MARMA: A Mobile Augmented Reality Maintenance Assistant for Fast-Track Repair Procedures in the Context of Industry 4.0

Abstract

The integration of exponential technologies in the traditional manufacturing processes constitutes a noteworthy trend of the past two decades, aiming to reshape the industrial environment. This kind of digital transformation, which is driven by the Industry 4.0 initiative, not only affects the individual manufacturing assets, but the involved human workforce, as well. Since human operators should be placed in the centre of this revolution, they ought to be endowed with new tools and through-engineering solutions that improve their efficiency. In addition, vivid visualization techniques must be utilized, in order to support them during their daily operations in an auxiliary and comprehensive way. Towards this end, we describe a user-centered methodology, which utilizes augmented reality (AR) and computer vision (CV) techniques, supporting low-skilled operators in the maintenance procedures. The described mobile augmented reality maintenance assistant (MARMA) makes use of the handheld’s camera and locates the asset on the shop floor and generates AR maintenance instructions. We evaluate the performance of MARMA in a real use case scenario, using an automotive industrial asset provided by a collaborative manufacturer. During the evaluation procedure, manufacturer experts confirmed its contribution as an application that can effectively support the maintenance engineers.

Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review

“…This is because sensor-based tracking technically requires intensive tracking algorithms [11] and advanced deep learning methods such as CNN [161] to boost the tracking performance and reduce latency, thus achieving the real-time feature. Finally, a model-based tracking method is applied when a 3D CAD model of the tracked object's parts are available to extract, analyze and determine the pose and position for later recognition and tracking [8,73,139].…”

“…[138][139][140][141][142][143][144][145][146][147][148][149][150][151][152][153][154][155][156][157] [7,11,158-187] [8,30,188-210] [211-219]…”

Quizzes Via Augmented Reality on Learning Management System: A Case Study of Moodle

“…Not only did AR enrich the user experience by representing the simulation of any complex process using 3D virtual objects (Keller et al, 2021), but it also helped to decide if the process failed or succeeded. Consequently, it is highly used in chemical experiments (Xiao et al, 2020), maintenance assistance (Konstantinidis et al, 2020), military applications, and others, but especially for training purposes (Sorko & Brunnhofer, 2019).…”

A Product Pose Tracking Paradigm Based on Deep Points Detection

“…During the last decade, significant effort has been given in facilitating contemporary digital technologies into the manufacturing procedure to comply with the Industry 4.0 scheme through Vertical Networking, Horizontal Integration, Through-Engineering, and early adaptation in Exponential Technologies [1,2]. Smart systems have been already introduced into the manufacturing procedure to increase the flexibility and productivity scale in different levels of infrastructure, such as remote control through the Internet of Things (IoT) [3], predictive maintenance [4], failure recovery from non-expert personnel [5,6], low-volume or high-variance production [7], workload scheduling [8], and more. With the means mentioned above, Computer-Aided Manufacturing (CAM) systems can be significantly improved to increase their autonomy and provide real-time analysis of their subjects using Artificial Intelligence (AI).…”