Monitoring and control of micromilling process represents a challenging task in metal cutting. The lack of static and dynamic stiffness due to the small tool size represents one of the most important issues that has to be tackled in order to provide satisfactory control of the process. In this paper different methods for identifying micro-end milling dynamics for process monitoring are proposed. On one side a parametric approach based on the identification of machining system ODPs (Operation Dynamic Parameters) has been designed. On the other hand a non-parametric approach, based on the calculation of system Lyapunov exponents has been also tested to verify whether generalized methods for assessing system dynamics are also applicable in micromachining where the process nonlinearity's can become relevant, thus limiting the effectiveness of other monitoring methods.
Parametric and non-parametric identification of micromilling dynamics
Marco Leonesio;
2015
Abstract
Monitoring and control of micromilling process represents a challenging task in metal cutting. The lack of static and dynamic stiffness due to the small tool size represents one of the most important issues that has to be tackled in order to provide satisfactory control of the process. In this paper different methods for identifying micro-end milling dynamics for process monitoring are proposed. On one side a parametric approach based on the identification of machining system ODPs (Operation Dynamic Parameters) has been designed. On the other hand a non-parametric approach, based on the calculation of system Lyapunov exponents has been also tested to verify whether generalized methods for assessing system dynamics are also applicable in micromachining where the process nonlinearity's can become relevant, thus limiting the effectiveness of other monitoring methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.