@article{58492,
  abstract     = {{A coupled finite plasticity ductile damage and failure model is proposed for the finite element simulation of clinch joining, which incorporates stress-state dependency and regularisation by gradient-enhancement of the damage variable. Ductile damage is determined based on a failure indicator governed by a failure surface in stress space. The latter is exemplary chosen as a combination of the Hosford–Coulomb and Cockcroft–Latham–Oh failure criteria for the high and low stress triaxiality range, respectively, to cover the wide stress range encountered in forming. Damage is coupled to elasto-plasticity to capture the damage-induced degradation of the stiffness and flow stress. This affects the material behaviour up to failure, thereby realistically altering the stress state. Consequently, especially for highly ductile materials, where substantial necking and localisation precede material fracture, the failure prediction is enhanced. The resulting stress softening is regularised by gradient-enhancement to obtain mesh-objective results. The analysis of a modified punch test experiment emphasises how the damage-induced softening effect can strongly alter the actual stress state towards failure. Moreover, the impact of successful regularisation is shown, and the applicability of the damage and failure model to clinch joining is proven.}},
  author       = {{Friedlein, Johannes and Mergheim, Julia and Steinmann, Paul}},
  issn         = {{0022-5096}},
  journal      = {{Journal of the Mechanics and Physics of Solids}},
  keywords     = {{Finite plasticity, Ductile damage, Gradient-enhancement, Stress-state dependency, Failure}},
  publisher    = {{Elsevier BV}},
  title        = {{{Modelling of stress-state-dependent ductile damage with gradient-enhancement exemplified for clinch joining}}},
  doi          = {{10.1016/j.jmps.2025.106026}},
  volume       = {{196}},
  year         = {{2025}},
}

@inproceedings{62080,
  abstract     = {{The failure behavior of fiber reinforced polymers (FRP) is strongly influenced by their microstructure, i.e. fiber arrangement or local fiber volume content. However, this information cannot be directly used for structural analyses, since it requires a discretization on micrometer level. Therefore, current failure theories do not directly account for such effects, but describe the behavior averaged over an entire specimen. This foundation in experimentally accessible loading conditions leads to purely theory based extension to more complex stress states without direct validation possibilities. This work aims at leveraging micro-scale simulations to obtain failure information under arbitrary loading conditions. The results are propagated to the meso-scale, enabling efficient structural analyses, by means of machine learning (ML). It is shown that the ML model is capable of correctly assessing previously unseen stress states and therefore poses an efficient tool of exploiting information from the micro-scale in larger simulations.}},
  author       = {{Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}},
  booktitle    = {{Sheet Metal 2025}},
  editor       = {{Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}},
  isbn         = {{978-1-64490-354-4}},
  keywords     = {{Failure, Fiber Reinforced Plastic, Machine Learning}},
  pages        = {{260–267}},
  publisher    = {{Materials Research Forum LLC, Materials Research Foundations}},
  title        = {{{Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning}}},
  doi          = {{10.21741/9781644903551-32}},
  year         = {{2025}},
}

@article{58491,
  abstract     = {{<jats:p>Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.</jats:p>}},
  author       = {{Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert, Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}},
  issn         = {{2504-4494}},
  journal      = {{Journal of Manufacturing and Materials Processing}},
  keywords     = {{ductile damage, stress-state dependency, failure, parameter identification, punch test, clinching}},
  number       = {{4}},
  publisher    = {{MDPI AG}},
  title        = {{{Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining}}},
  doi          = {{10.3390/jmmp8040157}},
  volume       = {{8}},
  year         = {{2024}},
}

@inproceedings{33991,
  abstract     = {{In the course of digitalization, digital platforms are unleashing their full disruptive potential and are already dominating the first industries (e.g., hotel industry). As a result of this success, more and more companies want to build their own platforms and participate in the success. However, building and operating a digital platform involves multiple challenges and most of such ambitions fail. Since most digital platforms fail, strategic leadership of digital platforms must consider both success factors and reasons for platform failure. For this purpose, we conducted a systematic literature analysis and identified 24 success as well as failure factors in 9 dimensions. From a scientific perspective, the article provides a structured analysis of success and failure factors of digital platforms, which previously did not exist in literature. Practitioners can use the resulting knowledge base to successfully manage platform activities and avoid pitfalls.}},
  author       = {{Özcan, Leon and Koldewey, Christian and Duparc, Estelle and van der Valk, Hendrik and Otto, Boris and Dumitrescu, Roman}},
  keywords     = {{Digital Platform, Multi-sided Market, Two-sided Market, Success Factor, Failure Factor}},
  location     = {{Minneapolis}},
  title        = {{{Why do Digital Platforms succeed or fail? - A Literature Review on Success and Failure Factors}}},
  year         = {{2022}},
}

@inproceedings{27652,
  abstract     = {{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.
}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
  booktitle    = {{VDI-Berichte 2391}},
  isbn         = {{978-3-18-092391-8}},
  issn         = {{0083-5560 }},
  keywords     = {{run-to-failure, rubber-metal element, bearing prognostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection}},
  location     = {{Würzburg}},
  pages        = {{197 -- 210}},
  publisher    = {{VDI Verlag GmbH}},
  title        = {{{Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten }}},
  year         = {{2021}},
}

