## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training
### AR for Manufacturing Process Analysis  
The incorporation of AR in industrial manufacturing represents a significant advancement in the field, offering innovative solutions to a range of challenges, including real-time monitoring, failure mode analysis, manufacturing simulation, workflow optimisation, predictive maintenance, quality control analysis, resource allocation strategies and energy efficiency. Through _real-time monitoring_, AR enables a more comprehensive visualisation of the underlying structural information, integrating real and virtual elements. In their study, [PERSON] and [PERSON] [69] explored the potential of AR in mechanical engineering training, aiming to provide a more detailed visualisation of mechanical processes and their associated variables. Concurrently, [PERSON] et al. [74] developed a method integrating deep learning and AR to monitor in real time the actions of robots and their interaction with operators. In the field of _failure mode analysis_, [PERSON] et al. [72] have made significant advances with the creation of MANTRA, a system that fuses AR with infrared thermography to identify failures in industrial maintenance activities. This combination facilitates a more detailed visualisation and richer interaction with structural elements, integrating simulations, animations and sounds. This innovative approach not only enables the inspection of machinery and equipment from different perspectives and with different layers of virtual information, but also promotes greater efficiency in the industrial sector. In _manufacturing simulation_, AR facilitates the understanding and analysis of manufacturing processes. [PERSON] et al. [60] have made a significant contribution to this field with the development of an AR-based simulation system for mechanical assembly, with a particular focus on worker-centred smart manufacturing. [PERSON] et al. [44] conducted a detailed analysis of augmented simulations in assembly lines, highlighting the various applications and advances in this field. In the context of _workflow optimisation_, AR has proven to be a valuable tool in assembly lines. In this context, studies by [PERSON] et al. [77] and [PERSON] et al. [98] represent an important breakthrough, experimentally exploring the effect of AR in improving industrial production. Both investigations focus on how AR can improve the efficiency of assembly processes and reduce the mental workload of operators. The ability of AR to automatically overlay 3D models enables users to manipulate these models, including rotating, tilting and zooming them. This facilitates insight and decreases the time taken to complete tasks, with fewer failures than traditional methods. Consequently, more efficient and accurate planning and workflows are achieved. In the field of _predictive maintenance_, AR can be used to enrich the visualisation of data and intrinsic details, facilitating a broader understanding of the abstract concepts and algorithms that govern manufacturing processes. [PERSON] et al. [61] implemented facility segmentation to achieve a more accurate representation of aspects such as stress, strain and strength limits in various structural joints. In a similar vein, [PERSON] et al. [64] designed an AR-assisted system for dynamic gesture recognition and prediction in the context of industrial assembly, which enhances the early identification of potential defects. Both investigations demonstrate the advantages of AR in the field of predictive maintenance, as it enables the adaptation, updating and expansion of models without the need for high costs or compromises to safety. In terms of _quality control analysis_, AR has emerged as a valuable tool, allowing for more accurate and detailed assessment. A study by [PERSON] et al. [78] explored the application of AR in quality assessment, making use of hand tool tracking through machine vision techniques.