## 3 Materials and Methods  
### System Architecture  
The quality control context our solution aims to specifically improve is the final workstation of the vehicle assembly line, where non-conformities are detected by a human worker circling around the vehicle to inspect it. This factory in particular assembles the multi-brand k9 van model, which is customized according to each customer order, meaning there is no set pattern on which model variant the worker will inspect on any given day, outside of some being far more common than others. The worker is expected to compare the assembled vehicle with the paper list, but it was reported that many instead only consult it when faced with the less common models. The reason given was workers must perform parallel tasks that require their hands to be free, so short-term memorization becomes preferable to constant paper consultation. However, this exacerbates the aforementioned problem of mental fatigue. As for the inspection submission process in the observed factory, the worker has to abandon his post and walk to a terminal at the end of the line to manually input the report into the database, which is an added source of physical fatigue. Therefore, to achieve our goal of addressing these issues via the replacement of the paper conformity list with a digital counterpart, our hybrid quality control support tool must [10; 26; 27]:  
* Describe the conforming vehicle in the factory's database, along with other relevant metrics;
* Access the information via scanning of the barcode placed on the assembled vehicle;
* Use an automated detection system to perform the initial quality inspection of the vehicle;
* Use a wearable device (e.g., Smartglasses) to display the vehicle's conformity list alongside the results of the non-conformity detection, both in a visual format;
* Allow the worker to confirm or cancel each of the results, or submit undetected non-conformities to the final inspection report;
* Allow the worker to attach a photo and/or text report to each individual non-conformity;
* Update the database information with the results of the automated detection and the final inspection report submitted wirelessly by the worker;
* Use the submitted feedback to refine the automated detection system and improve its performance. The final design of the quality control support tool is divided into two sections: the automated detection, and the human interaction modules (Figure 2). The automated detection of non-conformities is performed at the beginning of the workstation's line, where a set of mounted cameras are installed to capture the vehicle from multiple, fixed angles as it enters, in order to reduce the system's error. The module begins by detecting the vehicle's barcode sticker and use it to fetch the respective information from the factory's database. It then takes the list of parts that form the fully conforming vehicle and comparesthem to the detected ones in the assembled vehicle. The result of the comparison serves as that vehicle's non-conformity list, which is stored in the database. The human interaction (or information visualization) module, on the other hand, involves the visualization of the digital information on a wearable device, which the workers use to perform the inspection on each vehicle. When the worker uses the device's camera to scan the barcode, the module accesses the factory's database and fetches the updated part list. That information is used to generate a visual representation of the conformity list of the parts specific to that quality control post, with all the non-conforming parts flagged by the automated detection highlighted in red. The worker circles the assembled vehicle to perform the inspection on each side, comparing the observed parts with the ones presented on the wearable device. The worker uses the interface to confirm if the non-conformity results of the automated detection highlighted on the device are indeed correct, or removes the incorrect ones.