Maintenance and its cost continue, over the years, drawing the attention of production management, since the unplanned failures decrease the reliability of the system and the return of investments. Maintenance services of manufactured products are among the most common services in the industry; they account for more than half of the total costs and influence the environmental impact of the product. In order for manufacturers to increase their productivity, by performing accurate and quick maintenance, advanced monitoring systems should be considered in order to easily detect machine tool failures before they occur. Toward that end, a cloud-based platform for condition-based preventive maintenance, supported by a shop-floor monitoring service and an augmented reality (AR) application, is proposed as a product-service system (CARM2-PSS). The proposed AR maintenance service consists of algorithms of automated generation of assembly sequences, part movement scripts, and improved interface that aim to maximize existing knowledge usage while creating vivid AR service instructions. Moreover, the proposed monitoring system is supported by a wireless sensor network (WSN), and is deployed on a Cloud environment together with the AR tool. The monitoring system monitors the status of the machine tools, calculates their remaining operating time between failures (ROTBF), and identifies the available windows of the machine tools in order to perform the AR remote maintenance. In order to validate the proposed methodology and calculate its impact, it is applied in a real-life case study of a white-goods industry.
Cloud-Based Augmented Reality Remote Maintenance Through Shop-Floor Monitoring: A Product-Service System Approach
Manuscript received October 24, 2016; final manuscript received January 10, 2017; published online January 30, 2017. Editor: Y. Lawrence Yao.
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Mourtzis, D., Vlachou, A., and Zogopoulos, V. (January 30, 2017). "Cloud-Based Augmented Reality Remote Maintenance Through Shop-Floor Monitoring: A Product-Service System Approach." ASME. J. Manuf. Sci. Eng. June 2017; 139(6): 061011. https://doi.org/10.1115/1.4035721
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