Publication result detail

The IMOCO4.E reference framework for intelligent motion control systems

Mohamed, S.; Van Der Veen, G.; Kuppens, H.; Vierimaa, M.; Kanellos, T.; Stoutjesdijk, H.; Masiero, R.; Määttä, K.; Van Der Weit J. W.; Ribeiro, G.; Bergmann, A.; Colombo, D.; Arenas, J.; Keary, A.; Goubej, M.; Rouxel, B.; Kilpeläinen, P.; Kadikis, R.; Armendia, M.; Blaha, P.; Stokkermans, J.; Čech, M.; Beltman, A.

Original Title

The IMOCO4.E reference framework for intelligent motion control systems

English Title

The IMOCO4.E reference framework for intelligent motion control systems

Type

Paper in proceedings (conference paper)

Original Abstract

Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial 'know-how' of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale 'Intelligent Motion Control under Industry4.E' (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework's applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.

English abstract

Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial 'know-how' of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale 'Intelligent Motion Control under Industry4.E' (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework's applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.

Keywords

AI; cyber-physical systems; data management; digital twin; edge computing; mechatronics; motion control; reference framework; smart system integration

Key words in English

AI; cyber-physical systems; data management; digital twin; edge computing; mechatronics; motion control; reference framework; smart system integration

Authors

Mohamed, S.; Van Der Veen, G.; Kuppens, H.; Vierimaa, M.; Kanellos, T.; Stoutjesdijk, H.; Masiero, R.; Määttä, K.; Van Der Weit J. W.; Ribeiro, G.; Bergmann, A.; Colombo, D.; Arenas, J.; Keary, A.; Goubej, M.; Rouxel, B.; Kilpeläinen, P.; Kadikis, R.; Armendia, M.; Blaha, P.; Stokkermans, J.; Čech, M.; Beltman, A.

RIV year

2024

Released

12.09.2023

Publisher

IEEE

Location

Sinaia, Romania

ISBN

979-8-3503-3991-8

Book

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Pages from

1

Pages to

8

Pages count

8

BibTex

@inproceedings{BUT187824,
  author="Mohamed, S. and Van Der Veen, G. and Kuppens, H. and Vierimaa, M. and Kanellos, T. and Stoutjesdijk, H. and Masiero, R. and Määttä, K. and Van Der Weit J. W. and Ribeiro, G. and Bergmann, A. and Colombo, D. and Arenas, J. and Keary, A. and Goubej, M. and Rouxel, B. and Kilpeläinen, P. and Kadikis, R. and Armendia, M. and Blaha, P. and Stokkermans, J. and Čech, M. and Beltman, A.",
  title="The IMOCO4.E reference framework for intelligent motion control systems",
  booktitle="IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",
  year="2023",
  pages="1--8",
  publisher="IEEE",
  address="Sinaia, Romania",
  doi="10.1109/ETFA54631.2023.10275410",
  isbn="979-8-3503-3991-8"
}