@article{63611,
  abstract     = {{When humans interact with artificial intelligence (AI), one desideratum is appropriate trust. Typically, appropriate trust encompasses that humans trust AI except for instances in which they either explicitly notice AI errors or are suspicious that errors could be present. So far, appropriate trust or related notions have mainly been investigated by assessing trust and reliance. In this contribution, we argue that these assessments are insufficient to measure the complex aim of appropriate trust and the related notion of healthy distrust. We introduce and test the perspective of covert visual attention as an additional indicator for appropriate trust and draw conceptual connections to the notion of healthy distrust. To test the validity of our conceptualization, we formalize visual attention using the Theory of Visual Attention and measure its properties that are potentially relevant to appropriate trust and healthy distrust in an image classification task. Based on temporal-order judgment performance, we estimate participants' attentional capacity and attentional weight toward correct and incorrect mock-up AI classifications. We observe that misclassifications reduce attentional capacity compared to correct classifications. However, our results do not indicate that this reduction is beneficial for a subsequent judgment of the classifications. The attentional weighting is not affected by the classifications' correctness but by the difficulty of categorizing the stimuli themselves. We discuss these results, their implications, and the limited potential for using visual attention as an indicator of appropriate trust and healthy distrust.}},
  author       = {{Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}},
  issn         = {{1664-1078}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{appropriate trust, healthy distrust, visual attention, Theory of Visual Attention, human-AI interaction, Bayesian cognitive model, image classification}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}}},
  doi          = {{10.3389/fpsyg.2025.1694367}},
  volume       = {{16}},
  year         = {{2026}},
}

@article{64916,
  abstract     = {{The joining of dissimilar materials, such as steel and aluminum, entails significant challenges during thermal curing processes due to differing coefficients of thermal expansion. This study addresses the formation of “viscous fingering” instabilities in structural adhesive joints, which are induced by thermally driven relative displacements during the liquid phase of the adhesive. Using a component-like specimen “bridge specimen,” the dependency of this phenomenon on process temperature and structural stiffness (rivet distance) was characterized. Experimental results reveal that while the relative displacement scales cubically with the free buckling length, the resulting adhesive area reduction follows an exponential trend, leading to a loss of effective bond area of up to 79%, which significantly compromises the joint strength in automotive applications. To predict these process-induced defects, a thermo-chemo-viscoelastic-viscoplastic adhesive model implemented in LS-DYNA was applied. The model combines curing kinetics, viscoelastic relaxation, and pressure-dependent plasticity and features a geometric damage parameter (D) that captures the adhesive area reduction caused by viscous fingering as an exponential function of the accumulated normal strain in the liquid phase. This damage parameter, calibrated on base-specimen level, was transferred to the component geometry. The simulation demonstrated high predictive accuracy with a maximum deviation of the adhesive area reduction of 3.1% compared to experimental data. This validates the model’s capability to predict manufacturing-induced damage in complex hybrid structures solely based on thermal boundary conditions.}},
  author       = {{Al Trjman, Mohamad and Beule, Felix and Teutenberg, Dominik and Meschut, Gerson and Riese, Julia}},
  issn         = {{0021-8464}},
  journal      = {{The Journal of Adhesion}},
  keywords     = {{Adhesive area reduction, CED coating process, delta alpha problem, epoxy structural adhesive, influence of manufacture, multi-material design, numerical simulation (FEM), relative displacements, viscous fingering (saffman-taylor-instability).}},
  pages        = {{1--24}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Experimental characterization and numerical analysis of the influence of the CED coating process on viscous fingering formation in hybrid-jointed mixed structures}}},
  doi          = {{10.1080/00218464.2026.2644394}},
  year         = {{2026}},
}

