Research Papers

Microstructure Feature Recognition for Materials Using Surfacelet-Based Methods for Computer-Aided Design-Material Integration

[+] Author and Article Information
Namin Jeong

The George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405

David W. Rosen

The George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mail: david.rosen@me.gatech.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 21, 2014; final manuscript received September 18, 2014; published online October 24, 2014. Assoc. Editor: Joseph Beaman.

J. Manuf. Sci. Eng 136(6), 061021 (Oct 24, 2014) (10 pages) Paper No: MANU-14-1240; doi: 10.1115/1.4028621 History: Received April 21, 2014; Revised September 18, 2014

With the material processing freedoms of additive manufacturing (AM), the ability to characterize and control material microstructures is essential if part designers are to properly design parts. To integrate material information into Computer-aided design (CAD) systems, geometric features of material microstructure must be recognized and represented, which is the focus of this paper. Linear microstructure features, such as fibers or grain boundaries, can be found computationally from microstructure images using surfacelet based methods, which include the Radon or Radon-like transform followed by a wavelet transform. By finding peaks in the transform results, linear features can be recognized and characterized by length, orientation, and position. The challenge is that often a feature will be imprecisely represented in the transformed parameter space. In this paper, we demonstrate surfacelet-based methods to recognize microstructure features in parts fabricated by AM. We will provide an explicit computational method to recognize and to quantify linear geometric features from an image.

Copyright © 2014 by ASME
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Fig. 1

Proposed reverse engineering of material process

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Fig. 2

Geometry of 2D Radon transform

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Fig. 3

Single linear feature and its shape of butterfly wings: (a) single linear feature and (b) shape of butterfly wing of linear feature

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Fig. 4

Simple example of an over and under-represented feature

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Fig. 5

Simple synthetic fiber-reinforced microstructure and surfacelet representations: (a) fiber-reinforced composite microstructure and (b) radon transform

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Fig. 6

Schematic of linear feature of characterization

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Fig. 7

The Radon transforms butterfly wings: (a) cross points of butterfly wings and (b) slope of butterfly wings

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Fig. 8

IN100 voxel data set

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Fig. 9

Cross section of part of IN100 example

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Fig. 10

Result of the Radon transform with cross points of the butterfly wings for IN 100 example: (a) recognized line segments and (b) reconstructed grains

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Fig. 11

Reconstructed grain boundaries of the IN 100 example using the surfacelet method: (a) recognized line segments and (b) recognized grains

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Fig. 12

Line overlay method results of IN 100 example: (a) recognized line segments and (b) recognized grains

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Fig. 13

Surfacelet method results for BurTi-Ti64 composite sample: (a) original BurTi-Ti64 microstructure and (b) recognized grain boundaries

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Fig. 14

Line overlay results for the BurTi-Ti64 example: (a) recognized grain boundaries and (b) recognized grains

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Fig. 15

Acicular α-phase Ti64 alloy

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Fig. 16

Recognized laths in the highlighted region.



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