SUMMARY

The primary objective of this study is to enhance the productivity of the micromilling process through an improved understanding of the unique properties of the microscale. Micromilling is a material removal method that is preferred for the production of a wide variety of micro components, due to its potential for high precision and applicability to a wide range of geometries and materials. However, the micromilling process suffers from low productivity due to feedrate limitations, short life of tools resulting in high production cost, and required high precision due to small feature size. An increase of productivity at the microscale requires consideration of unique microscale scale effects in process parameter selection to allow for increased material removal rate, improved stability, and increased tool life. Early results from this study have identified the following scale-effects as critical barriers to productivity enhancement in the micromilling processes: (a) the increased significance of tool size to feature size ratio, (b) the impact of sampling rate, and (c) the impact of spindle power characteristics. In order to compensate for these effects, this study has introduced new methods of feedrate optimization and path planning. The new method of feedrate optimization presented in this study achieves increased feedrates through two methods of intelligent segmentation: curvature-based and stability-based. Preliminary numerical simulation of these methods has shown up to a 2x increase in productivity, without loss of precision. A new path planning method is also being investigated to compensate for scale effects. The research approach being followed in this method is to improve productivity by incorporating spindle power characteristics into an optimization scheme for determining the optimal set of tool sizes. Finalization of this approach, including more efficient methods of optimization, is a part of the ongoing work.