Automated Identification of Linear Machine Tool Model Parameters Using Global Sensitivity Analysis

Abstract

High-fidelity machine tool models are needed for condition monitoring, machine tool development, and process simulation. To accurately predict the dynamic behavior of their real counterparts, these models have to be identified, meaning that the values for the involved physical model parameters have to be found by comparing the model with measured data from its real counterpart. As of now, this can only be performed automatically for comparably simple models, which are only valid under limiting assumptions. In contrast, parameter identification for predictive high-fidelity models requires cumbersome manual effort in many intermediate steps. The present work addresses this problem by showing how to automatically identify the parameters of a complex structural dynamic machine tool model using global sensitivity analysis. The capability of the proposed approach is demonstrated in two steps for simulated reference data: first, with a model being able to perfectly replicate the reference data, and second, with a disturbed model, which can only approximate the reference because modeling is present. It is shown that, in both cases, globally valid model parameters, which lead to high conformity with the reference data, can be found, paving the way for calibrating models based on experimental reference data in future work.

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

“…Methods to identify the parameters of existing models require user expertise and are not fully automated, making them impractical for non-experts [15,17,20]. Furthermore, they often require digital models of the machine components.…”

“…Furthermore, they often require digital models of the machine components. Additionally, the quality of these models directly affects the quality of the final simulation results [16,17].…”

“…In this way, the dimensionality problem is mitigated. In subsequent research, Ellinger and Zaeh [17] used the GSA in combination with an iterative approach to estimate the parameters of complex models of machine tool structures. The parameter optimization is conducted by minimizing the difference between the measured behavior of the machine tool and the simulated behavior of its model.…”

“…Compared to methods like Ellinger's [16,17], which require extensive optimizations, our approach has a significantly faster computation time. The reason for this is that the parameter space is smaller, and the expected displacement is calculated analytically instead of performing a time-consuming numerical simulation.…”

Review on errors and transmission characteristics of the planetary roller screw mechanism

“…In the last decade, due to the generation of hydraulic replacement of planetary roller screws, they have been heavily used in new energy vehicles, 18 highprecision machine tools, 19,20 aerospace, 21 and other fields. [22][23][24] Furthermore, the PRSM has been continuously favored by the above fields because of its excellent performance and can effectively solve the bottleneck problems under limited usage space.…”