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

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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.

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Abbreviations

B :

STL model of blade

Tr j :

j’th triangular

B i :

i’th sub-block

m :

Number of triangular facets

V i :

Normal vector of i’th triangular

C :

An initial clustering set (ICS)

L :

Normalized Laplace matrix

F :

Matrix by N eigenvectors normalized

C i :

i’Th initial clustering set (ICS)

n :

Surface normal vector

h :

Thickness of the printing layer

E i :

Staircase effect of an ICS

Ne ij :

Adjacent degree of Ci and Cj

l ij :

Diagonal length of the union set box of Ci and Cj

θ i :

Angle between normal vector ni and corresponding MPO Pi

\({\mathbf{n}}_{{Tr}_{j}}\) :

Surface normal vector of the Trj

n 0 :

Number of sets contained in D0

S :

Sum of angles for a sub-block

n :

Number of sub-blocks

P i :

Main printing orientation of i’th sub-block

D 0 :

Candidate set of MPO vector

D 1 :

Initial clustering voting vector set

n 2 :

Number of initial clustering set (ICS)

W :

Affinity matrix

N :

Optimal sub-block number

f :

Eigenvector of the first N eigenvalues(sortrows) in L

\({\text{D}}_{{{\varvec{i}}}_{0}}^{0}\) :

Printing orientation

x :

Printing layer offset in normal

x i :

i-Th triangular facet surface

S(C i):

Area of ICS Ci

l i :

Diagonal length of spatial bounding box of Ci

θ c :

Criterion angle

\({\mathbf{P}}_{{Tr}_{j}}\) :

MPO of corresponding sub-block for Trj

n j :

Number of facets contained in the sub-block where Trj is located

SF i :

Area of facet Tri

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Funding

This work is jointly supported by the National Science and Technology Major Project (No. 2017ZX04011013) and the Fundamental Research Funds for the Central Universities (No. 31020190502007, No. 31020200504003) and Natural Science Basic Research Program of Shaanxi (No. 2020JQ183).

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Authors and Affiliations

  1. Key Laboratory of High Performance Manufacturing for Aero Engine, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi Province, China

    Chenglin Li, Baohai Wu, Zhao Zhang & Ying Zhang

  2. Department of Mechanical Engineering, National University of Singapore, 117576, Singapore, Singapore

    Chenglin Li

Authors
  1. Chenglin Li
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  2. Baohai Wu
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  3. Zhao Zhang
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  4. Ying Zhang
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Contributions

Chenglin Li undertook the development of the process planning presented in this paper, supervised by Baohai Wu, Zhao Zhang and Ying Zhang.

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Correspondence to Baohai Wu.

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All co-authors have seen and agree with the contents of the manuscript and there are no conflicts of interest and financial disclosures to report. The authors also claim that none of the material in the paper has been published or is under consideration for publication elsewhere.

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Li, C., Wu, B., Zhang, Z. et al. A novel process planning method of 3 + 2-axis additive manufacturing for aero-engine blade based on machine learning. J Intell Manuf 34, 2027–2042 (2023). https://doi.org/10.1007/s10845-021-01898-6

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