Superabrasive microgrinding wheels are used for jig grinding of microstructures using various grinding approaches. The desire for increased final geometric accuracy in microgrinding leads to the need for improved process modeling and understanding. An improved understanding of the source of wheel topography characteristics leads to better knowledge of the interaction between the individual grits on the wheel and the grinding workpiece. Analytic stochastic modeling of the abrasives in a general grinding wheel is presented as a method to stochastically predict the wheel topography. The approach predicts the probability of the number of grits within a grind wheel, the individual grit locations within a given wheel structure, and the static grit density within the wheel. The stochastic model is compared to numerical simulations that imitate both the assumptions of the analytic model where grits are allowed to overlap and the more realistic scenario of a grind wheel where grits cannot overlap. A new technique of grit relocation through collective rearrangement is used to limit grit overlap. The results show that the stochastic model can accurately predict the probability of the static grit density while providing results two orders of magnitude faster than the numerical simulation techniques. It is also seen that grit overlap does not significantly impact the static grit density allowing for the simpler, faster analytic model to be utilized without sacrificing accuracy.

References

References
1.
Malkin
,
S.
, and
Guo
,
C.
,
2008
,
Grinding Technology: Theory and Applications of Machining With Abrasives
,
ASCE
,
Reston, VA
.
2.
Marinescu
,
I.
,
Hitchiner
,
M.
,
Uhlmann
,
E.
, and
Inasaki
,
I.
,
2007
,
Handbook of Machining With Grinding Wheels
,
CRC Press
,
Boca Raton, FL
.
3.
Brinksmeier
,
E.
,
Aurich
,
J.
,
Govekar
,
E.
,
Heinzel
,
C.
,
Hoffmeister
,
H.
,
Klocke
,
F.
,
Peters
,
J.
,
Rentsch
,
R.
,
Stephenson
,
D.
, and
Uhlmann
,
E.
,
2006
, “
Advances in Modeling and Simulation of Grinding Processes
,”
CIRP Ann.-Manuf. Technol.
,
55
(
2
), pp.
667
696
.10.1016/j.cirp.2006.10.003
4.
Hou
,
Z.
, and
Komanduri
,
R.
,
2003
, “
On the Mechanics of the Grinding Process-Part I. Stochastic Nature of the Grinding Process
,”
Int. J. Mach. Tools Manuf.
,
43
(
15
), pp.
1579
1593
.10.1016/S0890-6955(03)00186-X
5.
Park
,
H.
,
2008
, “
Development of Micro-Grinding Mechanics and Machine Tools
,” Ph.D. thesis, Georgia Institute of Technology, Atalnta, GA.
6.
Koshy
,
P.
,
Jain
,
V.
, and
Lal
,
G.
,
1993
, “
A Model for the Topography of Diamond Grinding Wheels
,”
Wear
,
169
(
2
), pp.
237
242
.10.1016/0043-1648(93)90304-5
7.
Koshy
,
P.
,
Jain
,
V.
, and
Lal
,
G.
,
1997
, “
Stochastic Simulation Approach to Modelling Diamond Wheel Topography
,”
Int. J. Mach. Tools Manuf.
,
37
(
6
), pp.
751
761
.10.1016/S0890-6955(96)00086-7
8.
Gong
,
Y.
,
Wang
,
B.
, and
Wang
,
W. S.
,
2002
, “
The Simulation of Grinding Wheels and Ground Surface Roughness Based on Virtual Reality Technology
,”
J. Mater. Process. Technol.
,
129
(
1
), pp.
123
126
.10.1016/S0924-0136(02)00589-7
9.
Hines
,
W. W.
,
Montgomery
,
D. C.
,
Goldsman
,
D. M.
, and
Borror
,
C. M.
,
2003
, Probability and Statistics in Engineering 4th Edition, Wiley.
10.
Davis
,
C.
,
1974
, “
The Dependence of Grinding Wheel Performance on Dressing Procedure
,”
Int. J. Mach. Tool Des. Res.
,
14
(
1
), pp.
33
52
.10.1016/0020-7357(74)90010-9
11.
Chen
,
X.
, and
Rowe
,
W.
,
1996
, “
Analysis and Simulation of the Grinding Process. Part I: Generation of the Grinding Wheel Surface
,”
Int. J. Mach. Tools Manuf.
,
36
(
8
), pp.
871
882
.10.1016/0890-6955(96)00116-2
12.
He
,
D.
,
Ekere
,
N.
, and
Cai
,
L.
,
1999
, “
Computer Simulation of Random Packing of Unequal Particles
,”
Phys. Rev. E
,
60
(
6
), pp.
7098
7104
.10.1103/PhysRevE.60.7098
13.
Chang
,
H. C.
, and
Wang
,
J. J. J.
,
2008
, “
A Stochastic Grinding Force Model Considering Random Grit Distribution
,”
Int. J. Mach. Tools Manuf.
,
48
(
12
), pp.
1335
1344
.10.1016/j.ijmachtools.2008.05.012
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