3D Microstructure Reconstruction of Phase-Banded Duplex Steels for Micromechanical Modeling (Panel Smart Digitalisation, E311)

• René Zandomeni (IDEeP)

Room: 311 Understanding the micromechanical behavior and degradation mechanisms of duplex stainless steels, especially under hydrogen exposure, is crucial for designing safe and durable materials for the green energy transition. However, investigating local strain partitioning and hydrogen-assisted damage requires realistic three dimensional (3D) representative volume elements (RVEs) that capture both anisotropic, phase-banded spatial morphologies and grain-resolved crystallographic statistics. This work introduces a novel methodology that combines the reconstruction of grain-resolved two-phase RVEs considering lower and higher-order grain and phase statistics and enables virtual material modeling without relying on expensive, destructive 3D characterization. First, a 3D microstructure containing the spatial phase distribution as a binary phase mask is optimized from 2D EBSD data using directional two-point correlation functions in MCRpy. Second, this phase mask serves as a spatial boundary condition in Kanapy to drive phase-constrained stochastic grain packing and orientation distribution assignment. A distance-transform clearance check and penalized voxelization routine prevent unphysical grain penetration across phase interfaces. Validated on DIN 1.4462 duplex stainless steel across three heat-treatment states (1050 °C, 1150 °C, and 1250 °C), the workflow accurately reconstructs the phase-banded morphology and polycrystalline aggregate, e.g. grain size, orientation distribution. By serving as input for micromechanical finite-element simulations, these 3D RVEs enable the systematic study of localized phase-boundary interactions and microstructural susceptibility to hydrogen embrittlement. This novel methodology advances virtual materials engineering, enabling targeted microstructural optimization for critical applications in future energy systems.

In-situ-Charakterisierung von Prozessabhängigen Materialgrößen durch magnetomechanisch gekoppelte Finite-Elemente Simulation (Panel Smart Digitalisation, E311)

• Marvin Henkel (Fakultät W, IfPaA)

Room: 311 Das 4D-Printing beschreibt die additive Fertigung von Smart Materials, die durch die Reaktion auf einen äußeren Stimulus eine gezielte Eigenschafts- oder Formänderung vollziehen (Aktivierung). Voraussetzung hierfür ist, dass diese Eigenschaftsänderung im Vorfeld über ein gezieltes äußeres Feld in die Bauteilstruktur eingebracht wurde (Programmierung). Magnetresponsive Elastomere (MRE) – wie thermoplastische Copolyester-Matrices mit eingelagerten Strontiumferrit-Mikropartikeln – bieten dabei den Vorteil, dass sie schnell, fesselfrei und in ihrer Intensität steuerbar mehrfach aktiviert werden können. Während bestehende Simulationsmodelle meist auf idealisierten Materialparametern beruhen, vernachlässigen sie den signifikanten Einfluss des additiven Fertigungsprozesses sowie die prozessinduzierte Anisotropie. Die Stärke der eingebrachten Magnetisierung hängt jedoch stark von der Bauteilgeometrie, der PartikelMatrix-Interaktion und den Druckparametern ab. Eine konventionelle Messung des magnetischen Profils über standardisierte Prüfverfahren (wie z. B. Permagraphen) gibt darüber nur bedingt Aufschluss, da hierbei keine realen Fertigungsgeometrienn berücksichtigt werden. Um diese Lücke zu schließen, wird anhand eines 4D-gedruckten bionischen Aktuators ein Simulationsmodell auf Basis einer origamibasierten Magnetisierungstechnik aufgebaut. Dieses Modell wird mit real gemessenen Kraft-Weg-Kurven gespeist, was eine in-situ durchgeführte inverse Parameteridentifikation für das magnetische Materialmodell nach Jiles-Atherton ermöglicht. Die erforderlichen mechanischen für die multiphysikalisch gekoppelte Simulation in COMSOL Multiphysics stammen aus zyklischen Zugprüfversuchen eigengefertigter Proben. Der dargestellte Ansatz erlaubt eine präzise Vorhersage des Kopplungsverhaltens und bildet die Grundlage für eine fertigungsgerechte, wissensbasierte Materialprogrammierung komplexer MREKomponenten in der Soft-Robotik.