@inproceedings{22507,
  abstract     = {{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.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
  booktitle    = {{Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)}},
  keywords     = {{Wind turbine diagnostics, bearing diagnostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection, fault detection, failure detection}},
  title        = {{{On the applicability of time series features as health indicators for technical systems operating under varying conditions}}},
  year         = {{2021}},
}

@article{23431,
  abstract     = {{As an effective and accurate method for modelling composite materials, mean-field homogenization is still not well studied in modelling non-linear and damage behaviours of UD composites. Investigated micro FE-simulations show that the matrix of UD composites exhibits different average plastic behaviour, named as average asymmetric matrix plasticity (AAMP), when the composite behaves different under shear, longitudinal and transverse loadings. In this study, a non-linear mean-field debonding model (NMFDM) combining a mean-field model and a fibre–matrix interface debonding model, is developed to simulate UD composites under consideration of AAMP, fibre–matrix interface damage and progressive failure. AAMP is considered by using so-called stress mode factor, which is expressed in terms of basic invariants of the matrix deviatoric stress tensor and is used as an indicator for detection of differences in the loading mode. The material behaviour of UD composites with imperfect interface is assumed identical as for perfect interface and stiffness reduced fibres. Progressive failure criteria are established with consideration of fibre breakage and matrix crack for different fibre orientations. As a representative example for the NMFDM, a C30/E201 UD composite is studied. To verify the model, experiments are conducted on polymers, carbon fibres and UD CFRPs. Finally, the model is applied to simulate a perforated CFRP laminate, which shows excellent prediction ability on deformation, debonding and progressive failure.}},
  author       = {{Cheng, C. and Wang, Z. and Jin, Z. and Ju, X. and Schweizer, Swetlana and Tröster, Thomas and Mahnken, Rolf}},
  issn         = {{1359-8368}},
  journal      = {{Composites Part B: Engineering}},
  keywords     = {{Non-linear mean-field homogenization Average asymmetric plasticity of matrix Fibre–matrix interface debonding Micro-mechanical FE-simulation Progressive failure}},
  title        = {{{Non-linear mean-field modelling of UD composite laminates accounting for average asymmetric plasticity of the matrix, debonding and progressive failure}}},
  doi          = {{10.1016/j.compositesb.2021.109209}},
  volume       = {{224}},
  year         = {{2021}},
}

@inbook{21542,
  abstract     = {{Using near-field (NF) scan data to predict the far-field (FF) behaviour of radiating electronic systems represents a novel method to accompany the whole RF design process. This approach involves so-called Huygens' box as an efficient radiation model inside an electromagnetic (EM) simulation tool and then transforms the scanned NF measured data into the FF. For this, the basic idea of the Huygens'box principle and the NF-to-FF transformation are briefly presented. The NF is measured on the Huygens' box around a device under test using anNF scanner, recording the magnitude and phase of the site-related magnetic and electric components. A comparison between a fullwave simulation and the measurement results shows a good similarity in both the NF and the simulated and transformed FF.Thus, this method is applicable to predict the FF behaviour of any electronic system by measuring the NF. With this knowledge, the RF design can be improved due to allowing a significant reduction of EM compatibility failure at the end of the development flow. In addition, the very efficient FF radiation model can be used for detailed investigations in various environments and the impact of such an equivalent radiation source on other electronic systems can be assessed.}},
  author       = {{Schröder, Dominik and Lange, Sven and Hangmann, Christian and Hedayat, Christian}},
  booktitle    = {{Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis}},
  isbn         = {{9781839530494}},
  keywords     = {{Huygens' box, NF-to-FF transformation, efficient FF radiation model, FF behaviour, EMI assessment, PCB, near-field measurements, efficient radiation model, far-field behaviour, RF design process, far-field prediction, Huygens'box principle, fullwave simulation, electronic system radiation, equivalent radiation source, electromagnetic simulation tool, near-field scan data, EM compatibility failure reduction}},
  pages        = {{315--346 (32)}},
  publisher    = {{ The Institution of Engineering and Technology (IET)}},
  title        = {{{Far-field prediction combining simulations with near-field measurements for EMI assessment of PCBs}}},
  doi          = {{10.1049/pbcs072e_ch14}},
  year         = {{2020}},
}

@inproceedings{4463,
  abstract     = {{Academic success in Higher Education is influenced by a number of different factors. This paper tackles the question if the individual levels of motivation, anxiety, enjoyment and self-efficacy, measured immediately before entering university, influence the probability of academic success. Former studies have shown an influence of the high school grade, the learning environment and motivational variables. They do not investigate, however, the individual levels of the mentioned constructs before the beginning of the studies. This research was conducted at the University of St. Gallen/Switzerland. The sample includes 695 first-year students who provided information about the individual level of the mentioned constructs. 
Descriptive statistics show that on average the students are highly motivated, have a high level of self-efficacy and are looking forward to their studies before their beginning. Yet, there are students who have a high level of fear of failure in the study in spite of their high motivation and self-efficacy. A logistic regression shows that there is a significant effect of fear of failure on the probability of study success. This paper shows that fear of failure can increase the probability of academic failure and thus become a self-fulfilling prophecy. It confirms fear as an important factor for academic success. Furthermore, other important factors for academic success, for example the high school grade, could be confirmed in this study}},
  author       = {{Brahm, Taiga and Jenert, Tobias and Wagner, Dietrich}},
  keywords     = {{Quantitative methods, Student learning, Emotion and Cognition, Social sciences, Higher education, Motivation and Emotion, Fear of Failure}},
  location     = {{Zypern}},
  title        = {{{The self-fulfilling prophecy of fear of academic failure}}},
  year         = {{2015}},
}

