Die 10 zuletzt veröffentlichten Dokumente
Machine learning-assisted passive thermography has emerged as a powerful tool for characterizing fatigue damage through self-heating-induced temperature hotspots, with its accuracy depending critically on the precise determination of the governing thermal properties. This work proposes a nondestructive method to simultaneously recover the transverse conductivity k_ct and the film coefficient h ─ two thermal parameters that are typically difficult to measure. To this end, a near-infrared laser in conjunction with a DLP-based spatial light modulator was used to create artificial temperature hotspot distributions by illuminating one side of a ±45° glass-epoxy composite laminate. The generated surface temperature distribution on the opposite side ─ denoted as ground truth (GT) ─ was recorded by a cooled midwave infrared thermal camera. Bayesian optimization (BO) was subsequently employed to iteratively suggest the best k_ct and h values for use in a 3D FEM thermal model, which in turn generated synthetic thermal images as similar as possible to the GT. Different BO runs converged after about 10 iterations, yielding effective values of k_ct = 0.40 (W/m K) and h = 5.78 W/(m^2 K), from where the loss objective decreased by a factor of ca. 3 within only seven iterative rounds. A final experimental validation using these optimized parameters successfully reproduced three independent thermal images with only minor deviations, demonstrating the robustness and applicability of the proposed approach.
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how different fine-tuning strategies influence the prediction of harmonic phonon band structures, thermal properties, and the potential energy surface along imaginary phonon modes. We achieve substantial accuracy improvements with minimal additional data, with as few as 10 additional training structures already yielding significant gains. In addition to existing approaches, we introduce Equitrain, a finetuning framework that implements LoRA-based adaptation. Across 53 materials systems, we show that fine-tuned models consistently outperform both the underlying pretrained model and models trained from scratch. Equitrain achieves the best overall performance, and our results demonstrate that fine-tuning enables accurate phonon predictions.
Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.
Tensile Test Ontology (TTO)
(2026)
This is the tensile test ontology (TTO) in version 3.0.0 as developed on the basis of the tensile test standard ISO 6892-1:2019-11: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature.
The TTO was developed in the frame of the project Plattform MaterialDigital (PMD). The TTO provides conceptualizations valid for the description of tensile tests and corresponding data in accordance with the respective test standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed upon by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. Due to the import of the PMD core ontology (PMDco), here in version 3.0.0, the interoperability of tensile test data is enhanced and querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated.
Linear, low-molar-mass poly(trimethylene terephthalate) (PTT) was synthesized via the polycondensation of 1,3-propanediol and dimethyl terephthalate and investigated by matrix-assisted laser desorption/ionization time of flight (MALDI TOF) mass spectrometry, size exclusion chromatography (SEC), differential scanning calorimetry (DSC) and x-ray scattering. The obtained PTT was used in crystalline plaque or powder form for studies of its solid-state polycondensation (SSP). The combination of powder and vacuum increased the number-average molecular weight (Mn) by a factor of three. Interestingly, even-numbered cycles with degrees of polymerization (DPs) between six and 16 were preferentially formed. Annealing after doping with tin catalysts produced three cyclic main reaction products (C10, C12 and C14), which suggest that thermodynamic control of transesterification processes favors the formation of three types of monodisperse extended-ring crystallites (ERC) with thicknesses of 5, 6 of 7 repeat units. Additionally, a predominantly cyclic PTT was prepared, once again demonstrating the formation of even-numbered ERC upon annealing. Crystallinities calculated from WAXS measurements showed satisfactory agreement with those determined by DSC. SAXS measurements confirmed that the crystal thickness defined by ERC is also valid for long chains and large cycles that crystallize with chain folding.
Verdeckte Risse in Eisenbahn-Spannbetonschwellen können zum plötzlichen Versagen der Schwelle und dem Verlust der Spurhaltefähigkeit führen. Im schlimmsten Fall kann dies bei Überfahrt eines Schienenfahrzeugs eine Entgleisung bewirken. Einige Risstypen entstehen im Inneren der Schwelle und breiten sich unerkannt aus. Dies führt weit vor der äußeren Sichtbarkeit bereits zu einer deutlichen Minderung der Querschnittsfestigkeit und damit Instabilität der Schwelle.
