One of the projector gray code calibration patterns Figure 8: Projector validation pattern (projected white squares)
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual an...
... Так, созданы портативные и удобные в использо вании мобильные AR-устройства (от augmented realityдополненная реальность) [17,18]. Их широко применяют при обучении операторов и прочего персонала навыкам сборки, монтажа и обслуживания в целях сокращения длительного чтения документации [19,20]. В другом исследовании предложена виртуальная среда для обучения работе на насосных станциях. ...
In the era of digital technology, more and more of it finds application in various industries. This paper proposes to use technology of augmented reality for maintenance of stirred tank reactors. The proposed approach can be applied to any type of equipment, as it can be easily integrated with the existing automation systems and does not require much investment at the initial stage, implying gradual optimization and functionality build-up. This paper de- scribes the basic set of functional requirements of an augmented reality-based maintenance system, methods of assessing the system performance, as well as scaling-up and streamlining prospects. The paper also describes how such systems can be integrated with existing control systems of a production company. The effectiveness of the developed augmented reality-based system was verified by determining the average execution time of each service stage and processing the outcomes using the Mann-Whitney U test. The use of the augmented reality system resulted in the reduction of the average service time by 2.3 times, while the maintenance efficiency increased by 5%.
Human–Robot Collaboration is a critical component of Industry 4.0, contributing to a transition towards more flexible production systems that are quickly adjustable to changing production requirements. This paper aims to increase the natural collaboration level of a robotic engine assembly station by proposing a cognitive system powered by computer vision and deep learning to interpret implicit communication cues of the operator. The proposed system, which is based on a residual convolutional neural network with 34 layers and a long-short term memory recurrent neural network (ResNet-34 + LSTM), obtains assembly context through action recognition of the tasks performed by the operator. The assembly context was then integrated in a collaborative assembly plan capable of autonomously commanding the robot tasks. The proposed model showed a great performance, achieving an accuracy of 96.65% and a temporal mean intersection over union (mIoU) of 94.11% for the action recognition of the considered assembly. Moreover, a task-oriented evaluation showed that the proposed cognitive system was able to leverage the performed human action recognition to command the adequate robot actions with near-perfect accuracy. As such, the proposed system was considered as successful at increasing the natural collaboration level of the considered assembly station.
Smart manufacturing supported by emerging Industry 4.0 technologies is a key driver to realize mass product customizations. Augmented reality (AR) has been commonly applied to facilitate manual operations with ambient intelligence by overlaying virtual information on physical scenes. In most modern factories, maintenance remains an indispensable process that is difficult or yet to be fully automated. Several studies have previously reviewed AR-based maintenance across all industrial sectors, whereas those specific to manufacturing did not necessarily involve maintenance. Hence, this paper presents a systematic literature review on AR-assisted maintenance in manufacturing with a focus on the operator’s needs. A generic process has been proposed to classify the maintenance operations examined in the past studies into four sequential steps and to analyze the classification results based on the geographical location, maintenance type, AR technical elements, and integrated external sensors. The findings thus derived are expected to provide design guidelines for implementing AR applications with practical values to aid manual maintenance in future smart manufacturing environments.
Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future.
Maintenance of technical equipment in manufacturing is inevitable for sustained productivity with minimal downtimes. Elimination of unscheduled interruptions as well as real-time monitoring of equipment health can potentially benefit from adopting augmented reality (AR) technology. How best to employ this technology in maintenance demands a fundamental comprehension of user requirements for production planners. Despite augmented reality applications being developed to assist various manufacturing operations, no previous study has examined how these user requirements in maintenance have been fulfilled and the potential opportunities that exist for further development. Reviews on maintenance have been general on all industrial fields rather than focusing on a specific industry. In this regard, a systematic literature review was performed on previous studies on augmented reality applications in the maintenance of manufacturing entities from 2017 to 2021. Specifically, the review examines how user requirements have been addressed by these studies and identifies gaps for future research. The user requirements are drawn from the challenges encountered during AR-based maintenance in manufacturing following a similar approach to usability engineering methodologies. The needs are identified as ergonomics, communication, situational awareness, intelligence sources, feedback, safety, motivation, and performance assessment. Contributing factors to those needs are cross-tabulated with the requirements and their results presented as trends, prior to drawing insights and providing possible future suggestions for the made observations. Keywords: Augmented reality; Maintenance; Usability; User requirements
It was explored that instructions for manual industrial installation are better than instructions on a stationary monitor in a head-worn Virtual Reality Display (AR-HWD). A prototype consisting of virtual instruction screens was designed for two instance assembly tasks. In a comparative analysis, participants carried out the tasks with instructions through an AR-HWD and a stationary screen. The task performance and user experience were measured through questionnaires, interviews, and observation notes. The study showed that the consumers had the enjoyment of exploring the technology and were enthusiastic. The perceived utility in the current situation was different, but the users saw a tremendous opportunity for the future with AR-HWDs. The accuracy of the task with the ARHWD directions was as strong as on the screen. AR-HWDs are a better solution than a stationary screen, but technical limitations are required and new technology employees need to be educated in order to make their application effective.