This work addresses the advanced probabilistic modeling of the stochastic nature of
microgrinding in the machining of high-aspect ratio, ceramic micro-features. The
heightened sensitivity of such high-fidelity workpieces to excessive grit cutting force
drives a need for improved stochastic modeling. Statistical propagation is used to
generate a comprehensive analytic probabilistic model for static wheel topography.
Numerical simulation and measurement of microgrinding wheels show the model
accurately predicts the stochastic nature of the topography when exact wheel
specifications are known. Investigation into the statistical scale affects associated
microgrinding wheels shows that the decreasing number of abrasives in the wheel
increases the relative statistical variability in the wheel topography although variability in
the wheel concentration number dominates the source of variance. An in situ
microgrinding wheel measurement technique is developed to aid in the calibration of the
process model to improve on the inaccuracy caused by wheel specification error. A
probabilistic model is generated for straight traverse and infeed microgrinding dynamic
wheel topography. Infeed microgrinding was shown to provide a method of measuring
individual grit cutting forces with constant undeformed chip thickness within the grind
zone. Measurements of the dynamic wheel topography in infeed microgrinding verified
the accuracy of the probabilistic model.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/49118 |
| Date | 20 September 2013 |
| Creators | Kunz, Jacob Andrew |
| Contributors | Mayor, J. Rhett |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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