@article{63765,
  abstract     = {{Rubber-metal bushings (RMB) are critical components in multi-body systems, such as vehicles and industrial machinery, due to their ability to enable relative motion, dampen vibrations, and transmit forces. However, their nonlinear behavior challenges accurate modeling. Traditional physics-based models often fail to balance simplicity, accuracy, and computational efficiency. The growing availability of experimental data offers opportunities to improve RMB modeling through hybrid and data-driven approaches. This study evaluates physics-based, hybrid, and data-driven methods based on predictive accuracy, modeling effort, and computational cost. Hybrid approaches, combining machine learning techniques with physics-based models, are investigated to leverage their complementary strengths. Results show that hybrid methods enhance accuracy for simpler models with a modest increase in computational time. This highlights their potential to simplify RMB modeling while balancing accuracy and efficiency, offering insights for advancing multi-body system simulations. Building on these insights, data-driven methods are explored for their ability to provide surrogate models for dynamical systems without requiring expert knowledge. Experiments reveal that while simple data-driven methods approximate system behavior when data has low variance, they fail with trajectories of widely varying frequency and amplitude.}},
  author       = {{Wohlleben, Meike Claudia and Schütte, Jan and Berkemeier, Manuel Bastian and Sextro, Walter and Peitz, Sebastian}},
  issn         = {{1384-5640}},
  journal      = {{Multibody System Dynamics}},
  pages        = {{1–21}},
  title        = {{{Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}}},
  doi          = {{10.1007/s11044-026-10146-9}},
  year         = {{2026}},
}

@inproceedings{64826,
  author       = {{Kelber, Max and Brück, Steffen and Bhardwaj, Nishant and Aimiyekagbon, Osarenren Kennedy and Naumann, Rolf and Sextro, Walter}},
  booktitle    = {{Tagungsband Rad-Schiene-Tagung 2026}},
  isbn         = {{978-3-96892-332-1}},
  location     = {{Dresden}},
  pages        = {{206 – 208}},
  publisher    = {{DVV Media Group GmbH - Eurailpress}},
  title        = {{{Methodik zur Untersuchung der Fahrwerksparameter von Schienenfahrzeugen auf Basis optischer Schwingungsmessungen an einer ortsfesten Messstelle}}},
  year         = {{2026}},
}

@inproceedings{64787,
  abstract     = {{This study proposes a fault diagnostics methodology that addresses the challenges posed by highly imbalanced datasets typical of railway applications, where faulty conditions constitute the minority class. Fault diagnostics is performed from the component level upward, considering each sensor’s proximity to its respective critical component. Advanced signal analysis, feature engineering, and automated data-driven model generation techniques were explored to achieve comprehensive diagnostics, such that the model development process accounts for variations in the operating conditions and differing levels of information availability. The proposed methodology is evaluated on datasets from the MONOCAB, for scenarios with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data challenge, which focused on fault diagnostics of railway systems under various fault modes and operating conditions.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas and Beck, Maximilian and Sextro, Walter}},
  booktitle    = {{PHM Society Asia-Pacific Conference}},
  keywords     = {{MONOCAB, Beijing Data Challenge, Diagnostics of railway systems}},
  title        = {{{Multilevel fault diagnostics for railway applications using limited historical data}}},
  doi          = {{10.36001/phmap.2025.v5i1.4449}},
  volume       = {{5}},
  year         = {{2025}},
}