@techreport{65021,
  abstract     = {{Several early music projects, such as the Stanford Josquin Project, have demonstrated the potential for attaining valuable new musicological insights using a corpus-based approach. However, the available musical corpora tend to be relatively small and exhibit considerable variation in encoding practices. Aspiring corpus researchers are confronted with a lack of suitable data, which needs to be addressed before they can embark on their proper research. The EarlyMuse Short Term Scientific Mission CORSICA has surveyed the current state of corpus creation and digital editing in early music. Based on this information, it has developed a vision for the future of corpus building in this field, which aims to speed up the production of digital encodings while respecting the autonomy of the encoders and acknowledging their efforts. This is important because much high-quality encoding is carried out outside the field of professional musicology, and engaging citizen scientists could help address the current shortage of research data. The CORSICA team‘s vision is informed not only by a study of the available data, standards and technologies, but also by Human-Computer Interaction, placing human goals and values before the creation of technology and work processes. The core of the vision is that successful corpus creation must be an inclusive endeavour in terms of both technology and human participation. The report concludes with an implementation plan outlining the initial steps required to realise the vision.}},
  author       = {{Wiering, Frans and Bergwall, Erik and van Berchum, Marnix and Goebl, Werner and Van Kranenburg, Peter and Lewis, David and Plaksin, Anna Viktoria Katrin and Rodríguez-García, Esperanza and Smith, David J. and Visscher, Mirjam and Weigl, David M.}},
  keywords     = {{citizen science, crowdsourcing, digital editions of music, early music, human computer interaction, music corpora, music encoding, musicology}},
  title        = {{{Making Corpus Creation in Early Music Rewarding and Effective: Finding the Optimum Between Standardisation and Autonomy}}},
  doi          = {{10.5281/zenodo.18413961}},
  year         = {{2026}},
}

@article{59755,
  abstract     = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,
trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,
for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated
classification as advice. Research to improve such interactions often aims to foster the
user’s trust, which in turn should improve the combined human-AI performance. Given that AI
models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is
an equally interesting target of research.
We created two image classification scenarios in which the participants received mock-up
AI advice. The quality of the advice decreases for a phase of the experiment. We studied the
task performance, trust and distrust of the participants, and tested whether an instruction to
remain skeptical and review each piece of advice led to a better performance compared to a
neutral condition. Our results indicate that this instruction does not improve but rather worsens
the participants’ performance. Repeated single-item self-report of trust and distrust shows an
increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no
difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory
analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether
the problems of under- and over-reliance have been mitigated. We discuss implications of our
results for the usage of disclaimers before interacting with AI, as prominently used in current
LLM-based chatbots, and for trust and distrust research.}},
  author       = {{Peters, Tobias Martin and Scharlau, Ingrid}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}},
  title        = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}},
  doi          = {{10.3389/fpsyg.2025.1574809}},
  volume       = {{16}},
  year         = {{2025}},
}

@article{58163,
  abstract     = {{Fibre-reinforced polymers are increasingly used due to their high specific strength, making them suitable for local sheet metal reinforcement. This allows improved overall mechanical properties with reduced wall thickness of the sheet metal part and, thus, lower weight of the components. One of the main focuses of research into such hybrid structures is on the adhesive properties and the respective failure behaviour of the interfaces. Generally, the failure behaviour under the influence of mechanical loads can be divided into adhesive, cohesive and mixed-mode failure. The correlation between observed failure behaviour and adhesion properties of the hybrid composite materials is analysed in detail in this work. The hybrid composite consists of an aluminium sheet of the alloy EN AW‑6082 T6 and thermoset carbon fibre-reinforced plastic (CFRP) prepreg. The aluminium sheet was laser pretreated before hybrid production to improve the adhesion properties. The specimens studied were produced by the prepreg pressing process, in which the components are cured and joined simultaneously. The influences of the thickness of the CFRP part, the layup, the fibre orientation at the boundary layer, and the laser pretreatment parameters on the properties of the hybrid joints were investigated.}},
  author       = {{Wu, Shuang and Delp, Alexander and Freund, Jonathan and Walther, Frank and Haubrich, Jan and Löbbecke, Miriam and Tröster, Thomas}},
  issn         = {{0021-8464}},
  journal      = {{The Journal of Adhesion}},
  keywords     = {{Prepreg pressing process, hybrid joints, laser surface pretreatment, intrinsic manufacturing, CFRP, aluminium, materials engineering}},
  pages        = {{1--26}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Correlation between interlaminar shear strength of CFRP and joint strength of aluminium-CFRP hybrid joints}}},
  doi          = {{10.1080/00218464.2024.2439956}},
  year         = {{2025}},
}

