## 3 Results and Discussion
### Answering RQ1 and RQ2
Another interesting AR-assisted quality study relevant to the automotive industry is investigated for car body fitting, correcting alignment errors [189]. Alignment car panels of exterior bodywork to satisfy the specific tolerances is a challenging task in automotive assembly. The workers need to be guided during the panel fitting operations to reduce errors and performance time. In addition, correcting the positioning of body work components is a key operation in automotive assembly, which is time-consuming and characterized by a strong dependence between the achievable results and the skill level of the worker performing the operation. To solve this, an AR prototype system is developed for supporting the operator during complex operations relating to the dedicated phase of panel fitting for car body assembly by providing gap and flushness information to correct the alignment errors. This system also provides the feature of converting the information on gap and flushness between car panels measured by sensors into AR instructions to support the workers for correcting alignment errors. The main elements of the solution consist of measuring sensors positioned on the wrist of a 6-axis articulated robot for gap and flushness data acquisitions and an AR system utilized for providing instructions and visual aids to the worker through a Head-Mounted Device (HMD). Gap and flush measurements of the component are first acquired for each control point (CP) and analyzed by comparing the extracted features with reference values to decide whether the component position needs correcting with further manual adjustments. Thus, the AR system starts guiding the operator by showing proper assembly instructions. During adjustment operations, gap and flushness are continuously measured and checked, creating, if necessary, further instructions until the assembly phase is completed. With this approach, the system has some outstanding features: immediate detection of alignment errors, in-process selection of the recovery procedure, accurate guidance for reduced time and procedural errors in task execution, real-time information without diverting the worker from the assembly process, fast feedback after adjusting and easy use, thanks to the integration of the real environment and the AR instructions in the user's field of view. A verified step and a test is carried out for the developed system. The results show a potential for further integration and industry adoption. With the advantage of immediate detection of alignment errors, the same assembly procedure has been able to be completed almost 4 times faster with the AR tool. The data collected from 10 tests are also less dispersed, indicating the robustness of the procedure conducted with the support of the AR system. The gap and flushness are reduced from 12.77 mm and 3.05 mm to 7.17 mm and 0.33 mm, respectively. Besides that, the AR system also helped in increasing assembly effectiveness and efficiency as well as reducing errors. The correct positioning of bodywork components no longer depends on the experience and dexterity of the operator. For further improvement, system setup time needs to be minimized, implementing an Artificial Neutral Network (ANN) to support the measurement for gap and flushness error detection as well as reducing the collected data. At this point, it is found in this SLR that although the AR-based application proved its strength in assisting quality activities, there are still challenges and limitations. In general, the current AR-assist quality applications can be classified into three groups depending on the features and objectives of their approach: AR as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. The details are included in the following Table 7: