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Research Papers

Surface Topography of Additive Manufacturing Parts Using a Finite Difference Approach

[+] Author and Article Information
Saeed Jamiolahmadi

Department of Automotive, Mechanical and
Manufacturing Engineering,
Faculty of Engineering and Applied Science,
University of Ontario Institute of Technology,
2000 Simcoe Street North,
Oshawa, ON L1H 7K4, Canada
e-mail: Saeed.Jamiolahmadi@uoit.ca

Ahmad Barari

Mem. ASME
Department of Automotive, Mechanical and
Manufacturing Engineering,
Faculty of Engineering and Applied Science,
University of Ontario Institute of Technology,
2000 Simcoe Street North,
Oshawa, ON L1H 7K4, Canada
e-mail: Ahmad.Barari@uoit.ca

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 14, 2014; final manuscript received September 12, 2014; published online October 24, 2014. Assoc. Editor: David L. Bourell.

J. Manuf. Sci. Eng 136(6), 061009 (Oct 24, 2014) (8 pages) Paper No: MANU-14-1178; doi: 10.1115/1.4028585 History: Received April 14, 2014; Revised September 12, 2014

Inspection of surface integrity in additive manufacturing (AM) parts essentially needs a detailed understanding of their actual surface topography. Today's optical surface topography and roughness measurement sensors only provide information of the discrete points measured from the manufactured surface and not a detailed reconstruction of the surface topography. This paper presents a finite difference approach for reconstruction of the surface topography using sample measured data points. The developed methodology can be used in the surface quality inspection of the additive manufactured parts, their surface texture modeling, surface integrity analysis, and in planning for the required postprocessing or down-stream surface finish processes suitable for them. The methodology is fully implemented, and variety of experiments is conducted. The results show that the developed methodology is successful to reconstructed surface topography of the AM parts.

Copyright © 2014 by ASME
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References

Figures

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Fig. 1

u–v–e deviation space, detailed deviation zone representation

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Fig. 2

Additive manufactured part

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Fig. 3

Actual microscopic view of AM surface

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Fig. 4

Surface deviation space using 10% of initial points before iteration runs

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Fig. 5

Estimated surface topography using 10% of initial points after 50 iteration runs

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Fig. 6

Estimated surface topography using 10% of initial points after 100 iteration runs

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Fig. 7

Estimated surface topography using 10% of initial points after 500 iteration runs

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Fig. 8

3D surface reconstructed using 10% of initial points after 500 runs

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Fig. 9

Convergence of FDM while using 10% of initial points

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Fig. 10

Surface deviation space using 50% of initial points before iteration runs

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Fig. 11

Estimated surface topography using 50% of initial points after 50 iteration runs

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Fig. 12

Estimated surface topography using 50% of initial points after 100 iteration runs

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Fig. 13

Estimated surface topography using 50% of initial points after 500 iteration runs

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Fig. 14

3D surface reconstructed using 50% of initial points after 500 runs

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Fig. 15

Convergence of FDM while using 50% of initial points

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Fig. 16

Surface deviation space using 90% of initial points before iteration runs

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Fig. 17

Estimated surface topography using 90% of initial points after 50 iteration runs

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Fig. 18

Estimated surface topography using 90% of initial points after 100 iteration runs

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Fig. 19

Estimated surface topography using 90% of initial points after 500 iteration runs

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Fig. 20

3D surface reconstructed using 90% of initial points after 500 runs

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Fig. 21

Convergence of FDM while using 90% of initial points

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Fig. 22

Estimated roughness after 500 runs versus percentage of points used in FDM

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Fig. 23

Normalized difference of the estimated roughness and original roughness for percentage of points used in FDM

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