Surface finish prediction models for precision grinding of silicon
Conventional grinding of silicon substrates results in poor surface quality unless they are machined in ductile mode on expensive ultra-precision machine tools. However, precision grinding can be used to generate massive ductile surfaces on silicon so that the polishing time can be reduced imme...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/14277/ http://irep.iium.edu.my/14277/ http://irep.iium.edu.my/14277/5/Surface_finish_prediction_models_for_precision_grinding_of_silicon2012.pdf |
| Summary: | Conventional grinding of silicon substrates
results in poor surface quality unless they are machined in
ductile mode on expensive ultra-precision machine tools.
However, precision grinding can be used to generate
massive ductile surfaces on silicon so that the polishing
time can be reduced immensely and surface quality
improved. However, precision grinding has to be planned
with reliability in advance and the process has to be
performed with high rates of reproducibility. Therefore, this
work reports the empirical models developed for surface
parameters Ra, Rmax, and Rt with precision grinding
parameters, depths of cut, feed rates, and spindle speeds
using conventional numerical control machine tools with
Box–Behnken design. Second-order models are developed
for the surface parameters in relation to the grinding
parameters. Analysis of variance is used to show the
parameters as well as their interactions that influence the
roughness models. The models are capable of navigating
the design space. Also, the results show large amounts of
ductile streaks at depth of cut of 20 μm, feed rate of
6.25 mm/min, and spindle speed of 70,000 rpm with a
43-nm Ra. Optimization experiments by desirability
function generate 37-nm Ra, 400-nm Rmax, and 880-nm
Rt with massive ductile surfaces.
Keywords Precision grinding . Box–Behnken design .
Silicon . Surface roughness parameters . Empirical models .
Analysis of variance |
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