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ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment

Author

Listed:
  • Yuqian Lu

    (The University of Auckland)

  • Hongqiang Wang

    (EagleBurgmann Australasia Pty. Ltd)

  • Xun Xu

    (The University of Auckland)

Abstract

The ever-increasing distributed, networked and crowd-sourced cloud environment imposes the need of a service-oriented product data model for explicit representation of service requests in global manufacturing-service networks. The work in this paper aims to develop such a description framework for products based on semantic web technologies to facilitate the make-to-individual production strategy in a cloud manufacturing environment. A brief discussion on the requirements of a product data model in cloud manufacturing and research on product data modelling is given in the first part. A systematic ontology development methodology is then proposed and elaborated. The ontology called ManuService has been developed, consisting of all necessary concepts for description of products in a service-oriented business environment. These concepts include product specifications, quality constraints, manufacturing processes, organisation information, cost expectations, logistics requirements, and etcetera. ManuService ontology provides a module-based, reconfigurable, privacy-enhanced and standardised approach to modelling customised manufacturing service requests. An industrial case is presented to demonstrate possible applications using ManuService ontology. Comprehensive discussions are given thereafter, including a pilot application of a software package for semantic-based product design and a semantic web-based module for intelligent knowledge-based decision-making based on ManuService. ManuService forms the basis for collaborative service-oriented business interactions, intelligent and secure service provision in cloud manufacturing environment.

Suggested Citation

  • Yuqian Lu & Hongqiang Wang & Xun Xu, 2019. "ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 317-334, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1250-x
    DOI: 10.1007/s10845-016-1250-x
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    Citations

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    Cited by:

    1. Juan José Montero Jiménez & Rob Vingerhoeds & Bernard Grabot & Sébastien Schwartz, 2023. "An ontology model for maintenance strategy selection and assessment," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1369-1387, March.
    2. Jiatong Yu & Jiajue Wang & Taesoo Moon, 2022. "Influence of Digital Transformation Capability on Operational Performance," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    3. Jiatong Yu & Taesoo Moon, 2021. "Impact of Digital Strategic Orientation on Organizational Performance through Digital Competence," Sustainability, MDPI, vol. 13(17), pages 1-15, August.
    4. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
    5. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    6. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.
    7. Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.

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