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Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting

Author

Listed:
  • Huaxian Wei

    (Shantou University
    Ministry of Education of China)

  • Bijan Shirinzadeh

    (Monash University)

  • Xiaodong Niu

    (Shantou University)

  • Jian Zhang

    (Shantou University)

  • Wei Li

    (China University of Mining and Technology)

  • Alessandro Simeone

    (Shantou University)

Abstract

3D printing offers great potential for developing complex flexure mechanisms. Recently, thickness-correction factors (TCFs) were introduced to correct the thickness and stiffness deviations of powder-based metal 3D printed flexure hinges during design and analysis. However, the reasons for the different TCFs obtained in each study are not clear, resulting in a limited value of these TCFs for future design and fabrication. Herein, the influence of the porous layer of 3D printed flexure hinges on the hinge thickness is investigated. Samples of parallelogram flexure mechanisms (PFMs) were 3D printed using selective laser melting (SLM) and 316L stainless steel powder. A 3D manufacturing error analysis was completed for each PFM sample via 3D scanning, surface roughness measurement and morphological observation. The thickness of the porous layer of the flexure hinge was independent of the designed hinge thickness and remained close to the average powder particle diameter. The effective hinge thickness could be estimated by subtracting twice the value of the porous layer thickness from the designed value. Guidelines based on finite element analysis and stiffness experiments are proposed. The limitations of the presented method for evaluating the effective hinge thickness of flexure hinges 3D printed via SLM are also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01621-x
    DOI: 10.1007/s10845-020-01621-x
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    References listed on IDEAS

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    1. William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
    2. 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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