Current methods for predicting the dynamic behavior of the milling process are limited because they do not provide combined stability and accuracy information. Therefore, the selection of machining parameters is commonly based upon limited information or experience. The result is unnecessary part errors that are created from tool vibrations. These errors are a limiting factor when producing precision components (e.g. thin wall structures and miniature components).
Tool vibrations impose severe limitations on industrial capability: 1) reduced accuracy; 2) a poor surface finish; and 3) increased costs which are linked to instability. Although research studies target these limitations for conventional size tools, the differences and role of dynamics at miniature levels has remained virtually unstudied. This research seeks to develop analysis and sensing methods for conventional to miniature milling dynamics; these developments will enable Smart Machine Tools that account for the effects of vibration. Dynamical system analysis and modeling efforts provide the crucial first steps for integrating control strategies into smart machines.
This research seeks to advance industrial capability for conventional to miniature machining applications, develop new capabilities for monitoring rotating shaft motions, and provide advances in miniature part production (e.g. small medical devices and MEMS). Outreach and recruiting efforts focus on K-12 students and undergraduate students, particularly from traditionally underrepresented groups, through: 1) developing a "So you want to be an engineer?" outreach program with a hands-on children's learning museum; 2) two existing UF outreach programs. Furthermore, this research develops tools that enhance the infrastructure for future research and seeks to enable the industrial application of miniature machining dynamics.
