Structural and Mechanical Changes of AlMgSi0.5_{0.5} Alloy during Extrusion by ECAP Method

SPD (several plastic deformations) methods make it possible to obtain an ultrafine-grained structure (UFG) in larger volumes of material and thus improve its mechanical properties. The presented work focuses on the structural and mechanical changes of aluminium alloy AlMgSi$_{0.5}$ (EN AW 6060) during processing by repeated extrusion through the ECAP rectangular channel. After a four-pass extrusion, the samples’ microstructures were observed using an optical microscope, where refinement of the material grains was confirmed. Tensile tests determined the extrusion forces and allowed interpretation of the changes in the mechanical properties of the stressed alloy. The grain size was refined from 28.90 μm to 4.63 μm. A significant improvement in the strength of the material (by 45%) and a significant deterioration in ductility (to 60%) immediately after the first extrusion was confirmed. The third pass through the die appeared to be optimal for the chosen deformation path, while after the fourth pass, micro-cracks appeared, significantly reducing the strength of the material. Based on the measurement results, new analytical equations were formulated to predict the magnitude or intensity of the volumetric and shape deformations of the structural grain size and, in particular, the adequate increase in the strength and yield point of the material

Laser-induced reactions of 4-aminobenzenthiol species adsorbed on Ag, Au, and Cu plasmonic structures followed by SERS spectroscopy. The role of substrate and excitation energy - surface-complex photochemistry and plasmonic catalysis

This study focuses on investigating the laser-induced reactions of various surface complexes of 4-aminobenzenethiol on Ag, Au, and Cu surfaces. By utilizing different excitation wavelengths, the distinct behavior of the molecule species on the plasmonic substrates was observed. Density functional theory (DFT) calculations were employed to establish the significant role of chemical enhancement mechanisms in determining the observed behavior. The interaction between 4-aminobenzenethiol (4-ABT) molecules and plasmonic surfaces led to the formation of surface complexes with absorption bands red-shifted into the visible and near-infrared regions. Photochemical transformations were induced by excitation wavelengths from these regions, with the nature of the transformations varying based on the excitation wavelength and the plasmonic metal. Resonance with the electronic absorption transitions of these complexes amplifies surface-enhanced Raman scattering (SERS), enabling the detailed examination of ongoing processes. A kinetic study on the Ag surface revealed processes governed by both first- and second-order kinetics, attributed to the dimerization process and transformation processes of individual molecules interacting with photons or plasmons. The behavior of the molecules was found to be primarily determined by the position and variability of the band between 1170 and 1190 cm-1, with the former corresponding to molecules in the monomer state and the latter to dimerized molecules. Notably, laser-induced dimerization occurred most rapidly on the Cu surface, followed by Ag, and least on Au. These findings highlight the influence of plasmonic surfaces on molecular behavior and provide insights into the potential applications of laser-induced reactions for surface analysis and manipulation.University of Chemistry and Technology Prague; Grantová Agentura České Republiky, GA ČR, (20–08679S)Grantov? Agentura Cesk? Republiky; University of Chemistry and Technology Pragu

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network

The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life