TY - CONF AB - Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space. AU - Rook, Jeroen AU - Trautmann, Heike AU - Bossek, Jakob AU - Grimme, Christian ID - 48896 KW - configuration KW - multi-modality KW - multi-objective optimization SN - 978-1-4503-9268-6 T2 - Proceedings of the Genetic and Evolutionary Computation Conference Companion TI - On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems ER - TY - JOUR AB - Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers. AU - Wever, Marcel Dominik AU - Tornede, Alexander AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 21004 JF - IEEE Transactions on Pattern Analysis and Machine Intelligence KW - Automated Machine Learning KW - Multi Label Classification KW - Hierarchical Planning KW - Bayesian Optimization SN - 0162-8828 TI - AutoML for Multi-Label Classification: Overview and Empirical Evaluation ER - TY - JOUR AB - Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available. AU - Schneider, Stefan Balthasar AU - Khalili, Ramin AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Hecker, Artur ID - 21808 JF - Transactions on Network and Service Management KW - network management KW - service management KW - coordination KW - reinforcement learning KW - self-learning KW - self-adaptation KW - multi-objective TI - Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning ER - TY - GEN AB - Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches. AU - Schneider, Stefan Balthasar AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 33854 KW - mobility management KW - coordinated multipoint KW - CoMP KW - cell selection KW - resource management KW - reinforcement learning KW - multi agent KW - MARL KW - self-learning KW - self-adaptation KW - QoE TI - DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning ER - TY - JOUR AB - With the rapid progress of technological development, self-efficacy in reference to digital devices (i.e., information and computer technology [ICT] self-efficacy) is an important driver that helps students to deal with technological problems and support their lifelong learning processes. Schools, peers, and home learning environments are important sources for the development of positive self-efficacy. Expanding on previous research, we investigated the associations between different aspects of the digital home learning environment and students’ ICT self-efficacy. The moderation effects of gender were also tested. A total of 651 children answered a questionnaire about different digital home learning environment dimensions and estimated their ICT self-efficacy using an adapted scale—Schwarzer and Jerusalem’s (1999) general self-efficacy scale. Using the structural equation modeling technique, a digital home learning environment containing six different qualities of parental support was investigated. Families’ cultural capital, parents’ attitudes toward the Internet, and shared Internet activities at home contributed positively to ICT self-efficacy. We observed small gender differences, with the moderation effect being nonsignificant. The results help researchers and practitioners to understand how different dimensions of the digital home learning environment support ICT self-efficacy. We will discuss how parents can enhance the home learning environment and how teachers can integrate this knowledge into formal education. AU - Bonanati, Sabrina AU - Buhl, Heike M. ID - 32558 IS - 2 JF - Learning Environments Research KW - Digital media use KW - Gender KW - Home learning environment KW - ICT self-efcacy KW - Motivation KW - Parental involvement SN - 1387-1579 TI - The digital home learning environment and its relation to children’s ICT self-efficacy VL - 25 ER - TY - GEN AB - This study examines the relation between voluntary audit and the cost of debt in private firms. We use a sample of 4,058 small private firms operating in the period 2006‐2017 that are not subject to mandatory audits. Firms decide for a voluntary audit of financial statements either because the economic setting in which they operate effectively forces them to do so (e.g., ownership complexity, export‐oriented supply chain, subsidiary status) or because firm fundamentals and/or financial reporting practices limit their access to financial debt, both reflected in earnings quality. We use these factors to model the decision for voluntary audit. In the outcome analyses, we find robust evidence that voluntary audits are associated with higher, rather than lower, interest rate by up to 3.0 percentage points. This effect is present regardless of the perceived audit quality (Big‐4 vs. non‐Big‐4), but is stronger for non‐Big‐4 audits where auditees have a stronger position relative to auditors. Audited firms’ earnings are less informative about future operating performance relative to unaudited counterparts. We conclude that voluntary audits facilitate access to financial debt for firms with higher risk that may otherwise have no access to this form of financing. The price paid is reflected in higher interest rates charged to firms with voluntary audits – firms with higher information and/or fundamental risk. AU - Ichev, Riste AU - Koren, Jernej AU - Kosi, Urska AU - Sitar Sustar, Katarina AU - Valentincic, Aljosa ID - 37136 KW - private firms KW - voluntary audit KW - cost of debt KW - self‐selection bias KW - risk TI - Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection of Risk ER - TY - CHAP AB - Self-piercing riveting is an established technique for joining multi-material structures in car body manufacturing. Rivets for self-piercing riveting differ in their geometry, the material used, the condition of the material and their surface condition. To shorten the manufacturing process by omitting the heat treatment and the coating process, the authors have