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Research Papers

Stochastic Characteristics in Microgrinding Wheel Static Topography

[+] Author and Article Information
Jacob A. Kunz

George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: jacobakunz@gatech.edu

J. Rhett Mayor

Associate Professor
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: rhett.mayor@me.gatech.edu

Contributed by the Manufacturing Engineering of ASME for publication in the JOURNAL OF MICRO- AND NANO-MANUFACTURING. Manuscript received June 18, 2013; final manuscript received January 17, 2014; published online February 21, 2014. Assoc. Editor: Stefan Dimov.

J. Micro Nano-Manuf 2(2), 021001 (Feb 21, 2014) (9 pages) Paper No: JMNM-13-1052; doi: 10.1115/1.4026545 History: Received June 18, 2013; Accepted January 17, 2014; Revised January 17, 2014

Superabrasive grind wheels are used for the machining of brittle materials such as tungsten carbide. Stochastic modeling of the wheel topography can allow for statistical bounding of the grind force characteristics allowing improved surface quality without sacrificing productivity. This study utilizes a machine vision method to measure the wheel topography of diamond microgrinding wheels. The results showed that there are large variances in wheel specifications from the manufacturer and that microgrinding wheels suffer from statistical scaling effects that increase wheel-to-wheel variability in the topography. Analysis of the static grit density values measured on the microgrinding wheels showed that the distributions provided by both analytic stochastic and numerical simulation models accurately predicted the static grit density within a significance level of 5%. Utilizing only manufacturer-supplied specifications caused the models to predict the static grit density with errors as large as 25.3% of the predicted value leading to a need for improved wheel tolerancing and in situ wheel measurement. The spacings between the grits on the wheel surface were shown to be independent of direction and can best be described by a loglogistic distribution.

Copyright © 2014 by ASME
Topics: Wheels , Density
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References

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Figures

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Fig. 1

SEM image of 1 mm OD, metal bonded, #220 grit diamond grind wheel

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Fig. 2

Microscope image of 1 mm OD, #220 grit diamond grind wheel in a microscope at 10× magnification

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Fig. 3

Stitched imaged surface of 0.5 mm OD, #200 grit diamond grind wheel composed from 61 individual images

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Fig. 4

Machine vision algorithm for locating individual grits

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Fig. 5

Effects of machine vision algorithm steps on select region of a #200 microgrinding wheel

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Fig. 6

Bond thickness measurements showing no definite variation across different wheel diameters

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Fig. 7

Bond thickness measurements showed no definite variation across different wheel widths

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Fig. 8

Bond thickness measurements showed no definite variation across different grit sizes

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Fig. 9

Normal probability plot for the measured wheel concentration number across all wheels

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Fig. 10

Normal probability plot for the measured bond thickness across all wheels

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Fig. 11

Normal probability plot for the shank diameter error across all wheels

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Fig. 12

Normal probability plot for the wheel width error across all wheels

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Fig. 13

Histogram of static grit density residual error between experimental measurement and (a) numerical simulation model, and (b) analytic stochastic model

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Fig. 14

Potential scale effect of higher static grit density relative standard deviation in microgrinding wheels due to the few number of grits in the wheel

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Fig. 15

Histogram of the circumferential grit spacings in wheel 6.2

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Fig. 16

Frequency PDF of measured circumferential grit spacings in wheel 6.2 with fitted distribution comparison between the currently used rayleigh distribution and proposed (a) lognormal distribution, and (b) loglogistic distribution

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