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Data-driven prediction of keyhole features in metal additive manufacturing based on physics-based simulation

Author

Listed:
  • Ziyuan Xie

    (National University of Singapore)

  • Fan Chen

    (National University of Singapore)

  • Lu Wang

    (National University of Singapore)

  • Wenjun Ge

    (National University of Singapore)

  • Wentao Yan

    (National University of Singapore)

Abstract

The defect formation is closely related to molten pool and keyhole features in metal additive manufacturing. Experimentation and physics-based simulation methods to capture the molten pool and keyhole features are expensive and time-consuming. A data-driven method is proposed in this work to efficiently predict the molten pool and keyhole features characterized by a series of fitting curves under given manufacturing parameters, instead of simply predicting the molten pool and keyhole sizes. The database consists of simulation cases with the high-fidelity thermal-fluid flow model. Molten pool melting regime, keyhole stability and keyhole type are recognized with the neural net pattern recognition. With the Gaussian process regression model, the keyhole dimensions are predicted and the keyhole contour is reconstructed. The comparison between predicted data and physics-based simulation results demonstrates the feasibility and accuracy of our data-driven model. Meanwhile, the predicted results can guide the selection of manufacturing parameters in actual production, and are also helpful to the further study of pores in additive manufacturing in academic research.

Suggested Citation

  • Ziyuan Xie & Fan Chen & Lu Wang & Wenjun Ge & Wentao Yan, 2024. "Data-driven prediction of keyhole features in metal additive manufacturing based on physics-based simulation," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2313-2326, June.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:5:d:10.1007_s10845-023-02157-6
    DOI: 10.1007/s10845-023-02157-6
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    References listed on IDEAS

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    1. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    2. S. Mohammad H. Hojjatzadeh & Niranjan D. Parab & Wentao Yan & Qilin Guo & Lianghua Xiong & Cang Zhao & Minglei Qu & Luis I. Escano & Xianghui Xiao & Kamel Fezzaa & Wes Everhart & Tao Sun & Lianyi Chen, 2019. "Pore elimination mechanisms during 3D printing of metals," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    3. Chu Lun Alex Leung & Sebastian Marussi & Robert C. Atwood & Michael Towrie & Philip J. Withers & Peter D. Lee, 2018. "In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    4. 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.
    5. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    6. Aiden A. Martin & Nicholas P. Calta & Saad A. Khairallah & Jenny Wang & Phillip J. Depond & Anthony Y. Fong & Vivek Thampy & Gabe M. Guss & Andrew M. Kiss & Kevin H. Stone & Christopher J. Tassone & J, 2019. "Dynamics of pore formation during laser powder bed fusion additive manufacturing," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    7. Yuze Huang & Tristan G. Fleming & Samuel J. Clark & Sebastian Marussi & Kamel Fezzaa & Jeyan Thiyagalingam & Chu Lun Alex Leung & Peter D. Lee, 2022. "Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    8. Chenglin Li & Baohai Wu & Zhao Zhang & Ying Zhang, 2023. "A novel process planning method of 3 + 2-axis additive manufacturing for aero-engine blade based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2027-2042, April.
    9. 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.
    10. Osama Aljarrah & Jun Li & Alfa Heryudono & Wenzhen Huang & Jing Bi, 2023. "Predicting part distortion field in additive manufacturing: a data-driven framework," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1975-1993, April.
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