@inproceedings{62079,
  abstract     = {{This paper investigates two modeling approaches for the simulation of the deformation and decomposition behavior of preconsolidated rovings above the thermoplastic matrix{\textquoteright} melting temperature. This is crucial for capturing the local material structure after processes introducing highly localized deformation such as mechanical joining processes between metal and fiber reinforced thermoplastics (FRTP). A generic finite element (FE) model is developed, incorporating interfaces discretized through either cohesive zone (CZ) elements or Coulomb friction-based contacts. The material parameters for the FE elements are derived from the initial stiffness of a statistical volume element (SVE) at micro scale modelled with an Arbitrary-Lagrange-Eulerian method for three load cases. The CZ properties calculated are based on the shear viscosity of the composite. The CZ and contact modelling approaches are evaluated using three load cases of the SVE, comparing force-displacement curves. Under simple loading conditions, such as normal pressure tension and bending, both methods produce similar results; however, in complex load cases, the CZ approach shows clear advantages in handling interface interactions and shows robust simulations. The CZ approach thus presents a promising method for simulating roving decomposition in FRTP-metal joining applications above the matrix{\textquoteright} melting temperature.}},
  author       = {{Gröger, Benjamin and 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     = {{Finite Element Method (FEM), Process, Thermoplastic Fiber Reinforced Plastic}},
  pages        = {{268–275}},
  publisher    = {{Materials Research Forum LLC, Materials Research Foundations}},
  title        = {{{Modeling approaches for the decomposition behavior of preconsolidated rovings throughout local deformation processes}}},
  doi          = {{10.21741/9781644903551-33}},
  year         = {{2025}},
}

@inproceedings{61149,
  abstract     = {{The use of continuous fiber-reinforced thermoplastics (FRTP) in automotive industry increases due to their excellent material properties and possibility of rapid processing. The scale spanning heterogeneity of their material structure and its influence on the material behavior, however, presents significant challenges for most joining technologies, such as self-piercing riveting (SPR). During mechanical joining, the material structure is significantly altered within and around the joining zone, heavily influencing the material behavior. A comprehensive understanding of the underlying phenomena of material alteration during the SPR process is essential as basis for validating numerical simulations. This study examines the material structure at ten stages of a step-setting test of SPR with two FRTP sheets with glass-fiber reinforcement. Utilizing X-ray computed tomography (CT), the damage phenomena within different areas of the setting test are analyzed three-dimensionally and key parameters are quantified. Dominating phenomena during the penetration of the rivet into the laminate are fiber failure (FF), interfiber failure (IFF) and fiber bending, while delamination, fiber kinking and roving splitting are also observed. At the final stages, the bottom layers of the second sheet collapse and form a bulge into the cavity of the die.}},
  author       = {{Dargel, Alrik and Gröger, Benjamin and Schlichter, Malte Christian and Gerritzen, Johannes and Köhler, Daniel and Meschut, Gerson and Gude, Maik and Kupfer, Robert}},
  booktitle    = {{Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025)}},
  editor       = {{Gomes, J.F. Silva and Meguid, Shaker A.}},
  isbn         = {{9789727523238}},
  keywords     = {{self-piercing riveting, computed tomography, thermoplastic composites, process-structure-interaction}},
  location     = {{Porto}},
  publisher    = {{FEUP}},
  title        = {{{LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION}}},
  doi          = {{10.24840/978-972-752-323-8}},
  year         = {{2025}},
}

