## 3 Results and Discussion
### Answering RQ3 to RQ5
Furthermore, this tracking method is widely used for applications that provide guidance for operators during process preparation, which is time-consuming, error-prone and costly for small batch production, such as the setup of die cutters in the packaging industry [198], the setup of machine tools in the smart factory [213] or programming the trajectory of touching probes for aligning the raw material with the machine reference frame [214]. Finally, maker-based tracking is the prominent choice when it comes to AR-based solutions that support the welding process in the automotive industry. It was used in an AR-based quality control solution to enhance in-situ inspection of spot welding in the automotive industry [209], to guide the manual spot-welding process in order to ensure the in-line quality [205] or to assist intuitively in welding robot programming [218]. Despite its advantage, the effectiveness of the marker-based tracking method is not reliable in a working environment that may cause occlusion or damage the marker tag, such as workshops, production lines, etc. Therefore, several AR-assisted manufacturing applications also considered and employed other tracking methods. In a series of works [30; 210], a markerless tracking methodology is developed to optimize the quality control procedure for automotive parts in terms of measuring deviation errors. An algorithm for extracting and comparing two consecutive 3D point clouds of the workstation, which are captured by the camera, is developed for this specific industrial case. In [189], the same approach, with control points instead of 3D point clouds, was used in enhancing the panel alignment process in car body assembly. In another scenario, some applications integrate the cloud database with the asset determining a priori in different locations of the plant to support fault diagnosis of the aseptic production line [220], enhance event-driven for AR-assisted maintenance and scheduling for a remote machine [138]. Other relevant applications using markerless tracking can be seen in Table 15. It is crucial to note that, the markerless or NFT methods often require lots of computing algorithms [65; 72; 197; 4], as well as a powerful computing system [30; 189; 192; 210] and a robust information system architecture or cloud platform [138; 220]. The AR-hybrid tracking method in manufacturing usually employs data from additional sensors to increase the tracking speeds, reduce the latency and lighten the computing burden of markerless tracking algorithms. In addition, using sensory data also boosts the tracking performance of marker-based tracking or the model-based tracking method. In more detail, a hybrid tracking method using the marker and sensor data was applied in a series of studies to support evaluating the impact of using Hololens 1 for assembly work instructions [52; 53]. A mock wing assembly task following paper instructions, traditional digital instructions including tablet model-based instructions (MBIs), desktop MBIs and AR-based instructions using different AR hardware such as a tablet and Hololens 1 was implemented to compare the data in terms of completion time, net promoter score (NPS)and error count. The results of these papers provide good references for manufacturing stakeholders regarding the benefits of diverse AR technologies that could be used for manual assembly tasks as well as to address some limitations of using a Hololens for larger-scale applications. In other scenarios, additional sensors are integrated to reduce the latency of markerless tracking systems to support design discrepancy inspections and annotations for flange systems in the Baker Hughes company [188,195]. Although the sensor-based technique is usually integrated with other tracking techniques to form hybrid tracking situations, as mentioned in the above part, it is rarely applied alone for AR-based solutions in manufacturing.