Along with the pressure and the abrasive medium used, temperature plays an important role in controlling the AFM process. In general, higher temperatures will lead to a decreasing viscosity of the medium, which causes lower material removal rates. On the other hand, material removal rates can be increased by raising the pressure and reducing the flow cross-section in the fixture, due to an increase in the fluid’s velocity. With knowledge of these dependencies it is possible to design processes for complex-shaped ceramic workpieces. Nevertheless, process design in AFM is often still based on empirical studies and personal experience. Newest developments in process design use computational fluid dynamics (CFD) to correlate the results of empirical studies with results from a simulated flow of the abrasive medium [54].
The most important step in modeling the flow during AFM with CFD is describing the medium’s viscoelastic properties in a suitable material model. First, values for the viscosity depending on the shear rate are measured with a rheometer. Then, under the assumption of a one-phase fluid as the abrasive medium, the viscoelastic behavior can be described with the Ostwald–de-Waele-model as exponentially decreasing over the shear rate. With a known material model for an abrasive medium, the flow along simply shaped geometries in the AFM process is simulated, taking boundary conditions such as the applied pressure and the initial temperature into consideration. As a result, from these simulations, the values for pressure and velocity are known locally along the workpiece. These values can be correlated with the results, such as surface roughness or edge rounding, from actual machining experiments on simply shaped parts. These correlations can be used to predict the results for AFM of complex-shaped parts if the local values for velocity and pressure are known from CFD simulations [54]. This new method for process design can help accelerate process optimization for AFM and reduce its costs, especially in productions with small batch sizes.
Future Prospects