## 3 Results and Discussion
### Answering RQ1 and RQ2
The correct position for the comparator in each step indicated by green boxes is augmented onto the RGB-D live video stream using the same screen with other methods. In this method, whenever the comparator is detected in the correct position, the measure is taken automatically. The validation of the correct comparator positioning is also used to trigger the transition to the next assembly stage. With this approach, an AR-based quality control system provides automatic in-process instructions for the next steps of measuring and accurate guidance to speed up the workers' efficiency. A test is carried out with seven operators: four inexperienced users and three experienced users. As a result, the experienced participants performed faster in both non-AR and AR-based methods, but the difference was smaller with the AR-based method. After implementation in an industrial setup by operators working on the shop floor in the metal industry, it was shown that AR-based systems help to reduce by 36% the execution time of a complex quality control procedure, allowing an increase of 57% in the number of tests performed in a certain period of time. It is also concluded that the AR system can prevent users from making costly errors during peak production times, though this has not been tested yet. Besides that, the risk of human errors is also reduced. In another scenario, an AR inspection tool is developed based on a user-centered design approach, following the standard ISO 9241-210:2019 to support workers during assembly error detection in an industry 4.0 context [188]. Once again, it is mentioned that the inspection activities naturally require high mental concentration and time when using traditional paper-based detection methods. Besides that, when the geometric complexity of the product grows, the probability that an operator makes mistakes also increases. In order to solve this, the research proposed and developed a novel AR tool to assist operators during inspection activities by overlaying 3D models onto real prototypes. When errors are detected, the users can add an annotation by using the virtual 3D models. The AR tool is then tested in a case study of assembly inspection of an auxiliary baseplate system (14 m long and 6 m wide) used for providing oil lubrication of turbine bearings and managing oil pressure and temperature. 16 engineers and factory workers of the Baker Hughes plant, skilled in the use of smartphone and tablet devices but novices to AR technology, were selected for the test. Five markers (rigid plastic QR code size of 150 mm x 150 mm x 1 mm) were placed 1.5 m apart, along with the system for the tracking method. The users went through a demo, performed training steps and indicated a set of six tasks: framing a marker and visualizing the AR scene; detecting a design discrepancy and adding the relative 3D annotation; taking a picture of design discrepancies detected during the task; changing the size of the 3D annotations added during task 2; framing marker 4 and hiding the 3D model of the filter component; sending a picture and 3D annotations to the technical office. By adopting multiple markers to minimize tracking errors, freedom of movement for the user when inspecting large-size products is ensured. Analysis of Variance (ANOVA) is used to evaluate the number of errors and completion time, while System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX) are applied to evaluate user acceptance. The ANOVA and SUS results showed that a low number of errors occurred during the interaction of the user with the proposed tool, which means that the AR tool is easy and intuitive to use. Thus, the AR tool could be efficiently adopted to support workers during the inspection activities for detecting design discrepancies. Nevertheless, the NASA-TLX test proved that the developed AR tool minimizes the cognitive load of divided attention induced to both the physical prototype and the related design data.