Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.

Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen: Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw. Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit gilt es in allen Bausteinen zu berücksichtigen. Dieser Beitrag konzentriert sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt. Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert. Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten Betriebs- und Umgebungsbedingungen robust sind.

Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions.

Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.

In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.

Ultrasonic wire bonding is a process to form electrical connections in electronics well established industry. Typically, a clamping tool is pressed on the wire and forced to vibrate at relative high frequency 40 to 100 kHz. The ultrasonic vibration is transmitted through the wire into the interface between wire and substrate. Due to frictional processes, contamination like oxide layers are removed from the contact zone, the surface roughness is reduced, and with increasing bond duration an metallic connection of wire and substrate is established. It is known that the amount of ultrasonic energy over time directly influences the strength and reliability of the bond connection, but the determination of optimum bond parameters is still a challenging experimental task. For this, in the past different model approaches have been presented, to calculate the bond quality by simulation. Measuring the friction between wire and substrate to validate these models is a challenging task at ultrasonic bonding frequency. Therefore a versatile test rig for bonding experiments at frequencies lower than 1 kHz is setup to get detailed insight into the different phases of the connection process. It includes a piezoelectric force sensor for the measurement of the three-dimensional process forces, an electrodynamic shaker for the vibration excitation and a conventional tension-compression testing machine to apply the bond normal force. Using this test rig, it is possible to observe the different phases of bond formation in detail, validate and enhance existing models and finally optimize bond parameters for different processes.

In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.

In der Entwicklung mechatronischer Systeme spielt die Steigerung der Verlässlichkeit und somit auch der Zuverlässigkeit und der funktionalen Sicherheit eine entscheidende Rolle. Die modellbasierte Entwicklung liefert in Kombination mit unterstützender Software einen wichtigen Beitrag zur Absicherung der Verlässlichkeit mechatronischer Systeme in frühen Entwicklungsphasen. In der Nutzungsphase ermöglichen aktuelle Verfahren der Zustandsüberwachung und moderne Methoden der Regelungstechnik eine effektive Absicherung. Modelle aus der Entwicklung mechatronischer Systeme enthalten weitreichende Informationen über die Architektur, das Verhalten und die Verlässlichkeit eines Systems. Diese Modelle können als Grundlage für die Erstellung eines Digitalen Zwillings für die vorausschauende Instandhaltung verwendet und mit Zustandsdaten des realen Systems kombiniert werden. Die Nutzung der Modelle für den Digitalen Zwilling bietet weitreichende Potenziale und vereinfacht dessen Erzeugung. Die Veröffentlichung beschreibt Rahmenbedingungen der Integration und stellt die Potenziale des Digitalen Zwillings zur vorausschauenden Instandhaltung dar.

Die Achse als einzige Verbindung zwischen Fahrzeugaufbau und Rad hat die Hauptaufgabe das Rad auf der Straße zuführen. Kinematisch betrachtet übernimmt die Radaufhängung, als Teil der Achse, die Funktion, zwischen Rad und Fahrzeugaufbaueinen vertikalen Freiheitsgrad zur Aufnahme von Fahrbahnunebenheiten zu realisieren. Die aus der RadhubundElastokinematik resultierenden Radstellungsänderungen bestimmen dabei maßgeblich die Fahrdynamik. Zur objektivenBeurteilung von Radaufhängungen ist eine genaue Charakterisierung der Radhub- und Elastokinematik erforderlich.Daher wurde zur Identifikation der kinematischen, elastokinematischen und dynamischen Radaufhängungseigenschaftenam Lehrstuhl für Dynamik und Mechatronik der Universität Paderborn ein Halbachsprüfstand entwickelt. Bei der Auslegungwurde Wert auf ein möglichst breites Einsatzspektrum gelegt. Es können verschiedene Typen von Einzelradaufhängungenin Serien- oder Prototypenkonfiguration am Prüfstand analysiert werden. Er ermöglicht eine Identifikation derdynamischen Radstellungsänderungen unter verschiedenen fahrdynamischen Lastfällen und regellosen Anregungen.

In a variety of industrial applications, liquids are atomized to produce aerosols for further processing. Example applications are the coating of surfaces with paints, the application of ultra-thin adhesive layers and the atomization of fuels for the production of combustible dispersions. In this publication different atomizing principles (standing-wave, capillary-wave, vibrating-mesh) are examined and discussed. Using an optimized standing-wave system, tough liquids with viscosities of up to about 100 Pas could be successfully atomized.

Remaining useful lifetime (RUL) predictions as part of a condition monitoring system are focused in more and more research and industrial applications. To establish an efficient and precise estimate of the RUL of a technical product, different uncertainties have to be handled. To minimize the uncertainties of the RUL estimation, a reliable and accurate prognostic approach as well as a good failure threshold are important. Regarding the failure threshold, most often an expert sets a fixed failure threshold. However, neither the a priori known failure threshold nor a fixedthreshold value are feasible in every application. Especially in the case of varying characteristics of the monitored system, an adaptive failure threshold is of great importance concerning the accuracy of the RUL estimation. Rubber-metal-elements, which are used in a wide range of applications for vibration and sound isolation, are mon-itored by thermocouples to allow for lifetime predictions. Therefore, the element’s state is described by its temper-ature during its service life. Aiming to establish accurate RUL predictions of a rubber-metal-element, uncertainties due to nonlinear material characteristics and changing operational conditions have to be considered. Consequently, different temperature-based failure threshold definitions are implemented and compared within a particle filtering approach.

Gummi-Metall-Teile (GM-Teile) werden zur Schwingungsreduktion u. a. in Windenergieanlagen eingesetzt. Mögliche Anwendungen der Teile liegen in Wellen-, Generator- und Getriebelagerungen, Lagern für die Gondel und ihre Komponenten sowie in Drehmomentstützen. Mit dem Ziel eine prädiktive Instandhaltung zu realisieren, soll eine Zustandsüberwachung für die GM-Teile entwickelt werden. Diese Entwicklung basiert auf der Umsetzung diverser Schritte. Neben der funktionalen Betrachtung wird zwingend auch die konstruktive Integration der Sensoren in das überwachte Teil berücksichtigt. Der Schwerpunkt dieser Arbeit liegt auf der verwendeten Messgröße Temperatur, die mittels ausgewählter Sensorik detektiert wird. Dabei werden Lebensdauerversuche unter instationären Betriebsbedingungen durchgeführt, um diese Messdaten zu generieren. In der Datenauswertung werden sie hinsichtlich der Degradierung des GM-Teils analysiert und für die Ermittlung der nutzbaren Restlebensdauer verwendet. Rubber-metal-elements are used for isolation of vibrations e. g. in wind turbines. Possible applications of the elements are shaft bearings, generator bearings, gearbox bearings, bearings for the nacelle and its components and torque supports. In order to realize predictive maintenance, an accurate condition monitoring system for rubber-metal-elements should be developed. During that development different aspects have to be implemented. Additionally to the functional analysis, the constructive integration of the sensors into the monitored part is mandatory. The focus of this work is on the measured variable temperature, which is detected by means of appropriate sensors. Thereby lifetime tests are run under non-stationary operating conditions to generate temperature measurements. During data analysis, the measured data is analyzed regarding the degradation of the rubber-metal-elements and remaining useful lifetimes are estimated.

In der Entwicklung mechatronischer Systeme spielt die Steigerung der Verlässlichkeit und somit auch der Zuverlässigkeit und der funktionalen Sicherheit eine entscheidende Rolle. Die modellbasierte Entwicklung liefert in Kombination mit unterstützender Software einen wichtigen Beitrag zur Absicherung der Verlässlichkeit mechatronischer Systeme in frühen Entwicklungsphasen. In der Nutzungsphase ermöglichen aktuelle Verfahren der Zustandsüberwachung und moderne Methoden der Regelungstechnik eine effektive Absicherung. Modelle aus der Entwicklung mechatronischer Systeme enthalten weitreichende Informationen über die Architektur, das Verhalten und die Verlässlichkeit eines Systems. Diese Modelle können als Grundlage für die Erstellung eines Digitalen Zwillings für die vorausschauende Instandhaltung verwendet und mit Zustandsdaten des realen Systems kombiniert werden. Die Nutzung der Modelle für den Digitalen Zwilling bietet weitreichende Potenziale und vereinfacht dessen Erzeugung. Die Veröffentlichung beschreibt Rahmenbedingungen der Integration und stellt die Potenziale des Digitalen Zwillings zur vorausschauenden Instandhaltung dar.

