A characterization of composite materials of a polyester matrix reinforced with natural fibers of Cuban henequen and organophilized Cuban bentonite was carried out, the experimental results of which are used to formulate, implement, and validate a scalar damage model. The fibers were added on a weight basis of 15, 20, and 25% as a reinforcing material, and organophilized Cuban bentonite, in amounts of 3, 5, and 7% by weight, was added. Samples containing only polymer and fibers showed the best mechanical performance for 25% of fiber, with increases of 51% in the tensile strength and 169% on the impact resistance. Samples containing polymer and clay showed the best results for 5% of bentonite, with increases of 89% in the tensile strength and 83% on the impact resistance, and samples containing 25% fiber and 5% clay were also prepared and achieved a 98% increase in the tensile strength and 219% in the impact strength. The scalar damage model deals with an internal variable, which corresponds to the damage variable, together with a failure surface that allows identifying the mechanical state of the material dependent on the mechanical stress, obtaining an adequate correlation with the results of the experimental tests.

In this study, the ballistic effectiveness of Kevlar 29 composites was analyzed by combining 400 and 460 GSM grammages with a polyurethane matrix. Plates measuring 300 mm × 250 mm were fabricated using hand lamination and compression techniques, with reinforcement designs consisting of 10, 14, and 18 layers of Kevlar 29 fabric oriented at a 90° angle. Ballistic tests were conducted following the parameters of the NIJ 0108.01 standard, applying five impacts with 9 mm FMJ and 22 (5.5 mm) caliber bullets. Both the composites and pure Kevlar were evaluated. Post-damage visual analysis was conducted on the front and back faces, as well as the interior of the composite, to identify delamination and fractures. The results show that .22 caliber bullets were captured at various stacking levels depending on the configuration, whereas 9 mm bullets penetrated all the plates. Localized fractures and delamination associated with the impacts were observed, highlighting the importance of stacking design and grammage in the material’s energy dissipation capacity.

This work presents an integrated experimental and machine learning study on the fire performance of sisal fiber-reinforced polyester composites treated with magnesium hydroxide as a flame retardant. A total of 43 small-scale fire resistance tests were conducted in a custom-built gas-fired furnace following ISO 834 and NCh935/2 standards. Key parameters—including fiber content, flame retardant proportion, catalyst, and accelerator—were correlated with burn time and mass loss. Linear regression revealed negligible to weak correlations, while nonlinear models (Random Forest, Support Vector Regression, and Deep Neural Network) showed improved predictive capacity. The Deep Neural Network achieved the best performance (MSE = 0.061, R² = 0.334). Experimental results confirm that magnesium hydroxide substantially increases burn time, whereas sisal fiber content alone has a minimal effect on fire resistance. This study highlights an affordable strategy for enhancing the fire safety of bio-based composites and demonstrates the potential of machine learning to optimize material formulations. Future research should expand the dataset and validate the models through standardized large-scale fire tests. However, the findings are limited to small-scale fire resistance tests under controlled laboratory conditions and should not be generalized to full-scale structural applications without further validation.

This paper presents the design, development, and preliminary validation of a haptic virtual reality simulator for Shielded Metal Arc Welding (SMAW) at the Pontificia Universidad Católica de Valparaíso, Chile, aimed at enhancing psychomotor training for mechanical engineering students in line with Industry 4.0 demands. The system integrates Unity 3D, a commercial haptic device, and a custom 3D-printed electrode holder replicating the welding booth, enabling interaction through visual, auditory, and tactile feedback. Thirty students with minimal welding experience and seven experts participated in usability and realism assessments. The results showed that 80% of students perceived motor skill improvement, 60% rated realism as adequate, and 90% preferred hybrid training (simulator + workshop). The prototype was practically implemented at the mechanical engineering school, requiring only a mid-range workstation, the Touch haptic device, and the developed software, demonstrating feasibility in real academic settings. The findings indicate potential to build confidence, support motor coordination, and provide a safe, resource-efficient training environment, while experts emphasized the need for automated feedback and improved haptic fidelity. The modular architecture allows scalability, extension to other welding processes, and adaptation for inclusive education. This prototype demonstrates how locally developed immersive technologies can modernize technical education while promoting sustainability, accessibility, and skill readiness.

Ornamental palm pruning residues represent a locally abundant, underutilized lignocellulosic waste stream with potential as a waste-valorized epoxy reinforcement. This study investigates the flexural behavior of particulate epoxy composites reinforced with Washingtonia robusta leaf stalk residue, evaluating the influence of reinforcement granulometry on mechanical and microstructural response. Four specimen families were fabricated from a Bisphenol A/F epoxy resin cured with a cycloaliphatic amine hardener: neat resin (RS, reference) and composites reinforced with fine (RF), coarse (RG) and mixed-fraction (RM) particles at 20 vol.% loading. Flexural properties were assessed by three-point bending and fracture surfaces were characterized by SEM. The neat resin exhibited a non-monotonic, viscoelastic-dominated response with no fracture within the extended deformation range tested, whereas all reinforced systems fractured within a substantially narrower window (~8–14.5 mm). RF showed the highest observed flexural modulus (≈15.8 GPa), followed by RM (≈15.4 GPa) and RG (≈14.2 GPa). These differences were not statistically significant (one-way ANOVA, p > 0.05). Damage tolerance followed a similar descriptive trend: RG failed earliest, linked to large interfacial pull-out cavities; RF delayed fracture through crack deflection; and RM showed the most favorable overall balance, combining a modulus comparable to RF with superior crack path tortuosity. These results indicate the potential of Washingtonia robusta, particularly in mixed-granulometry form, as a candidate reinforcement for semi-structural epoxy biocomposites, pending further characterization of properties such as tensile strength, impact resistance, moisture absorption, and long-term durability.

Seaweed polysaccharide films are potential alternatives to petroleum-derived flexible packaging, but literature data are sparse and heterogeneous. This study evaluates whether composition-only machine-learning models can prioritize reported seaweed formulations for experimental follow-up near a nominal low-density polyethylene (LDPE) mechanical target of 15 MPa tensile strength and 300% elongation at break. A published dataset of 115 formulations with 41 compositional predictors was analyzed using a regularized linear baseline (ridge regression), random forest, and a deliberately shallow, regularized XGBoost model. Model performance was estimated using repeated five-fold cross-validation (10 repeats; 50 test-fold evaluations). For tensile strength, mean R² was 0.617 ± 0.174 for random forest and 0.628 ± 0.150 for XGBoost, with corresponding RMSE values of 12.35 ± 3.52 and 12.27 ± 3.84 MPa. For elongation, random forest and XGBoost reached mean R² values of 0.498 ± 0.194 and 0.470 ± 0.188, respectively. The linear baseline was materially less stable, indicating that the available composition-property relations are not adequately represented by a global linear model. The maximum observed elongation was 172.5%, which is 42.5% below the 300% screening target; equivalently, the target lies 73.9% above the dataset maximum. Consequently, the model cannot establish LDPE equivalence or credible extrapolation to the target. Residual diagnostics, learning curves, and SHAP analyses were added to bound interpretation. The defensible outcome is experimental prioritization within the observed domain, not inverse design or material substitution.