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A novel process planning method of 3 + 2-axis additive manufacturing for aero-engine blade based on machine learning

Author

Listed:
  • Chenglin Li

    (Northwestern Polytechnical University
    National University of Singapore)

  • Baohai Wu

    (Northwestern Polytechnical University)

  • Zhao Zhang

    (Northwestern Polytechnical University)

  • Ying Zhang

    (Northwestern Polytechnical University)

Abstract

Additive manufacturing (AM) is an emergingly technology in aerospace such as aero-engine blade fabrication, which has benefits in complex shape creation with little post processing required. In this paper, a machine learning algorithm is proposed for powder-saving and support-free process planning in multi-axis metal AM, improving the printing efficiency and the surface quality of printed blade. Firstly, a self-adaptive spectral clustering algorithm is developed to carry out two functions: one is to decompose the blade into sub-blocks in a global view; the other one is to automatically obtain the optimal clustering number, addressing the contradiction issue between printing efficiency and decomposition performance. Secondly, the global constraint formula and the normalized area weight are introduced to obtain main printing orientations (MPOs). Each sub-block can be built along the corresponding MPO with high-quality surface, free support, and low powder leakage. A sample blade is built on the 3 + 2 axis laser metal deposition (LMD) machine to validate the feasibility of the proposed method. Experimental results indicate that the proposed method has advantages of less powder consumption, higher decomposition performance and printing efficiency compared to the existed method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01898-6
    DOI: 10.1007/s10845-021-01898-6
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    References listed on IDEAS

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    1. Salomé Sanchez & Divish Rengasamy & Christopher J. Hyde & Grazziela P. Figueredo & Benjamin Rothwell, 2021. "Machine learning to determine the main factors affecting creep rates in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2353-2373, December.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    3. Dongbo Wu & Hui Wang & Kaiyao Zhang & Bing Zhao & Xiaojun Lin, 2020. "Research on adaptive CNC machining arithmetic and process for near-net-shaped jet engine blade," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 717-744, March.
    4. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    5. A. Chabot & N. Laroche & E. Carcreff & M. Rauch & J.-Y. Hascoët, 2020. "Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1191-1201, June.
    6. Donghua Zhao & Weizhong Guo, 2020. "Mixed-layer adaptive slicing for robotic Additive Manufacturing (AM) based on decomposing and regrouping," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 985-1002, April.
    7. 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.
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