Für die Zerstäubung hochviskoser Flüssigkeiten werden neben Düsenzerstäubern vor allem UltraschallStehwellenzerstäuber angewendet [1]. Diese ermöglichen ohne weitere Maßnahmen zwar keine gerichtete Zerstäubung, benötigen jedoch im Gegensatz zu Düsenzerstäubern keine hohen Drücke und haben keine hohen Austrittsgeschwindigkeiten. Zur Erzeugung der Ultraschallwellen werden typischerweise piezoelektrische, mit Bolzen verschraubte LangevinWandler verwendet [1-4], die eine starke Schallabstrahlung bei einer elektrischen Eingangsleistung von bis zu einigen Kilowatt erzeugen können. Wie bei jedem anderen schwingenden System emittiert der Ultraschallwandler zunächst eine Wanderwelle. Mit einem Reflektor, der gegenüber der Sonotrode angeordnet ist, wird eine stehende Welle erzeugt. Im Resonanzabstand zwischen Reflektor und Wandler werden abgestrahlte und reflektierte Wellen so überlagert, dass höhere Schalldruckamplituden erzielt werden. Ein einfacher Ansatz zur Maximierung des Schallpegels im Stehwellenfeld ist die Erhöhung der Schwingungsamplituden des Wandlers, die jedoch zu Schäden oder zumindest zu einer Verringerung der Lebensdauer führen kann. Hohe Schalldrücke werden auch bei geringen Abständen zwischen Wandler und Reflektor erreicht. Das Volumen des Schallfeldes ist in diesem Fall jedoch für die meisten Prozesse zu klein. Ein weiterer Ansatz ist die Verwendung zweier entgegengesetzt angeordneter Wandler [5]. In diesem Fall erfordert jedoch die Erzeugung einer stehenden Welle eine genaue Abstimmung von Frequenz und Phase beider Wandler, was eine komplexe Steuerung erfordert. Ebenso ist es möglich, geometrische Randbedingungen des Stehwellensystems zu optimieren, sodass es zu optimaler Interferenz der Wellen kommt. Im Folgenden wird der Anschaulichkeit halber vereinfachend angenommen, dass der Wandler an seiner Sonotrodenoberfläche einzelne Schallstrahlen aussendet, die in Nähe des Wandlers nahezu parallel verlaufen und sich mit zunehmender Entfernung vom Wandler auffächern. Ein einfaches Stehwellensystem, bestehend aus ebener Sonotrode und ebenem Reflektor, erzeugt bei kleinem Abstand zwischen Sonotrode und Reflektor sehr hohe Schallpegel, da nahezu sämtliche ausgesandten Schallstrahlen in Richtung der Sonotrode reflektiert werden positive Interferenz entsteht. Erhöht man jedoch den Abstand zwischen Sonotrode und Reflektor, so nehmen die Verluste durch Schallstrahlen, die den Prozessraum verlassen, zu. Wie Abbildung 1 gezeigt, werden nur Schallstrahlen, die in etwa parallel zur Rotationsachse verlaufen, zum Wandler zurück reflektiert und tragen zum Stehwellenfeld bei. Die Strahlen haben zudem abhängig vom Abstrahlwinkel unterschiedliche Weglängen. Die Stehwellenbedingung ist demnach nur für Strahlen in der Nähe der Rotationsachse exakt erfüllt. Um dies zu vermeiden, müssen die Geometrien von Wandler und Reflektor optimiert werden. In den folgenden Abschnitten wird zunächst ein Optimierungsansatz vorgestellt. Mithilfe eines FiniteElemente-Modells werden die Auswirkungen einer optimierten Geometrie auf den maximalen Schalldruckpegel untersucht. Ergebnisse werden durch Messungen an einem experimentellen Aufbau eines Stehwellensystems validiert. Es wird gezeigt, wie sich die Optimierung der geometrischen Randbedingungen auf die Zerstäubung hochviskoser Flüssigkeiten auswirkt.

As the emerging digitalization of technical systems offers immense opportunities to be exploited by means of bigdata analysis, ubiquitous computing and largely networked systems, the digital twin comes into focus to combineall these aspects to an attendant model of an individual system during design phase as well as during operation.Since state-of-art technical systems are growing increasingly complex due to inherent intelligence and increasingfunctionality, i. e. autonomous behavior so far, it becomes considerably challenging to ensure reliability for thosesystems. Many methods were developed to support a reliability focused design or reliability-by-design approachesto tackle this challenge during design process. In field, data-based methods, i. e. condition monitoring enabled bythe rise of machine learning approaches, are exploited to ensure a reliable operation based on the current conditionof the monitored system. In order to take advantage of existing models of system reliability during design phaseand condition monitoring systems during operation, a method is proposed to combine both approaches in order toset up a digital twin with focus on system reliability. The base model of the digital twin is taken from the systemreliability model from the design phase and is used during operation and therein updated to the current reliabilitybased on the state estimation of the condition monitoring system. The approach is illustrated with a case study of arolling bearing test rig.

Rubber-metal-elements are used in a wide range of applications for vibration and sound isola- tion. Nowadays it is state of the art to calculate the lifetimes of these elements under mechanical stress prior to their service life. To establish more reliable and safer rubber-metal-elements, continuous monitoring by dif- ferent sensors can be used. Especially prognostics enable a rise in reliability, availability and safety. To estab- lish these advantages, estimating the remaining useful lifetime of rubber-metal-elements should be realized during its service life based on current information on its condition. Therefore a suitable measure to monitor the condition of the element is necessary. This work focuses on temperature signals. This approach allows in- cluding the ambient temperature and thereby involving changing operating conditions. For estimating the RUL of rubber-metal-elements a model-based prognostics approach based on particle filtering is proposed. Its performance is analyzed regarding relevant parameters to enable the best performance for the applied data.

State-of-the-art industrial compact high power electronic packages require copper-copper interconnections with larger cross sections made by ultrasonic bonding. In comparison to aluminium-copper, copper-copper interconnections require increased normal forces and ultrasonic power, which might lead to substrate damage due to increased mechanical stresses. One option to raise friction energy without increasing vibration amplitude between wire and substrate or bonding force is the use of two-dimensional vibration. The first part of this contribution reports on the development of a novel bonding system that executes two-dimensional vibrations of a tool-tip to bond a nail- like pin onto a copper substrate. Since intermetallic bonds only form properly when surfaces are clean, oxide free and activated, the geometries of tool-tip and pin were optimised using finite element analysis. To maximize the area of the bonded annulus the distribution of normal pressure was optimized by varying the convexity of the bottom side of the pin. Second, a statistical model obtained from an experimental parameter study shows the influence of different bonding parameters on the bond result. To find bonding parameters with the minimum number of tests, the experiments have been planned using a D-optimal experimental design approach.

Ultrasonic bonding and welding are common friction based approaches in the assembly of power electronics. Interconnections with cross-sections of 0.3 mm² up to 12 mm² made from copper are well suited in high power applications. For increasing friction energy, which is responsible for bond formation, a two-dimensional vibration approach is applied to newly developed interconnection pins. Using two-dimensional vibration for bonding requires identification of suitable bonding parameters. Even though simulation models of wire bonding processes exist, parameters for the two-dimensional pin-bonding process cannot be derived accurately yet. Within this contribution, a methodology and workflow for experimental studies identifying a suitable bond parameter space are presented. The results of a pre-study are used to set up an extensive statistical parameter study, which gives insights about the bond strength change due to bond process parameter variation. By evaluation of electrical data captured during bonding, errors biasing the resulting shear forces are identified. All data obtained during the experimental study is used to build a statistical regression model suitable for predicting shear forces. The accuracy of the regression model’s predictions is determined and the applicability to predict process parameters or validate simulation models is assessed. Finally, the influence of the tool trajectory on the bond formation is determined, comparing one dimensional, elliptic and circular trajectories.

