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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. [ABSTRACT FROM AUTHOR]

Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis (GSA). It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9% with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the GSA for parameter identification. [ABSTRACT FROM AUTHOR]