## 3 Results and Discussion
### Answering RQ3 to RQ5
The QFD-AHP methodology mentioned in Layer 1 could be utilized to systematically evaluate all these elements. * Tracking methods  
By having a general view about how displaying devices are utilized in manufacturing through the above analysis, a conclusion of display devices selection for AR assisting in the quality field can be made. By considering the specific working conditions/requirements such as human working environment, movements, flexibility, etc., as well as the advantages/disadvantages of each display technology, the appropriate display devices can be evaluated and selected for each specific AR-assisted quality application. The QFD  
Tracking plays an important role in the real-time AR assisting in manufacturing application. It calculates the pose and position of the physical components as well as the relative pose of the camera to those components in real-time. The orientation (6 DoF) and the position of an object form a pose. High accuracy in tracking this provides the users' location and their movements in reference to the surrounding environments, which is an important requirement for AR-based manufacturing applications [243], except in some applications using SAR. Tracking technology is one of the main challenges affecting AR application in support of intelligent manufacturing [2]. Robustness and low latency at an acceptable computation cost also need considering in terms of AR tracking. It is essential to distinguish between recognition and tracking. Recognition seeks to estimate the camera posture without relying on any previous information provided by the camera. When the AR system is initialized or at any time when there is a tracking failure, recognition is made. In contrast, tracking aims to analyze the camera pose based on the camera's previous frame [244]. Currently, there are three main categories of tracking techniques, known as computer vision-based (CV-based), sensor-based and hybrid-tracking, the latter of which utilizes both  
Figure 14: Number of articles about AR tracking methods in manufacturing over years 2010-2021. Figure 15: Distribution of tracking methods in terms of tracking methods from 2010-2021. CV-based and sensor-based tracking techniques at the same time [188]. CV-based tracking techniques are usually utilized for indoor environments and can be classified into three categories in terms of \"a priori\" methods, which are: marker-based tracking, markerless tracking (feature-based or natural feature tracking NFT) and model-based tracking. \"A priori\" is predefined knowledge about the object, which would be tracked. It could be a marker, a feature map or a model regarding marker-based, markerless and model-based tracking techniques, respectively. In order to initialize \"a-priori\" knowledge to support the CV-based tracking methods, \"ad-hoc\" methods can be applied to create the information that establishes \"a priori\" knowledge. In addition, \"ad-hoc\" could provide marker tracking methods or feature tracking methods based on Optical flow, Parallel Tracking and Mapping (PTAM) and Simultaneous Localization and Mapping (SLAM) [34]. A sensor-based method tracks the location of the sensor, which could be Radio Frequency Identification (RFID), a magnetic sensor, an ultrasonic sensor, a depth camera, an inertial sensor, infra-red (IR), GPS, etc. Marker-based tracking is the most utilized method for AR-based solutions in the manufacturing context, with 63 articles--31.5% of 200 selected articles or 47% of 133 articles that have a contribution to tracking content. This shows a steady trend in applying marker-based tracking methods for AR-based manufacturing solutions over the period from 2010 to 2021, which reached a peak in 2019 and then slowly reduced in 2020 and 2021. In these two years 2020 and 2021, markerless tracking has grown as the dominant method for AR-based manufacturing solutions.