Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification

Abstract

Dynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building the model, the parameterization of the model, that is, determining the parameter values the model is based upon, is the most crucial step. This paper describes the application of both sensitivity-based and black-box parameter identification to a machine tool. It further provides a comparison between these two methods and the method of sequential assembly. It is shown that both methods can increase the mode shape conformity by more than 25% and significantly reduce damping deviations. However, sensitivity-based parameter identification is the most economical method, offering the chance to update a dynamic machine tool model within minutes.