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Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

Author

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  • Amro M. Farid

    (Masdar Institute of Science & Technology
    MIT Mechanical Engineering)

Abstract

In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems.

Suggested Citation

  • Amro M. Farid, 2017. "Measures of reconfigurability and its key characteristics in intelligent manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 353-369, February.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0983-7
    DOI: 10.1007/s10845-014-0983-7
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    References listed on IDEAS

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    1. Mostafa G. Mehrabi, A.Galip Ulsoy, Yoram Koren, 2000. "Reconfigurable manufacturing systems and their enabling technologies," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 1(1), pages 114-131.
    2. D. Wu & L. Zhang & J. Jiao & R. Lu, 2013. "SysML-based design chain information modeling for variety management in production reconfiguration," Post-Print hal-00846436, HAL.
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    Cited by:

    1. Inas S. Khayal & Amro M. Farid, 2018. "Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes," Complexity, Hindawi, vol. 2018, pages 1-24, March.
    2. Ashutosh Singh & Shashank Gupta & Mohammad Asjad & Piyush Gupta, 2017. "Reconfigurable manufacturing systems: journey and the road ahead," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1849-1857, November.
    3. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    4. Bashir Salah & Mustufa Haider Abidi & Syed Hammad Mian & Mohammed Krid & Hisham Alkhalefah & Ali Abdo, 2019. "Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    5. Battaïa, Olga & Dolgui, Alexandre & Guschinsky, Nikolai, 2023. "MIP-based heuristics for combinatorial design of reconfigurable rotary transfer machines for production of multiple parts," International Journal of Production Economics, Elsevier, vol. 262(C).
    6. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.

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