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Abstract: The paper explores enhancing the mechanical behavior of stochastic lattice structures through a semi-controlled design approach. By leveraging the Gibson-Ashby model and predefined geometric routines, the study aims to optimize the mechanical response of lattice structures under compressive stress. Transitioning from stochastic to semi-controlled tessellation using Rhinoceros 7 software enables more predictable deformation behavior. Design parameters such as node formation, strut thickness, and lattice generation patterns are correlated with relative density to regulate stiffness and strength. Experimental validation using Acrylic Styrene Acrylonitrile (ASA) filament demonstrates the effectiveness of the proposed design model. The research emphasizes the importance of understanding internal mechanics by introducing a novel design approach to control geometry and topology arrangement in shaping lattice properties. By introducing a semi-controlled mechanism, the study seeks to improve the reliability and uniformity of mechanical responses in lattice structures. The findings highlighted the benefits of semi-controlled design approaches in achieving tailored mechanical properties. Specimens were compression tested in quasi-static uniaxial loading and showed that structures created with parabolic distribution dimensioned by hp=0.5hv originated the most reliable and most vital mechanical response compared with other design models, including typical Voronoi distribution. The improved mechanical response in between proposed design models constantly progressed by about 15% on average consecutively, starting from the parabolic distribution dimensioned by hp=1.0hv as the weakest ranked, up to the best one, dimensioned by hp=0.5hv , even better than the typical Voronoi distribution. The proposed design model has introduced an entirely novel approach that significantly enhances the product’s volume tessellation using routines that guarantee the validity of geometric and topologic entities. Uniaxial compression tests on lattice blocks highlighted the effect of the proposed approach on the mechanical properties of these structures, having shown particularly crucial repeatability and stability. Overall, the paper contributes to advancing the field of lightweight lattice structures through the novel design methodology and material characterization.
Keywords: Voronoi tessellation, lightweight design, node connectivity, mechanical response
Published in DKUM: 15.01.2026; Views: 0; Downloads: 0
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Abstract: In the article we present the method of visual response to literary texts, using the example of a folk tale from a foreign civilization circle titled "The Story of Caiman". In considering the perceptions of pupils in the 5th grade of elementary school, our goal is to examine teacher competences for developing the pupilsʹ ability to perceive, understand and evaluate literary texts and on this basis, to assess the justification of content integration (JCI) in the curriculum of university teacher education. This problem was observed within the framework of the interdisciplinary connection between literature and fine arts, since it is only possible to achieve optimal use of aesthetically designed text as a didactic tool for the externalization of the learner's dialogue with literary proverbs, thus checking to what extent teachers are trained in these methods (Visual Response Method) in the communication model of literary education.
Keywords: competences of elementary education teachers, reception of youth literary texts, psychonarratology, recreational literarydidactic method of visual response, characterization, ethical judgment, modality
Published in DKUM: 18.06.2025; Views: 0; Downloads: 3
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Abstract: Drawing is a well-established method to improve the mechanical properties of wet-spun fibers, as it orients the polymer chains, increases the chain density, and homogenizes the microstructure. This work aims to investigate how drawing variables, such as the draw ratio, drawing speed, and temperature affect the elastic modulus (E) and the strain at break (εB) of biobased wet-spun fibers constituted by neat polylactic acid (PLA) and a PLA/poly(dodecamethylene 2,5-furandicarboxylate) (PDoF) (80/20 wt/wt) blend. Drawing experiments were conducted with a design of experiment (DOE) approach following a 24 full factorial design. The results of the quasi-static tensile tests on the drawn fibers, analyzed by the analysis of variance (ANOVA) and modeled through the response surface methodology (RSM), highlight that the presence of PDoF significantly lowers E, which instead is maximized if the temperature and draw ratio are both low. On the other hand, εB is enhanced when the drawing is performed at a high temperature. Finally, a genetic algorithm was implemented to find the optimal combination of drawing parameters that maximize both E and εB. The resulting Pareto curve highlights that the temperature influences the mechanical results only for neat PLA fibers, as the stiffness increases by drawing at lower temperatures, while optimal Pareto points for PLA/PDoF fibers are mainly determined by the draw ratio and the draw rate.
Keywords: fibers, poly(lactic acid), furanoate polyesters, drawing, response surface methodology, genetic algorithms
Published in DKUM: 24.03.2025; Views: 0; Downloads: 8
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Keywords: cardiac arrest, cardiopulmonary resuscitation, football broadcast, emotional response, social media, users
Published in DKUM: 16.07.2024; Views: 126; Downloads: 15
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Abstract: Three different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes (approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites. Production was realised through mechanical alloying in pre-processing and compocasting processes. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. Experimental results were analysed by applying the response surface methodology (RSM) and a suitable mathematical model for the wear rate of tested nanocomposites was developed. Appropriate wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of the predicting methods showed that ANN is more efficient in predicting wear.
Keywords: ZA-27 alloy, Al2O3 nanoparticles, nanocomposites, wear, response surface methodology, artificial neural network
Published in DKUM: 20.03.2024; Views: 239; Downloads: 16
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