@inproceedings{60958,
  abstract     = {{Large Language Models (LLMs) excel in understanding, generating, and processing human language, with growing adoption in process mining. Process mining relies on event logs that capture explicit process knowledge; however, knowledge-intensive processes (KIPs) in domains such as healthcare and product development depend on tacit knowledge, which is often absent from event logs. To bridge this gap, this study proposes a LLM-based framework for mobilizing tacit process knowledge and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs can capture tacit process knowledge through targeted queries and systematically integrate it into event logs. This study presents a novel approach combining LLMs, knowledge management, and process mining, advancing the understanding and management of KIPs by enhancing knowledge accessibility and documentation.}},
  author       = {{Brennig, Katharina}},
  booktitle    = {{AMCIS 2025 Proceedings. 11.}},
  keywords     = {{Process Mining, Large Language Model, Knowledge Management, Knowledge-Intensive Process, Tacit Knowledge}},
  location     = {{Montréal}},
  title        = {{{Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes}}},
  year         = {{2025}},
}

@article{55400,
  abstract     = {{This study contributes to the evolving field of robot learning in interaction
with humans, examining the impact of diverse input modalities on learning
outcomes. It introduces the concept of "meta-modalities" which encapsulate
additional forms of feedback beyond the traditional preference and scalar
feedback mechanisms. Unlike prior research that focused on individual
meta-modalities, this work evaluates their combined effect on learning
outcomes. Through a study with human participants, we explore user preferences
for these modalities and their impact on robot learning performance. Our
findings reveal that while individual modalities are perceived differently,
their combination significantly improves learning behavior and usability. This
research not only provides valuable insights into the optimization of
human-robot interactive task learning but also opens new avenues for enhancing
the interactive freedom and scaffolding capabilities provided to users in such
settings.}},
  author       = {{Beierling, Helen and Beierling, Robin  and Vollmer, Anna-Lisa}},
  journal      = {{Frontiers in Robotics and AI}},
  keywords     = {{human-robot interaction, human-in-the-loop learning, reinforcement learning, interactive robot learning, multi-modal feedback, learning from demonstration, preference-based learning, scaffolding in robot learning}},
  publisher    = {{Frontiers }},
  title        = {{{The power of combined modalities in interactive robot learning}}},
  volume       = {{12}},
  year         = {{2025}},
}

@article{61327,
  abstract     = {{Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or
adapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement
learning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive
opportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected
data, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized
area of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the
learning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others)
and holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through
interaction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more
meaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result
in robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to
provide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and
current open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context.
The paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to
broad recommendations and a call for action for the growing HIRL community}},
  author       = {{Baraka, Kim  and Idrees, Ifrah and Faulkner, Taylor Kessler and Biyik, Erdem and Booth, Serena and Chetouani, Mohamed and Grollman, Daniel H. and Saran, Akanksha and Senft, Emmanuel and Tulli, Silvia and Vollmer, Anna-Lisa and Andriella, Antonio and Beierling, Helen and Horter, Tiffany and Kober, Jens and Sheidlower, Isaac and Taylor, Matthew E. and van Waveren, Sanne and Xiao, Xuesu}},
  journal      = {{Transactions on Human-Robot Interaction}},
  keywords     = {{Robot learning, Interactive learning systems, Human-robot interaction, Human-in-the-loop machine learning, Teaching and learning}},
  title        = {{{Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}}},
  year         = {{2025}},
}

@article{61339,
  author       = {{Protte, Marius and Djawadi, Behnud Mir}},
  journal      = {{Frontiers in Behavioral Economics}},
  keywords     = {{cheating, human-machine interaction, ambiguity, verification process, algorithm aversion, algorithm appreciation}},
  pages        = {{1645749}},
  title        = {{{Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty}}},
  doi          = {{10.3389/frbhe.2025.1645749}},
  volume       = {{4}},
  year         = {{2025}},
}

@inproceedings{62116,
  author       = {{Böer, Nils Tobias and Güldenpenning, Iris and Weigelt, Matthias}},
  booktitle    = {{57th Herbsttreffen der experimentellen Kognitionspsychologie (HExKoP)}},
  keywords     = {{Deception, Sport Psychology, Social Interaction}},
  location     = {{Trier}},
  title        = {{{The mere presence of a social partner modulates fake-production costs}}},
  year         = {{2025}},
}