@inproceedings{64800,
  abstract     = {{Intensive ultrasonic cleaning of surfaces by means of a lead-free ultrasonic transducer with focusing sonotrode
Ultrasonic cleaning baths are probably a coincidental development: After underwater sonars had already been successfully used to detect submarines before 1920, it was probably observed in this environment that the ultrasonic oscillators not only showed a self-cleaning effect but also cavitation damage. At the beginning of the 1950s, the first ultrasonic cleaning devices finally came onto the market. Today, the range of applications ranges from household appliances for jewellery and eyewear cleaning to classic cleaning baths for metal parts and systems for cleaning highly sensitive electronic components. There is a certain gap in handheld, mobile cleaning equipment. Although devices for spot cleaning of textiles are known, the cleaning effect is usually low. 
Due to the directive 2011/65/EU on the restriction of the use of hazardous substances in electrical and electronic equipment (RoHS) [1] lead should no longer be used in technical devices. As today’s standard ceramics for medium and high-power ultrasonic transducers typically contain lead, there is a need to explore the use of lead-free ceramics in this field. Honda [2] already offers a cleaning transducer based on lead-free piezoelectric ceramics, but it is designed to be used in cleaning baths.
This article presents the model-based development of a highly innovative ultrasonic cleaner. On the one hand, lead-free piezoelectric ceramics are used, and on the other hand, a special sonotrode has been developed that concentrates the sound in such a way that a strong cavitation and thus cleaning effect is achieved with comparatively low power in a short time. Coupled field finite element method was used to find an appropriate geometry for the focussing sonotrode. The comparison of simulation and measurement results shows that the lead-free piezoceramics used do their job well and can keep up with standard ceramics, but more ceramic volume is needed to achieve same power. An advanced control concept was elaborated to ensure continuous hard cavitation at varying distances between the sonotrode and the part to be cleaned. Cleaning results for different surfaces and contaminations are presented. The concept of the focusing sonotrode shows that a convincing cleaning result can be achieved even with low power and in short time, provided that the oscillation system and control electronics are suitably coordinated.

References
[1] http://data.europa.eu/eli/dir/2011/65/2024-08-01 
[2] https://en.honda-el.co.jp/product/ceramics/lineup/lead_off/lead-off 
}},
  author       = {{Hemsel, Tobias and Scheidemann, Claus and Bornmann, Peter and Littmann, Walter and Sextro, Walter}},
  location     = {{Paderborn, Germany}},
  title        = {{{Intensive ultrasonic cleaning of surfaces by means of lead-free ultrasonic transducer with focussing sonotrode}}},
  year         = {{2025}},
}

@inproceedings{61755,
  author       = {{Scheidemann, Claus and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Vilnius, Lithuania}},
  title        = {{{Time dependent material characteristics of prestressed piezoelectric ceramics in langevin transducers}}},
  year         = {{2025}},
}

@inproceedings{61757,
  author       = {{Scheidemann, Claus and Porzenheim, Julius and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Paderborn, Germany}},
  title        = {{{Investigation of the Setting Behaviour of Mechanically Biased Piezoelectric Ultrasonic Transducers}}},
  year         = {{2025}},
}

@article{57829,
  abstract     = {{Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program \emph{SPP~2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics}, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics.}},
  author       = {{de Payrebrune, Kristin M. and Flaßkamp, Kathrin and Ströhla, Tom and Sattel, Thomas and Bestle, Dieter and Röder, Benedict and Eberhard, Peter and Peitz, Sebastian and Stoffel, Marcus and Rutwik, Gulakala and Aditya, Borse and Wohlleben, Meike Claudia and Sextro, Walter and Raff, Maximilian and Remy, C. David and Yadav, Manish and Stender, Merten and van Delden, Jan and Lüddecke, Timo and Langer, Sabine C. and Schultz, Julius and Blech, Christopher}},
  journal      = {{Technische Mechanik - European Journal of Engineering Mechanics}},
  number       = {{1}},
  pages        = {{1--23}},
  title        = {{{The impact of AI on engineering design procedures for dynamical systems}}},
  doi          = {{10.24352/UB.OVGU-2025-037}},
  volume       = {{45}},
  year         = {{2025}},
}

@inbook{62988,
  author       = {{Amakor, Augustina C. and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter and Peitz, Sebastian}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783032045546}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}}},
  doi          = {{10.1007/978-3-032-04555-3_21}},
  year         = {{2025}},
}

