Search Results (3,428)

Traditional modular pipelines in autonomous driving (AD) frequently suffer from error accumulation and delayed responsiveness during safety-critical events. Although Embodied Intelligence (EI) introduces a paradigm shift through internal “World Models” for proactive risk mitigation, a substantial gap remains between high-level cognitive theories and real-time, safety-certified deployment. This paper bridges that gap by proposing an Embodied System Integration Methodology (ESIM), which translates cognitive models into fielded robotic systems. Grounded in a “Perception-Imagination-Execution” (PIE) cognitive architecture, ESIM treats risk prediction as an uncertainty-driven, counterfactual closed-loop sensorimotor process. Unlike passive prediction models, the framework employs a Bayesian uncertainty-gated mechanism that selectively triggers a World Model to simulate future risk scenarios only when perceptual degradation occurs. We validate this methodology through a multi-paradigm study spanning three distinct levels: an academic prototype on edge computing platforms, an industrial implementation adhering to ASIL-D (Automotive Safety Integrity Level D) constraints, and an open-source simulation platform. The results demonstrate that by applying hardware acceleration and asynchronous pipelines, the ESIM framework consistently maintains end-to-end latencies within 10–20 ms across heterogeneous hardware. We explicitly address the engineering trade-offs in latency, hardware heterogeneity, and optimization, and establish mathematically grounded probabilistic safety boundaries for black-box neural architectures. Finally, we discuss the framework’s scalability in extreme scenarios, coupling with SLAM pipelines, privacy-preserving federated learning, and generalization potential in the low-altitude economy. Full article

Microelectromechanical systems (MEMS) gyroscopes are now standard rate sensors in inertial navigation, automotive electronics, industrial automation, and medical instrumentation because they are inexpensive, compact, and readily integrated. Their accuracy, however, degrades with temperature: damping and quadrature coupling change, and readout-electronics behavior shifts, producing temperature-dependent zero-rate-output drift, elevated random noise, and poorer long-term stability. Hardware- and structure-based temperature compensation address part of the problem but carry cost and generality penalties, which has moved recent work toward data-driven software-based temperature-drift compensation. This review focuses on the fusion algorithms that have come to dominate that literature, organized as a four-stage pipeline: signal decomposition, learning-based drift modeling, adaptive filtering, and signal reconstruction. We examine how optimizer-tuned variational mode decomposition and improved empirical-mode-decomposition variants separate temperature-related components from noise; how deep temporal networks and optimizer-coupled learners model the nonlinear, time-lagged drift; and how adaptive Kalman variants and time-frequency filtering reconstruct a stable output. We close by identifying four open problems that recur across the recent gyroscope-specific work—cross-device generalization, temperature hysteresis, embedded real-time deployment, and physics-informed lightweight modeling. Full article

The automotive industry is adopting the Industry 4.0 model to reduce failures in electrical resistance spot welding by using automated non-destructive testing systems. This study presents a real-time machine vision system that has been implemented on an automotive cabin assembly line to detect weld defects in door frames. The system extracts the physical parameters of each spot weld, including nugget diameter and heat-affected zone, to identify failing robots and welding points and prioritise maintenance actions (maintenance-free, predictive, preventive or corrective). Machine learning models were trained using images from 674 cabins collected over 30 continuous hours. YOLOv8 was used for spot weld detection and feature extraction. A neural network with linear discriminant analysis was applied for defect classification, achieving 95.80% accuracy, 95.61% precision, 96.00% recall and 95.80% F1-score. Additionally, a convolutional neural network was developed for maintenance prediction, achieving 94.72% precision, 95.87% accuracy, 93.97% F1-score and 94.42% recall. The results demonstrate the effectiveness of real-time defect detection and reliable maintenance prediction, supporting informed decision-making and efficient resource management. Full article