@article{60837,
  abstract     = {{In light of growing demands for resource efficiency and sustainability in vehicle engineering, the environmentally compatible separation of structural adhesive joints is gaining increasing relevance. This study presents a comparative analysis of two physically based debonding methods: the established hot-air process and a cryogenic cold process based on liquid nitrogen (LN2). The primary objective is to assess the ecological impact and process-related sustainability of both approaches.
Experimental investigations were conducted on a component-representative triple-sheet structure that simulates common automotive flange joints. Thermal input was applied either by convective heating using a hot air gun or by direct cooling through a contact-based LN2 tool. The resulting temperature profiles were recorded using spatially distributed thermocouples. Subsequently, the outer panel was selectively debonded to replicate a repair scenario, and the mechanical integrity of the remaining adhesive joint was evaluated through Mode I testing of L-shaped specimens. Process data served as input for an Life Cycle Assessment (LCA) according to DIN EN ISO 14040.
The cryogenic method achieved a 40% reduction in carbon footprint compared to the hot-air process (0.337 kg vs. 0.559 kg CO2-equivalents), primarily due to its shorter process time and more efficient heat transfer. While the hot-air method’s impact is mainly driven by electrical energy use, that of the cold method stems from cryogenic media consumption. Notwithstanding certain disadvantages in specific impact categories, the LN2-based process exhibits a superior overall ecological performance and signifies a promising solution for repair- and recycling-oriented adhesive separation in structural vehicle applications.}},
  author       = {{Jordan, Alex and Hermelingmeier, Lucas and Gilich, Julian and Meschut, Gerson and De Santis, Marco Sebastian and Schlüter, Alexander}},
  issn         = {{2666-3309}},
  journal      = {{Journal of Advanced Joining Processes}},
  keywords     = {{Sustainable debonding, Structural adhesives, Sustainable joining technologies, Life Cycle Assessment (LCA), Automotive repair process, Economically efficient debonding}},
  publisher    = {{Elsevier}},
  title        = {{{Comparison of the economic efficiency and sustainability of two debonding processes for structurally bonded sills}}},
  doi          = {{10.1016/j.jajp.2025.100332}},
  volume       = {{12}},
  year         = {{2025}},
}

@article{65007,
  author       = {{Knaup, Felix and Schöppner, Volker}},
  journal      = {{International Polymer Processing}},
  keywords     = {{CFD simulation, melting modeling, melting process, polymer extrusion, single-screw extruder}},
  title        = {{{Improvement of a numerical two-phase simulation model for single-screw plasticizing extruders based on experimental investigations}}},
  doi          = {{10.1515/ipp-2025-0072}},
  year         = {{2025}},
}

@inproceedings{54513,
  abstract     = {{Learning argumentative writing is challenging. Besides writing fundamentals such as syntax and grammar, learners must select and arrange argument components meaningfully to create high-quality essays. To support argumentative writing computationally, one step is to mine the argumentative structure. When combined with automatic essay scoring, interactions of the argumentative structure and quality scores can be exploited for comprehensive writing support. Although studies have shown the usefulness of using information about the argumentative structure for essay scoring, no argument mining corpus with ground-truth essay quality annotations has been published yet. Moreover, none of the existing corpora contain essays written by school students specifically. To fill this research gap, we present a German corpus of 1,320 essays from school students of two age groups. Each essay has been manually annotated for argumentative structure and quality on multiple levels of granularity. We propose baseline approaches to argument mining and essay scoring, and we analyze interactions between both tasks, thereby laying the ground for quality-oriented argumentative writing support.}},
  author       = {{Stahl, Maja and Michel, Nadine and Kilsbach, Sebastian and Schmidtke, Julian  and Rezat, Sara and Wachsmuth, Henning}},
  keywords     = {{Annotation, Corpus, Argumentative Structure}},
  title        = {{{A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality}}},
  doi          = {{10.48550/arXiv.2404.02529}},
  year         = {{2024}},
}

