Johannes Ellinger’s research while affiliated with Technical University of Munich and other places

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.

The dynamic characterization of structures by means of modal parameters offers many valuable insights into the vibrational behavior of these structures. However, modal parameter estimation has traditionally required expert knowledge and cumbersome manual effort such as, for example, the selection of poles from a stabilization diagram. Automated approaches which replace the user inputs with a set of rules depending on the input data set have been developed to address this shortcoming. This paper presents an alternative approach based on Bayesian optimization. This way, the possible solution space for the modal parameter estimation is kept as widely open as possible while ensuring a high accuracy of the final modal model. The proposed approach was validated on both a synthetic test data set and experimental modal analysis data of a machine tool. Furthermore, it was benchmarked against a similar tool from a well-known numerical computation software application.

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.

Chatter vibrations originated by the machine structure are a major limitation for the productivity of ram based machines performing heavy duty operations. Consequently, the damping of the machine structure has a capital importance. It is known that interfaces and guideways are the main origin of damping. Recently, the use of active dampers has been introduced in industry. In this work, the damping of hydrostatic and rolling guideways with and without active damping has been experimentally identified and compared using receptance coupling. The results show that hydrostatic guidance can introduce 3–4 times more damping than a roller based system. However, the introduction of active damping is a game changer enhancing damping more than 30 times.

Due to their critical influence on manufacturing accuracy, machine tool feed drives and the monitoring of their condition has been a research field of increasing interest for several years already. Accurate and reliable estimates of the current condition of the machine tool feed drive’s components ball screw drive (BSD) and linear guide shoes (LGSs) are expected to significantly enhance the maintainability of machine tools, which finally leads to economic benefits and smoother production. Therefore, many authors performed extensive experiments with different sensor signals, features and components. Most of those experiments were performed on simplified test benches in order to gain genuine and distinct insights into the correlations between the recorded sensor signals and the investigated fault modes. However, in order to build the bridge between real use cases and scientific findings, those investigations have to be transferred and performed on a more complex test bench, which is close to machine tools in operation. In this paper, a condition monitoring test cycle is developed for such a test bench. The developed test cycle enables the recording of a re-producible data basis, on which models for the condition monitoring of BSDs and LGSs can be based upon.

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.

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.

Ungeplante Maschinenausfälle führen zu Stillständen in der Produktion, die große Auswirkungen auf die Wertschöpfungskette eines Unternehmens haben. Der Einsatz von Predictive Maintenance (PdM) entlang dieser Kette erhöht die Maschinenverfügbarkeit und sichert einen reibungslosen Produktionsablauf. Im Rahmen verschiedener Forschungsprojekte am iwb werden Anwendungsfälle für PdM in der Fertigung, der Montage und der Produktionssteuerung betrachtet. Dieser Beitrag beleuchtet individuelle Herausforderungen, Lösungsansätze und Grenzen von PdM im produktionstechnischen Umfeld. Unplanned machine downtimes can have a big impact on the value chain of a company. Predictive Maintenance (PdM) shows the potential to increase machine availability and secures smooth production processes. Different research projects at iwb examine applications of PdM within manufacturing and assembly, as well as the integration of such approaches in production planning. This article highlights individual challenges, solutions and limits of PdM within production.

Kurzfassung Um spanende Werkzeugmaschinen möglichst profitabel betreiben zu können, muss der aktuelle Verschleißzustand der Maschinen hinreichend genau bekannt sein. Dies kann durch kontinuierliche Zustandsüberwachung erreicht werden. Neuartige, direkt in die Komponenten des Antriebsstranges von Werkzeugmaschinen integrierte Sensoren bieten die Möglichkeit, dies auf einfache und kostengünstige Weise durchzuführen. Als Indikator für Verschleiß kann dabei direkt das dynamische Verhalten des Antriebsstranges herangezogen und so eine hohe Anlagenverfügbarkeit sichergestellt werden.