Research Papers

Data Fusion Strategy for Multiscale Surface Measurements

[+] Author and Article Information
Suresh K. Ramasamy

e-mail: sureshramasamy@gmail.com

Brian D. Boudreau

Department of Mechanical Engineering
and Engineering Science,
University of North Carolina at Charlotte,
9201 University City Blvd.,
Charlotte, NC 28223

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MICRO AND NANO-MANUFACTURING. Manuscript received May 30, 2012; final manuscript received January 25, 2013; published online March 25, 2013. Assoc. Editor: Brad Nelson.

J. Micro Nano-Manuf 1(1), 011004 (Mar 25, 2013) (6 pages) Paper No: JMNM-12-1032; doi: 10.1115/1.4023755 History: Received May 30, 2012; Revised January 25, 2013

Interdisciplinary research efforts have started focusing on the development of multiscale models and development of designer multiscale surfaces exhibiting specific properties at different scales for a specific purpose. With the rapid evolution of these new engineered surfaces for microelectromechanical systems (MEMS), microfluidics, etc., there is a strong need for developing tools to measure and characterize these surfaces at different scales. In order to obtain all meaningful details of the surface at various required scales, one is left with the only option of measuring the surface using multiple technologies using a combination of instruments. The majority of hardware-based approaches focus on the development of systems housing multiple technologies/capabilities into a single frame. These systems enable the user to obtain different surface maps using various technologies, but the user does not readily have the ability to combine all the obtained data into one single dataset. The effective approach toward multiscale measurement and characterization would be to use the individual measurement tools and finding a method to relate the individual coordinate systems and use an offline virtual tool to unify, manipulate, segment, merge, and retrieve data. Shape primitives and focus-based fusion strategies cannot be used as every data point in the data sets under consideration has to be treated as essentially at optimal focus. A multiscale data fusion strategy results in edge effects on nonplanar and high aspect ratio surfaces. An optimized fusion strategy, the “FWR method,” for the surface metrology domain is proposed where the subimages obtained from discrete wavelet frame (DWF) were separated into three regimes—form, waviness, and roughness—and fusion was not performed on subimages in the form regime. This approach effectively eliminates the edge effects. Individual data-point-level fusion was successfully demonstrated on Fresnel microlens array surface data as a case study of a nondirectional engineered surface with high aspect ratio.

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Grahic Jump Location
Fig. 1

Fresnel microlens array at (a) 5X magnification and (b) 20X magnification

Grahic Jump Location
Fig. 2

Schematic of FWR data fusion for surface metrology datasets (a) original data, (b) precondition (outlier removal, resample, and resize), (c) coarse registration, (d) fine registration after control point detection, (e) multiscale decomposition on selected same size area from both data, (f) multiscale fusion, (g) inverse transform on fused subdatasets to obtain fused data, (h) fused data replaced to the original location

Grahic Jump Location
Fig. 3

Six wavelet planes obtained from: (a) low magnification data; (b) high magnification data to obtain and (c) new set of six wavelet planes

Grahic Jump Location
Fig. 4

Fused data on Fresnel lens

Grahic Jump Location
Fig. 5

Zoomed in view of fused data on Fresnel lens

Grahic Jump Location
Fig. 6

(a) Fused data obtained when all planes are considered for fusion; (b) fused data obtained when only roughness and waviness planes are considered for fusion



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