@unpublished{55159,
  abstract     = {{We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus.
Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schrödinger equation.
The article constitutes an extension of our previous article  arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes.}},
  author       = {{Offen, Christian}},
  keywords     = {{System identification, inverse problem of variational calculus, Gaussian process, Lagrangian learning, physics informed machine learning, geometry aware learning}},
  pages        = {{28}},
  title        = {{{Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification}}},
  year         = {{2024}},
}

@inproceedings{49430,
  abstract     = {{Within the current energy and environmental crisis, new material- and energy-saving processes are needed. For this reason, this study focuses on the development of a new forming technology for Ti-6Al-4V sheet metal. It is based on combination of solution treatment by resistive heating with rapid tool-based quenching and subsequent annealing. This new “TISTRAQ” process is comparable with press-hardening already known for steels and hot die quenching known for aluminium alloys. One of the main influencing factors for this process is the heat transfer coefficient (HTC). It is an important driver for adjustment of basic parameters, as selection of tool material or the forming speed but also plays an important role while elaborating temperature distribution in the numerical model. Therefore, a new and unique test rig was developed to determine the HTC and to perform tool-based heat treatment at specimen level under laboratory conditions. The test rig was used to investigate the influence of the titanium-tool-lubricant system on HTC and cooling rate. Further the effect of heat treatment in the test rig and tool-based quenching on microstructure and mechanical properties was studied. To improve the prediction of the temperature distribution of the titanium during cooling, the HTC was integrated into the numerical process simulation}},
  author       = {{Kaiser, Maximilian Alexander and Höschen, Fabian and Pfeffer, Nina and Merten, Mathias and Meyer, Thomas and Marten, Thorsten and Rockicki, Pawel and Höppel, Heinz Werner and Tröster, Thomas}},
  booktitle    = {{IOM3. Chapter 14: Forming, Machining & Joining [version 1; not peer reviewed]}},
  keywords     = {{Interfacial heat transfer coefficient, Ti-6Al-4V, nonisothermal forming, thermomechanical processing, TISTRAQ process}},
  location     = {{Edinburgh}},
  title        = {{{The new TISTRAQ process: Solution treatment with rapid quenching and annealing for Ti-6Al-4V sheet metal part forming - investigation on heat transfer coefficient and influence on cooling rates}}},
  doi          = {{doi.org/10.7490/f1000research.1119929.1}},
  year         = {{2024}},
}

@inproceedings{49437,
  abstract     = {{The phase and TTT diagrams of the Ti-6Al-4V system allow the development of a new forming process for a more energy- and materialefficient production of sheet metal parts. This new “TISTRAQ” process is composed of two steps. In terms of process technology, the first step is comparable to a direct press-hardening process already well known for steels. In this step, the Ti-6Al-4V sheet material is resistively heated to a temperature below β-transus Tβ and, after a very short holding time, simultaneously formed and quenched by use of water cooled tools. Thereby, the β phase undergoes a martensitic transformation. The second step is a subsequent short-time annealing, which leads to a hardening of the material. In this work, a new test rig using resistive heating technique was used in order to produce
different solution treated and tool quenched (STQ) and subsequently annealed (STA) states. In this paper, the effects of heating rate, solution treatment temperature and holding time on microstructure and mechanical properties are addressed. For the characterisation, tensile testing and scanning electron microscopy were used. By the systematic variation of applied processing parameters, dominating effects on microstructure and mechanical properties were evaluated. For example, the solution treatment temperature was found to have a significant effect on microstructural features and characteristic strength and strain values. The obtained results reveal a high potential for future technical applications.}},
  author       = {{Pfeffer, Nina and Kaiser, Maximilian Alexander and Meyer, Thomas and Göken, Mathias and Höppel, Heinz Werner}},
  booktitle    = {{IOM3. Chapter 14: Forming, Machining & Joining [version 1; not peer reviewed]}},
  keywords     = {{Ti-6Al-4V, thermomechanical processing, resistive heating, quench-forming, process parameter-microstructure-properties relationship}},
  location     = {{Edinburgh}},
  title        = {{{The new TISTRAQ process: Solution treatment with rapid quenching and annealing for Ti-6Al-4V sheet metal part forming - the effect of processing parameters on microstructure and mechanical properties}}},
  doi          = {{https://doi.org/10.7490/f1000research.1119929.1}},
  year         = {{2024}},
}

