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Research Papers

Increasing Part Accuracy in Additive Manufacturing Processes Using a k-d Tree Based Clustered Adaptive Layering

[+] Author and Article Information
Neeraj Panhalkar

Center for Global Design and Manufacturing,
Department of Mechanical and
Materials Engineering,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: panhalnm@mail.uc.edu

Ratnadeep Paul

Center for Global Design and Manufacturing,
Department of Mechanical and
Materials Engineering,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: paulrp@ucmail.uc.edu

Sam Anand

Center for Global Design and Manufacturing,
Department of Mechanical and
Materials Engineering,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: sam.anand@uc.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 15, 2014; final manuscript received September 11, 2014; published online October 24, 2014. Assoc. Editor: Darrell Wallace.

J. Manuf. Sci. Eng 136(6), 061017 (Oct 24, 2014) (10 pages) Paper No: MANU-14-1216; doi: 10.1115/1.4028586 History: Received April 15, 2014; Revised September 11, 2014

Additive manufacturing (AM) is widely used in aerospace, automobile, and medical industries for building highly accurate parts using a layer by layer approach. The stereolithography (STL) file is the standard file format used in AM machines and approximates the three-dimensional (3D) model of parts using planar triangles. However, as the STL file is an approximation of the actual computer aided design (CAD) surface, the geometric errors in the final manufactured parts are pronounced, particularly in those parts with highly curved surfaces. If the part is built with the minimum uniform layer thickness allowed by the AM machine, the manufactured part will typically have the best quality, but this will also result in a considerable increase in build time. Therefore, as a compromise, the part can be built with variable layer thicknesses, i.e., using an adaptive layering technique, which will reduce the part build time while still reducing the part errors and satisfying the geometric tolerance callouts on the part. This paper describes a new approach of determining the variable slices using a 3D k-d tree method. The paper validates the proposed k-d tree based adaptive layering approach for three test parts and documents the results by comparing the volumetric, cylindricity, sphericity, and profile errors obtained from this approach with those obtained using a uniform slicing method. Since current AM machines are incapable of handling adaptive slicing approach directly, a “pseudo” grouped adaptive layering approach is also proposed here. This “clustered slicing” technique will enable the fabrication of a part in bands of varying slice thicknesses with each band having clusters of uniform slice thicknesses. The proposed k-d tree based adaptive slicing approach along with clustered slicing has been validated with simulations of the test parts of different shapes.

Copyright © 2014 by ASME
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References

Figures

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Fig. 2

k-d tree representation of the 3D dataset (adapted from Ref. [12])

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Fig. 3

k-d tree for the 3D dataset

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Fig. 4

Virtual manufacturing of a cylinder (adapted from Ref. [2])

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Fig. 5

(a) Uniform slice thickness and (b) adaptive slice thickness

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Fig. 6

(a) CAD model of a sample part I and (b) STL representation of the sample part I

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Fig. 7

k-d tree representation with the original set of STL vertices (adapted from Ref. [12])

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Fig. 8

Calculation of cusp height within a cuboid in 2D view

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Fig. 9

Discretization of an STL facet

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Fig. 10

(a) Original STL representation of the sample part I and (b) discretized STL representation of the sample part I

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Fig. 11

Partial k-d tree representation with the discretized STL points

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Fig. 12

Complete k-d tree representation using the discretized STL points (adapted from Ref. [12])

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Fig. 13

Criteria to divide slice thickness at z-level (adapted from Refs. [12] and [17])

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Fig. 14

Virtual manufacturing of sample part 1 using k-d tree adaptive slice thicknesses [12]

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Fig. 15

(a) CAD model—sample part II, (b) k-d tree—sample part II, and (c) virtual manufacturing using k-d tree adaptive slicing [12]

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Fig. 16

(a) CAD model—sample part III, (b) k-d tree—sample part III, and (c) virtual manufacturing using k-d tree adaptive slicing [12]

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Fig. 17

(a) CAD model—test part II, (b) k-d tree—test part II, and (c) virtual manufacturing using k-d tree adaptive slicing

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Fig. 18

(a) CAD model with all features and (b) STL file of the feature selected for profile error evaluation

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Fig. 19

(a) CAD model, (b) feature 1, and (c) feature 2 selected for profile error evaluation

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Fig. 20

(a) Virtual model using slices from k-d tree and (b) virtual model using banded slicing approach

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Fig. 21

Voxelization of a part at a particular level

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