The increasing deployment of lithium-ion batteries in electromobility, industrial logistics, and stationary energy storage systems has introduced new operational safety challenges associated with thermal runaway, fire development, and the release of hazardous substances. Although significant attention has been devoted to battery design and fire suppression technologies, less emphasis has been placed on the development of integrated monitoring systems capable of identifying abnormal thermal behaviour during routine storage and handling operations. This paper presents an operational framework for the early detection of thermal anomalies in lithium-ion battery storage facilities based on the integration of thermal imaging technology, multi-level alarm logic, automated notification processes, and predefined response procedures. The proposed framework was developed using a risk-based approach and implemented within an industrial environment where lithium-ion batteries and battery modules are routinely stored and handled. The methodology included hazard identification, determination of critical monitoring zones, configuration of thermal detection devices, establishment of alarm thresholds, and integration with existing fire protection infrastructure. Particular attention was devoted to ensuring rapid identification, localization, verification, and escalation of abnormal thermal conditions before the occurrence of visible fire manifestations. The implemented monitoring framework comprised a total of 21 thermal imaging cameras, including four fixed radiometric thermal imaging cameras and seventeen local thermal monitoring cameras, covering five risk-prioritized monitoring zones within an industrial lithium-ion battery storage facility. During operational deployment, the system recorded 21 Yellow Alerts, 6 Red Alerts, and 4 false alarms, with an average response time of 4.2 min. Experimental verification further demonstrated that, although directly exposed battery modules were measured at approximately 60 °C, enclosure within the battery pack significantly attenuated the externally detectable thermal signature, with surface temperatures decreasing to approximately 23–31 °C after prolonged enclosure. The results demonstrate that the proposed framework enables continuous operational monitoring, supports timely identification of abnormal thermal behaviour, and provides a structured basis for rapid decision-making and emergency response in industrial lithium-ion battery storage facilities. The integration of thermal monitoring with structured alarm management and response procedures creates a comprehensive safety chain that contributes to reducing the probability of delayed incident recognition. The presented approach provides practical guidance for industrial operators seeking to improve lithium-ion battery safety and may serve as a foundation for the future development of operational safety requirements for battery storage facilities. The principal contribution of this study is the documented implementation and operational evaluation of an integrated thermal monitoring and response system under routine automotive production conditions. Full article

High-velocity oxy-fuel (HVOF)-sprayed NiCrBSi coatings containing 0, 10, 30, and 50 wt.% WC-Co were evaluated on AZ91D magnesium alloy to determine how the discrete reinforcement level affects surface topography, phase constitution, Vickers microhardness, dry-sliding friction, mass loss, and wear-track microchemistry. As-sprayed surfaces were characterized by three-dimensional profilometry; coating cross-sections and worn surfaces by optical microscopy and SEM/EDS; phase constitution by XRD; and mechanical response by HV_0.1 indentation. Dry-sliding tests were performed at 10, 30, and 50 N over 100–1000 m. Increasing WC-Co content raised Sa from 8.8 ± 0.3 to 13.0 ± 0.5 µm and Vickers microhardness from 776 ± 4 to 959 ± 5 HV_0.1. XRD indicated a γ-Ni-based matrix containing boride/carbide constituents, while WC, W₂C, and Co became increasingly prominent in the reinforced coatings. Boride assignments are based on diffraction evidence, whereas B and C EDS signals were treated semi-quantitatively. The 50 wt.% WC-Co coating exhibited the lowest mass loss and mean coefficient of friction at every load. Its mean friction coefficients were 0.31, 0.35, and 0.41 at 10, 30, and 50 N, corresponding to reductions of 40.1%, 38.9%, and 36.2% relative to AZ91D. At 1000 m, its mass-normalized wear rate indices were 9.0 × 10⁻⁴, 4.0 × 10⁻⁴, and 5.3 × 10⁻⁴ mg N⁻¹ m⁻¹, respectively. Post-wear mapping showed the largest field-scale W-Co-rich fraction in the 50 wt.% coating; however, isolated spectra containing more than 94 wt.% Mg are compatible with local coating penetration/substrate exposure and/or Mg-rich debris. The 50 wt.% composition therefore provided the best combined response among the four tested levels, while intermediate compositions are required to identify a continuous-composition optimum. Full article

In response to common issues with traditional automotive switches, such as poor contact of terminals, low durability, and weak vibration resistance, this paper proposes and designs a novel high-performance automotive toggle switch. Through structural design and parameter optimization, a new solution is provided to enhance the structural strength and service life of automotive electronic components. After completing three-dimensional modeling based on SolidWorks 2025, a full set of simulation analyses was carried out using ANSYS Workbench 2024 R2. After structural optimization, the maximum stress of the core valve stem decreased from 17.19 MPa to 14.877 MPa, a reduction of 13.5%; meanwhile, the fatigue life increased to 2.51 times that before optimization, indicating that for polycarbonate materials, a slight reduction in stress can significantly slow the rate of component damage accumulation. The switch’s first-order natural frequency is 1171.7 Hz, and a random vibration analysis of the switch was conducted according to the industry standard ISO 16750-3:2023. Under excitations covering the entire 2000 Hz frequency range, the switch structure did not show deformation or fatigue risks caused by resonance, indirectly confirming that vibration energy density is often more concentrated at low frequencies. This study not only completes the innovative design and performance verification of the novel toggle switch but also demonstrates that the comprehensive research methods employed provide a systematic analytical approach for developing high-performance, highly reliable automotive electronic components under stringent industry standards. Full article