@inproceedings{64804,
  abstract     = {{Das Ultraschallschweißen ist in der Verpackungs-, Halbleiter- und Automobilindustrie weit verbreitet. Neben dem Schweißen von Blechen bietet es die Möglichkeit, Folien oder Hülsen zu verschweißen. Konventionelle Schweißsysteme arbeiten mit Längs- oder Biegeschwingungen, deren Hauptanteil in der Schweißebene liegt. Der orthogonale Anteil verursacht zusätzliche Belastungen im Schweißgut. Bei der Verwendung von Torsionsschwingungen wird die orthogonale Komponente der Schwingung nahezu eliminiert. 
In diesem Beitrag wird ein System vorgestellt, bei dem die Torsionsschwingung durch tangentiale Polarisation der Piezokeramiken erzeugt wird. Der Transducer ist axial oberhalb des Schweißpunktes platziert, sodass die Normalkraft momentfrei aufgebracht wird. Das Schweißwerkzeug weicht beim Schweißvorgang daher seitlich nicht aus. Zudem wird das Schweißen an schwer zugänglichen Positionen vereinfacht, da der Systemaufbau deutlich schlanker ist als konventionelle Ultraschallschweißsysteme.
Die Auslegung des Torsionsschwingsystems stellt eine Herausforderung dar. Insbesondere muss die Lagerung des Schwingers betrachtet werden, da diese die Normalkraft übertragen und zugleich die Schwingung nicht beeinträchtigen soll. Der Schweißprozess bewirkt eine Verschiebung von Schwingungsknoten und Resonanzfrequenzen. Im Rahmen des Vortrags wird ein Finite-Elemente-Simulationsmodell vorgestellt, das in Kombination mit einem Lastmodell das Systemverhalten während des Schweißprozesses abbildet. Die Geometrie des Transducers wurde schrittweise so angepasst, dass die Schwingamplitude im Lagerungspunkt minimiert wird.  
}},
  author       = {{Dohmen, Markus Daniel and Bornmann, Peter and Littmann, Walter and Hemsel, Tobias and Sextro, Walter}},
  title        = {{{Modellgestützte Optimierung eines Ultraschall-Torsionsschweißsystems}}},
  year         = {{2025}},
}

@article{58556,
  abstract     = {{To predict and prevent uneven tire wear in addition to a reduction of overall tire wear, it is essential to estimate not only the total amount of wear but also how the wear is distributed across the tire width. This requires knowledge of the frictional power distribution in the tire contact patch, which is the basis for calculating tire wear using a wear law. Usually, only 3D structural tire models can generate such distributed contact results. However, they involve high computational costs and cannot be used for comprehensive optimization of a vehicle’s suspension system with respect to tire wear characteristics. Hence, this contribution presents a methodology on how to accelerate the prediction of the frictional power distribution using two components: The structural tire model is replaced by an empirical tire model that on its own is not able to generate distributed contact results. Therefore, an artificial neural network is trained to predict the desired contact results from the kinematic quantities calculated by the empirical tire model. In the initial training phase, both components are fitted to data generated by the original complex tire model. After training, the empirical tire model can replace the structural tire model in vehicle simulations, resulting in significantly shorter calculation times. The simulation results are fed into the artificial neural network, which predicts the frictional power distributions over the tire width with negligible additional effort. Overall, the methodology reduces calculation time for the prediction of tire wear based on virtual test drives to approximately 25% of the time needed when using structural tire models.}},
  author       = {{Muth, Lars and Zharia, Raphael and Sahin, Hürkan and Sextro, Walter}},
  journal      = {{Tire Science and Technology}},
  publisher    = {{The Tire Society}},
  title        = {{{Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network}}},
  doi          = {{https://doi.org/10.2346/TST-24-009}},
  year         = {{2025}},
}

@unpublished{60881,
  abstract     = {{<jats:p>Hybrid modeling aims to combine physical and data-driven models to increase simulation accuracy without losing physical interpretability. In the context of dynamic mechanical systems, this enables the compensation of modeling inaccuracies that arise from simplifications, missing effects, or uncertain parameters. In this work, a hybrid model is used as a starting point, in which the discrepancy between simulation and measurement is learned and compensated by a data-driven correction element. To integrate such models into commercial multibody system (MBS) software like MSC Adams and Simpack, the formulation is adapted to operate directly on the force level. This allows implementation via standard co-simulation interfaces without modifying the system’s differential equations or solvers. The method is demonstrated using a single-mass oscillator with synthetic measurement data. Results show that the coupled simulation works reliably and that the hybrid model significantly improves accuracy while remaining compatible with established industrial simulation workflows.</jats:p>}},
  author       = {{Wohlleben, Meike Claudia and Linneweber, Jill Mercedes and Schütte, Jan and Sextro, Walter}},
  publisher    = {{MDPI AG}},
  title        = {{{Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach}}},
  year         = {{2025}},
}

