in adjusting to the evolving working environment. This study presents a smart and user-centric manufacturing information recommendation system that harnesses the vision and text dual encoder-based multimodal deep learning model to offer the most relevant information based on the worker's vision and query, which can support human-robot collaboration (HRC) in a mixed reality (MR) environment. The proposed recommendation model can assist the worker by analyzing the manufacturing environment image acquired from smart glasses, the worker's specific question, and the related manufacturing document. By establishing correlations between the MR-based visual information and the worker's query using the multimodal deep learning model, the proposed approach identifies the most suitable information to be recommended. Furthermore, the recommended information can be visualized through MR smart glasses to support HRC. For quantitative and qualitative evaluation, we compared the proposed model with existing vision-text dual models, and the results demonstrated that the proposed approach outperformed previous studies. Thus, the proposed approach has the potential to assist workers more effectively in MR-based manufacturing environments, enhancing their overall productivity and adaptability.