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

Chenglin Li, Baohai Wu (), Zhao Zhang and Ying Zhang
Additional contact information
Chenglin Li: Northwestern Polytechnical University
Baohai Wu: Northwestern Polytechnical University
Zhao Zhang: Northwestern Polytechnical University
Ying Zhang: Northwestern Polytechnical University

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 4, No 28, 2027-2042

Abstract: 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.

Keywords: Powder-saving; Multi-axis LMD for blade; Self-adaptive spectral clustering; Support-free printing (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01898-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01898-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-021-01898-6

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().