Ultrasonic wire bonding is used to connect the electrical terminals of semiconductor modules in power electronics. Mul- tiple influencing factors in wedge/wedge bonding are known and extensively investigated. A constructively settable but rarely examined parameter is the bonding frequency. In case of bonding on challenging substrates, e.g. supple substruc- tures, a high influence of the working frequency is observed. The choice of the working frequency is typically based on experimental investigations for a certain component or substrate and needs to be evaluated anew for new applications. A profound understanding of the influence of the working frequency is required to achieve a reliable bond process and a short process development. Here a generalized model for the numerical simulation of the bond formation with respect to the dynamics of the substructure is presented. The simulation results are compared to experiments using 300 µm copper wire at different working frequencies and geometries of the substructure.

Ultrasonic wedge/wedge-wire bonding is used to connect electrical terminals of semiconductor modules in power electronics. The wire is clamped with a tool by a normal force and ultrasonic vibration is transmitted through the wire into the interface between wire and substrate. Due to frictional processes contaminations like oxide layers are removed from the contact zone and the surface roughness is reduced, thus the real contact area is increased. In the next step of bond formation, thermomechanical forces create micro-junctions between the wire and substrate and the bond strength increases. The bond parameters like the bond normal force, the ultrasonic vibration amplitude and the geometry of the clamping tool show a high influence on the strength and reliability of the wire bond and need to be investigated in detail. Therefore, in this contribution the dynamical behaviour of the ultrasonic system, the wire and the substrate are modeled in form of substructures, which are connected by the friction contacts between tool and wire and between wire and substrate. Approaches for modelling the time variant contact behaviour, the substrate dynamics, and the model order reduction for a time efficient simulation are described to simulate the full bonding process.

Ultrasonic wire bonding is an indispensable process in the industrial manufacturing of semiconductor devices. Copper wire is increasingly replacing the well-established aluminium wire because of its superior electrical, thermal and mechanical properties. Copper wire processes differ significantly from aluminium processes and are more sensitive to disturbances, which reduces the range of parameter values suitable for a stable process. Disturbances can be compensated by an adaption of process parameters, but finding suitable parameters manually is difficult and time-consuming. This paper presents a physical model of the ultrasonic wire bonding process including the friction contact between tool and wire. This model yields novel insights into the process. A prototype of a multi-objective optimizing bonding machine (MOBM) is presented. It uses multi-objective optimization, based on the complete process model, to automatically select the best operating point as a compromise of concurrent objectives.

Zuverlässigkeit, Sicherheit und Verfügbarkeit gewinnen bei der Anwendung von technischen Systemen eine immer größere Bedeutung. Aus diesem Grund hat sich Condition Monitoring, die Zustandsüberwachung eines technischen Produkts, in verschiedenen Industriebranchen etabliert. Die sensorbasierte Überwachung eines Produkts während seiner Betriebsdauer in Kombination mit Condition Monitoring Methoden ermöglichen die Bestimmung des aktuellen Zustands des Produkts und somit eine Diagnose, ob das Produkt seine ihm zugeschriebene Funktion zum aktuellen Zeitpunkt erfüllt. Neben Diagnosen bietet Condition Monitoring auch die Möglichkeit Prognosen aufzustellen, dabei wird die restliche Nutzungsdauer des Produkts aufbauend auf geeigneten Sensordaten geschätzt. So kann eine intelligente Wartungsplanung umgesetzt werden, die im Gegensatz zu klassischen Ansätzen keine festen Wartungsintervalle benötigt und die Nachteile einer rein reaktiven Wartung kompensiert. Stattdessen ist es möglich ein Element bis vor das Ende seiner Lebensdauer zu nutzen und erst dann zu warten, um eine optimale Nutzung zu gewährleisten. Durch eine Bestimmung der verbleibenden Restlebensdauer während des Betriebs ist eine optimale Wartungsplanung möglich, wodurch die Verfügbarkeit und die Auslastung der überwachten Produkte signifikant gesteigert werden kann. In dieser Arbeit soll ein produktspezifisches Condition Monitoring System für Gummi-Metall-Elemente entwickelt werden. Diese Elemente werden zur Federung, Geräusch- und/oder Schwingungsisolation in vielen verschiedenen Anwendungen eingesetzt, wie bspw. in Nutz- und Schienenfahrzeugen oder Windenergieanlagen. In Industrie und Forschung werden bereits Zustandsüberwachungen von Systemen mit integrierten Gummi-Metall-Elementen eingesetzt, allerdings noch keine Condition Monitoring Systeme zur alleinigen Zustandsüberwachung dieser Elemente. Aktuell ist es üblich die Lebensdauer dieser Elemente aufbauend auf beschleunigten Lebensdauerversuchen und Erfahrungswerten abzuschätzen. Mit dem Ziel die Lebensdauer des fokussierten Produkts präziser vorherzusagen und damit eine intelligente Wartungsplanung zu ermöglichen, wird die Entwicklung eines Condition Monitoring Systems für Gummi-Metall-Elemente angestrebt und in dieser Arbeit erläutert.

In vielen verschiedenen Industriezweigen hat sich Condition Monitoring aufgrund seiner finanziellen und sicherheitstechnischen Vorteile bereits etabliert. Um die Verlässlichkeit und die Auslastung zu steigern, sowie um die Lebenszykluskosten zu reduzieren, steigt auch im Schienenfahrzeugbereich die Anzahl an eingesetzten Condition Monitoring Systemen. Studien zu Versagensmodi von Schienenfahrzeugen haben gezeigt, dass Versagensursachen meistens in den Radprofilen oder im Fahrwerk liegen [1]. Wird das Fahrwerk heute mittels Condition Monitoring überwacht, werden hierfür häufig Sensoren an den Wagons angebracht, um bspw. deren Schwingungen zu kontrollieren [2, 3]. In dieser Arbeit liegt der Fokus auf Gummi-Metall-Elementen (GM-Elementen) der Jörn GmbH.; als elastische Lager im Drehgestell sind diese Teil des Fahrwerks eines Schienenfahrzeugs. Mit dem Ziel die Wartungsplanung dieser Elemente zu optimieren, ist untersucht worden, ob diese Elemente einzeln mittels Condition Monitoring überwacht werden können. Die hierfür durchgeführten beschleunigten Lebensdauertests werden im nächsten Abschnitt erläutert. Anschließend werden die modellbasierten Methoden dargestellt, die aufbauend auf den im Versuch aufgezeichneten Daten eine Prognose der nutzbaren Restlebensdauer (RUL, remaining useful lifetime) der GM-Elemente aufstellen. Im letzten Abschnitt folgen eine kurze Zusammenfassung und ein Ausblick.