In recent years, the automotive industry has increasingly adopted aluminum alloys in vehicle bodies to improve fuel efficiency. However, it is known that applying resistance spot welding—widely used in automotive production lines—to join steel and aluminum alloys results in the formation of brittle intermetallic compounds at the joint interface, leading to reduced joint strength and increased variability. In this study, the anchor effect—defined as mechanical interlocking at the interface—was examined, and the effect of introducing a rectangular interface on tensile shear strength (TSS) was investigated. The rectangular interface was formed by pre-machining a groove on the steel sheet prior to welding. The effect of the rectangular interface on TSS was examined by comparing joints with and without the interface. The results showed that TSS increased in joints with the rectangular interface. Furthermore, TSS was influenced by the groove geometry, as variations in groove depth and width resulted in changes in TSS. These results indicate that appropriate control of the interface geometry can effectively enhance joint strength. Full article

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration. Full article

Accurate classification of carbon deposit severity for automotive engines is critical for routine vehicle inspection and exhaust emission pollution control. Although deep learning-based methods have made certain progress in carbon deposit visual detection, they face prominent drawbacks in real industrial deployment because endoscopic carbon deposit images carry subtle inter-class fine-grained textures and lack sufficient labeled training samples, which severely impair conventional models’ classification performance and prevent them from satisfying the accuracy requirements of practical inspection tasks. This paper proposes an improved EfficientNet-B0 framework for fine-grained identification of cylinder wall carbon deposit severity. By introducing the DiffuseMix data augmentation algorithm, label confusion and overfitting are avoided. At the same time, a dual-path weighted feature fusion structure (DPWFFS) is designed to achieve bidirectional complementary of shallow and deep features, enhancing the representation ability of local discriminative features of carbon deposits. Quantitative experiments conducted on our self-built cylinder wall carbon deposit dataset demonstrate that the proposed improved model achieves a classification accuracy of 90.33%, which substantially outperforms the original EfficientNet-B0 baseline. The presented method provides an effective technical reference for the intelligent automatic identification of engine carbon deposit severity. Full article

Aluminium is increasingly being adopted in large-scale structural applications due to its high strength-to-weight ratio, corrosion resistance, and excellent recyclability, making it attractive for automotive, aerospace, infrastructure, and renewable energy sectors. Despite these advantages, welding remains a major barrier to the wider use of aluminium in load-carrying structures. This challenge is particularly critical for precipitation-hardened 6xxx series aluminium alloys, where welding-induced thermal cycles can lead to significant strength degradation in the heat-affected zone (HAZ). Strength losses of up to approximately 50% may occur as a result of precipitate dissolution and coarsening. In addition, weld quality can be further compromised by defects such as porosity and lack of fusion. Laser-based welding has emerged as a promising alternative to conventional arc welding due to its high energy density and low overall heat input. These characteristics enable narrower HAZ, reduced distortion, and improved mechanical performance of welded joints, while also offering high productivity and potential cost reductions in the fabrication of large aluminium structures. This paper details industrial challenges, weldability limits, and the mechanisms of beam oscillation, which has emerged as a primary solution for stabilising the keyhole, suppressing porosity, and refining microstructure. A case study has also been included in this paper to show the potential of laser oscillation in pore suppressing in hybrid laser-arc welding (HLAW) of 6082 aluminium. Full article

In recent years, the use of aluminum alloys in the automotive industry has been increasingly promoted to achieve lightweight vehicle bodies. Meanwhile, resistance spot welding (RSW) has long been widely employed for automotive body assembly, and thus considerable efforts have been devoted to improving the weldability of aluminum alloys by RSW. In particular, the stability of nugget formation, i.e., the molten region, during consecutive welding has emerged as a critical technical challenge. In this study, electrode wear, which is a key factor influencing nugget formation, was investigated to improve the continuous spot weldability of aluminum alloys. The relationship between the extent and morphology of electrode wear and nugget formation behavior was systematically examined. Furthermore, the electrode wear morphology that enhances continuous weldability was identified. The results revealed that stable nugget formation during consecutive welding requires the generation of sufficient heat at the center of the joint during the initial stage of current flow. Moreover, it was demonstrated that achieving such heat concentration at the joint center necessitates maintaining electrode wear as uniform as possible. Full article

