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Statistical modeling and optimization of the drawing process of bioderived polylactide/poly(dodecylene furanoate) wet-spun fibers
Daniele Rigotti, Giulia Fredi, Davide Perin, Dimitrios Bikiaris, Alessandro Pegoretti, Andrea Dorigato, 2022, izvirni znanstveni članek

Opis: 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.
Ključne besede: fibers, poly(lactic acid), furanoate polyesters, drawing, response surface methodology, genetic algorithms
Objavljeno v DKUM: 24.03.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,23 MB)
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Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction
Aleksandar Vencl, Petr Svoboda, Simon Klančnik, Adrian But, Miloš Vorkapić, Marta Harničárová, Blaža Stojanović, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: ZA-27 alloy, Al2O3 nanoparticles, nanocomposites, wear, response surface methodology, artificial neural network
Objavljeno v DKUM: 20.03.2024; Ogledov: 239; Prenosov: 13
Celotno besedilo (14,10 MB)
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