elaborated a concept for the use of stainless steel with high strain hardening as a rivet material. The focus of the present investigation is on the evaluation of the influences of the rivet’s geometry and material on its deformation behaviour. Conventional rivets of types P and HD2, a rivet with an improved geometry made of treatable steel 38B2, and rivets made of the stainless steels 1.3815 and 1.4541 are examined. The analysis is conducted by means of multi-step joining tests for two material combinations comprising high-strength steel HCT70X and aluminium EN AW-5083. The joints are cut to provide a cross-section and the deformation behaviour of the different rivets is analysed on the basis of the measured changes in geometry and hardness. In parallel, an examination of the force-stroke curves provides further insights. It can be demonstrated that, besides the geometry, the material strength, in particular, has a significant influence on the deformation behaviour of the rivet. The strength of steel 1.4541 is seen to be too low for the joining task, while the strength of steel 1.3815 is sufficient, and hence the investigation confirms the capability of rivets made of 1.3815 for joining even challenging material combinations. AU - Uhe, Benedikt AU - Kuball, Clara-Maria AU - Merklein, Marion AU - Meschut, Gerson ED - Daehn, Glenn ED - Cao, Jian ED - Kinsey, Brad ED - Tekkaya, Erman ED - Vivek, Anupam ED - Yoshida, Yoshinori ID - 22930 KW - Self-piercing riveting KW - Lightweight design KW - Deformation behaviour KW - Stainless steel KW - High nitrogen steel T2 - Forming the Future - Proceedings of the 13th International Conference on the Technology of Plasticity. The Minerals, Metals & Materials Series. TI - Self-Piercing Riveting Using Rivets Made of Stainless Steel with High Strain Hardening ER - TY - CONF AB - The use of high-strength steel and aluminium is rising due to the intensified efforts being made in lightweight design, and self-piercing riveting is becoming increasingly important. Conventional rivets for self-piercing riveting differ in their geometry, the material used, the condition of the material and the coating. To shorten the manufacturing process, the use of stainless steel with high strain hardening as the rivet material represents a promising approach. This allows the coating of the rivets to be omitted due to the corrosion resistance of the material and, since the strength of the stainless steel is achieved by cold forming, heat treatment is no longer required. In addition, it is possible to adjust the local strength within the rivet. Because of that, the authors have elaborated a concept for using high nitrogen steel 1.3815 as the rivet material. The present investigation focusses on the joint strength in order to evaluate the capability of rivets in high nitrogen steel by comparison to conventional rivets made of treatable steel. Due to certain challenges in the forming process of the high nitrogen steel rivets, deviations result from the targeted rivet geometry. Mainly these deviations cause a lower joint strength with these rivets, which is, however, adequate. All in all, the capability of the new rivet is proven by the results of this investigation. AU - Uhe, Benedikt AU - Kuball, Clara-Maria AU - Merklein, Marion AU - Meschut, Gerson ID - 22274 KW - Self-piercing Riveting KW - Joining Technology KW - Rivet Geometry KW - Rivet Material KW - High Nitrogen Steel KW - Joint Strength TI - Strength of self-piercing riveted Joints with conventional Rivets and Rivets made of High Nitrogen Steel ER - TY - JOUR AB - Ultrasonic wire bonding is a solid-state joining process, used in the electronics industry to form electrical connections, e.g. to connect electrical terminals within semiconductor modules. Many process parameters affect the bond strength, such like the bond normal force, ultrasonic power, wire material and bonding frequency. Today, process design, development, and optimization is most likely based on the knowledge of process engineers and is mainly performed by experimental testing. In this contribution, a newly developed simulation tool is presented, to reduce time and costs and efficiently determine optimized process parameter. Based on a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire bonding process are considered using finite element simulation for the complex plastic deformation of the wire and reduced order models for the transient dynamics of the transducer, wire, substrate and bond formation. The model parameters such as the coefficients of friction between bond tool and wire and between wire and substrate were determined for aluminium and copper wire in experiments with a test rig specially developed for the requirements of heavy wire bonding. To reduce simulation time, for the finite element simulation a restart analysis and high performance computing is utilized. Detailed analysis of the bond formation showed, that the normal pressure distribution in the contact between wire and substrate has high impact on bond formation and distribution of welded areas in the contact area. AU - Schemmel, Reinhard AU - Krieger, Viktor AU - Hemsel, Tobias AU - Sextro, Walter ID - 21436 JF - Microelectronics Reliability KW - Ultrasonic heavy wire bonding KW - Co-simulation KW - ANSYS KW - MATLAB KW - Process optimization KW - Friction coefficient KW - Copper-copper KW - Aluminium-copper SN - 0026-2714 TI - Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization VL - 119 ER - TY - JOUR AB - Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments. AU - Grimme, Christian AU - Kerschke, Pascal AU - Aspar, Pelin AU - Trautmann, Heike AU - Preuss, Mike AU - Deutz, André H. AU - Wang, Hao AU - Emmerich, Michael ID - 46318 JF - Computers & Operations Research KW - Multimodal optimization