A zone-layered trimming method for ceramic core of aero-engine blade based on an advanced reconfigurable laser processing system

doi:10.1007/s11465-022-0675-5

Front. Mech. Eng.

2022, Vol. 17

Issue (2) : 19 https://doi.org/10.1007/s11465-022-0675-5

RESEARCH ARTICLE

A zone-layered trimming method for ceramic core of aero-engine blade based on an advanced reconfigurable laser processing system

Xiaodong WANG^1,², Dongxiang HOU³, Bin LIU^1,², Xuesong MEI^1,²(

), Xintian WANG^1,², Renhan LIAN^1,²

¹. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
². Shaanxi Key Laboratory of Intelligent Robots, Xi’an 710049, China
³. Artificial Intelligence Department, Beijing University of Posts and Telecommunications, Beijing 100876, China

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Abstract

Ceramic structural parts are one of the most widely utilized structural parts in the industry. However, they usually contain defects following the pressing process, such as burrs. Therefore, additional trimming is usually required, despite the deformation challenges and difficulty in positioning. This paper proposes an ultrafast laser processing system for trimming complex ceramic structural parts. Opto-electromechanical cooperative control software is developed to control the laser processing system. The trimming problem of the ceramic cores used in aero engines is studied. The regional registration method is introduced based on the iterative closest point algorithm to register the path extracted from the computer-aided design model with the deformed ceramic core. A zonal and layering processing method for three-dimensional contours on complex surfaces is proposed to generate the working data of high-speed scanning galvanometer and the computer numerical control machine tool, respectively. The results show that the laser system and the method proposed in this paper are suitable for trimming complex non-datum parts such as ceramic cores. Compared with the results of manual trimming, the method proposed in this paper has higher accuracy, efficiency, and yield. The method mentioned above has been used in practical application with satisfactory results.

Keywords ceramic parts trimming computer-aided laser manufacturing 3D vision reconfigurable laser processing system

Corresponding Author(s): Xuesong MEI

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 01 March 2022 Issue Date: 10 June 2022

Cite this article:

Xiaodong WANG,Dongxiang HOU,Bin LIU, et al. A zone-layered trimming method for ceramic core of aero-engine blade based on an advanced reconfigurable laser processing system[J]. Front. Mech. Eng., 2022, 17(2): 19.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-022-0675-5
https://academic.hep.com.cn/fme/EN/Y2022/V17/I2/19

Fig.1 Defects such as burrs and blockage of the ceramic core: (a) turbine blade and ceramic core and (b) burrs and blockage on the ceramic core.

Fig.2 Laser beam quality measurement: (a) measuring device and (b) beam quality.

Fig.3 Laser energy distribution near the laser focus (a) for different radii on the focal plane near the focus and (b) maximum energy density at different locations.

Fig.4 Effect of laser incident angle on the shape and size of the spot.

Fig.5 Change of the laser spot size with the angle of incidence: (a)

r min / r

and (b)

r max / r

Fig.6 Grooving experiment device under different laser incident angles: (a) experimental equipment and (b) experimental material.

Fig.7 Width of the groove near the focus.

Fig.8 Width of the groove at different incidence angles under the microscope 1000 times magnification: (a)

θ = 0 ∘

, (b)

θ = 1 ∘

, (c)

θ = 2 ∘

, (d)

θ = 3 ∘

, (e)

θ = 4 ∘

, and (f)

θ = 5 ∘

Fig.9 Edges of the ceramic core designated for trimming.

Fig.10 Contour clusters extracted by CAM software of the ceramic core designated for trimming.

Fig.11 k-means clustering results: (a) normalized processing; (b) non-normalized processing.

Tab.1 Clustering centers after k-means clustering

Fig.12 Schematic diagram of WCS and LCS.

Fig.13 LCS results of the contour cluster.

Fig.14 Schematic representation of layering and projection of the contour cluster: (a) Contour 2, (b) Contour 3, and (c) Contour 1.