In der Windenergieindustrie haben die Größen Zuverlässigkeit, Sicherheit und Verfügbarkeit eine enorme Bedeutung erlangt aufgrund des Trends Windenergieanlagen zur optimalen Windausnutzung an schwer zugänglichen Positionen aufzustellen, wie bspw. Offshore. Dies führt zu erschwerten Wartungsbedingungen und damit zu höheren Kosten. Der Einsatz von Condition Monitoring hat sich in dieser Industrie etabliert, denn diese Technik ermöglicht eine Zustandsdiagnose des überwachten Systems und eine Prognose seiner nutzbaren Restlebensdauer (remaining useful life: RUL), jeweils basierend auf geeigneten Sensordaten. In dieser Arbeit wird ein Konzept für ein produktspezifisches Condition-Monitoring-System für Gummi-Metall-Elemente (GM-Elemente) vorgestellt, welches den Schwerpunkt auf die Prognose der RUL dieser Elemente setzt. In Windenergieanlagen werden zahlreiche GM-Elemente zur Geräusch- und Schwingungsisolation verwendet. Der Einsatz des hier vorgestellten produktspezifischen Condition-Monitoring-Systems kann somit einen erheblichen Beitrag zum verlässlichen Betrieb von Windenergieanlagen liefern, da die Überwachung einzelner Komponenten in die Zustandsüberwachung der gesamten Anlage integriert und dadurch der Betrieb der Anlage optimiert werden kann. In dieser Arbeit werden einige Herausforderungen diskutiert, die sich bei der Entwicklung eines Condition-Monitoring-Systems für GM-Elemente ergeben. So wird evaluiert, welche Größen sich zur Beschreibung der Alterung eines spezifischen Elements eignen und wie diese gemessen werden können. Temperaturen werden bereits in einigen technischen Systemen, wie auch in Windenergieanlagen, aufgezeichnet und ausgewertet, aber ihr Potential für die Bestimmung der RUL der überwachten Komponente ist noch nicht ausgeschöpft. Hier wird eine Lösungsmöglichkeit vorgestellt, die auf Temperatursensoren aufbaut. Als Grundlage für die Entwicklung des Condition-Monitoring-Systems wurden beschleunigte Lebensdauerversuche der GM-Elemente auf einem Versuchsstand zur Schwingungsanalyse durchgeführt. In diesen Lebensdauerversuchen wird die mechanische Alterung eines GM-Elements über einen kraftgeregelten Hydraulikzylinder erzielt. Dabei wird das Ende der Lebensdauerversuche in einem ersten Schritt über die Wegamplitude des Zylinders bestimmt. Während dieser Versuche wurden diverse Sensoren eingesetzt. Die aufgezeichneten Temperaturdaten zeigen, dass sich Temperaturmessungen eignen die Lebensdauer von GM-Elementen mittels Condition Monitoring Prognosemethoden zu schätzen.

The integrated modeling of behavior and reliability in system development delivers a model-based approach for reliability investigation by taking into account the dynamic system behavior as well as the system architecture at different phases of the development process. This approach features an automated synthesis of a reliability model out of a behavior model enabling for the closed loop modeling of degradation of the system and its (dynamic) behavior. The approach is integrated into the development process following Systems Engineering. It is based on standard models used in model-based development methodologies i.e. SysML or Matlab/Simulink. In addition to the theoretical description of the necessary steps the procedure is validated by an application example at two stages of the development process.

Piezoelectric transducers are used in a wide range of applications. Reliability of these transducers is an important aspect in their application. Prognostics, which involve continuous monitoring of the health of technical systems and using this information to estimate the current health state and consequently predict the remaining useful lifetime (RUL), can be used to increase the reliability, safety, and availability of the transducers. This is achieved by utilizing the health state and RUL predictions to adaptively control the usage of the components or to schedule appropriate maintenance without interrupting operation. In this work, a prognostic approach utilizing self-sensing, where electric signals of a piezoelectric transducer are used as the condition monitoring data, is proposed. The approach involves training machine learning algorithms to model the degradation of the transducers through a health index and the use of the learned model to estimate the health index of similar transducers. The current health index is then used to estimate RUL of test components. The feasibility of the approach is demonstrated using piezoelectric bimorphs and the results show that the method is accurate in predicting the health index and RUL.

ln der industriellen Fertigung werden zum Transport von Bauteilen häufig Förderketten genutzt. Obwohl die Förderketten meist nicht direkt mit den Arbeitsmedien in Berührung kommen, werden sie indirekt durch vagabundierende Stäube und Pulver, die an der geölten Kette anhaften, im Laufe der Zeit stark verschmutzt. Ein derart im Betrieb verschmutztes Kettenglied ist in Abbildung 1 dargestellt. Um die Lebensdauer der Ketten zu erhöhen und das Herunterfallen von Schmutzpartikel auf die Produkte zu vermeiden, muss die Kette regelmäßig gereinigt werden. Ziel des hier beschriebenen Forschungsvorhabens ist die Entwicklung eines Systems, das in der Lage ist, ein einzelnes Kettenglied in unter 60 s mittels Ultraschall zu reinigen. In [1] wurde in ersten Versuchen nachgewiesen, dass Stabschwinger in Abhängigkeit des Sonotrodenabstands zum Reinigungsobjekt und der Ultraschallamplitude eine intensive Reinigungswirkung entfalten. Das Konzept der Reinigungsanlage sieht deshalb vor, im ersten Schritt die stark verschmutzten Kettenglieder durch ein hochintensives Kavitationsfeld von direkt eingetauchten Stabschwingern vorzureinigen und anschließend schwer zugängliche Be- reiche wie Hinterschneidungen oder Bohrungen mittels konventioneller Tauchschwinger von Verschmutzungen zu befreien. Für den Stabschwinger wird die sogenannte - Sonotrode untersucht; diese wird unter anderem auch in der Sonochemie verwendet. Ein wesentliches Merkmal der Sonotrode ist eine hohe Amplitudenübersetzung bei einer gleichzeitig großen Abstrahlfläche. Neben dem Entwurf mittels der L /2 -Synthese wird die Reinigungswirkung der Sonotrode in Abhängigkeit der Ultraschallamplitude und dem Abstand zum Reinigungsobjekt in einer Versuchsreihe untersucht. Zur genaueren Betrachtung der Reinigungs- mechanismen eines Stabschwingers werden abschließend Hochgeschwindigkeitsaufnahmen vorgestellt und analysieren.

Intelligente technische Systeme sind durch einen erhöhten Funktionsumfang charakterisiert, der diese dazu befähigt, autonom auf wechselnde Umgebungsbedingungen, Anforderungen und inhärente Systemzustände zu reagieren. Dies kann mit den Methoden der Selbstoptimie-rung erreicht werden. Hier werden mit Verfahren der Mehrzieloptimierung mögliche Betriebs-punkte des Systems bestimmt zwischen denen das System im Betrieb autonom auswählt und somit eine Verhaltensadaption erwirkt. Zur Berechnung der Betriebspunkte ist es notwendig ein Modell des Systemverhaltens aufzustellen und das Verhalten hinsichtlich verschiedener, meist konfliktärer, Ziele zu quantifizieren. Bei der Modellierung des Systemverhaltens und der Formulierung der Ziele stellt die Absiche-rung der Verlässlichkeit auf Grund der zunehmenden Systemkomplexität eine große Heraus-forderung dar, der im Entwicklungsprozess begegnet werden muss. Die Implementierung von Selbstoptimierung bietet darüber hinaus in Kombination mit einer Zustandsüberwachung im Betrieb die Möglichkeit einer zuverlässigkeitsbasierten Verhaltensanpassung, deren Potential zu einer Steigerung der Verlässlichkeit genutzt werden kann. In dieser Arbeit werden die Entwicklung intelligenter technischer Systeme und die damit ver-bundenen notwendigen Entwicklungsschritte zur Absicherung der Verlässlichkeit anhand von selbstoptimierenden Systemen betrachtet. Dazu gehören die Formulierung verlässlichkeitsre-levanter Ziele und die Implementierung einer Zustandsüberwachung als Basis für eine zuver-lässigkeitsbasierte Verhaltensanpassung. Es werden auf Grundlage einer Beschreibung der Entwicklungsschritte, Potentiale zur Steigerung der Verlässlichkeit sowie Chancen und zukünf-tige Herausforderungen herausgestellt und diskutiert.

