Large volume metrology process model: measurability analysis with integration of metrology classification model and feature-based selection model

Chun Hung Cheng, Dehong Huo, Xi Zhang, Wei Dai, Paul G. Maropoulos

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators.

    Original languageEnglish
    Title of host publicationProceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology
    EditorsGeorge Q. Huang, K.L. Mak, Paul G. Maropoulos
    Place of PublicationBerlin (DE)
    PublisherSpringer
    Pages1013-1026
    Number of pages14
    ISBN (Electronic)978-3-642-10430-5
    ISBN (Print)978-3-6421-0429-9
    DOIs
    Publication statusPublished - 2010
    Event6th CIRP International Conference on Digital Enterprise Technology - Hong Kong, China
    Duration: 14 Dec 200916 Dec 2009

    Publication series

    NameAdvances in Intelligent and Soft Computing
    PublisherSpringer
    Volume66
    ISSN (Print)1867-5662

    Conference

    Conference6th CIRP International Conference on Digital Enterprise Technology
    Abbreviated titleDET 2009
    CountryChina
    CityHong Kong
    Period14/12/0916/12/09

    Keywords

    • assembly planning
    • feature-based selection model
    • measurability analysis
    • metrology classification model
    • process modelling

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  • Research Output

    High accuracy mobile robot positioning using external large volume metrology instruments

    Wang, Z., Liang, M. & Maropoulos, P. G., 2011, In : International Journal of Computer Integrated Manufacturing. 24, 5, p. 484-492 9 p.

    Research output: Contribution to journalSpecial issue

    Open Access
    File
  • Design of an information system for metrology contents

    Ferri, C., Jamshidi, J., Loftus, C. & Maropoulos, P., 2010, Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology. Huang, G. Q., Mak, K. L. & Maropoulos, P. G. (eds.). Berlin (DE): Springer, p. 957-970 14 p. (Advances in Intelligent and Soft Computing; vol. 66).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • High accuracy mobile robot positioning using an external large volume metrology instrument

    Wang, Z., Liang, M. & Maropoulos, P. G., 2010, Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology. Huang, G. Q., Mak, K. L. & Maropoulos, P. G. (eds.). Berlin (DE): Springer, p. 621-630 10 p. (Advances in Intelligent and Soft Computing; vol. 66).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Cite this

    Cheng, C. H., Huo, D., Zhang, X., Dai, W., & Maropoulos, P. G. (2010). Large volume metrology process model: measurability analysis with integration of metrology classification model and feature-based selection model. In G. Q. Huang, K. L. Mak, & P. G. Maropoulos (Eds.), Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology (pp. 1013-1026). (Advances in Intelligent and Soft Computing; Vol. 66). Springer. https://doi.org/10.1007/978-3-642-10430-5_78