Search Results (1,242)

Polyetheretherketone (PEEK) is a semi-crystalline thermoplastic polymer which, due to its very high mechanical properties and high chemical resistance, has found application in the automotive, aerospace, chemical, food and medical (biomedical engineering) industries. Owing to the use of additive technologies, particularly the Fused Filament Fabrication (FFF) method, this material is the most widely used plastic to produce skull reconstruction implants, parts of dental implants and orthopedic implants, including spinal, knee and hip implants. PEEK enables the creation of personalized implants, which not only have greater elasticity compared to implants made of metal alloys but also resemble the physical properties of the cortical layer of human bone in terms of their mechanical properties. Therefore, the aim of this article is to characterize polyether ether ketone as an alternative material used in the manufacturing of implants in orthopedics and dentistry. Full article

Magnetron Sputter Vacuum Deposition (MSVD) has undergone significant advancements since its inception. This review explores the evolution of MSVD, encompassing its fundamental principles, various techniques (including reactive sputtering, pulsed magnetron sputtering, and high-power impulse magnetron sputtering), and its wide-ranging industrial applications. While detailing the advantages of high deposition rates, versatility in material selection, and precise control over film properties, the review also addresses inherent challenges such as low target utilization and plasma instability. A significant portion focuses on the crucial role of MSVD in the automotive industry, highlighting its use in creating durable, high-quality coatings for both aesthetic and functional purposes. The transition from traditional electroplating methods to more environmentally friendly MSVD techniques is also discussed, emphasizing the growing demand for sustainable manufacturing processes. This review concludes by summarizing the key advancements, remaining challenges, and potential future trends in magnetron sputtering technologies. Full article

With the development of the automotive industry, the performance of commercial vehicle braking systems is crucial for road traffic safety. However, traditional braking systems are no longer able to meet the growing demand for response speed, control accuracy, and adaptability to complex operating conditions. To this end, this article focuses on improving the braking performance of commercial vehicles, designs and develops a new integrated high-response-speed EBS, explains its structure and function, proposes a pressure delay compensation control method for wire-controlled braking systems, establishes relevant models, designs control processes, and conducts braking simulations. Braking experiments are also conducted on a commercial 6 × 4 tractor on different road surfaces. The research results show that the system has good braking response performance under typical working conditions such as low adhesion, high adhesion, and opposite docking. The braking time is short (for example, the initial braking time at 40 km/h on high-adhesion roads is only 2.209 s, and the initial braking time at 50 km/h on opposite roads is 6.68 s), and the braking safety performance is superior, meeting the requirements of relevant standards. The contribution of this study lies in the proposed time delay compensation control method for wire-controlled braking, which effectively solves the problem of low control accuracy caused by time delay in wire-controlled braking systems. The integrated EBS designed integrates multiple functions, improves driving safety and comfort, and provides strong support for the upgrade of commercial vehicle braking technology, with good application prospects. Full article

The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

Shift organizations in automotive manufacturing often rely on manual task allocation, resulting in inefficiencies, human error, and increased workload for supervisors. This research introduces an automated solution using the Kuhn-Munkres algorithm, integrated with the Moodle learning management system, to optimize task assignments based on operator qualifications and task complexity. Simulations conducted with real industrial data demonstrate that the proposed method meets operational requirements, both logically and mathematically. The system improves the start of shifts by assigning simpler tasks initially, enhancing operator confidence and reducing the need for assistance. It also ensures that task assignments align with required training levels, improving quality and process reliability. For industrial practitioners, the approach provides a practical tool to reduce planning time, human error, and supervisory burden, while increasing shift productivity. From an academic perspective, the study contributes to applied operations research and workforce optimization, offering a replicable model grounded in real-world applications. The integration of algorithmic task allocation with training systems enables a more accurate matching of workforce capabilities to production demands. This study aims to support data-driven decision-making in shift management, with the potential to enhance operational efficiency and encourage timely start of work, thereby possibly contributing to smoother production flow and improved organizational performance. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

Fiber-reinforced composites are gaining more importance across different fields such as aeronautics, automotives, high-performance sporting equipment, etc., where decreasing weight while improving mechanical properties of polymers is fundamental. This article explores the mechanical behavior of fiber-reinforced polyester composite materials, highlighting their advantages and applications in various industrial fields. Usually, composite materials consist of a polyester matrix reinforced with different types of fibers, such as glass, carbon, or Kevlar, which provide superior mechanical characteristics. This study analyzed the tensile strength, bending resistance, and resilience of glass fiber composites, emphasizing the importance of proper fiber selection and manufacturing processes. These materials stand out for their excellent strength-to-weight ratio and are widely used in the fabrication of tanks in various industries. Experimental results demonstrated tensile strength (Rm) around 115 MPa, Shore D hardness values of 88 units, and impact toughness (resilience) of 2.7 J/cm². Based on the composite materials’ behavior in testing, the article further offers practical recommendations for the effective deployment of these composites in the fabrication of various types of industrial reservoirs. Full article

Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. Full article

This study introduces a time-efficient rotating bending fatigue testing system featuring 11 dual-end spindles, enabling simultaneous testing of 22 specimens. Designed for high-throughput fatigue life (S–N curve) assessment, the system theoretically allows over 93% reduction in total test duration, with 87.5% savings demonstrated in validation experiments using AISI 304 stainless steel. The PLC-based architecture provides autonomous operation, real-time failure detection, and automatic cycle logging. ER16 collet holders are easily replaceable within one minute, and all the components are selected from widely available industrial-grade parts to ensure ease of maintenance. The modular design facilitates straightforward adaptation to different station counts. The validation results confirmed an endurance limit of 421 MPa, which is consistent with the established literature and within ±5% deviation. Fractographic analysis revealed distinct crack initiation and propagation zones, supporting the observed fatigue behavior. This high-throughput methodology significantly improves testing efficiency and statistical reliability, offering a practical solution for accelerated fatigue life evaluation in structural, automotive, and aerospace applications. Full article

This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and constructed QA, RAG, and Multi-Turn datasets to reflect realistic troubleshooting scenarios. To overcome limitations of baseline RAG models, we proposed an enhanced architecture that incorporates sentence-level similarity annotations and parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) using the bLLossom-8B language model and BAAI-bge-m3 embedding model. Experimental results show that the proposed system achieved improvements of 3.0%p in BERTScore, 3.0%p in cosine similarity, and 18.0%p in ROUGE-L compared to existing RAG systems, with notable gains in image-guided response accuracy. A qualitative evaluation by 20 domain experts yielded an average satisfaction score of 4.4 out of 5. This study presents a practical and extensible AI framework for multimodal document understanding, with broad applicability across automotive, industrial, and defense-related technical documentation. Full article

This study introduces a novel approach for minimizing assembly errors in wind turbine gearboxes using a hybrid optimization algorithm, Particle Swarm-Bacteria Foraging Optimization (PSBFO). By integrating error-driven task sequencing and real-time error feedback with the PSBFO algorithm, we developed a comprehensive framework tailored to the unique challenges of gearbox assembly. The PSBFO algorithm combines the global search capabilities of PSO with the local refinement of BFO, creating a unified framework that efficiently explores task sequencing, minimizing misalignment and torque misapplication assembly errors. The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. The 50 to 5 error reduction represents a significant decrease in assembly errors from an unoptimized (50) to an optimized (5) sequence, achieved through the PSBFO algorithm, by minimizing dimensional deviations, torque mismatches, and alignment errors across 26 critical gearbox components. While the primary focus is on wind turbine gearbox applications, this approach has the potential for broader applicability in error-prone assembly processes in industries such as automotive and aerospace, warranting further validation in future studies. Full article

Efficient lubrication lowers friction, wear, and energy losses in automotive drivetrain components. Advanced lubricants are key to sustainable transportation performance, durability, and efficiency. This study analyzes the tribological performance of Group III base oil with CuO and ZnO nanoadditive mixtures. These additives enhance the performance of Group III base oils, making them highly relevant for automotive lubricant applications. An Optimol SRV5 tribometer performed ball-on-disk sliding contact tests with 100Cr6 steel specimens subjected to a 50 N force and a temperature of 100 °C. The test settings are designed to mimic the boundary and mixed lubrication regimes commonly seen in the automobile industry. During the tests, the effect of nanoparticles on friction was measured. Microscopic wear analysis was performed on the worn specimens. The results demonstrate that adding 0.3 wt% CuO nanoparticles to Group III base oil achieves a 19% reduction in dynamic friction and a 47% decrease in disk wear volume compared to additive-free oil. Notably, a 2:1 CuO-to-ZnO mixture produced synergy, delivering up to a 27% friction reduction and a 54% decrease in disk wear. The results show the synergistic effect of CuO and ZnO in reducing friction and wear on specimens. This study highlights the potential of nanoparticles for lubricant development and automotive applications. Full article

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing and manufacturing a MacPherson steering knuckle using topology optimization (TO), additive manufacturing, and rapid investment casting (RIC). Static structural simulations confirmed the mechanical integrity of the optimized design, with stress and strain values remaining within the elastic limits of the SG A536 iron alloy. The TO process achieved a 30% reduction in mass, resulting in lower material use and production costs. Additive manufacturing of optimized geometry reduced resin consumption by 27% and printing time by 9%. RIC simulations validated efficient mold filling and solidification, with porosity confined to removable riser regions. Life cycle assessment (LCA) demonstrated a 27% reduction in manufacturing environmental impact and a 31% decrease throughout the component life cycle, largely due to vehicle lightweighting. The findings highlight the potential of integrated TO and advanced manufacturing techniques to produce structurally efficient and environmentally sustainable automotive components. This workflow offers promising implications for broader industrial applications that aim to balance mechanical performance with ecological responsibility. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article