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. 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 global sensitivity analysis for parameter identification.
Ball screws and linear guides are among the key components of machine tools. Abrasive wear causes a loss in stiffness of these components over time affecting the attainable manufacturing precision and, eventually, leads to failures and costly down-time. In order to control these effects, the condition of the crucial feed drive components needs to be monitored. This paper shows, how the feed drive condition can be monitored by looking at the modal parameters of the system. It will be shown, that preload loss cannot only be detected globally, but can be traced back to the worn component. A distinct test cycle was developed for this purpose.
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.
Kurzfassung
Der wirtschaftliche Einsatz von Werkzeugmaschinen ist maßgeblich abhängig von dem erreichbaren Zeitspanvolumen sowie den Stillstandszeiten aufgrund von Wartungsmaßnahmen. Es ist folglich der stete Wunsch von produzierenden Unternehmen, die Belegungszeit und die Anzahl notwendiger Instandhaltungsmaßnahmen zu reduzieren. Der Digitale Zwilling bietet durch einen permanenten Datenaustausch zwischen der realen Werkzeugmaschine und deren virtueller Repräsentation die Möglichkeit, Bearbeitungsprozesse unter Einhaltung der statischen und dynamischen Lastgrenzen zu optimieren und die Restlebensdauer von Maschinenkomponenten zu prognostizieren.
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