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Integrated Production and Maintenance Scheduling Through Machine Monitoring and Augmented Reality: An Industry 4.0 Approach

  • Dimitris Mourtzis
  • Ekaterini Vlachou
  • Vasilios Zogopoulos
  • Xanthi Fotini
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 513)

Abstract

Maintenance tasks are a frequent part of shop floor machines’ schedule, varying in complexity, and as a result in required time and effort, from simple cutting tool replacement to time consuming procedures. Nowadays, these procedures are usually called by the machine operator or shop floor technicians, based on their expertise or machine failures, commonly without flagging the shop floor scheduling. Newer approaches promote mobile devices and wearables as a mean of communication among the shop floor operators and other departments, to quickly notify for similar incidents. Shop floor scheduling is frequently highly influenced by maintenance tasks, thus the need to include them into the machine schedule has arisen. Moreover, production is highly disturbed by unexpected failures. As a result, the last few years through the industry 4.0 paradigm, production line machinery is more and more equipped with monitoring software, so as to flag the technicians before a maintenance task is required. Towards that end, an integrated system is developed, under the Industry 4.0 concept, consisted of a machine tool monitoring tool and an augmented reality mobile application, which are interfaced with a shop-floor scheduling tool. The mobile application allows the operator to monitor the status of the machine based on the data from the monitoring tool and decide on immediately calling AR remote maintenance or scheduling maintenance tasks for later. The application retrieves the machine schedule, providing the available windows for maintenance planning and also notifies the schedule for the added task. The application is tested on a CNC milling machine.

Keywords

Maintenance Scheduling Augmented reality Machine monitoring Industry 4.0 

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Dimitris Mourtzis
    • 1
  • Ekaterini Vlachou
    • 1
  • Vasilios Zogopoulos
    • 1
  • Xanthi Fotini
    • 1
  1. 1.Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and AeronauticsUniversity of PatrasRio PatrasGreece

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