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An application to Stereolithography of a feature recognition algorithm for manufacturability evaluation

Author

Listed:
  • Giampaolo Campana

    (University of Bologna)

  • Mattia Mele

    (University of Bologna)

Abstract

Additive manufacturing processes are experiencing extraordinary growth in present years. Concerning the production of goods by using this technology, expertise and know-how are today relevant while process simulation needs to be extensively validated before acquiring the necessary reliability, which is already achieved and established for a number of manufacturing processes. The objective of the present work is the development of a new algorithm for feature recognition, which is the first step towards an application of rules for manufacturability to digital models. The proposed approach was specifically conceived for design for additive manufacturing (DfAM). The method starts from a graph-based representation of geometric models that is the base for the definition of new and original geometrical entities. Then, an algorithm-based process has been identified and proposed for their detection. Eventually, these geometrical entities have been used for comparison with rules and constraints of DfAM in order to point out possible critical issues for manufacturability. A self-developed plugin software was implemented for the application of proposed procedure in Computer Aided Design systems. Several applications of a set of DfAM rules are provided and tested to validate the method by means of case studies. As a conclusion, such an application demonstrated the suitability of the approach for detections of features that are relevant to an early investigation into Stereolithography manufacturability. Presented approach could be helpful during early phases of product development for detecting critical manufacturing issues and thus for realising an assistant-tool that can help designers by displaying potential solutions to overcome them. Since the very first steps of product design, this integration of manufacturing knowledge allows for a reduction of a number of potential errors occurring during product fabrication and then for a decrease of required time for product development.

Suggested Citation

  • Giampaolo Campana & Mattia Mele, 2020. "An application to Stereolithography of a feature recognition algorithm for manufacturability evaluation," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 199-214, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1441-8
    DOI: 10.1007/s10845-018-1441-8
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    Citations

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    Cited by:

    1. Huaxian Wei & Bijan Shirinzadeh & Xiaodong Niu & Jian Zhang & Wei Li & Alessandro Simeone, 2021. "Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1411-1420, June.
    2. Ruihuan Ge & Joseph Flynn, 2022. "A computational method for detecting aspect ratio and problematic features in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 519-535, February.

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