arXiv subjects

Martin Gurka

Publications and source records attributed to Martin Gurka.

3 recordsLinked to original sources

arXiv2026

3D Finite Element-Based Multiphysics Simulation of a Shape Memory Alloy Hybrid Composite Module

Shape adaptive shape memory alloy hybrid composites (SMAHCs) are composites that incorporate shape memory alloys (SMAs) to realize shape transformation. Despite the availability of numerous analytical and finite element models for predicting the transient response of SMAHCs, many approaches exhibit limitations with respect to the thermomechanical coupling and comprehensive experimental validation. Therefore, this paper presents a coupled, multiphysics, 3D finite element approach for the simulation of a SMAHC actuator, integrating mechanical, thermal and electromagnetic solvers in the Finite Element Code ANSYS LS-DYNA. The proposed approach employs a micromechanical constitutive model implemented in ANSYS LS-DYNA, to accurately capture the complex thermomechanical phase transformation of SMAs. A key feature of the model is the ability to prescribe a defined martensitic pre-strain through a preceding simulation step, in which an initially scaled SMA wire is mechanically loaded and stretched to its nominal length. This procedure enables partial detwinning of the martensitic microstructure and provides a physically motivated initialization of the material state. Joule heating of the SMA wires, as well as varying mechanical loads and ambient temperature conditions, are explicitly considered. The simulation results are validated against experimental data and a fully coupled transient staggered scheme model to assess the predictive capability of the 3D approach. The results show good qualitative agreement, reproducing the characteristic hysteresis of actuator deflection as a function of temperature. Quantitatively, the predicted deflections are of the correct order of magnitude, although marginally outside the 95 % experimental confidence interval. Overall, a consistent trend between simulation and experiment is observed, giving rise to possibility of simulating more complex SMAHC systems.

physics.app-ph↗

arXiv2026

Additively manufactured Shape Memory Alloy Hybrid Composites with a polymer matrix featuring a re-entrant honeycomb structure

Stereolithography (SLA) and Tailored Fiber Placement (TFP) were combined to fabricate shape memory alloy hybrid composites (SMAHC) featuring a three-layer structure and exhibiting out of plane bending deformation when activated, in a fully integrated, additive manufacturing process. SMA wires as active elements were attached to a textile reinforcement layer, which then was embedded within a UV-curable polymer matrix and combined with a geometrically tailored toplayer, featuring the re-entrant honeycomb architecture. Exploiting the design freedom of SLA, the overall mechanical response of the SMAHC can be systematically adjusted, enabling controlled out-of-plane bending during thermal activation. Two different SMA integration strategies - manual embedding and automated TFP were investigated to assess their influence on actuation behavior, reproducibility, and deformation behaviour. A total of eight geometric configurations were manufactured and experimentally characterized using synchronized optical measurements. The results demonstrate that the combination of SLA-based fabrication and textile-mediated SMA integration enables precise control over the actuation response, while the use of re-entrant honeycomb structures provides an effective approach to tailor stiffness and deformation characteristics. In particular, the automated TFP integration yields improved reproducibility and more symmetric deformation behavior compared to manual fabrication. The presented approach establishes a fully additive manufacturing route for SMAHCs, enabling the realization of structurally integrated, morphing composite systems with programmable mechanical properties.

physics.app-ph↗

arXiv2026

Data-Driven Automated Identification of Optimal Feature-Representative Images in Infrared Thermography Using Statistical and Morphological Metrics

Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features such as subsurface defects. However, most IRT post-processing methods generate image sequences in which defect visibility varies strongly across time, frequency, or coefficient/index domains, making the identification of defect-representative images a critical challenge. Conventional evaluation metrics, such as the signal-to-noise ratio (SNR) or the Tanimoto criterion, often require prior knowledge of defect locations or defect-free reference regions, limiting their suitability for automated and unsupervised analysis. In this work, a data-driven methodology is proposed to identify images within IRT datasets that are most likely to contain and represent structural features, particularly anomalies and defects, without requiring prior spatial information. The approach is based on three complementary metrics: the Homogeneity Index of Mixture (HI), which quantifies statistical heterogeneity via deviations of local intensity distributions from a global reference distribution; a Representative Elementary Area (REA), derived from a Minkowski-functional adaptation of the Representative Elementary Volume concept to two-dimensional images; and a geometrical-topological Total Variation Energy (TVE) index, also based on two-dimensional Minkowski functionals, designed to improve sensitivity to localized anomalies. The framework is validated experimentally using pulse-heated IRT data from a carbon fiber-reinforced polymer (CFRP) plate containing six artificial defects at depths between 0.135 mm and 0.810 mm, and is further supported by one-dimensional N-layer thermal model simulations. The results demonstrate robust and unbiased ranking of image sequences and provide a reliable basis for automated defect-oriented image selection in IRT.

cs.CV↗