A training and implementation concept for specialist German language skills, designed for a German-speaking call center in Poland (Panels Sustainable Futures & Health and Well-Being, E310)

• David Wierzbicki (ChallengeEU, University of Warmia and Mazury in Olsztyn)

Room: E311 / E310 The increasing internationalisation of customer service operations has created a growing demand for employees who are able to use foreign languages not only communicatively, but also effectively within highly specialised professional contexts. German-speaking call centers in Poland represent a particularly relevant example, as their employees are required to combine advanced general language proficiency with specialised vocabulary, standardised communication patterns, procedural knowledge, and the ability to respond appropriately to situation-specific customer needs. This presentation outlines the development of a training and implementation concept for specialist German language skills designed specifically for a German-speaking call center operating in Poland. The concept constitutes the practical outcome of an interdisciplinary research project combining applied linguistics, specialist language research, language pedagogy, and the requirements of professional customer-service environments. The proposed concept goes beyond conventional foreign-language training by integrating language competence with professional action competence. Its implementation includes needs analysis, corpus-informed selection of training content, scenario-based learning, simulation of authentic customer interactions, systematic feedback, and evaluation of learning outcomes. The concept is designed as a flexible framework that can be adapted to specific customer-service processes, target groups, and organisational requirements. The presentation discusses both the research foundations and practical implementation potential of the concept. It also addresses the challenges involved in transferring linguistic research findings into an organisational training environment, including the balance between standardisation and authentic communication, the changing nature of specialist vocabulary, and the need to align educational objectives with measurable workplace competencies.

Agentic AI for Systematic Engineering Problem Analysis in Sustainable Process Design (Panel Smart Digitalisation, E311)

• Mas'udah Mas'udah (Hochschule Offenburg)

Room: 311 Engineering problems in sustainable process design are often introduced as broad stakeholder objectives, such as improving efficiency, reducing costs, and enhancing environmental performance. However, these objectives may not sufficiently describe system boundaries, causal relationships, technical constraints, and engineering contradictions. This can limit the effectiveness of subsequent idea generation and concept development. This contribution presents a multi-role agentic AI framework for systematic analysis of engineering problems. The framework uses four specialised AI roles that sequentially perform problem framing, cause-and-effect analysis, contradiction identification, and problem prioritisation, thereby transforming broad stakeholder objectives into a structured engineering problem formulation. The framework was implemented using GPT-5.5 and benchmarked against a conventional single-role baseline using a biodiesel production case study focused on the transesterification stage. In this process, feedstock variability, catalyst efficiency, operational costs, and sustainability trade-offs create substantial analytical complexity. Both configurations were evaluated using a hybrid AI-human expert approach across four criteria: problem-framing completeness, causal reasoning quality, contradiction identification, and stakeholder-objective alignment. Across five experimental runs per configuration, the multi-role framework significantly outperformed the single-role baseline across all criteria, achieving an overall score of 4.60 compared with 2.82 out of 5 (p = 0.031). The largest improvements were observed in contradiction identification and causal reasoning quality. Notably, the multi-role framework identified approximately three times as many engineering contradictions per run and more consistently captured both technical and environmental trade-offs. These findings suggest that decomposing engineering problem analysis into role-differentiated analytical stages produces more systematic and comprehensive problem formulations than conventional single-role LLM interactions. The results demonstrate the potential of agentic AI to support early-stage sustainable process design.

It's Complicated: Sustained Emotional Bonds With AI (Panels Sustainable Futures & Health and Well-Being, E310)

• Xenia Armstrong (ACI)

Room: 310 Chances are you've typed something personal into ChatGPT, Replika, or a similar system: a bad day, a late-night thought, a question you weren't ready to ask a person. You're not alone. Conversational AI is increasingly becoming a space for emotional disclosure, comfort, and even companionship. But what actually happens to us when these interactions aren't a one-off, but repeated over weeks or months? The honest answer is: we don't really know. Existing research is fragmented, dominated by single short sessions, and – crucially – often uses AI systems whose "empathic" behaviour is never precisely specified, making findings hard to compare or replicate. This PhD project tackles that gap through a cumulative research design (two controlled experiments and one longitudinal study) built around one guiding question: How do affective dynamics emerge and evolve in sustained emotional human–AI interaction, and which mechanisms and individual factors shape these processes? Study 1 tests whether more emotionally expressive AI communication causally shapes how we feel. Study 2 asks why, focusing on perceived empathy as the underlying mechanism. Study 3 follows people over several weeks to see how these bonds actually develop over time. A central contribution is technical: rather than relying on vague or inconsistent "empathic" chatbots, this project builds an AI system whose empathy can be dialed up or down like a control knob (independent of what it actually says!) using a technique called activation steering, grounded in an established empathy framework. This turns "AI empathy" from a fuzzy concept into something measurable, controllable, and reproducible – a tool other researchers can build on. The result: causal, mechanistic, and longitudinal insight into what it really means to form an ongoing emotional bond with AI, with direct relevance to digital companionship, mental health support, and AI for populations of all ages.