ABSTRACT
The article shows the usage of swarming algorithms for reconstructing the heat transfer coefficient regarding the continuity boundary condition. Numerical calculations were performed using the authors' own application software with classical forms of swarm algorithms implemented. A functional determining error of the approximate solution was used during the numerical calculations. It was minimized using the artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). The considered in paper geometry comprised a square (the cast) in a square (the casting mold) separated by a heat-conducting layer with the coefficient [Formula: see text]. Due to the symmetry of that geometry, for calculations, only a quarter of the cast-mold system was considered. A Robin's boundary condition was assumed outside the casting mold. Both regions' inside boundaries were insulated, but between the regions, a continuity boundary condition with nonideal contact was assumed. The coefficient of the thermally conductive layer was restored using the swarm algorithms in the interval [Formula: see text]] and compared with a reference value. Calculations were carried out using two finite element meshes, one with 111 nodes and the other with 576 nodes. Simulations were conducted using 15, 17, and 20 individuals in a population with 2 and 6 iterations, respectively. In addition, each scenario also considered disturbances at 0[Formula: see text], 1[Formula: see text], 2[Formula: see text], and 5[Formula: see text] of the reference values. The tables and figures present the reconstructed value of the [Formula: see text] coefficient for ABC and ACO algorithms, respectively. The results show high satisfaction and close agreement with the predicted values of the [Formula: see text] coefficient. The numerical experiment results indicate significant potential for using artificial intelligence algorithms in the context of optimization production processes, analyze data, and make data-driven decisions.
ABSTRACT
High filled polylactide/multiwall carbon nanotube composites were subjected to multiple extrusions using single-screw and twin-screw extruders. Samples of the processed composites were characterized by SEM, XRD, Raman, and FTIR spectroscopy. Thermal and rheological properties were investigated by DSC and MFR analyses. Subsequent extrusions resulted in decreased torque and process efficiency, which is a consequence of the viscosity reduction of PLA. Thermal and rheological properties of composites changed after each extrusion as well. As revealed by DSC analyses, cold crystallization temperature showed a tendency to decrease after each process, whereas cold crystallization enthalpy ΔHcc increased significantly. Melt flow rate, which is indicative of the polymer degradation, increased after each extrusion.
ABSTRACT
To assess the impact of graphite fillers on the thermal processing of graphite/poly(lactic acid) (PLA) composites, a series of the composite samples with different graphite of industrial grade as fillers was prepared by melt mixing. The average size of the graphite grains ranged between 100 µm and 6 µm. For comparative purposes, one of the carbon fillers was expandable graphite. Composites were examined by SEM, FTIR, and Raman spectroscopy. As revealed by thermogravimetric (TG) analyses, graphite filler slightly lowered the temperature of thermal decomposition of the PLA matrix. Differential scanning calorimetry (DSC) tests showed that the room temperature crystallinity of the polymer matrix is strongly affected by the graphite filler. The crystallinity of the composites determined from the second heating cycle reached values close to 50%, while these values are close to zero for the neat polymer. The addition of graphite to PLA caused a slight reduction in the oxidation induction time (OIT). The melt flow rate (MFR) of the graphite/PLA composites was lower than the original PLA due to an increase in flow resistance associated with the high crystallinity of the polymer matrix. Expandable graphite did not cause changes in the structure of the polymer matrix during thermal treatment. The crystallinity of the composite with this filler did not increase after first heating and was close to the neat PLA MFR value, which was extremely high due to the low crystallinity of the PLA matrix and delamination of the filler at elevated temperature.
