Search Results (838)

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

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. 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

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

LEDs are readily controllable and demonstrate rapid switching capabilities. These attributes facilitate their efficient integration across a broad spectrum of applications. Indeed, their inherent versatility renders them ideally suited for diverse sectors, including consumer electronics, traffic signage, automotive technology, and architectural illumination. Furthermore, LEDs serve as effective light sources for applications in spectroscopy, agriculture, pest control, and wireless optical transmission. The capability to choice high-efficiency LED devices with a specified dominant wavelength renders them particularly well-suited for integration into underwater optical communication systems. In this paper, we present the state-of-the-art of Light-Emitting Diodes (LEDs) for use in underwater wireless optical communications (UOWC). In particular, we focus on the challenges posed by water turbidity and evaluate the optimal wavelengths for communication in coastal environments, especially in the presence of chlorophyll or suspended particulate matter. Given the growing development and applications of underwater optical communication, it is crucial that the topic becomes not only a subject of research but also part of the curricula in technical school and universities. To this end, we introduce a simple and cost-effective UOWC system designed for educational purposes. Some tests have been conducted to evaluate the system’s performance, and the results have been reported. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article

This study investigates the development of a tire wear model using finite element techniques. Experimental testing was conducted using the Hoosier R25B slick tire mounted onto a Mustang Dynamometer (MD-AWD-500) in the Automotive Center of Excellence, Oshawa, Ontario, Canada. A general acceleration/deceleration procedure was performed until the battery was completely exhausted. A high-fidelity finite element tire model using Virtual Performance Solution by ESI Group, a part of Keysight Technologies, was developed, incorporating highly detailed material testing and constitutive modeling to simulate the tire’s complex mechanical behavior. In conjunction with a finite element model, Archard’s wear theory is implemented algorithmically to determine the wear and volume loss rate of the tire during its acceleration and deceleration procedures. A novel application using a modified wear theory incorporates the temperature dependence of tread hardness to measure tire wear. Experimental tests show that the tire loses 3.10 g of mass within 45 min of testing. The results from the developed finite element model for tire wear suggest a high correlation to experimental values. This study demonstrates the simulated model’s capability to predict wear patterns, ability to quantify tire degradation under dynamic loading conditions and provides valuable insights for optimizing performance and wear estimation. Full article

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article

The aim of the present study is to investigate the influence of Nickel–Hexagonal Boron Nitride (Ni-hBN) nanocomposite coatings, deposited using the pulse reverse current electrodeposition technique. This experimental study focuses on assessing the tribological and corrosion properties of the produced coatings on the SS304 substrate. The microhardness of the as-deposited (AD) sample and heat-treated (HT) sample were 49% and 83.8% higher compared to the control sample. The HT sample exhibited a grain size which was approximately 9.7% larger than the AD sample owing to the expansion–contraction mechanism of grains during heat treatment and sudden quenching. Surface roughness reduced after coating, where the Ni-hBN-coated sample measured a roughness of 0.43 µm compared to 0.48 µm for the bare surface. The average coefficient of friction for the AD sample was 42.4% lower than the bare surface owing to the self-lubricating properties of nano hBN. In particular, the corrosion rate of the AD sample was found to be 0.062 mm/year, which was lower than values reported in other studies. As such, findings from the present study can be particularly beneficial for applications in the automotive and aerospace industries, where enhanced wear resistance, reduced friction, and superior corrosion protection are critical for components such as engine parts, gears, bearings and shafts. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

The manufacture of lightweight components is one of the most important requirements in the automotive and aerospace industries. Gears, on the other hand, are among the heaviest parts in terms of their total weight. Accordingly, a spur gear was considered, the body of which was configured as a lattice structure to make it lightweight. In addition, the structure was optimized by topology optimization using ProTOP software. Subsequently, the gear was manufactured by a selective laser melting process by using a strong and lightweight material, namely Ti-6Al-4V. This study defeated the problems of manufacturing orientation, surface roughness, support structure, and bending due to the high thermal gradient in the selective laser melting process. To experimentally investigate the benefits of such a lightweight gear body structure, a new test rig with a closed loop was developed. This rig enabled measurements of strains in the gear ring, hub, and tooth root. The experimental results confirmed that a specifically designed and selectively laser-melted, lightweight cellular lattice structure in the gear body can significantly influence strain. This is especially significant with respect to strain levels and their time-dependent variations in the hub section of the gear body. Full article

Warehouses are vital in linking production to consumption, often using a forward–reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas—intermediate zones between forward and reserve locations—to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model’s effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management. Full article

Additive Manufacturing (AM) techniques, such as Fused Deposition Modeling (FDM) and Stereolithography (SLA), are increasingly adopted in various high-demand sectors, including the aerospace, biomedical engineering, and automotive industries, due to their design flexibility and material adaptability. However, the tribological performance and surface integrity of parts manufactured by AM are the biggest functional deployment challenges, especially in wear susceptibility or load-carrying applications. The current review provides a comprehensive overview of the tribological challenges and surface engineering solutions inherent in FDM and SLA processes. The overview begins with a comparative overview of material systems, process mechanics, and failure modes, highlighting prevalent wear mechanisms, such as abrasion, adhesion, fatigue, and delamination. The effect of influential factors (layer thickness, raster direction, infill density, resin curing) on wear behavior and surface integrity is critically evaluated. Novel post-processing techniques, such as vapor smoothing, thermal annealing, laser polishing, and thin-film coating, are discussed for their potential to endow surface durability and reduce friction coefficients. Hybrid manufacturing potential, where subtractive operations (e.g., rolling, peening) are integrated with AM, is highlighted as a path to functionally graded, high-performance surfaces. Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. Through material-level innovations, process optimization, and surface treatment techniques integration, the article provides actionable guidelines for researchers and engineers aiming at performance improvement of FDM and SLA-manufactured parts. Future directions, such as smart tribological, sustainable materials, and AI-based process design, are highlighted to drive the transition of AM from prototyping to end-use applications in high-demand industries. Full article