Integration of composites into the fabrication process of structural assemblies within the aerospace, automotive, marine or civil engineering industries represents a rational solution adopted by leading companies which are guided by the necessity for novel low-weight, high-strength, and high-stiffness materials. During the manufacturing process and throughout the service life, fiber-reinforced polymer structures are subjected to impact loading, either accidentally or as an inherent requirement of the operational cycle. Firstly, general aspects regarding impact loading and some parameters used for its characterization are briefly described. Recent progress regarding the influence of the stacking sequence, fiber type, and impactor geometry on the impact performance of Kevlar and glass fiber reinforced composite materials is emphasized. Additionally, the effects of environmental factors (such as temperature, UV radiation, or humidity) on the impact energy absorbed by polymers reinforced with each of the two types of synthetic fibers are presented. Finally, the importance of directing the researcher’s judgment towards improving the characteristics of materials subjected to impact, from a sustainable perspective, is motivated through the presentation of the impact behavior of polymer composites reinforced with Kevlar fibers or glass fibers hybridized with vegetable fibers. Full article

Automotive systems are experiencing a rapid increase in complexity driven by the transition towards software-defined vehicles, autonomous functionalities and increasingly interconnected E/E architectures. This transformation intensifies variability across hardware and software domains and challenges established engineering approaches. Traditional modular product development (MPD) provides structural mechanisms to manage hardware complexity, while systems and software product line engineering (SPLE) offers methods for managing software variability. However, these paradigms are typically applied in isolation and lack an integrated methodology capable of addressing cross-domain variability and architectural synchronization in automotive systems. This paper investigates how SPLE and MPD can be systematically integrated to manage variability and architectural complexity in automotive systems. Following a design-oriented research approach, industry requirements are derived from an automotive case study at an OEM. Existing SPLE and modularization approaches are analyzed against these requirements, revealing gaps in cross-domain traceability, synchronization mechanisms, and lifecycle coordination. Based on this analysis, we propose an integrated methodology that combines variability modeling principles from SPLE with architectural modularization concepts. The approach enables management of module structures, supporting system-level consistency in automotive environments. The main contribution is a model-based-integration framework that bridges variability management and modular architecture design to address increasing system complexity in the automotive industry. Full article

Designing products that feel simple and intuitive while remaining efficient to manufacture and meaningful across cultures remains a key challenge in contemporary product development. Although minimalist design has achieved commercial success, most methodologies fail to systematically connect aesthetic intentions with engineering, usability, and production realities. This study proposes a four-phase iterative framework—Framing, Translation, Materialisation, and Experience that integrates human-centred design, simplicity heuristics—including ‘SHE’ (Shrink, Hide, Embody) tactics, quantitative aesthetic measurement, Design for X methods, and cross-cultural considerations to operationalise minimalist principles such as formal reduction, seriality, and industrial materiality. An empirical case study applied the framework to three competing automotive interior concepts through image-based evaluation by 113 respondents. Results provided preliminary evidence that a balanced hybrid approach consistently outperformed both traditional button-heavy and extreme single-screen minimalist designs across measures of usability, sense of order, trust, and user recommendation. Findings confirm that effective minimalism does not merely remove elements but strategically redistributes complexity into interface logic, production systems, and material quality. The framework offers designers a structured yet flexible path to create manufacturable, user-validated, and culturally sensitive minimalist products. Full article

Aluminum honeycomb structures are widely used in the aeronautical, aerospace, marine, and automotive industries due to their excellent stiffness-to-weight ratio. However, machining these structures remains highly challenging because their thin, highly flexible cell walls are susceptible to plastic deformation and geometric defects. To overcome these limitations, this study proposes an innovative machining approach that combines longitudinal-torsional ultrasonic vibration-assisted machining (LT-UVAM) with a 55-tooth CZD10 cutting tool. A three-dimensional finite element model was developed using Abaqus/Explicit 2017 to simulate the dynamic interactions between the cutting tool and the honeycomb cell walls during the milling process. Following experimental validation on a high-speed machining center, the model was employed to investigate the effects of cutting and vibration parameters on the machining performance. The results demonstrate that longitudinal-torsional ultrasonic vibration coupling significantly reduces the cutting forces, resulting in a 26% to 42% reduction in the axial force component (Fz). Furthermore, vibration assistance effectively limits cell wall deflection, reducing the stress levels by up to 60% in the thinnest walls while maintaining them below the critical Euler buckling load. Furthermore, an ultrasonic vibration frequency of 22.5 kHz almost completely eliminates plastic deformation, while a vibration amplitude of 25 µm significantly reduces tool wear by promoting intermittent tool–workpiece contact, thereby facilitating chip evacuation. Ultimately, the LT-UVAM process produces finer and more uniform chips, leading to improved machining quality, enhanced dimensional accuracy, and extended tool life. Full article