Complex thermo-kinetic interactions during metal additive manufacturing reduce the homogeneity of the microstructure of the produced samples. Understanding the effect of processing parameters over the resulting mechanical properties is essential for adopting and popularizing this technology. The present work is focused on the effect of laser power, scanning speed, and hatch spacing on the relative density, microhardness, and microstructure of 316L stainless steel processed by laser powder bed fusion. Several characterization techniques were used to study the microstructure and mechanical properties: optical, electron microscopies, and spectrometry. A full-factorial design of experiments was employed for relative density and microhardness evaluation. The results derived from the experimental work were subjected to statistical analysis, including the use of analysis of variance (ANOVA) to determine both the main effects and the interaction between the processing parameters, as well as to observe the contribution of each factor on the mechanical properties. The results show that the scanning speed is the most statistically significant parameter influencing densification and microhardness. Ensuring the amount of volumetric energy density (125 J/mm³) used to melt the powder bed is paramount; maximum densification (99.7%) is achieved with high laser power and low scanning speed, while hatch spacing is not statistically significant.

Rubber bushings and mounts are vastly used in automotive applications as support and interface elements. In suspension systems, they are commonly employed to interconnect the damping structure to the chassis. Therein, the viscoelastic nature of the material introduces a desirable filtering effect to reduce mechanical vibrations. When designing a suspension system, available literature often deals with viscoelastic mounts by introducing a linear or nonlinear stiffness behavior. In this context, the present paper aims at representing the rubber material using a proper viscoelastic model with the selection of different in-wheels motors. Thus, the mount dynamic behavior’s influence in a suspension is studied and discussed thoroughly through numerical simulations and sensitivity analyses. Furthermore, guidelines are proposed to orient the designer when selecting these elements.

Glass fiber reinforced polymers (GFRPs) have drawn significant attention given their lightweight, mechanical resistance and tunable properties through constituent selection. Due to environmental concerns, research efforts have focused on incorporating sustainable materials, such as bio-epoxy resins, to reduce the ecological impact of GFRPs. This study characterizes a GFRP containing a bio-epoxy resin matrix, various loadings of titanium dioxide (TiO₂) nanoparticles, and a stabilized arrangement of glass fiber. The unreinforced composite exhibited a tensile strength and modulus of 214 MPa, and 13 GPa, respectively, and a flexural strength and modulus of 375 MPa and 14.5 GPa, respectively. The addition of TiO₂ produced an improvement in mechanical response for all the composites. The formulation with 1 wt.% TiO₂ showed the best tensile response with an improvement of 13% and 14% for its tensile strength, and modulus, respectively; meanwhile, the composites with 2 wt.% TiO₂ attained an improvement of 19% and 40% for the flexural strength and modulus, respectively. Scanning electron microscopy (SEM) revealed significant changes in the fracture mechanism of the composites, while energy-dispersive spectroscopy (EDS) confirmed an even nanoparticle distribution. Additionally, machine learning (ML) models were developed to predict the mechanical response as a function of the TiO₂ content.

Polymer matrix composites have become one of the most developed materials due to the possibility of tailored mechanical, physical, or thermal properties. However, important environmental concerns have arisen within conventional thermoset polymers due to the depletion of the non-renewable resources used in their production. For this reason, bio-epoxy resins have been developed by replacing a fraction of the petroleum-based components with renewable materials. In addition, machine learning algorithms have become a powerful tool to estimate material properties in nanocomposites, which might be further corroborated by experimental means. Therefore, this study developed a composite material with a bio-epoxy resin matrix reinforced with graphene oxide (GO). Experimental and theoretical densities revealed a linear relationship between the GO loading and the pore volume fraction of the composites. An estimation of various mechanical properties is listed for a bio-epoxy resin: ultimate tensile strength (UTS) (63.4 MPa), modulus of elasticity (2.74 GPa), flexural strength (95.32 MPa), and flexural modulus of elasticity (2.52 GPa). Composites including 0.1, 0.3, and 0.5 wt. % showed an improvement in the aforementioned properties, while the composites, including 0.8 and 1.2 wt. % exhibit a decrease in the overall mechanical response. Evaluation of tensile and flexural fracture surfaces revealed a noticeable strengthening mechanism in the composites after the addition of the reinforcement. Differential scanning calorimetry results demonstrated increased glass transition temperature as the GO filler loading increased. Machine learning allows the prediction of the UTS as a function of GO content with high accuracy.

Graphene oxide (GO) has garnered significant interest due to its exceptional and tunable properties, which make it a promising candidate for a wide range of engineering applications, including composite material fabrication and water treatment. In this study, GO was synthesized from graphite flakes using a modified Hummers method involving a reduced amount of sulfuric acid. The resulting material was characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). These techniques enabled a clear differentiation between the morphology of the synthesized GO and that of the original graphite. The GO exhibited a substantially altered structure, with increased thickness likely due to the incorporation of oxygen-containing functional groups on its basal plane. FTIR analysis confirmed the presence of characteristic functional groups such as hydroxyl, carbonyl, and carboxyl. XPS analysis revealed that the elemental composition of the synthesized GO consisted of approximately 69.7% carbon and 29.9% oxygen, with a trace amount of sulfur attributed to the reagents used in the synthesis. The observed changes in morphology and composition suggest the successful synthesis of GO with potential for functionalization and application in diverse engineering contexts.