SUMMARY

This work addresses the advanced stochastic modeling of microgrinding for the purpose of improved process control in the machining of high-aspect ratio, ceramic micro-features. The extreme 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 stochastic 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 measured. Investigation into the statistical scale affects associated microgrinding wheels showed 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. An analytic stochastic 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 analytic stochastic model.