Wire bonding has been an established packaging technology for decades. When introducing copper as wire material for high power applications, adaptations to the bonding process and to machines became necessary. Here, challenges occur due to the stiffer wire material and changing oxide layers on the contact partners. To achieve sufficient process stability, a clean bond area is required, which can only be achieved with high shear stresses in the contact partners surfaces. These necessitate high normal forces to plastically deform the wire and substrate. To achieve such high stresses in the contact area, the bonding tool needs to be able to transmit the needed tangential forces to the top side of the wire. The wire itself performs a shear movement and transmits the force into the contact area to clean the contaminant and oxide layers and to level the desired bond surfaces. The main function of the tool is to transmit these forces. If the bond tool can only transmit low forces in the direction of excitation, the parameter space for a stable bond process is severely restricted. Here, a modeling approach to estimate how well different tool shapes meet the demand of transmitting high tangential forces is presented. The model depends on wire deformation and thus on the ultrasonic softening effect.

The transportation of dry fine powders is an emerging technologic task, as in biotechnology, pharmaceutical or coatings industry particle sizes of processed powders are getting smaller and smaller. Fine powders are primarily defined by the fact that adhesive and cohesive forces outweigh the weight forces. This leads to mostly unwanted agglomeration (clumping) and adhesion to surfaces, what makes it more difficult to use conventional conveyor systems (e. g. pneumatic or vibratory conveyors) for transport. A rather new method for transporting these fine powders is based on ultrasonic vibrations, which are used to reduce friction and adhesion between powder and the substrate. One very effective set-up consists of a pipe, which vibrates harmoniously in axial direction at low frequency combined with a pulsed radial high frequency vibration. The high frequency vibration accelerates the particles perpendicular to the surface of the pipe, which in average leads to lower normal and thereby smaller friction force. With coordinated friction manipulation the powder acceleration can be varied so that the powder may be greatly accelerated and only slightly decelerated in each excitation period of the low frequency axial vibration of the pipe. The amount of powder flow is adjustable by vibration amplitudes, frequencies, and pulse rate, which makes the device versatile for comparable high volume and fine dosing using one setup. Within this contribution an experimental set-up consisting of a pipe, a solenoid actuator for axial vibration and a piezoelectric actuator for the radial high frequency vibration is described. An analytical model is shown, that simulates the powder velocity. Finally, simulation results are validated by experimental data for different driving parameters such as amplitude of low frequency vibration, pipe material and inclination angle.

Ultrasonic wire bonding is a common technology for connecting electrodes of electronic components like power modules. Nowadays, bond connections are often made of copper instead of aluminum due to its thermal and mechanical assets. One of the main cost factors in the wire bonding process is the acquisition cost of consumables such as bonding tools. For copper wire bonding tool lifetime is much lower than for aluminium bonding. This paper presents a micro wear model for wedge/wedge bonding tools that was validated by observing wear patterns with a scanning electron microscope. The wear coefficient is determined in long-term bonding tests. The application of Fleischer´s wear approach incorporating frictional power to a finite element simulation of the bonding processes is used to shift element nodes depending on the rising frictional power for finite element modeling. The presented simulation method can be used to take tool wear into consideration for creating tools with increased lifetime. This enables the production of reliable bond connections using heavy as well as thin wire of any material. The paper discusses the predominant influences of wear on the main tool functions and their changes over tool life. Furthermore, the influence of the tool groove angle on the tool wear was investigated. One of the main results is that the wear is largest during the last phase of each bonding process, when the contact area between tool and wire is largest.

Redundancy is a common approach to improve system reliability, availability and safety in technical systems. It is achieved by adding functionally equivalent elements that enable the system to remain operational even though one or more of those elements fail. This paper begins with an overview on the various terminologies and methods for redundancy concepts that can be modeled sufficiently using established reliability analysis methods. However, these approaches yield very complex system models, which limits their applicability. In current research, Bayesian Networks (BNs), especially Dynamic Bayesian Networks (DBNs) have been successfully used for reliability analysis because of their benefits in modeling complex systems and in representing multi-state variables. However, these approaches lack appropriate methods to model all commonly used redundancy concepts. To overcome this limitation, three different modeling approaches based on BNs and DBNs are described in this paper. Addressing those approaches, the benefits and limitations of BNs and DBNs for modeling reliability of redundant technical systems are discussed and evaluated.

Tire-wheel assembly is the only connection between road and vehicle. Contacting directly with road within postcard size of contact area, it is mounted and guided by the suspension system. Therefore kinematics and compliances of suspension system greatly influence the frictional coupling of tire tread elements and road surface asperities by affecting pressure and sliding velocity distribution in the contact zone. This study emphasizes the development of a numerical methodology for frictional rolling contact analysis with focus on interaction of suspension system dynamics and tire-road contact using ADAMS. For this purpose a comprehensive flexible multibody system of the multi-link rear suspension is established, where both flexible and rigid bodies are modeled to allow large displacements with included elastic effects. To meet accuracy requirements for the high frequency applications, such as road excitations, the amplitude- and frequency-dependency of rubber-metal bushings is included. Furthermore the proposed flexible viscoelastic suspension model is enhanced by a Flexible Ring Tire Model (FTire), which describes a 3D tire dynamic response and covers any road excitations by tread submodel connected to road surface model. Concerning the verification and validation procedure numerous experiments are carried out to confirm the validity and the accuracy of both the developed submodels and the entire model. The devised approach makes it possible to investigate the influence of suspension system design on dynamical rolling contact and to evaluate tire tread wear. Therefore it can be a useful tool to predict frictional power distribution within the contact area under more realistic conditions.

This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of datadriven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research

Usage of copper wire bonds allows to push power boundaries imposed by aluminum wire bonds. Copper allows higher electrical, thermal and mechanical loads than aluminum, which currently is the most commonly used material in heavy wire bonding. This is the main driving factor for increased usage of copper in high power applications such as wind turbines, locomotives or electric vehicles. At the same time, usage of copper also increases tool wear and reduces the range of parameter values for a stable process, making the process more challenging. To overcome these drawbacks, parameter adaptation at runtime using self-optimization is desired. A self-optimizing system is based on system objectives that evaluate and quantify system performance. System parameters can be changed at runtime such that pre-selected objective values are reached. For adaptation of bond process parameters, model-based self-optimization is employed. Since it is based on a model of the system, the bond process was modeled. In addition to static model parameters such as wire and substrate material properties and vibration characteristics of transducer and tool, variable model inputs are process parameters. Main simulation result is bonded area in the wiresubstrate contact. This model is then used to find valid and optimal working points before operation. The working point is composed of normal force and ultrasonic voltage trajectories, which are usually determined experimentally. Instead, multiobjective optimalization is used to compute trajectories that simultaneously optimize bond quality, process duration, tool wear and probability of tool-substrate contacts. The values of these objectives are computed using the process model. At runtime, selection among pre-determined optimal working points is sufficient to prioritize individual objectives. This way, the computationally expensive process of numerically solving a multiobjective optimal control problem and the demanding high speed bonding process are separated. To evaluate to what extent the pre-defined goals of self-optimization are met, an offthe- shelf heavy wire bonding machine was modified to allow for parameter adaptation and for transmitting of measurement data at runtime. This data is received by an external computer system and evaluated to select a new working point. Then, new process parameters are sent to the modified bonding machine for use for subsequent bonds. With these components, a full self-optimizing system has been implemented.

Multibody models of mechatronic systems are usually interdisciplinary and are continuously gaining complexity, due to a growing demand for comprehensive models of systems including effects of electro mechanics, elastic bodies, contacts and friction. To be capable of simulating large models with subassemblies and contact between bodies, reduction techniques are required, which need certain experience in the choice of parameters. This publication discusses different possibilities for the modal description of structures in flexible multibody models with application to an Adaptive Frontlighting System in ADAMS. It will be shown that mode count, assembling of structures before and after modal reduction and influence of damping parameters of particular structures and subassemblies affect the behavior of the entire system. A common reduction technique for flexible structures in multibody models is the component mode synthesis, which uses a certain number of modes for description of the modal behavior of a structure. The influence of the mode count will be shown by means of different modal descriptions of one structure that contributes to a comprehensive model. Another study will prove that modal data of subassemblies and assemblies of modal reduced single structures lead to different models. The definition of damping parameters depends on the number of structures that have been added to an assembly before modal reduction and on the number of modal reduced structures. The comparison of subassemblies and the entire model to experimental data will highlight the accuracy, computational overhead, complexity of models and modeling efficiency of the comprehensive model for the frontlighting system.