Fig.15 Layering and projection results of the contour clusters in LCS: (a) 3D data after layering of cluster 2, (b) 2D projection data in LCS of cluster 2, (c) 3D data after layering of cluster 10, (d) 2D projection data in LCS of cluster 10, (e) 3D data after layering of cluster 8, and (f) 2D projection data in LCS of cluster 8.

Fig.16 Five-axis ultrafast laser processing system.

Fig.17 Hardware integration scheme of the five-axis ultra-fast laser processing system.

Fig.18 Ceramic core trimming process.

Fig.19 Registration process between CAD extracted trajectory and the edges of the measured point cloud.

Fig.20 Online measurement of the ceramic core via stereo vision: (a) online measurement and (b) point cloud model.

Fig.21 Template trajectory registration: (a) before registration and (b) after registration.

Fig.22 Optical system: (a) laser side and (b) laser processing head side.

Fig.23 Laser scanning trajectory: (a) contour cluster 2, (b) cluster 10, and (c) cluster 8.

Fig.24 Trimming results of the ceramic core: (a) ceramic core before trimming, (b) burr and hole blockage of long groove, (c) burr and hole blockage of hole, (d) ceramic core after laser trimming, (e) long groove after laser trimming, (f) hole after laser trimming, (g) ceramic core after manual trimming, (h) long groove after manual trimming, and (i) hole after manual trimming.

Abbreviations
2D	Two-dimensional
CAD	Computer-aided design
CAM	Computer-aided manufacturing
CNC	Computer numerical control
ICP	Iterative closest point
LCS	Local coordinate system
LPL	Laser focus processing length
OEMCC	Opto-electromechanical cooperative control
WCS	Workpiece coordinate system
Variables
c_j	Coordinate of the cluster center
$C (i) (O WL (i), Z L (i))$	Center of the ith cluster
D	Diameter of the laser beam
E	Single pulse energy
f	Focal length of the field lens
floor(x)	Downward rounding of the variable x
h	Repetition frequency
H⁽ⁱ⁾	Transformation matrix from WCS O_W to LCS $O L (i)$
$Δ H$	Layering distance
$I 0$	Energy density at the center of the spot
$I th$	Ablation threshold of the material
J	Objective function
$k$	Preset number of categories
$M 2$	Mode parameter that characterizes the beam quality
$n$	Number of feature vectors
$N layer$	Layer number of the current cluster
$O L (i)$	ith LCS
$O W$	Workpiece coordinate system
$O WL (i)$	Coordinate of the coordinate origin $O L (i)$ of LCS in WCS $O W$
P	Laser power
$P m (i)$	Positioning coordinates of the mth layer of the ith contour cluster
r	Laser spot radius
$r max$	Radius size along the ellipse’s major axis
$r min$	Radius size along the ellipse’s minor axis
$V WZ$	Unit vector along the Z-axis in WCS
$w 0$	Radius of the laser beam at the waist
$w (z)$	Radius at various positions along the optical axis
$x^i$	Normalization of $x i$
$x i (j) (x i, y i, z i, u i, v i, w i)$	Six-dimensional data representing the feature vector
$x^i (x^i, y^i, z^i, u i, v i, w i)$	Normalization data
$x max$	Maximum values of $x i$
$x W (i)$	Data of the ith cluster in WCS
$x L (i)$	Data in LCS after the coordinate transformation of $x W (i)$
$X L (i)$	Unit vector along the X-axis in the ith LCS
$y^i$	Normalization of $y i$
$y max$	Maximum values of $y i$
$Y L (i)$	Unit vector along the Y-axis in the ith LCS
z	Distance to the center of the focus along the optical axis
$z^i$	Normalization of $z i$
$z max$ , $z min$	Maximum and minimum values of $z i$ , respectively
$z R$	Rayleigh length
$Z L (i)$	Unit vector along the Z-axis in the ith LCS
$λ$	Laser wavelength
$θ$	Incident angle of the laser

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