@inproceedings{55429,
  abstract     = {{A detailed understanding of the cognitive process underlying diagnostic reasoning in medical experts is currently lacking. While high-level theories like hypothetico-deductive reasoning were proposed long ago, the inner workings of the step-by-step dynamics within the mind remain unknown. We present a fully automated approach to elicit, monitor, and record diagnostic reasoning processes at a fine-grained level. A web-based user interface enables physicians to carry out a full diagnosis process on a simulated patient, given as a pre-defined clinical vignette. By collecting the physician’s information queries and hypothesis revisions, highly detailed diagnostic reasoning trajectories are captured leading to a diagnosis and its justification. Four expert epileptologists with a mean experience of 19 years were recruited to evaluate the system and share their impressions in semi-structured interviews. We find that the recorded trajectories validate proposed theories on broader diagnostic reasoning, while also providing valuable additional details extending previous findings.}},
  author       = {{Battefeld, Dominik and Mues, Sigrid and Wehner, Tim and House, Patrick and Kellinghaus, Christoph and Wellmer, Jörg and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 46th Annual Conference of the Cognitive Science Society}},
  keywords     = {{Differential Diagnosis, Diagnostic Reasoning, Reasoning Process Analysis, Seizure, Epilepsy}},
  location     = {{Rotterdam, NL}},
  title        = {{{Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories}}},
  year         = {{2024}},
}

@article{56089,
  abstract     = {{<jats:p>Additive manufacturing (AM) technologies enable near-net-shape designs and demand-oriented material usage, which significantly minimizes waste. This points to a substantial opportunity for further optimization in material savings and process design. The current study delves into the advancement of sustainable manufacturing practices in the automotive industry, emphasizing the crucial role of lightweight construction concepts and AM technologies in enhancing resource efficiency and reducing greenhouse gas emissions. By exploring the integration of novel AM techniques such as selective laser melting (SLM) and laser metal deposition (LMD), the study aims to overcome existing limitations like slow build-up rates and limited component resolution. The study’s core objective revolves around the development and validation of a continuous process chain that synergizes different AM routes. In the current study, the continuous process chain for DMG MORI Lasertec 65 3D’s LMD system and the DMG MORI Lasertec 30 3D’s was demonstrated using 316L and 1.2709 steel materials. This integrated approach is designed to significantly curtail process times and minimize component costs, thus suggesting an industry-oriented process chain for future manufacturing paradigms. Additionally, the research investigates the production and material behavior of components under varying manufacturing processes, material combinations, and boundary layer materials. The culmination of this study is the validation of the proposed process route through a technology demonstrator, assessing its scalability and setting a benchmark for resource-efficient manufacturing in the automotive sector.</jats:p>}},
  author       = {{Chalicheemalapalli Jayasankar, Deviprasad and Gnaase, Stefan and Kaiser, Maximilian Alexander and Lehnert, Dennis and Tröster, Thomas}},
  issn         = {{2075-4701}},
  journal      = {{Metals}},
  keywords     = {{additive manufacturing (AM), selective laser melting (SLM), laser metal deposition (LMD), hybrid manufacturing, process optimization, 316L, 1.2709}},
  number       = {{7}},
  publisher    = {{MDPI AG}},
  title        = {{{Advancements in Hybrid Additive Manufacturing: Integrating SLM and LMD for High-Performance Applications}}},
  doi          = {{10.3390/met14070772}},
  volume       = {{14}},
  year         = {{2024}},
}