This study investigated the influence of relative humidity (RH = 30%, 60 %, and 90 %) on the drying shrinkage behaviour of hybrid alkaline cement (HAC) systems, composed of 30 % Portland cement and 70 % bottom ash (BA) and/or rice husk ash (RHA). At RH < 30 %, HAC mortars exhibited shrinkage strains up to 7 times higher than Portland cement, whereas curing at RH > 90 % reduced shrinkage by nearly 15-fold compared to dry conditions. The 70RHA mixture demonstrated the highest shrinkage under low RH, associated with its refined micropore structure and elevated internal surface area. Nitrogen adsorption-desorption analysis revealed that low RH hindered pore refinement, while wet curing promoted gel densification, increasing the fraction of pores below 5 nm and improving compressive strength. At intermediate RH, mesopore volume increased after 28 days, consistent with microstructural changes associated with moisture exchange processes. This work provides a combined analysis of humidity-dependent shrinkage mechanisms and pore structure evolution in hybrid alkaline systems incorporating industrial and agricultural residues. The main contribution lies in identifying two distinct shrinkage regimes governed by pore-scale mechanisms under different RH conditions, offering practical guidance for curing optimisation and dimensional stability control in sustainable cementitious materials.
Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviations of simulation from measurements are ubiquitous even after model calibration due to the model discrepancy, which may result from deliberate modeling decisions, ignorance, or lack of knowledge. If the mismatch between simulation and measurements are deemed unacceptable, the model has to be improved. Targeted model improvement is challenging due to a non-local impact of model discrepancies on measurements and the dependence on sensor configurations. Many approaches to model improvement, such as Bayesian calibration with additive mismatch terms, gray-box models, symbolic regression, or stochastic model updating, often lack interpretability, generalizability, physical consistency, or practical applicability. This paper introduces a non-intrusive approach to model discrepancy analysis using mixture models. Instead of directly modifying the model structure, the method maps sensor readings to clusters of physically meaningful parameters, automatically assigning sensor readings to parameter vector clusters. This mapping can reveal systematic discrepancies and model biases, guiding targeted, physics-based refinements by the modeler. The approach is formulated within a Bayesian framework, enabling the identification of parameter clusters and their assignments via the Expectation-Maximization (EM) algorithm. The methodology is demonstrated through numerical experiments, including an illustrative example and a real-world case study of heat transfer in a concrete bridge.
BAM Inside #2/2026
(2026)
Interner E-Mail-Newsletter der BAM.
Influence of initial powder oxidation on mechanical properties of components fabricated via PBF-LB/M
(2026)
The reuse of feedstock in laser powder bed fusion of metals (PBF-LB/M) enhances sustainability and reduces production costs but is limited by progressive degradation of the material [1]. One contributor to powder degradation is oxidation, even in protective atmospheres [2]. This study investigates how the initial oxidation state of Haynes 282, a nickel-based alloy for high-temperature applications, influences its performance at both ambient and elevated temperatures (850 °C). For this purpose, four feedstock batches with oxygen contents ranging from approximately 140 ppm to 1400 ppm were produced by controlled oxidation of virgin powder in a laboratory furnace.
Microscopy analysis revealed that higher oxidation levels promoted irregular melt pool morphology and increased surface roughness, accompanied by a reduction in grain size. Electron probe microanalysis revealed that the oxide layers observed on the surface of the manufactured specimens in the as-build condition consisted primarily of aluminium and titanium oxides. Oxygen analysis via inert gas fusion on heat-treated specimens measured approximately 90 ppm in material from virgin feedstock and approximately 600 ppm in material from the most oxidized batch. Notwithstanding these discrepancies, the part porosity and the room-temperature tensile properties, including ultimate strength and elongation at break, remained largely unaltered. Conversely, creep testing at elevated temperatures under application-relevant conditions showed a pronounced decline in creep resistance and a corresponding increase in creep strain with rising oxygen content. However, both the tensile strength and the deformation mechanisms observed in high-temperature tensile tests remained comparable across all batches. These findings indicate that while the use of oxidized powder is viable for less demanding service conditions, strict powder quality control is imperative when manufacturing components for high-temperature applications.