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Research Papers

Multifeature-Fitting and Shape-Adaption Algorithm for Component Repair

[+] Author and Article Information
Renwei Liu

Laser-Aided Manufacturing Processes Lab,
Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: rl8q5@mst.edu

Zhiyuan Wang

Laser-Aided Manufacturing Processes Lab,
Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: zwc4b@mst.edu

Frank Liou

Michael and Joyce Bytnar Product Innovation
and Creativity Professor Laser-Aided
Manufacturing Processes Lab,
Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: liou@mst.edu

1Corresponding author.

Manuscript received February 18, 2017; final manuscript received June 8, 2017; published online December 18, 2017. Assoc. Editor: Tony Schmitz.

J. Manuf. Sci. Eng 140(2), 021003 (Dec 18, 2017) (19 pages) Paper No: MANU-17-1105; doi: 10.1115/1.4037107 History: Received February 18, 2017; Revised June 08, 2017

In recent years, the usage of additive manufacturing (AM) provides new capabilities for component repair, which includes low heat input, small heat-affected zone, and freeform near-net-shape fabrication. Because the geometry of each worn component is unique, the automated repair process is a challenging and important task. The focus of this paper is to investigate and develop a general best-fit and shape-adaption algorithm for automating alignment and defect reconstruction for component repair. The basic principle of using features for rigid-body best-fitting is analyzed and a multifeature-fitting method is proposed to best fit the 3D mesh model of a worn component and its nominal component. The multifeature-fitting algorithm in this paper couples the least-squares method and a density-based outlier detection method. These two methods run alternately to approach the best-fit result gradually and eliminate the disturbance caused from the defect geometry. The shape-adaption algorithm is used to do cross section comparison and defect reconstruction based on the best-fitted 3D model. A “point-line-surface” fracture surface detection method is proposed to construct fracture surface and the fracture surface boundary is dilated to trim the nominal 3D model to obtain defect geometry. Illustrative examples with typical components and different kinds of defects are used to demonstrate the flexibility and capability of using multifeature-fitting and shape-adaption algorithm developed in this paper.

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Figures

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Fig. 1

Rigid-body best-fit

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Fig. 2

Cross sections and features construction: (a) a 3D mesh model of nominal component and face selection, (b) cross sections and features of nominal, (c) a 3D mesh model of worn component and face selection, and (d) cross sections and features of worn model

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Fig. 3

An example of a 2D polygon

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Fig. 4

Multifeature-fitting algorithm

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Fig. 5

The initial spatial relationship of nominal and worn model's features

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Fig. 6

An example of feature-fitting algorithm based on least square and density-based outlier detection: (a) the first iteration result of feature fitting and outlier detection—(1) the first feature-fitting by least-squares method, (2) the first outlier detection after feature-fitting, and (3) features after taking out outliers, (b) the second iteration result of feature fitting and outlier detection—(1) the second feature-fitting by least-squares method, (2) the second outlier detection after feature-fitting, and (3) features after taking out outliers, and (c) the third iteration result of feature fitting and outlier detection—(1) the third feature-fitting by least-squares method, (2) the third outlier detection after feature-fitting, and (3) features after taking out outliers

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Fig. 7

The results of using feature fitting algorithm: (a) best-fitted features, (b) fitted cross sections after feature-fitting algorithm, and (c) fitted worn-area cross sections

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Fig. 8

Stages of shape-adaption algorithm

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Fig. 9

Point-line-surface cross section comparison processes: (a) first fracture segment detection, (b) fracture segments construction, and (c) fracture surface construction

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Fig. 10

An example of cross section comparison and fracture surface construction: (a) fracture point detection for worn cross sections of defect 1, (b) fracture segments construction of defect 1, (c) fracture surface construction of defect 1, and (d) fracture surface construction of defect 2

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Fig. 11

Vertices dilation for fracture surface boundary: (a) vertice dilation process and (b) dilated vertices and related lines

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Fig. 12

An example of fracture surface boundary dilation: (a) fracture surface boundary dilation of defect and (b) fracture surface boundary dilation of defect 2

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Fig. 13

Mesh trim in rhino for defect reconstruction: (a) fracture surface on the worn component model and (b) use dilated fracture surface to trim nominal

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Fig. 14

Mesh trim result for the example of a cone shape: (a) assemble defects with worn model, (b) defect 1, and (c) defect 2

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Fig. 15

Bearing house model and cross sections with features: (a) original model, (b) worn model, and (c) initial cross sections with features

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Fig. 16

Model level feature fitting for 1D feature—Bearing house: (a) initial features, (b) best-fitted features, and (c) best-fit result after model level feature-fitting

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Fig. 17

Cross section level's feature fitting—bearing house: (a) initial cross section, (b) best-fitted cross section, and (c) multifeature-fitting result after cross section level's

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Fig. 18

Shape-adaption algorithm—bearing house: (a) cross section comparison along the first axis, (b) cross section comparison along the second, and (c) fracture surface

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Fig. 19

Fracture surface dilation—bearing house: (a) dilated boundary vertices and (b) dilated fracture surface

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Fig. 20

Mesh trim and defect reconstruction—bearing house: (a) fracture surface on worn bearing house, (b) defect geometry, and (c) defect geometry with worn bearing house

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Fig. 21

Curved bladed model, adaptive slicing, and feature construction: (a) nominal model, (b) deformed model, and (c) initial cross sections and features

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Fig. 22

Multifeature-fitting results: (a) initial spatial relationship of features, (b) best-fitted features, and (c) best-fitted cross sections

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Fig. 23

Fracture surface construction on nominal blade model: (a) fracture surface detection for nominal model, (b) fracture surface construction, and (c) fracture surface on nominal model

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Fig. 24

Fracture surface construction on deformed blade model: (a) fracture surface detection on deformed model, (b) fracture surface construction, and (c) fracture surface on deformed model

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Fig. 25

Intersection surface on nominal blade: (a) dilated side surface on the deformed model, (b) intersection surface, and (c) intersection surface on the nominal model

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Fig. 26

Intersection surface on deformed blade: (a) dilated side surface on the nominal model to trim side surface on deformed model, (b) intersection surface on deformed model, and (c) intersection surface on deformed model

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Fig. 27

Intersection surface trim fracture surface on deformed blade: (a) intersection surface on nominal model to trim fracture surface on deformed model, (b) deformed geometry mesh, and (c) deformed geometry on the deformed blade model

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Fig. 28

Intersection surface trim fracture surface on original blade: (a) intersection surface on deformed model to trim fracture surface on nominal, (b) missed geometry, and (c) missed geometry on nominal model

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Fig. 29

Defect reconstruction result for deformed geometry: (a) deformed geometry, (b) missed geometry, and (c) assembled defects

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