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.

To reduce the carbon footprint of manufacturing processes, it is necessary to reduce the number of stages in the development process. To this end, integrating additive manufacturing processes with three-dimensional (3D) printing makes it possible to eliminate the need to use tooling for component manufacturing. Furthermore, using 3D printing allows the generation of complex models to optimize different components, reducing the development time and realizing lightweight structures that can be applied in different industries, such as the mobility industry. Printing process parameters have been studied to improve the mechanical properties of printed items. In this regard, although the failure of most structural components occurs under dynamic load, the majority of the evaluations are quasistatic. This work highlights an improvement in fatigue strength under dynamic loads in 3D-printed components through heat treatment. The fatigue resistance was improved regarding the number of cycles and the dispersion of results. This allows 3D-printed polylactic acid components to be structurally used, and increasing their reliability allows their evolution from a prototype to a functional component.

Recent research emphasizes the growing use of advanced composite materials in modern transportation, highlighting their superior weight-to-strength ratio. These materials are increasingly replacing steel and aluminium in housings to enhance sustainability, improve efficiency, and reduce emissions. Considering these advancements, this article reviews recent studies on composite materials, focusing on fatigue life assessment models. These models, which include performance degradation, progressive damage, and S–N curve models, are essential for ensuring the reliability of composite materials. It is noted that the fatigue damage process in composite materials is complex, as failure can occur in the matrix, reinforcement, or transitions such as interlaminar and intralaminar delamination. Additionally, the article critically examines the integration of artificial intelligence techniques for predicting the fatigue life of composite materials, offering a comprehensive analysis of methods used to indicate the mechanical properties of battery shell composites. Incorporating neural networks into fatigue life analysis significantly enhances prediction reliability. However, the model’s accuracy depends heavily on the comprehensive data it includes, including material properties, loading conditions, and manufacturing processes, which help to reduce variability and ensure the precision of the predictions. This research underscores the importance of continued advancements and their significant scientific contributions to transportation sustainability, especially in the context of emerging artificial intelligence technologies.

Energy absorption is a pivotal factor in impact mitigation, especially in engineering applications that demand lightweight and efficient mechanical absorbers. Traditionally, crash boxes are manufactured using metallic materials; however, advancements in additive manufacturing present new opportunities for producing lightweight prototypes quickly, without the need for specialized tooling. This study explores the potential of 3D-printed polylactic acid (PLA) components with tailored stiffness to optimize energy absorption performance. By combining material properties, manufacturing parameters, and design strategies, varying mechanical strengths were achieved. Furthermore, the performance of the best combination was enhanced through post-processing via thermal treatment, resulting in improved crushing metrics. Experimental results revealed a 33% increase in energy absorption for honeycomb structures with variable thickness designs, demonstrating the benefits of tailored stiffness distribution. These findings highlight the promise of variable-thickness 3D-printed composites as highly effective and customizable energy absorbers, offering innovative solutions for micromobility and other engineering applications.

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.

This study presents a multi-domain experimental framework for evaluating the environmental and energetic behavior of desktop fused deposition modeling (FDM) under controlled enclosure conditions. A sensor-instrumented data acquisition platform was developed to continuously monitor chamber temperature, relative humidity, electrical power demand, carbon dioxide, total volatile organic compounds (TVOCs), and fine particulate matter (PM_2.5) throughout four operational stages: Sensor Preheating, Printer Initialisation, Printing, and post-print Resting. An 18-run experimental matrix was implemented using PLA, ABS, and PETG filaments, combining two material-specific nozzle-temperature settings and three infill densities. Time-normalized datasets were used to compare emission and energy profiles across the complete print cycle, and a reduced factorial model was applied to explore the main and two-factor interaction effects of material, temperature condition, and infill density. The results revealed strongly material-dependent environmental signatures. PLA produced the strongest sensor-indicated particulate response, with several raw PM_2.5 outputs exceeding the AM1008W sensor’s specified upper measurement range of 1000 µg/m³. ABS exhibited the most persistent chemical outgassing, with an average TVOC concentration of 128.0 ppm and a maximum of 283.0 ppm, while PETG showed minimal particulate accumulation but temperature-dependent TVOC peaks approaching those observed for ABS. Energy consumption was governed primarily by material type, nozzle-temperature condition, and infill density: ABS and PETG required nearly identical cumulative electrical inputs of approximately 263 Wh per cycle, whereas PLA averaged about 211 Wh. The interaction analysis showed that maximum PM_2.5 concentration was significantly affected by material, temperature condition, and the Material × Temperature Condition interaction, whereas none of the two-factor interactions for maximum TVOC concentration reached statistical significance. Total energy consumption was mainly determined by the direct process settings. These findings demonstrate that desktop FDM sustainability and indoor air safety cannot be assessed solely from material selection or nominal operating temperature but must be understood through the coupled effects of polymer chemistry, thermal processing conditions, and printing demand. The results support the need for verified engineering controls, particularly enclosure or confinement combined with Local Exhaust Ventilation (LEV) for source capture, while HEPA and activated-carbon filtration may be incorporated as supplementary measures.