Abstract
The paper describes the architecture design and implementing solution for the digital twins of industrial robot, aggregated and embedded in the global health monitoring, maintenance and control system of manufacturing resources. Manufacturing scheduling and control system. The main functionalities of the digital twin are: monitoring the current status and quality of services performed by robots working in the shop floor, early detecting anomalies and unexpected events to prevent robot breakdowns and production stops, and forecasting robot performances and energy consumption. Machine learning techniques are applied in the cloud layer of the virtual twin for predictive, customized maintenance and optimized robot allocation in production tasks. The paper introduces a framework integrating the virtual robot twins in an ARTI-type control architecture, proposes a solution to implement the twin on a distributed cloud platform and exemplifies the concepts in a shop floor case study with SCARA assembly robots.
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Anton, F., Borangiu, T., Răileanu, S., Anton, S. (2020). Cloud-Based Digital Twin for Robot Integration in Intelligent Manufacturing Systems. In: Zeghloul, S., Laribi, M., Sandoval Arevalo, J. (eds) Advances in Service and Industrial Robotics. RAAD 2020. Mechanisms and Machine Science, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-48989-2_60
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DOI: https://doi.org/10.1007/978-3-030-48989-2_60
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