The transformation of powertrains, powered by internal combustion engines, into electrical systems generates new challenges in developing lightweight materials because electric vehicles are typically heavy. It is therefore important to develop new vehicles and seek more aesthetic and environmentally friendly designs whilst integrating manufacturing processes that contribute to reducing the carbon footprint. At the same time, this research explores the development of new prototypes and custom components using printed composite materials. In this framework, it is essential to formulate new approaches to estimate fatigue life, specifically for components tailored and fabricated with these kinds of advanced materials. This study introduces a novel fatigue life prediction approach based on an artificial neural network. When presented with given inputs, this neural network is trained to predict the accumulation of fatigue damage and the temperature generated during cyclic loading, along with the mechanical properties of the compound. Its validation involves comparing the network’s response with the load ratio result, which can be calculated using the fatigue damage parameter. Comparing both results, the network can successfully predict the fatigue damage accumulation; this implies an ability to directly employ data on the mechanical behavior of the component, eliminating the necessity for experimental testing. Then, the current study introduces a neural network designed to predict the accumulated fatigue damage in printed composite materials with an Onyx matrix and Kevlar reinforcement.

Obtain the modal frequencies of polymeric composite materials reinforced with unidirectional fibers (glass fiber and epoxy resin) for possible aeronautical applications. The objective of this study is to compare the behavior of an isotropic material with an orthotropic one. This comparison is to observe the influence of the material properties on its performance under dynamic conditions, where the modal frequencies of a material can directly affect the performance of each element of a structure. The first case describes the numerical and experimental identification of the modal frequencies of an isotropic material (6065 T5 aluminum). The second case study is presented to show how this methodology is adapted to the composite material. The experimental results are obtained by applying the impact hammer testing method. The comparison provides new insights into the modal behavior of vibrations in composite materials. A significant finding of this work is to provide a detailed analysis of the behavior of a unidirectional composite material in terms of the fiber’s orientation. Then, this work would be established the fundamentals of the composite material performance for rotative elements applications.

Additive manufacturing of polylactic acid reinforced with carbon fiber, specifically via fused deposition modeling, offers significant advantages for fabricating complex geometries; however, inherent extrusion defects often restrict its use in high-performance mechanical components. While post-printing annealing treatments promote polymer crystallization and interlayer cohesion, the highly non-linear effects of this process on PLA composites remain underexplored. This study investigates the specific effects of annealing temperature and exposure time on parametrically optimized 3D-printed polylactic acid reinforced with short carbon fiber. An orthogonal experimental design evaluates temperatures ranging from 55 °C to 95 °C and times between 4 and 12 h. The mechanical behavior was mapped utilizing Analysis of Variance and Response Surface Methodology, supported by Differential Scanning Calorimetry and dimensional analysis. The results revealed that annealing temperature is the governing factor, whereas exposure time has a marginal impact. An optimal window emerged between 75 °C and 85 °C. Relative to the as-printed baseline, optimal factors improved Ultimate Tensile Strength by 13.74%, ductility by 24.78%, bed surface hardness by 5.58% and Young’s modulus by 39%, driven by substantial matrix crystallinity increments (29.4%) verified via DSC. In contrast, top surface hardness was negatively affected by the heat treatment. Furthermore, temperatures exceeding 85 °C triggered severe dimensional distortions. Ultimately, this research provides experimentally validated statistical models for the reliable design of PLA-SCF components.