KW - Multi-objective continuous optimization KW - Landscape analysis KW - Visualization KW - Benchmarking KW - Theory KW - Algorithms SN - 0305-0548 TI - Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization VL - 136 ER - TY - CONF AB - In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited. AU - Bossek, Jakob AU - Neumann, Aneta AU - Neumann, Frank ID - 48853 KW - evolutionary algorithms KW - evolutionary diversity optimization KW - knapsack problem KW - tailored operators SN - 978-1-4503-8350-9 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms ER - TY - CONF AB - In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality. AU - Bossek, Jakob AU - Neumann, Frank ID - 48860 KW - evolutionary algorithms KW - evolutionary diversity optimization KW - minimum spanning tree KW - runtime analysis SN - 978-1-4503-8350-9 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem ER - TY - JOUR AB - We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient. AU - Bossek, Jakob AU - Neumann, Frank AU - Peng, Pan AU - Sudholt, Dirk ID - 48854 IS - 10 JF - Algorithmica KW - Dynamic optimization KW - Evolutionary algorithms KW - Running time analysis SN - 0178-4617 TI - Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem VL - 83 ER - TY - CONF AB - Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks. AU - Schneider, Stefan Balthasar AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 19609 KW - self-driving networks KW - self-learning KW - network coordination KW - service coordination KW - reinforcement learning KW - deep learning KW - nfv T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Self-Driving Network and Service Coordination Using Deep Reinforcement Learning ER - TY - GEN AB - The aim to reduce pollutant emission has led to a trend towards lightweight construction in car body development during the last years. As a consequence of the resulting need for multi-material design, mechanical joining technologies become increasingly important. Mechanical joining allows for the combination of dissimilar materials, while thermic joining techniques reach their limits. Self-piercing riveting enables the joining of dissimilar materials by using semi-tubular rivets as mechanical fasteners. The rivet production, however, is costly and time-consuming, as the rivets generally have to be hardened, tempered and coated after forming, in order to achieve an adequate strength and corrosion resistance. A promising approach to improve the efficiency of the rivet manufacturing is the use of high-strength high nitrogen steel as rivet material because these additional process steps would not be necessary anymore. As a result of the comparatively high nitrogen content, such steels have various beneficial properties like higher strength, good ductility and improved corrosion resistance. By cold bulk forming of high nitrogen steels high-strength parts can be manufactured due to the strengthening which is caused by the high strain hardening. However, high tool loads thereby have to be expected and are a major challenge during the production process. Consequently, there is a need for appropriate forming strategies. This paper presents key aspects concerning the process design for the manufacturing of semi-tubular self-piercing rivets made of high-strength steel. The aim is to produce the rivets in several forming stages without intermediate heat treatment between the single stages. Due to the high strain hardening of the material, a two stage forming concept will be investigated. Cup-backward extrusion is chosen as the first process step in order to form the rivet shank without forming the rivet foot. Thus, the strain hardening effects in the area of the rivet foot are minimized and the tool loads during the following process step can be reduced. During the second and final forming stage the detailed geometry of the rivet foot and the rivet head is formed. In this context, the effect of different variations, for example concerning the final geometry of the rivet foot, on the tool load is investigated using multistage numerical analysis. Furthermore, the influence of the process temperature on occurring stresses is analysed. Based on the results of the investigations, an adequate forming strategy and a tool concept for the manufacturing of semi-tubular self-piercing rivets made of high-strength steel are presented. ED - Kuball, Clara-Maria ED - Uhe, Benedikt ED - Meschut, Gerson ED - Merklein, Marion ID - 19976 KW - high nitrogen steel KW - self-piercing riveting KW - joining by forming KW - bulk forming KW - tool design TI - Process design for the forming of semi-tubular self-piercing rivets made of high nitrogen steel VL - 50 ER - TY - CONF AB - We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities. Ravi and Sinha (SODA 2004) introduced the model as the \emph{Multi-Commodity Facility Location Problem} (MFLP) and considered it an offline optimization problem. The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances. In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$. A request has to be connected to a set of facilities jointly offering the commodities demanded by it. In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each. To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time. We present results regarding the competitive ratio. On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( \Omega ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics. Here, \( n \) is the number of requests. On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} \cdot \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} \cdot \frac{\log n}{\log \log n} ) \)-competitive algorithm. Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function. AU - Castenow, Jannik AU - Feldkord, Björn AU - Knollmann, Till AU - Malatyali, Manuel AU - Meyer auf der Heide, Friedhelm ID - 17370 KW - Online Multi-Commodity Facility Location KW - Competitive Ratio KW - Online Optimization KW - Facility Location Problem SN - 9781450369350 T2 - Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures TI - The Online Multi-Commodity Facility Location Problem ER - TY - JOUR AU - Otroshi, Mortaza AU - Rossel, Moritz AU - Meschut, Gerson ID - 20143 JF - Journal of Advanced Joining Processes KW - Self-pierce riveting KW - Ductile fracture KW - Damage modeling KW - GISSMO damage model TI - Stress state dependent damage modeling of self-pierce riveting process simulation using GISSMO damage model VL - 1 ER - TY - JOUR AB - Im Artikel werden drei verschiedene Lernzugänge (kom-petenzorientiertes, ästhetisches und biographisches Lernen) vorgestellt und aus theoretischer Perspektive deren motivierender Gehalt für selbstreguliertes Lernen in Praxisphasen des Lehramtsstudiumsherausgearbeitet. Als theoretische Grund-lage dient die Selbstbestimmungstheorie als zentrale motivationale Theorie zur Erklärung selbstbestimmten Handelns. AU - Caruso, Carina AU - Adammek, Christine AU - Bonanati, Sabrina AU - Wiescholek, Sybille ID - 35298 IS - 1 JF - Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion KW - ästhetische Forschung KW - Biographiearbeit KW - Praxissemester KW - Professionalisierung KW - selbstreguliertes Lernen KW - Motivation / aesthetic research KW - biographical work KW - long-term internship KW - profes-sionalization KW - self-regulated learning KW - motivation SN - 2625-0675 TI - Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen VL - 3 ER - TY - JOUR AB - Helhmoltz–Kirchhoff equations of motions of vortices of an incompressible fluid in the plane define a dynamics with singularities and this leads to a Zermelo navigation problem describing the ship travel in such a field where the control is the heading angle. Considering one vortex, we define a time minimization problem which can be analyzed with the technics of geometric optimal control combined with numerical simulations, the geometric frame being the extension of Randers metrics in the punctured plane, with rotational symmetry. Candidates as minimizers are parameterized thanks to the Pontryagin Maximum Principle as extremal solutions of a Hamiltonian vector field. We analyze the time minimal solution to transfer the ship between two points where during the transfer the ship can be either in a strong current region in the vicinity of the vortex or in a weak current region. The analysis is based on a micro-local classification of the extremals using mainly the integrability properties of the dynamics due to the rotational symmetry. The discussion is complex and related to the existence of an isolated extremal (Reeb) circle due to the vortex singularity. The explicit computation of cut points where the extremal curves cease to be optimal is given and the spheres are described in the case where at the initial point the current is weak. AU - Bonnard, Bernard AU - Cots, Olivier AU - Wembe Moafo, Boris Edgar ID - 33866 JF - ESAIM: Control, Optimisation and Calculus of Variations KW - Computational Mathematics KW - Control and Optimization KW - Control and Systems Engineering SN - 1292-8119 TI - A Zermelo navigation problem with a vortex singularity VL - 27 ER - TY - GEN AB - Due to the trend towards lightweight design in car body development mechanical joining technologies become increasingly important. These techniques allow for the joining of dissimilar materials and thus enable multi-material design, while thermic joining methods reach their limits. Semi-tubular self-piercing riveting is an important mechanical joining technology. The rivet production, however, is costly and time-consuming, as the process consists of several process steps including the heat treatment and coating of the rivets in order to achieve an adequate strength and corrosion resistance. The use of high nitrogen steel as rivet material leads to the possibility of reducing process steps and hence increasing the efficiency of the process. However, the high tool loads being expected due to the high strain hardening of the material are a major challenge during the rivet production. Thus, there is a need for appropriate forming strategies, such as the manufacturing of the rivets at elevated temperatures. Prior investigations led to the conclusion that forming already at 200 °C results in a distinct reduction of the yield strength. To create a deeper understanding of the forming behaviour of high nitrogen steel at elevated temperatures, compression tests were conducted in a temperature range between room temperature and 200 °C. The determined true stress – true strain curves are the basis for the further process and tool design of the rivet production. Another key factor for the rivet manufacturing at elevated temperatures is the influence of the process temperature on the tribological conditions. For this reason, ring compression tests at room temperature and 200 °C are carried out. The friction factors are determined on the basis of calibration curves resulting from the numerical analysis of the ring compression process. The investigations indicate that the friction factor at 200 °C is significantly higher compared to room temperature. This essential fact has to be taken into account for the process and tool design for the rivet production using high nitrogen steel. ED - Kuball, Clara-Maria ED - Jung, R ED - Uhe, Benedikt ED - Meschut, Gerson ED - Merklein, Marion ID - 19974 KW - High nitrogen steel KW - Self-piercing riveting KW - Joining by forming KW - Bulk forming KW - Strain hardening TI - Influence of the process temperature on the forming behaviour and the friction during bulk forming of high nitrogen steel VL - 1 ER -