To increase quality and reliability of copper wire bonds, self-optimization is a promising technique. For the implementation of self-optimization for ultrasonic heavy copper wire bonding machines, a model of stick-slip motion between tool and wire and between wire and substrate during the bonding process is essential. Investigations confirm that both of these contacts do indeed show stick-slip movement in each period oscillation. In a first step, this paper shows the importance of modeling the stick-slip effect by determining, monitoring and analyzing amplitudes and phase angles of tooltip, wire and substrate experimentally during bonding via laser measurements. In a second step, the paper presents a dynamic model which has been parameterized using an iterative numerical parameter identification method. This model includes Archard’s wear approach in order to compute the lost volume of tool tip due to wear over the entire process time. A validation of the model by comparing measured and calculated amplitudes of tool tip and wire reveals high model quality. Then it is then possible to calculate the lifetime of the tool for different process parameters, i.e. values of normal force and ultrasonic voltage.

Changing manufacturing technologies or material in well-known processes has to be followed by an adaption of process parameters. In case of the transition from aluminum wire to copper wire in heavy wire bonding, the adaption effort is high due to the strongly different mechanical properties of the wire. One of these adaption aspects, apart from wire material, is the existent oxide layers on wire and substrate. The ductile aluminum oxide is not influencing the bonding process much, because it is supposed to break apart in case of plastic deformation. The lubricating copper oxide layer has to be removed before micro welds can develop. Therefore, in this paper, experiments are carried out at low frequency to determine the friction energy needed to abrade the copper oxide layer of wire and substrate, which is indicated by an increase in the resulting friction coefficient. The friction energy per contact area to remove the interfering layers at low frequency is compared to the real bonding process working at 58 kHz. In addition, a theoretical concept is being described to get a grasp of the occurring mechanism. In the end a proposal is given how to set bonding parameters to get the cleanest surfaces with the installed bond tool.

Die starke Integration von Sensorik, Aktorik, Hard- und Software stellt Herausforderungen an die Verlässlichkeit intelligenter mechatronischer Systeme dar. Diese Systeme verfügen aber auch über großes Potential zur Verbesserung ihrer Verlässlichkeit durch eine Anpassung des Systemverhaltens an den aktuellen Zustand. Um den Umfang der Systemmodelle zu reduzieren und die Anpassung des Systemverhaltens zu ermöglichen, sind fortschrittliche Modellierungsmethoden notwendig, mit denen die Verlässlichkeit in frühen Phasen des Entwicklungsprozesses sichergestellt und evaluiert werden kann. Von den Attributen der Verlässlichkeit ist insbesondere die Zuverlässigkeit in hohem Maße von den auftretenden Belastungen an den Komponenten und damit vom dynamischen Systemverhalten abhängig. Bisherige Modellierungsansätze bilden diese Abhängigkeit nur unzureichend ab. Es wird daher ein Ansatz zur integrierten Modellierung mechatronischer Systeme vorgestellt. Dieser ist in der Lage, sowohl die Dynamik als auch die Zuverlässigkeit des Systems abzubilden. Die Transformation eines Modells des dynamischen Systemverhaltens generiert dabei ein Zuverlässigkeitsmodell. Für typischerweise konkurrierende Ziele können mit Hilfe von Mehrzieloptimierungsverfahren Betriebspunkte eines Systems bestimmt werden. Das integrierte Modell kann zur Erzeugung von Zielfunktionen für die Dynamik als auch für die Zuverlässigkeit genutzt werden. Die Ergebnisse ermöglichen eine Verhaltensanpassung durch Wahl eines paretooptimalen Betriebspunkts während des Betriebs. Das vorgeschlagene Konzept zur integrierten Modellierung mechatronischer Systeme bietet aufgrund des modellbasierten Entwicklungsansatzes und der automatisierten Transformation eines Verlässlichkeitsmodells eine Reduktion der Benutzereingaben und eine Entlastung des Benutzers. Dadurch wird die Wahrscheinlichkeit von Benutzerfehlern gesenkt und die Verlässlichkeit bereits während der Entwicklung erhöht. Somit können Iterationsschleifen vermieden und die Entwicklungskosten gesenkt werden.

Intelligent mechatronic systems are able to autonomously adapt system behavior to current environmental conditions and to system states. To allow for such reactions, complex sensor and actuator systems as well as sophisticated information processing are required, making these systems increasingly complex. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability and in turn to increase reliability. The adaptation is based on switching selecting an appropriate working point at runtime. Multiple suitable working points can be found using multi-objective optimization techniques, which require an accurate system model including system reliability. At present, modeling of system reliability is a laborious manual task performed by reliability modelling experts. Despite actual system reliability being highly dependent on system dynamics, pre-existing system dynamics models and the resulting reliability model are at best loosely coupled. To allow for closer interaction among dynamics and reliability model and to ensure these are always synchronized, advanced modeling techniques are required. Therefore, an integrated model is introduced that reduces user input to a minimum and that integrates system dynamics and system reliability.

This paper presents a comparison of a number of prognostic methods with regard to algorithm complexity and performance based on prognostic metrics. This information serves as a guide for selection and design of prognostic systems for real-time condition monitoring of technical systems. The methods are evaluated on ability to estimate the remaining useful life of rolling element bearing. Run-to failure vibration and temperature data is used in the analysis. The sampled prognostic methods include wear-temperature correlation method, health state estimation using temperature measurement, a multi-model particle filter approach with model parameter adaptation utilizing temperature measurements, prognostics through health state estimation and mapping extracted features to the remaining useful life through regression approach. Although the performance of the methods utilizing the vibration measurements is much better than the methods using temperature measurements, the methods using temperature measurements are quite promising in terms of reducing the overall cost of the condition monitoring system as well as the computational time. An ensemble of the presented methods through weighted average is also introduced. The results show that the methods are able to estimate the remaining useful life within error bounds of +-15\%, which can be further reduced to +-5\% with the ensemble approach.

Intelligent mechatronic systems other the possibility to adapt system behavior to current dependability. This can be used to assure reliability by controlling system behavior to reach a pre-defined lifetime. By using such closed loop control, the margin of error of useful lifetime of an individual system is lowered. It is also possible to change the pre-defined lifetime during operation, by adapting system behavior to derate component usage. When planning maintenance actions, the remaining useful lifetime of each individual system has to be taken into account. Usually, stochastic properties of a fleet of systems are analyzed to create maintenance plans. Among these, the main factor is the probability of an individual system to last until maintenance. If condition-based maintenance is used, this is updated for each individual system using available information about its current state. By lowering the margin of error of useful lifetime, which directly corresponds to the time until maintenance, extended maintenance periods are made possible. Also using reliability-adaptive operation, a reversal of degradation driven maintenance planning is possible where a maintenance plan is setup not only according to system properties, but mainly to requirements imposed by maintenance personnel or infrastructure. Each system then adapts its behavior accordingly and fails according to the maintenance plan, making better use of maintenance personnel and system capabilities at the same time. In this contribution, the potential of maintenance plan driven system behavior adaptation is shown. A model including adaptation process and maintenance actions is simulated over full system lifetime to assess the advantages gained.