@inproceedings{63193,
  abstract     = {{The integration of data-driven models and specifically machine learning for conditon monitoring and predictive maintenance into companies, especially small and medium-sized enterprises, offers significant opportunities in reducing costs, operating more sustainably, and maintaining long-term competitiveness. However, many small and medium-sized enterprises lack the necessary resources and expertise to derive knowledge from data and integrate their own machine learning based solutions. To address this challenge, a framework is presented that enables the automated generation of data-driven models with a particular focus on condition monitoring and predictive maintenance, but applicable to other use cases as well. Using a dataset from the 2022 data challenge of the prognostics and health management society, it is demonstrated that the framework can generate high-performing models, achieving F1-scores up to 0.998, exemplarily for a classification task.}},
  author       = {{Löwen, Alexander and Quirin, Dennis and Hesse, Marc and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}},
  booktitle    = {{2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  location     = {{Porto}},
  publisher    = {{IEEE}},
  title        = {{{Facilitating the Automated Generation of Data-Driven Models for the Diagnostics and Prognostics of Technical Systems}}},
  doi          = {{10.1109/etfa65518.2025.11205799}},
  year         = {{2025}},
}

@article{51518,
  abstract     = {{In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Hemsel, Tobias and Sextro, Walter}},
  issn         = {{2079-9292}},
  journal      = {{Electronics}},
  keywords     = {{piezoelectric transducer, self-sensing, fault detection, diagnostics, hairline crack, condition monitoring}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  title        = {{{Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions}}},
  doi          = {{10.3390/electronics13030521}},
  volume       = {{13}},
  year         = {{2024}},
}

@inproceedings{55336,
  abstract     = {{Predicting the remaining useful life of technical 
systems has gained significant attention in recent years due to 
increasing demands for extending the lifespan of degrading system 
components. Therefore, already used systems are retrofitted by 
integrating sensors to monitor their performance and 
functionality, enabling accurate diagnosis of their condition and 
prediction of their remaining useful life. One of the main 
challenges in this field is identified in the missing data from the 
time where the retrofitted system has already run but without 
being monitored by sensors. In this paper, a novel approach for 
the combined diagnostics and prognostics of retrofitted systems is 
proposed. The methodology aims to provide an accurate diagnosis 
of the system’s health state and estimation of the remaining useful 
life by a combination of a machine learning and expert knowledge. 
To evaluate the effectiveness of the proposed methodology, a case 
study involving a retrofitted system in an industrial setting is 
selected and applied. It is demonstrated that the approach 
effectively diagnose the current system’s health state and 
accurately predict its remaining useful life, thereby enabling 
predictive maintenance and decision-making. Overall, our 
research contributes to advancing the field of condition 
monitoring for retrofitted systems by providing a comprehensive 
methodology that addresses the challenge of missing data.}},
  author       = {{Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}},
  booktitle    = {{Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}},
  isbn         = {{979-8-3503-6058-5}},
  keywords     = {{retrofit, diagnosis, prognostics, RUL prediction, missing data, ball bearings}},
  location     = {{Stockholm, Schweden}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}}},
  doi          = {{10.1109/PHM61473.2024.00038}},
  year         = {{2024}},
}

@inproceedings{56862,
  author       = {{Redeker, Magnus and Quirin, Dennis and Schroeder, Rafael and Klausmann, Tobias and Löwen, Alexander and Wollbrink, Alexander and Stichweh, Heiko and Althoff, Simon and Bender, Amelie and Sextro, Walter and Hesse, Marc}},
  booktitle    = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  publisher    = {{IEEE}},
  title        = {{{Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding Services in Production}}},
  doi          = {{10.1109/etfa61755.2024.10711095}},
  volume       = {{13}},
  year         = {{2024}},
}

@inproceedings{61756,
  author       = {{Scheidemann, Claus and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Hannover, Germany}},
  title        = {{{Characteristic behavior of lead-free and lead-containing piezo ring ceramics in ultrasonic transducers}}},
  year         = {{2024}},
}

