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Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework

Author

Listed:
  • Deyuan Ma

    (Huazhong University of Science & Technology)

  • Ping Jiang

    (Huazhong University of Science & Technology)

  • Leshi Shu

    (Huazhong University of Science & Technology)

  • Zhaoliang Gong

    (Huazhong University of Science & Technology)

  • Yilin Wang

    (Huazhong University of Science & Technology)

  • Shaoning Geng

    (Huazhong University of Science & Technology)

Abstract

Pore is one kind of the typical defects in aluminum alloys laser welding. Porosity is an important indicator for evaluating welding quality, and porosity assessment has attracted increasing attention. This paper presents a multi-fidelity deep learning framework (MFDLF) that enables online porosity prediction without post-weld destructive inspection or radioactive detection. In the proposed approach, the maximum temperature on the bottom wall of the keyhole acquired by numerical simulation is used as the data of fidelity 1 (F1), and the coherent optical measurement technology is used to acquire the keyhole depth as the data of fidelity 2 (F2). After extracting the respective four fluctuation characteristics of the multi-fidelity data separately, a sparse auto-encoder (SAE) is used to fuse the four characteristics into an overall feature. Based on the obvious correspondence between porosity and multi-fidelity fusion features, the MFDLF is constructed with tandem two deep belief network (DBN) models, where the former DBN utilizes process parameters to predict the overall feature of F1 data (Feature 1) that is difficult to obtain in real time. Feature 1 is combined with the overall feature of F2 data (Feature 2) that can be obtained online to predict porosity through the latter DBN. The results show that the MFDLF can predict porosity with significantly higher accuracy than the models constructed using only single-fidelity data.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02033-9
    DOI: 10.1007/s10845-022-02033-9
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    References listed on IDEAS

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    1. 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.
    2. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
    3. Zhengtao Gan & Orion L. Kafka & Niranjan Parab & Cang Zhao & Lichao Fang & Olle Heinonen & Tao Sun & Wing Kam Liu, 2021. "Universal scaling laws of keyhole stability and porosity in 3D printing of metals," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Qi Zhou & Youmin Rong & Xinyu Shao & Ping Jiang & Zhongmei Gao & Longchao Cao, 2018. "Optimization of laser brazing onto galvanized steel based on ensemble of metamodels," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1417-1431, October.
    5. Lu Yang & Hongquan Jiang, 2021. "Weld defect classification in radiographic images using unified deep neural network with multi-level features," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 459-469, February.
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