IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i2d10.1007_s10845-021-01857-1.html
   My bibliography  Save this article

A computational method for detecting aspect ratio and problematic features in additive manufacturing

Author

Listed:
  • Ruihuan Ge

    (University of Bath
    The University of Sheffield)

  • Joseph Flynn

    (University of Bath)

Abstract

In metal additive manufacturing, geometries with high aspect ratio (AR) features are often associated with defects caused by thermal stresses and other related build failures. Ideally, excessively high AR features would be detected and removed in the design phase to avoid unwanted failure during manufacture. However, AR is scale and orientation independent and identifying features across all scales and orientations is exceptionally challenging. Furthermore, not all high AR features are as easy to recognise as thin walls and fine needles. There is therefore a pressing need for further development in the field of problematic features detection for additive manufacturing processes. In this work, a dimensionless ratio (D1/D2) based on two distance metrics that are extracted from triangulated mesh geometries is proposed. Based on this method, geometries with different features (e.g. thin wall, helices and polyhedra) were generated and evaluated to produce metrics that are similar to AR. The prediction results are compared with known theoretical AR values of typical geometries.By combining this metric with mesh segmentation, this method was further extended to analyse the geometry with complex features. The proposed method provides a powerful, general and promising way to automatically detect high AR features and tackle the relevant defect issues prior to manufacture.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01857-1
    DOI: 10.1007/s10845-021-01857-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01857-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-021-01857-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Han Wang & Martin Baumers & Shreeja Basak & Yinfeng He & Ian Ashcroft, 2022. "The impact of the risk of build failure on energy consumption in additive manufacturing," Journal of Industrial Ecology, Yale University, vol. 26(5), pages 1771-1783, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01857-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.