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Machine learning integrated design for additive manufacturing

Author

Listed:
  • Jingchao Jiang

    (Singapore University of Technology and Design)

  • Yi Xiong

    (Singapore University of Technology and Design)

  • Zhiyuan Zhang

    (Singapore University of Technology and Design)

  • David W. Rosen

    (Singapore University of Technology and Design
    Georgia Institute of Technology)

Abstract

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and performance spaces. Furthermore, the primary advantage of ML over other surrogate modelling methods is the capability to model input–output relationships in both directions. That is, a deep neural network can model property–structure relationships, given structure–property input–output data. A case study was carried out to demonstrate the effectiveness of using ML to design a customized ankle brace that has a tunable mechanical performance with tailored stiffness.

Suggested Citation

  • Jingchao Jiang & Yi Xiong & Zhiyuan Zhang & David W. Rosen, 2022. "Machine learning integrated design for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1073-1086, April.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01715-6
    DOI: 10.1007/s10845-020-01715-6
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    References listed on IDEAS

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    1. Kyung-In Jang & Ha Uk Chung & Sheng Xu & Chi Hwan Lee & Haiwen Luan & Jaewoong Jeong & Huanyu Cheng & Gwang-Tae Kim & Sang Youn Han & Jung Woo Lee & Jeonghyun Kim & Moongee Cho & Fuxing Miao & Yiyuan , 2015. "Soft network composite materials with deterministic and bio-inspired designs," Nature Communications, Nature, vol. 6(1), pages 1-11, May.
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    Cited by:

    1. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.

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