# Augmented Reality-Based Framework Supporting Visual Inspection for Automotive Industry
## 1 Introduction  
Adopting the fourth industrial revolution (Industry 4.0) necessitates the use of a global and decentralized production technique [1]. This technique has to be mainly relied on fully-integrated collaborative manufacturing systems [2] presenting the concept of the Internet of Things (IoT) and real-time data transmission. The presence of such technology in the industrial network introduced the prominent concept of the Industrial Internet of Things (IIoT) which is one of the main pillars of Industry 4.0. IIoT refers to the collaboration of several components such as sensors, instruments, devices, computers, and even humans [3] allowing a better global visualization aiming at increased production [4] ensuring at the same time data security [5]. For the industry under study, which we keep anonymous for confidentiality reasons, emerging IIoT architecture should be considered for more standardized communication to implant Industry 4.0. Besides, the industry's main purpose is the design and specification of a cognitive assistant Augmented Reality (AR)-based, which should be defined to ensure the presentation of information with a less cognitive load to the operator. To minimize the processing time during the production process, the automotive industry under study decided to implement the inspection system just on the final station of the assembly line. The reason behind the choice of that station precisely, is that it is the only station where the vehicle defects are handled and corrected. Traditionally, the inspection and correction operation is carried out by a human operator, who, by reading an identification document that follows the vehicle along the production process, identifies the vehicle present at the station, and shows it on a screen. The screen is located at the edge of the production line next to this station in order to display all defects to be correctedin the vehicle. The operator memorizes this information and then goes to the vehicle to rectify the non-conformities and returns thereafter to the computer to confirm, in the system, the correction, or non-correction, of the defects. Thus, this task remains arduous and is presenting a high probability of errors. A similar process (wiring) in [6] confirms as well that these types of tasks are time-consuming regardless of the level of proficiency of technicians. As a solution, the authors in [7] prove the ability of AR systems in solving efficiency problems such as insufficient training workers or complex tasks.  
Therefore, for better organization and efficiency, a cognitive assistant system is proposed to quickly show the tasks to be performed after the vehicle has been recognized through the system. In this work, we have adopted Unity 3D for data visualization of the cognitive system given its flexibility and ability to build onto multiple platforms [8]. For data exploitation, we have acquired to Message Queuing Telemetry Transport (MQTT) [9] protocol via the IIoT platform given its messaging system simplicity, rapidity, and guaranteeing security [10]. The implementation process consists of the following steps: Firstly, a marker for vehicle identification is scanned and recognized. Secondly, the system brings the information related to the vehicle's non-conformity via the IIoT platform in front of the operator in a User Interface (UI). Then the operator would be able to interact with the UI to validate/invalidate the correction of the non-conformity.  
The rest of the paper is organized as follows. The existing literature on AR uses cases inside automotive industries is briefly discussed in Section 2. Section 3 outlines the proposed method for inspection to improve the productivity in the production line of the industry under study. The outcome of the proposed system is exhibited in Section 4, and Section 5 concludes the paper by exhibiting as well future perspectives.