Search

The assembly and maintenance of products in the aviation industry constitute a crucial aspect of the product life cycle, with numerous tasks still reliant on manual operations. In order to solve the problem of narrow operation spaces and blind areas in the processes of manual assembly and maintenance, we proposed an augmented reality (AR) assistant guidance method specifically designed for such scenarios. By employing a multi-modality anti-occlusion tracking algorithm, pose data of assembly parts can be obtained, upon which AR guidance information is displayed. Additionally, we proposed an assembly step identification method to alleviate user interaction pressure. We developed an AR visualization assistant guidance system and designed and conducted a user evaluation experiment to measure the learnability, usability, and mental effort required. The results demonstrate that our method significantly enhances training efficiency by 128.77%, as well as improving assembly and maintenance efficiency by 29.53% and 27.27% compared with traditional methods. Moreover, it has significant advantages in learnability, usability, and mental effort, providing a feasible and effective resolution for addressing blind areas during assembly and maintenance within the aviation industry. [ABSTRACT FROM AUTHOR]

The assembly and maintenance of products in the aviation industry constitute a crucial aspect of the product life cycle, with numerous tasks still reliant on manual operations. In order to solve the problem of narrow operation spaces and blind areas in the processes of manual assembly and maintenance, we proposed an augmented reality (AR) assistant guidance method specifically designed for such scenarios. By employing a multi-modality anti-occlusion tracking algorithm, pose data of assembly parts can be obtained, upon which AR guidance information is displayed. Additionally, we proposed an assembly step identification method to alleviate user interaction pressure. We developed an AR visualization assistant guidance system and designed and conducted a user evaluation experiment to measure the learnability, usability, and mental effort required. The results demonstrate that our method significantly enhances training efficiency by 128.77%, as well as improving assembly and maintenance efficiency by 29.53% and 27.27% compared with traditional methods. Moreover, it has significant advantages in learnability, usability, and mental effort, providing a feasible and effective resolution for addressing blind areas during assembly and maintenance within the aviation industry.

With the continuous improvement of function diversity, quality and reliability, the complexity and scale of significant products such as aerospace equipment are increasing. This trend poses severe challenges to the assembly process. The large scale of products requires the fixed-position assembly with multiple roles of workers in parallel, and it increases the difficulty of multi-person assembly. The high complexity of products makes the assembly process tedious. It is time-consuming and laborious for workers to understand the 2D documents, and it easily leads to mis-operation. The assembly process of complex products can produce huge amounts of data, which have the potential for improving efficiency and quality but remain underutilized. The high reliability of products puts forward high requirements for real-time detection of the assembly process. With the development of information technology, data driven approach and Augmented Reality (AR) provide an efficient way to change the above situation. Therefore, this paper proposes an intelligent collaboration assembly system (ICAS) combining real-time data driven method with AR technology. In the ICAS, real-time data driven method is used to realize the human-to-human and human-to-machine collaboration under the fixed-position assembly, and AR is introduced to the system to assist the assembly of large-scale complex products. The intelligent detection method in the ICAS combines 3D reconstruction with sensors measurement data, and the detection result is displayed in an intuitive way by AR. The proposed system is verified in the assembly process of a large-scale space deployable mechanism, and it paves the way for the further development of intelligent assembly in the future. [ABSTRACT FROM AUTHOR]