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Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process

Author

Listed:
  • Yilin Guo

    (National University of Singapore)

  • Wen Feng Lu

    (National University of Singapore)

  • Jerry Ying Hsi Fuh

    (National University of Singapore)

Abstract

In recent years, metal cellular structures have drawn attentions in various industrial sectors due to their design freedoms and abilities to achieve multi-functional mechanical properties. However, metal cellular structures are difficult to fabricate due to their complex geometries, even with modern additive manufacturing technologies such as the direct metal laser sintering (DMLS) process. Assessing the manufacturability of metal cellular structures via a DMLS process is a challenging task as the geometric features of the structures are complex. Besides, via a DMLS process, the manufacturability also depends on the cumulative deformation of the layers during the manufacturing process. Existing methods on Design for Additive Manufacturing (DFAM) provide design guidelines that are based on past successful printed designs. However, they are not effective in predicting the manufacturability of metal cellular structures. In this paper, we propose a semi-supervised deep learning based manufacturability assessment (SSDLMA) framework to assess whether a metal cellular structure can be successfully manufactured from a given DMLS process. To enable efficient learning, we represent the complex cellular structures as 3D binary arrays with a simple yet efficient voxelisation method. We then train a deep learning based classifier using only a small amount of experimental data by adopting a semi-supervised learning approach. By running real experiments and comparing with existing DFAM methods and machine learning models, we demonstrate the advantages of the proposed SSDLMA framework. The proposed framework can be extended to predict the manufacturability of various other complex geometries beyond cellular structure in a reliable way even with a small number of training data.

Suggested Citation

  • Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01575-0
    DOI: 10.1007/s10845-020-01575-0
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    References listed on IDEAS

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    1. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
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    Cited by:

    1. You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
    2. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    3. Thinh Quy Duc Pham & Truong Vinh Hoang & Xuan Tran & Quoc Tuan Pham & Seifallah Fetni & Laurent Duchêne & Hoang Son Tran & Anne-Marie Habraken, 2023. "Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1701-1719, April.
    4. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.
    5. Dongxiang Hou & Xiaodong Wang & Qing Song & Xuesong Mei & Haicheng Wang, 2024. "A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 331-341, January.

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