Intelligente technische Systeme, die in der Lage sind, sich an geänderte Umgebungsbedingungen anzupassen, ermöglichen eine Adaption anhand der aktuell erreichten Zuverlässigkeit. Zu diesem Zwecke kann ein geschlossener Regelkreis formuliert werden, der dazu geeignet ist, den Betriebspunkt des Systems während der gesamten Lebensdauer anzupassen. Dadurch wird eine harte Umschaltung während des Betriebs vermieden und die Verhaltensanpassung ist vom Nutzer weitgehend unbemerkt möglich. Dazu wird die aktuelle Restlebensdauer mit einer vorgegebenen Restlebensdauer verglichen. Durch Änderung der vorgegebenen Restlebensdauer lässt sich auch eine Anpassung der gewünschten Nutzungsdauer erreichen, beispielsweise um veränderte Wartungsintervalle einzuhalten. Zu diesem Zwecke ist es allerdings notwendig, die aktuell erreichte Zuverlässigkeit zu schätzen. Für die Regelung ist dabei die aktuelle Restlebensdauer der wichtigste Parameter, da er als Istwert direkt mit der gewünschten Restlebensdauer als Sollwert verglichen wird und als Reglereingang dient. Für die Genauigkeit der Regelung ist daher die Bestimmung der Restlebensdauer von entscheidender Bedeutung. Es wird ein Modell des Regelkreises vorgestellt, das auch den Einfluss einer fehlerhaften Restlebensdauerschätzung auf die Verhaltensanpassung abbildet. Dadurch ist es möglich, Grenzen der Verhaltensanpassung und die zur Einhaltung notwendige Genauigkeit der Restlebensdauerschätzung zu bestimmen. Es gibt zahlreiche Ansätze, die Restlebensdauer zu schätzen, die aufgeteilt werden in modellbasierte Verfahren und datengetriebene Verfahren. Die individuelle Eignung eines jeden Verfahrens sowie die Modellbildung oder die Nutzung geeigneter Algorithmen ist stark systemabhängig. Um die Auswahl von Verfahren und Modellen oder Algorithmen zu ermöglichen, werden zunächst die Anforderungen an die Restlebensdauerschätzung zur Nutzung als Regelungs-Istwert bestimmt. Verschiedene Verfahren werden sodann hinsichtlich ihrer Eignung evaluiert und Anwendungsgrenzen aufgezeigt.

Ultrasonic wire bonding is an indispensable process in the manufacturing of semiconductor components. It is used for interconnecting the silicon die to e.g. connectors in the housing or to other semiconductors in complex components. In high power applications, such as wind turbines, locomotives or electric vehicles, the thermal and mechanical limits of interconnects made from aluminum are nearing. The limits could be overcome using copper wire bonds, but their manufacturing poses challenges due to the harder material, which leads to increased wear of the bond tools and to less reliable production. To overcome these drawbacks, adaptation of process parameters at runtime is employed. However, the range of parameter values for which a stable process can be maintained is very small, making it necessary to compute suitable parameters beforehand. To this end, and to gain insights into the process itself, the ultrasonic bonding process is modeled. The full model is composed of several partial models, some of which were introduced before. This paper focuses on the modularization of the full model and on the interaction of partial models. All partial models are presented, their interaction is shown and the general outline of the simulation process is given.

The contact between viscoelastic materials e.g. elastomers and a rough surface leads to a special friction characteristic, which differs greatly in its properties comparing to other materials like metals. In practice, this friction combination occurs for example in the tire-road contact, or in the use of rubber gaskets. Due to the frictional forces a system is significantly influenced in its vibrational properties. The friction force is composed of two main components adhesion and hysteresis. The adhesion results from molecular bounds between the contact partners, while the deformation of the viscoelastic material by the roughness of the counter body leads to power loss. This internal friction results in an additional frictional force, which is described by the hysteresis. To simulate the frictional behaviour of elastomers on rough surfaces and thus to determine the energy dissipation in contact, it is necessary to develop a mechanical model which considers the roughness of the contact partners, as well as dynamic effects and the dependence on normal pressure and sliding speed. The viscoelastic material behaviour must also be considered. The contact between two rough surfaces is modelled as a rough rigid layer contacting a rough elas- tic layer. The elastic layer is modelled by point masses connected by Maxwell-elements. This allows the viscoelastic properties of the elastomer to be considered. The behaviour of whole system can be described by equations of motion with integrated constraints. The degrees of freedom of the model depends on the varying contact conditions. A point mass not in contact has two degrees of freedom. A point mass in contact moving along the roughness path can be described by only one degree of freedom. For each Maxwell-Element also an inner coordinate and thus a further degree of freedom is needed. Because of varying contact conditions dur- ing the simulation, the simulation interrupts in case the contact conditions change. Then the equations of motions are adapted with respect to the contact constraints. As a result of the simulation one obtain the energy dissipation and thus the friction char- acteristic during the friction process. It is possible to use these results in three dimensional point-contact elements in order to model contact surfaces on lager length scales.

To increase quality and reliability of copper wire bonds, self-optimization is a promising technique. For the implementation of self-optimization for ultrasonic heavy copper wire bonding machines, a model of stick-slip motion between tool and wire and between wire and substrate during the bonding process is essential. Investigations confirm that both of these contacts do indeed show stick-slip movement in each period oscillation. In a first step, this paper shows the importance of modeling the stick-slip effect by determining, monitoring and analyzing amplitudes and phase angles of tool tip, wire and substrate experimentally during bonding via laser measurements. In a second step, the paper presents a dynamic model which has been parameterized using an iterative numerical parameter identification method. This model includes Archard's wear approach in order to compute the lost volume of tool tip due to wear over the entire process time. A validation of the model by comparing measured and calculated amplitudes of tool tip and wire reveals high model quality. Then it is then possible to calculate the lifetime of the tool for different process parameters, i.e. values of normal force and ultrasonic voltage.

In order to increase mechanical strength, heat dissipation and ampacity and to decrease failure through fatigue fracture, wedge copper wire bonding is being introduced as a standard interconnection method for mass production. To achieve the same process stability when using copper wire instead of aluminum wire a profound understanding of the bonding process is needed. Due to the higher hardness of copper compared to aluminum wire it is more difficult to approach the surfaces of wire and substrate to a level where van der Waals forces are able to arise between atoms. Also, enough friction energy referred to the total contact area has to be generated to activate the surfaces. Therefore, a friction model is used to simulate the joining process. This model calculates the resulting energy of partial areas in the contact surface and provides information about the adhesion process of each area. The focus here is on the arising of micro joints in the contact area depending on the location in the contact and time. To validate the model, different touchdown forces are used to vary the initial contact areas of wire and substrate. Additionally, a piezoelectric tri-axial force sensor is built up to identify the known phases of pre-deforming, cleaning, adhering and diffusing for the real bonding process to map with the model. Test substrates as DBC and copper plate are used to show the different formations of a wedge bond connection due to hardness and reaction propensity. The experiments were done by using 500 $\mu$m copper wire and a standard V-groove tool.

Cavitation monitoring is desired to optimize the sonication for diverse sonochemical processes and to detect changes or malfunctions during operation. In situ cavitation measurements can be carried out by detection of the acoustic emissions of cavitation bubbles by sensors in the liquid. However, in harsh environments sensors might not be applicable. Thus, the impact of cavitation on the electrical signals of a piezoelectric transducer has been analyzed as alternative method to measure the threshold, strength and type of cavitation. The applicability has been tested in three different setups to evaluate the general- izability of extracted indicators. In all setups indicators for the cavitation thresholds could be derived from the current signal. In two setups features showed two thresholds that may be linked to the types of cavitation. However, only one feature derived from the current signal in one particular setup correlated to the strength of cavitation. Cavitation detection based on the current signal of the transducer is a useful method to detect cavitation in harsh environments and without perturbing the sound field. Once appli- cable indicators have been identified, they may easily be tracked during the process. However, for more detailed studies about the cavitation activity and its spatial distribution, measurements with in situ sensors are recommended.

