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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
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    References listed on IDEAS

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    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.
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    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.

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