@article{5714,
  abstract     = {{Coalition loyalty programs are on the rise, yet few studies investigate the impact of service failures in such programs. Using data from a retail context, the authors show that a program partner deemed responsible for a service failure suffers negative customer responses. However, customers’ perceptions of the benefits of the coalition loyalty program buffer these consequences. Perhaps most importantly, when customers perceive the program's special treatment benefits as low, direct and indirect spillover effects occur, such that a service failure by one program partner has a negative effect on customer loyalty toward the program itself.}},
  author       = {{Schumann, Jan H and Wünderlich, Nancy and Evanschitzky, Heiner}},
  journal      = {{Journal of Retailing}},
  keywords     = {{Service failure, Spillover effects, Buffering effect, Coalition loyalty program}},
  number       = {{1}},
  pages        = {{111--118}},
  publisher    = {{Elsevier}},
  title        = {{{Spillover Effects of Service Failures in Coalition Loyalty Programs: The Buffering Effect of Special Treatment Benefits.}}},
  volume       = {{90}},
  year         = {{2014}},
}

@inproceedings{9868,
  abstract     = {{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.}},
  author       = {{Althoff, Simon and Neuhaus, Jan and Hemsel, Tobias and Sextro, Walter}},
  booktitle    = {{Electronic Components and Technology Conference (ECTC), 2014 IEEE 64th}},
  keywords     = {{adhesion, circuit reliability, deformation, diffusion, fatigue cracks, friction, interconnections, lead bonding, van der Waals forces, Cu, adhering process, adhesion process, ampacity improvement, bond quality improvement, cleaning process, diffusing process, fatigue fracture failure, friction energy, friction model, heat dissipation, mechanical strength, piezoelectric triaxial force sensor, predeforming process, size 500 mum, total contact area, van der Waals forces, wedge copper wire bonding, Bonding, Copper, Finite element analysis, Force, Friction, Substrates, Wires}},
  pages        = {{1549--1555}},
  title        = {{{Improving the bond quality of copper wire bonds using a friction model approach}}},
  doi          = {{10.1109/ECTC.2014.6897500}},
  year         = {{2014}},
}

@techreport{20870,
  abstract     = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions.}},
  author       = {{Sievers, Sönke and Mokwa, Christopher Frederik}},
  keywords     = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}},
  pages        = {{31}},
  title        = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}},
  doi          = {{10.2139/ssrn.2100501}},
  year         = {{2012}},
}

@article{5196,
  abstract     = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions. }},
  author       = {{Mokwa, Christopher Frederik and Sievers, Sönke}},
  journal      = {{SSRN Electronic Journal}},
  keywords     = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}},
  title        = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}},
  doi          = {{10.2139/ssrn.2100501}},
  year         = {{2012}},
}

@inproceedings{9760,
  abstract     = {{Self-optimizing systems are able to adapt their behavior autonomously according to their current self-determined objectives. Unforeseen influences could lead to dependability-critical behavior of the system. Methods are required which secure self-optimizing systems during operation. These methods to increase the dependability of the system should already be taken into consideration in the design process. This paper presents a guideline for the dependability-oriented design of self-optimizing systems, which integrates established classical methods like failure mode and effects analysis as well as methods based on self-optimization. On the one hand self-optimization is used to increase the dependability of the system by integrating objectives like safety, availability, and reliability to the objectives of the system. On the other hand methods are required to ensure the self-optimization itself. As basis for this guideline serves the principle solution of the system. The six phases of the guideline extend the design process and lead to an enhanced principle solution. Additionally, the guideline illustrates phases to implement and validate the self-optimizing system. The proposed guideline is applied to an innovative rail-bound vehicle, called RailCab, which is equipped with self-optimizing function modules.}},
  author       = {{Sondermann-Wölke, Christoph and Hemsel, Tobias and Sextro, Walter and Gausemeier, Jürgen and Pook, Sebastian}},
  booktitle    = {{Industrial Informatics (INDIN), 2010 8th IEEE International Conference on}},
  keywords     = {{RailCab, dependability-critical behavior, dependability-oriented design, failure mode, rail-bound vehicle, secure self-optimizing systems, self-optimizing function modules, optimisation, railways, self-adjusting systems}},
  pages        = {{739 --744}},
  title        = {{{Guideline for the dependability-oriented design of self-optimizing systems}}},
  doi          = {{10.1109/INDIN.2010.5549490}},
  year         = {{2010}},
}