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.
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December 2014
Research-Article
Microstructure Feature Recognition for Materials Using Surfacelet-Based Methods for Computer-Aided Design-Material Integration
Namin Jeong,
Namin Jeong
The George W. Woodruff School
of Mechanical Engineering,
of Mechanical Engineering,
Georgia Institute of Technology
,Atlanta, GA 30332-0405
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David W. Rosen
David W. Rosen
1
The George W. Woodruff School
of Mechanical Engineering,
e-mail: david.rosen@me.gatech.edu
of Mechanical Engineering,
Georgia Institute of Technology
,Atlanta, GA 30332-0405
e-mail: david.rosen@me.gatech.edu
1Corresponding author.
Search for other works by this author on:
Namin Jeong
The George W. Woodruff School
of Mechanical Engineering,
of Mechanical Engineering,
Georgia Institute of Technology
,Atlanta, GA 30332-0405
David W. Rosen
The George W. Woodruff School
of Mechanical Engineering,
e-mail: david.rosen@me.gatech.edu
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. Dec 2014, 136(6): 061021 (10 pages)
Published Online: October 24, 2014
Article history
Received:
April 21, 2014
Revision Received:
September 18, 2014
Citation
Jeong, N., and Rosen, D. W. (October 24, 2014). "Microstructure Feature Recognition for Materials Using Surfacelet-Based Methods for Computer-Aided Design-Material Integration." ASME. J. Manuf. Sci. Eng. December 2014; 136(6): 061021. https://doi.org/10.1115/1.4028621
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