Wire bonding is the most common technology for connecting electronic components. Due to their efficiency bond interconnections made of copper wire are used for example in the aerospace and medical technology as well as in the fields of renewable energies. One of the main cost factors in the manufacturing process is the consumables like bonding tools. The technological transition to copper as wire material causes significant wear on the millimeter large effective contact area of the bonding tool. This wear leads to a loss by a factor of 30 of the number of reliable interconnections which can be produced by a single tool. To reduce setting-up time in the production and minimizing costs, an enlarged bonding tool lifetime is desirable. Consequently a better understanding of wear and recognition of wear pattern is required. Therefore, the paper presents an analyzing method of the tool topography change of a heavy wire bonding tool by using a confocal microscope. Furthermore, the paper discusses the identification of the main wear indicators by the help of the named topography change for different bond parameters, like ultrasonic power and tool geometry. Reference topography has been carried out by choosing typical parameters of the production line. To judge whether the quality requirement of the bond connections made by a single tool cannot be fulfilled shear test of the source bond have been carried out after a defined number of produced bond connections. Main steps of analysis: (I)Topography of the tool surface is sampled after a defined number of bonds by means of a confocal microscope to detect the wear progress.(II)The recorded data is filtered using Matlab. So, measurement errors can be eliminated and the topography can be overlaid more easy to identify differences between diverse tools or differences in wear stages of the same tool.(III)The subsequent discretization of the topography into sub volumes allows to (IV)describe the loss of volume depending on the position in the groove. Thereby, intermediate status of wear of one tool can be used to obtain a persistent description of the topography change over the number of produced bonds by interpolating the confocal data. Afterwards the persistent change of the groove flank has been analyzed for the named test series to identify the main wear indicators and their effect on shear forces. All worn tools show dominant areas for volume loss especially for plastic deformation and accordingly abrasion. These wear mechanism can be referred to the change of main parts of the groove geometry like the rounding of the front and back radius. The most volume loss was identified in the upper part of the tool flanks or rather at the transition from the groove flank to the front or back radius. Furthermore the observation of the center of the groove flank shows just a little change in volume. All in all, the identification of the wear indicators will be discussed with the objective of increasing the tool lifetime by optimizing the tool geometry without losses in bond quality and reliability.

Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.

With the paradigm shift towards prognostic and health management (PHM) of machinery, there is need for reliable PHM methodologies with narrow error bounds to allow maintenance engineers take decisive maintenance actions based on the prognostic results. Prognostics is mainly concerned with the estimation of the remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods is usually a function of the features extracted from the raw data obtained from sensors. In cases where the extracted features do not display clear degradation trends, for instance highly loaded bearings, the accuracy of the state of the art PHM methods is significantly affected. The data which lacks clear degradation trend is referred to as non-trending data. This study presents a method for extracting degradation trends from non-trending condition monitoring data for RUL estimation. The raw signals are first filtered using a discrete wavelet transform (DWT) denoising filter to remove noise from the acquired signals. Time domain, frequency domain and time-frequency domain features are then extracted from the filtered signals. An autoregressive model is then applied to the extracted features to identify the degradation trends. Features representing the maximum health information are then selected based on a performance evaluation criteria using extreme learning machine (ELM) algorithm. The selected features can then be used as inputs in a prognostic algorithm. The feasibility of the method is demonstrated using experimental bearing vibration data. The performance of the method is evaluated on the accuracy of RUL estimation and the results show that the method can be used to accurately estimate RUL with a maximum error of 10\%.

A model to calculate the locally resolved tangential contact forces of the wheel rail contact with respect to contact kinematics, material and surface properties as well as temperature is introduced. The elasticity of wheel and rail is modeled as an elastic layer consisting of point contact elements connected by springs to each other and to the wheel. Each element has two degrees of freedom in tangential directions. The resulting total stiffness matrix is reduced to calculate only the position of the elements in contact. Friction forces as well as contact stiffnesses are incorporated by a nonlinear force-displacement characteristic, which originates from a detailed contact model. The contact elements are transported through the contact zone in discrete time steps. After each time step an equilibrium is calculated. For all elements, their temperature and its influence on local friction are regarded by calculating friction power and temperature each time step.

A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test.

Many nonlinear mechanical oscillators show excitation-dependent behavior. In this paper, a new measurement approach is presented to analyze such structures. The main advantage of the presented method is the high efficiency, since measurement duration and loads to the structure are significantly reduced.

A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test. After a brief presentation of the algorithm, the validity scope of the approach is investigated with emphasis on an exemplary error investigation.

Power semiconductor modules are used to control and switch high electrical currents and voltages. Within the power module package wire bonding is used as an interconnection technology. In recent years, aluminum wire has been used preferably, but an ever-growing market of powerful and efficient power modules requires a material with better mechanical and electrical properties. For this reason, a technology change from aluminum to copper is indispensable. However, the copper wire bonding process reacts more sensitive to parameter changes. This makes manufacturing reliable copper bond connections a challenging task. The aim of the BMBF funded project Itsowl-InCuB is the development of self-optimizing techniques to enable the reliable production of copper bond connections under varying conditions. A model of the process is essential to achieve this aim. This model needs to include the dynamic elasto-plastic deformation, the ultrasonic softening effect and the proceeding adhesion between wire and substrate. This paper focusses on the pre-deformation process. In the touchdown phase, the wire is pressed into the V-groove of the tool and a small initial contact area between wire and substrate arise. The local characteristics of the material change abruptly because of the cold forming. Consequently, the pre-deformation has a strong effect on the joining process. In [1], a pre-cleaning effect during the touchdown process of aluminum wires by cracking of oxide layers was presented. These interactions of the process parameters are still largely unknown for copper. In a first step, this paper validates the importance of modeling the pre-deformation by showing its impact on the wire deformation characteristic experimentally. Creating cross-section views of pre-deformed copper wires has shown a low deformation degree compared to aluminum. By using a digital microscope and a scanning confocal microscope an analysis about the contact areas and penetration depths after touchdown has been made. Additionally, it has to be taken into account that the dynamical touchdown force depends on the touchdown speed and the touchdown force set in the bonding machine. In order to measure the overshoot in the force signals, a strain gauge sensor has been used. Subsequently, the affecting factors have been interpreted independently Furthermore, the material properties of copper wire have been investigated with tensile tests and hardness measurements. In a second step, the paper presents finite element models of the touchdown process for source and destination bonds. These models take the measured overshoot in the touchdown forces into account. A multi-linear, isotropic material model has been selected to map the material properties of the copper. A validation of the model with the experimental determined contact areas, normal pressures and penetration depths reveals the high model quality. Thus, the simulation is able to calculate and visualize the three dimensional pre-deformation with an integrated material parameter of the wire if the touchdown parameters of the bonding machine are known. Based on the calculated deformation degrees of wire and substrate, it is probably possible to investigate the effect of the pre-deformation on the pre-cleaning phase in the copper wire bonding.

In power electronics, ultrasonic wire bonding is used to connect the electrical terminals of power modules. To implement a self-optimization technique for ultrasonic wire bonding machines, a model of the process is essential. This model needs to include the so called ultrasonic softening effect. It is a key effect within the wire bonding process primarily enabling the robust interconnection between the wire and a substrate. However, the physical modeling of the ultrasonic softening effect is notoriously difficult because of its highly non-linear character and the absence of a proper measurement method. In a first step, this paper validates the importance of modeling the ultrasonic softening by showing its impact on the wire deformation characteristic experimentally. In a second step, the paper presents a data-driven model of the ultrasonic softening effect which is constructed from data using machine learning techniques. A typical caveat of data-driven modeling is the need for training data that cover the considered domain of process parameters in order to achieve accurate generalization of the trained model to new process configurations. In practice, however, the space of process parameters can only be sampled sparsely. In this paper, a novel technique is applied which enables the integration of prior knowledge about the process into the datadriven modeling process. It turns out that this approach results in accurate generalization of the data-driven model to unseen process parameters from sparse data.

Self-optimizing mechatronic systems allow the adaptation of the system’s behavior to the current situation. This can be used to actively adapt the behavior to the current degradation state of the system or of some of its components. To this end, the Multi-Level Dependability Concept has been developed. In this contribution, we show how the Multi-Level Dependability Concept has been applied to the active suspension module of an innovative rail-bound vehicle. For this module, the usage of control reconfiguration, which is a novel approach to exploit complex redundancy systems, is required. We show that by combining self-optimization with the possibilities given by control reconfiguration, the dependability of a complex mechatronic system can be greatly improved.