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A new method of reusing the manufacturing information for the slightly changed 3D CAD model

Author

Listed:
  • Jinfeng Liu

    (Jiangsu University of Science and Technology)

  • Xiaojun Liu

    (Southeast University)

  • Zhonghua Ni

    (Southeast University)

  • Honggen Zhou

    (Jiangsu University of Science and Technology)

Abstract

Nowadays, growing quantities of process models in the process planning system are generated in enterprises based on the 3D design model. Usually, these process models contain a great deal of process information. So, how to reuse the embedded manufacturing information plays an important role in the competitive global market for the enterprises increasingly, because it can save enormous time and cost. In this paper, a new method is presented to reuse the existing manufacturing information for the slightly changed 3D design model. First, the hierarchical data structure of process route is introduced to represent the dynamic evolution process of mechanical part model, which takes working procedure model as the carrier of manufacturing information. Second, a multilevel machining feature descriptor which captures different levels of information for each process is designed to search the query machining feature. Moreover, the proposed algorithm of obtaining the main machining feature face for accelerating feature match is given to filter out unmatched feature. Then, the strategies of reusing the manufacturing information are proposed: the matched machining features can automatically obtain the manufacturing information and the unmatched machining features are dealt with by interactive mode. Finally, some slightly changed aircraft structural parts are utilized to verify our proposed method for reusing the manufacturing information. A prototype system has been developed to verify the effectiveness of the proposed method.

Suggested Citation

  • Jinfeng Liu & Xiaojun Liu & Zhonghua Ni & Honggen Zhou, 2018. "A new method of reusing the manufacturing information for the slightly changed 3D CAD model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1827-1844, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1220-3
    DOI: 10.1007/s10845-016-1220-3
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    Cited by:

    1. Fangwei Ning & Yan Shi & Maolin Cai & Weiqing Xu, 2020. "Various realization methods of machine-part classification based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2019-2032, December.

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