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Cross-Layer Optimization Model Towards Service-Oriented Robotic Manufacturing Systems

[+] Author Affiliations
Jiaqiang Zhang, Quan Liu, Wenjun Xu, Zude Zhou

Wuhan University of Technology, Wuhan, China

Duc Truong Pham

University of Birmingham, Birmingham, UK

Paper No. MSEC2017-2703, pp. V003T04A026; 7 pages
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9
  • Copyright © 2017 by ASME


Service-oriented robotic manufacturing system is an integrated system, in which the industrial robots (IRs) operate within a service-oriented manufacturing model, and can be virtualized and servicelized as services, so as to provide on-demand, agile, configurable and sustainable manufacturing capability services to users in workshop environment. Manufacturing capability of such systems can be divided into three layers, including manufacturing cell layer, production process layer and workshop layer. However, most of existing works carried out the optimization on each layer individually. Manufacturing cells are the component parts of a production process, and there are close relationships between them and can effect the operation and performance for each other, therefore it is essential to jointly consider the manufacturing capability service optimization on both layers. In this context, a cross-layer optimization model is proposed to conquer the existing limitation and provide a comprehensive performance assurance to service-oriented robotic manufacturing systems. The proposed model has different decision-making mechanisms on each layer and the communications and interaction between the two layers can further coordinate the optimizations. A case study based on robotic assembly is implemented to demonstrate the availability and effectiveness of the proposed model.

Copyright © 2017 by ASME



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