Stochastic Characteristics in Microgrinding Wheel Static Topography using Machine Vision

Abstract

Superabrasive grinding wheels are used for the machining of brittle materials such 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. The numerical simulation and analytic models used to describe the wheel topography were seen to estimate the static grit density to within 4.5% using measured wheel geometry specifications. Utilizing only manufacturer-supplied specifications caused the models to predict the static grit density to within 24% leading to a need for improved wheel tolerancing and in situ wheel measurement.