@article{55568,
  abstract     = {{<jats:p>Historical condition monitoring data from technical systems can be utilized to develop data-driven models for predicting the remaining useful life (RUL) of similar systems, whereas the Health Index (HI) often is a crucial component. The development of robust and accurate models requires meaningful features that reflect the system’s degradation process, enabling an accurate prediction of the system's HI. Traditionally, the identification of those is supported by one of various feature ranking methods. In literature, feature interdependencies and their transferability across various similar systems are not sufficiently considered in feature selection, exacerbating the challenge of HI prediction posed by the scarcity of data and system diversity in real-world applications. This work addresses this gaps by demonstrating how filter-based feature selection, incorporating failure thresholds and cross correlations, enhances feature selection leading to improved HI prediction. The proposed methodology is applied to a novel dataset* obtained from run-to-failure experiments on geared motors conducted as part of this study, which presents the aforementioned challenges. It is revealed that classical feature selection, consisting of feature ranking only, leaves potential untapped, which is utilized by the proposed selection methodology. It is shown that the proposed feature selection methodology leads to the best result with a RMSE of 0.14 in predicting the HI of a constructive different gearbox, while the features, determined by classical feature selection, lead to a RMSE of 0.19 at best.</jats:p>}},
  author       = {{Löwen, Alexander and Wissbrock, Peter and Bender, Amelie and Sextro, Walter}},
  isbn         = {{978-1-936263-40-0}},
  journal      = {{PHM Society European Conference}},
  location     = {{Prague}},
  number       = {{1}},
  pages        = {{955--964}},
  publisher    = {{PHM Society}},
  title        = {{{Filter-based feature selection for prognostics incorporating cross correlations and failure thresholds}}},
  doi          = {{10.36001/phme.2024.v8i1.4075}},
  volume       = {{8}},
  year         = {{2024}},
}

@inproceedings{55631,
  abstract     = {{This paper investigates the remaining useful lifetime (RUL) estimation of bearings under dynamic, i.e., time-varying, operating conditions (OC). Unlike conventional studies that assume constant OC in bearing accelerated life tests, we introduce a dataset with time-varying OC during run-to-failure experiments, simulating real-world scenarios. We explore data-driven approaches to identify the transition point from a healthy to an unhealthy state and estimate the RUL. Additionally, we examine strategies for integrating OC information to enhance RUL estimations. These methodologies are evaluated through numerical experiments using various machine learning algorithms.}},
  author       = {{Javanmardi, Alireza and Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Kimotho, James Kuria and Sextro, Walter and Hüllermeier, Eyke}},
  booktitle    = {{PHM Society European Conference}},
  isbn         = {{978-1-936263-40-0}},
  location     = {{Prague, Czech Republic}},
  number       = {{1}},
  publisher    = {{PHM Society}},
  title        = {{{Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions}}},
  doi          = {{10.36001/phme.2024.v8i1.4101}},
  volume       = {{8}},
  year         = {{2024}},
}

@article{56113,
  abstract     = {{Abstract This study focuses on hybrid modeling approaches that combine physical and data-driven methods to create more effective dynamical system models. In particular, it examines discrepancy models, a type of hybrid model that integrates a physical system model with data-driven compensation for inaccuracies. The study applies two discrepancy modeling methods to a multibody system using discrepancies in the state vector and its time derivative, respectively. As an application example, a four-bar linkage with nonlinear damping is investigated, using a simplified conservative system as a physical model. The comparative analysis of the two methods shows that the continuous approach generally outperforms the discrete method in terms of accuracy and computational efficiency, especially for velocity prediction and prediction horizon. However, scenarios, where input signals for training and testing differ, present nuanced findings. When the continuous method is trained on complex signals (sine) and tested on simpler ones (stair), it struggles to deliver satisfactory results, exhibiting notably higher root mean square error (RMSE) values, particularly in angular velocity prediction. Conversely, training on simple signals (stair) and testing on complex ones (sine) surprisingly yields low RMSE values, indicating the continuous method’s adaptability. While the discrete method aligns more closely with expectations and performs better in certain scenarios, its results are consistently moderate, neither exceptional nor particularly poor. The study also introduces a selection framework for choosing the most suitable algorithm based on the specific characteristics of the modeling task. This framework provides guidance for researchers and practitioners in leveraging hybrid modeling effectively. Finally, the study concludes with an outlook on future research directions.}},
  author       = {{Wohlleben, Meike Claudia and Röder, Benedict and Ebel, Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}},
  journal      = {{PAMM}},
  pages        = {{e202400027}},
  title        = {{{Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction}}},
  doi          = {{https://doi.org/10.1002/pamm.202400027}},
  year         = {{2024}},
}

@inproceedings{51119,
  author       = {{Scheidemann, Claus and Hagedorn, Oliver Ernst Caspar and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Jeju, Korea}},
  title        = {{{Experimental Investigation of Bond Formation and Wire Deformation in the Ultrasonic Wire Bonding Process}}},
  year         = {{2023}},
}