A generic and modularized Digital twin enabled human-robot collaborationShow others and affiliations
2022 (English)In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom, IEEE, 2022, p. 66-73Conference paper, Published paper (Refereed)
Abstract [en]
Recently, the manufacturing paradigm shifts from mass production to mass customization, which results in urgently demands for the development of intelligent, flexible and automatic manufacturing systems for handling complex manufacturing tasks with high efficiency. The use of collaborative robots, an essential enabling technology for developing human-robot collaboration (HRC), is on the rise for human-centric intelligent automation design. An effective virtual simulation platform, which can continuously simulate and evaluate HRC performance in different working scenarios, is lacking in developing an HRC system in a sophisticated industrial arena. This paper presents a generic and modularized digital twin enabled HRC framework based on the synergy effect of human, robotic and environment-related factors to provide a flexible, compatible, re-configurable solution to ease the implementation of HRC in the real world. The feasibility of the proposed framework is validated through the practical implementation of a food packaging job, which involves a human operator and an ABB robotic arm collaboratively working together, on an industrial shop.
Place, publisher, year, edition, pages
IEEE, 2022. p. 66-73
Keywords [en]
Collaborative robots, Intelligent robots, Machine design, Automatic manufacturing systems, Complex manufacturing, Human-robot collaboration, Intelligent manufacturing system, Manufacturing paradigm, Mass customization, Mass production, Modularized, Paradigm shifts, Simulation platform, collaborative robot, Digital Twin
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
Virtual Manufacturing Processes; VF-KDO; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-22311DOI: 10.1109/ICEBE55470.2022.00021Scopus ID: 2-s2.0-85148656253ISBN: 978-1-6654-9244-7 (electronic)ISBN: 978-1-6654-9245-4 (print)OAI: oai:DiVA.org:his-22311DiVA, id: diva2:1740760
Conference
IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Funder
Knowledge Foundation
Note
© 2022 IEEE
This research was supported by the Knowledge Foundation (KKS, Sweden, through virtual Factory with Knowledge Driven Optimization (VF-KDO) project, EU FoF-06-2014 SYMBIO-TIC project (No.637107) and Natural Science Foundation of China (grant no. 61803169) and the Fundamental Research Funds for the Central Universities (grant no. 2662018JC029).
2023-03-022023-03-022024-07-08Bibliographically approved