IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i4d10.1007_s10845-021-01898-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01898-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01898-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angel-Iván García-Moreno, 2022. "A fast method for monitoring molten pool in infrared image streams using gravitational superpixels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1779-1794, August.
    2. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    3. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    4. Christoph March & Ina Schieferdecker, 2021. "Technological Sovereignty as Ability, Not Autarky," CESifo Working Paper Series 9139, CESifo.
    5. Pompeu Casanovas & Louis de Koker & Mustafa Hashmi, 2022. "Law, Socio-Legal Governance, the Internet of Things, and Industry 4.0: A Middle-Out/Inside-Out Approach," J, MDPI, vol. 5(1), pages 1-28, January.
    6. Anna Kwiotkowska & Radosław Wolniak & Bożena Gajdzik & Magdalena Gębczyńska, 2022. "Configurational Paths of Leadership Competency Shortages and 4.0 Leadership Effectiveness: An fs/QCA Study," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    7. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    8. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    9. 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.
    10. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
    11. Liangjie Xia & Yongwan Bai & Sanjoy Ghose & Juanjuan Qin, 2022. "Differential game analysis of carbon emissions reduction and promotion in a sustainable supply chain considering social preferences," Annals of Operations Research, Springer, vol. 310(1), pages 257-292, March.
    12. John Mugambwa Serumaga-Zake & John Andrew van der Poll, 2021. "Addressing the Impact of Fourth Industrial Revolution on South African Manufacturing Small and Medium Enterprises (SMEs)," Sustainability, MDPI, vol. 13(21), pages 1-31, October.
    13. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    14. Fadi Shehab Shiyyab & Abdallah Bader Alzoubi & Qais Mohammad Obidat & Hashem Alshurafat, 2023. "The Impact of Artificial Intelligence Disclosure on Financial Performance," IJFS, MDPI, vol. 11(3), pages 1-25, September.
    15. Zhaoyuan He & Paul Turner, 2021. "A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities," Logistics, MDPI, vol. 5(4), pages 1-22, December.
    16. Jia Liu & Jiafeng Ye & Daniel Silva Izquierdo & Aleksandr Vinel & Nima Shamsaei & Shuai Shao, 2023. "A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3249-3275, December.
    17. Andres Bustillo & Roberto Reis & Alisson R. Machado & Danil Yu. Pimenov, 2022. "Improving the accuracy of machine-learning models with data from machine test repetitions," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 203-221, January.
    18. Wurong Fu, 2021. "Macroscopic numerical model of reinforced concrete shear walls based on material properties," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1401-1410, June.
    19. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    20. Yue Wu & Dong-Shang Chang, 2024. "Decomposing the comprehensive efficiency of major cities into divisions on governance, ICT and sustainability: network slack-based measure model," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01898-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.