This review paper delves into the transformative synergy between 3D printing technology and the automotive and electric vehicle industries. 3D printing has revolutionized modern practices and stands at the forefront of manufacturing this innovation. This analysis is rooted in an exhaustive review of existing literature on automotive and EVs. Both domains are pivotal components of global transportation, and they are experiencing a paradigm shift through the adoption of 3DP. The present review systematically navigates the background of 3DP’s adaptation into the production processes of conventional and EVs. Through synthesizing diverse literature sources, the study highlights how 3DP is shaping vehicle manufacturing, spanning the spectrum from traditional internal combustion engine vehicles to emerging electric mobility. Furthermore, the exploration encompasses an in-depth investigation of the various 3DP materials utilized in components for these vehicles. It uncovers materials that contribute to heightened vehicle quality, performance enhancements, and a new realm of customization possibilities. Additionally, the unique material requirements of EVs are also addressed. By offering an extensive 3DP challenge, manufacturing prototypes, parts, batteries, and cost analysis of the existing body of knowledge, this paper serves as a guiding compass for industry stakeholders, researchers, and enthusiasts. It illuminates the potential of 3DP to modernize and redefine benchmarks of vehicle quality and steer the automotive and EV sectors toward a future of innovation, sustainability, and transformative progress.
This article investigates how a uniform high-frequency (HF) drive applied to each site of a weakly coupled discrete nonlinear resonator array can modulate the onsite natural stiffness and damping and thereby facilitate the active tunability of the nonlinear response and the phonon dispersion relation externally. Starting from a canonical model of parametrically excited van der Pol-Duffing chain of oscillators with nearest-neighbor coupling, a systematic two-widely separated time scale expansion (Direct Partition of Motion) has been employed, in the backdrop of Blekhman's perturbation scheme. This procedure eliminates the fast scale and yields the effective collective dynamics of the array with renormalized stiffness and damping, modified by the high-frequency drive. The resulting dispersion shift controls which normal modes enter the parametric resonance window, allowing highly selective activation of specific bulk modes through external HF tuning. The collective resonant response to the parametric excitation and mode selection by the HF drive has been analyzed and validated by detailed numerical simulations. The results offer a straightforward, experimentally tractable route to active control of response and channelize energy through selective mode activation in microelectromechanical system/nano electro-mechanical system arrays and related resonator platforms.
This paper presents an event-driven implementation of fixed-time sliding mode control (FxT-SMC) strategy for underactuated nonlinear systems, with application to quadrotor unmanned aerial vehicles. The event-triggered (ET) mechanism is designed to reduce communication and computation burdens while preserving the fixed-time convergence properties of the continuous controller. A nonsingular fixed-time sliding manifold is employed to ensure global convergence within a predefined time and to avoid singularities near the origin. When the tracking error enters a small neighborhood of the equilibrium, the manifold transitions smoothly to a quadratic form, ensuring continuity and eliminating potential singular behavior. Lyapunovbased analysis establishes global fixed-time stability (FxTS) under the ET framework, and Zeno behavior is excluded by design. Simulation results on a quadrotor UAV demonstrate that the proposed event-triggered FxT-SMC achieves precise trajectory tracking with reduced control updates by 74.2% and improved robustness against disturbances and model uncertainties.
The boom in urbanization has exerted heavy stress on energy requirements and infrastructural sustainability and buildings occupy approximately a quarter of the world energy consumption and a third of the greenhouse gas emission. This is the main issue that is addressed in the United Nations Sustainable Development Goal 11 (SDG 11), the goal that promotes sustainable cities and communities. The concept of Artificial Intelligence (AI) has already become a strong tool to enable real-time predictions of building energy, optimization, and control, though a thematic synthesis of this accumulation of research has stayed scarce. In this paper, the bibliometric and thematic analysis is performed on 947 articles that are indexed by Scopus and published in the period of 2020-2026 on the topic of using AI in the context of energy-efficient buildings. The findings indicate that there has been a sudden rise in the number of publications since 2020 with the greatest contributors being China, the United States, the United Kingdom, and India and prominent journals being Applied Energy and Sustainable Cities and Sustainability. It identifies five major research clusters, namely: HVAC optimization, occupancy detection, smart energy management systems, renewable energy integration and digital twins. In addition to deep learning and machine learning, which are the focus of modern studies, reinforcement learning and computer vision demonstrate good prospects of adaptive, occupantcentric control. Such issues as scalability, interoperability, data privacy, and deployment cost are still present, which reveals some directions on the way in which future research can be focused on the development of SDG 11.
The semiconductor industry plays a crucial part in technological innovation, although there are also severe environmental issues concerning the substantial use of resources and the emission of emissions. In this respect, the paper outlines the empirical assessment regarding the environmental effects concerning the use of energy, water, and GHGs in the production of semiconductors. Fab-operations at less than 28 nm technology nodes require some magnitude of over 500 GWh power and around 1,500,000 m3 ultra-pure water consumption annually per fab. GHG emissions vary from 2.8 to 4.5 kg CO2-eq per wafer, with process gases during etching and deposition being the main contributors. A significant positive relationship (r>0.82) was found between production quantity and emission intensity. The study's results provide a strong incentive for semiconductor manufacturers to continue and enhance their sustainable production processes and governance.
