The strategy of embedding conductive materials on polymeric matrices has produced functional and wearable artificial electronic skin prototypes capable of transduction signals, such as pressure, force, humidity, or temperature. However, these prototypes are expensive and cover small areas. This study proposes a more affordable manufacturing strategy for manufacturing conductive layers with 6 × 6 matrix micropatterns of RTV-2 silicone rubber and Single-Walled Carbon Nanotubes (SWCNT). A novel mold with two cavities and two different micropatterns was designed and tested as a proof-of-concept using Low-Force Stereolithography-based additive manufacturing (AM). The effect SWCNT concentrations (3 wt.%, 4 wt.%, and 5 wt.%) on the mechanical properties were characterized by quasi-static axial deformation tests, which allowed them to stretch up to ~160%. The elastomeric soft material’s hysteresis energy (Mullin’s effect) was fitted using the Ogden–Roxburgh model and the Nelder–Mead algorithm. The assessment showed that the resulting multilayer material exhibits high flexibility and high conductivity (surface resistivity ~7.97 × 104 Ω/sq) and that robust soft tooling can be used for other devices.
Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin–Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.
Natural porous structures are often anisotropic in their elastic properties, i.e., they have directional variations that are related to their topology and geometry. This paper presents the synthesis and simulation framework of novel families of non-cubic porous structures based on implicit modeling of cosine surfaces in the three-dimensional Euclidean space. The synthesis was performed using Field-Driven Design (FDD). An in-depth study of the elastic properties and simulated fatigue compression–compression behavior of a selection of five structures from the orthotropic anisotropy family is presented as case studies exposing the stretching-dominated mechanisms. The apparent properties characterized include Young’s modulus [Formula: see text], Poisson’s ratio [Formula: see text], shear modulus [Formula: see text], bulk modulus [Formula: see text], and relative density [Formula: see text]. A systematic approach to the characterization of average apparent properties was performed according to the schemes of Voigt, Reuss, and Hill. We show the anisotropic variation of the cosine surface–based porous structures using the Universal Elastic Anisotropy Index (AU) and compare it with six well-known triply periodic minimal surface (TPMS) structures by analyzing the stiffness tensor to validate and discuss which individual property (stiffness, rigidity, compressibility) has a higher impact on the final anisotropy value. We also provide a formal curvature analysis, based on the notions of mean [Formula: see text] and Gaussian [Formula: see text] curvatures to evidence the ridge-shaped surface in the structures. The proposed porous structures showed advantages when compared to cubic TPMS structures. From the data processing of the five analyzed porous structures, two synthesized structures have AU = 0.394 and 0.478, which are lower values than the structures based on Neovius and Schwarz’s Primitive surfaces with AU = 0.529 and 0.604. In addition, simulation results of cyclic compressive fatigue loading indicate that the fatigue resistance properties of the non-cubic porous structures are higher than the TPMS structures.
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