A simple and accurate method for the determination of Rh, Pd, and Pt in e-waste and spent automotive catalysts using HR-CS FAAS for assessing the value of secondary raw materials.

This work presents a simple and accurate method for the fast sequential determination of Rh, Pd, and Pt in spent automotive catalysts and e-wastes using high-resolution continuum source flame atomic absorption spectrometry (HR-CS FAAS). Extensive research was carried out in model systems on the impact of potential interfering substances on analyte's signals measured in two types of flame (air-C2H2 and N2O-C2H2). Mutual analyte interactions were also taken into account. Different background corrections offered by the HR-CS AAS spectrometer were tested to obtain interference-free analyte signals and the best detectability. Using an air-C2H2 flame and 1 % La solution as a spectrochemical buffer provided good sensitivity and accurate determinations of Rh, Pd, and Pt using a simple calibration graph. Microwave-assisted leaching of PGE from waste samples with aqua regia at 240 °C for 60 min efficiently leached all target metals, which significantly simplified and shortened the sample preparation step. The detectability of the method (detection limit of 0.4, 0.6, and 5 mg kg-1 for Rh, Pd, and Pt, respectively) and precision (< 7 %) were satisfactory. The accuracy of the method was confirmed by analysis of certified reference materials (spent automotive catalyst (ERM-EB504), electronic scrap (BAM-M505a)), and calculated zeta score values. The recoveries for Rh, Pd, and Pt in ERM-EB504 were 93, 101, and 96 %, respectively, and for Pd in BAM-M505a, 97 %. The developed method can be used to assess the value of secondary raw materials, such as various types of spent catalysts and e-waste containing Rh, Pd, and Pt.

A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

Iron capture mechanism for harmless recovering platinum group metals from spent automobile catalyst.

Automotive catalysts are the largest consumption source of platinum group metals (PGMs). When it exceeds its useful life, spent automotive catalysts (SACs) are the most important secondary PGMs resource and are classified as hazardous solid waste. Recycling SAC is a promising solution to alleviate the shortage of PGMs resources for projects and reduce environmental pollution. The technology for recovering PGMs by iron-melting collection can obtain Fe-PGMs alloy and harmless glass slag. In this paper, the spontaneous aggregation and growth behaviour of Fe and PGMs in slag at melting temperature were studied, and the settling velocity of Fe-PGMs particles in the slag was calculated to be 6.68 × 10-3 m/s. The effects of melting time, melting temperature and Fe dosage on PGMs recovery were determined, and the optimal conditions were 10 wt% Fe, 1500°C and 40 min. The toxicity test verifies that the slag obtained is a clean slag harmless to the environment. This work explains the mechanism of Fe collection of PGMs and provides a pathway for efficient and harmless recovery of PGMs from SAC.

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

Exploring Bio-Based Polyurethane Adhesives for Eco-Friendly Structural Applications: An Experimental and Numerical Study.

In response to heightened environmental awareness, various industries, including the civil and automotive sector, are contemplating a shift towards the utilization of more sustainable materials. For adhesive bonding, this necessitates the exploration of materials derived from renewable sources, commonly denoted as bio-adhesives. This study focuses on a bio-adhesive L-joint, which is a commonly employed configuration in the automotive sector for creating bonded structural components with significant bending stiffness. In this investigation, the behavior of joints composed of pine wood and bio-based adhesives was studied. Two distinct configurations were studied, differing solely in the fiber orientation of the wood. The research combined experimental testing and finite element modeling to analyze the strength of the joints and determine their failure mode when subjected to tensile loading conditions. The findings indicate that the configuration of the joint plays a crucial role in its overall performance, with one of the solutions demonstrating higher strength. Additionally, a good degree of agreement was observed between the experimental and numerical analyses for one of the configurations, while the consideration of the maximum principal stress failure predictor (MPSFP) proved to accurately predict the location for crack propagation in both configurations.

Evaluation of Vibration Damping Enhancement in Laminated Aluminum Sheets for Automotive Application.

In this research, the vibration damping characteristics of the laminated aluminum sheets (LAS) were evaluated in a sheet specimen and an automotive dash panel and compared with those of the monolithic aluminum sheet (MAS). The LAS was fabricated with two 5xxx series aluminum alloy (AA) sheets (AA5052-O) with a thickness of 0.7 mm by inserting an acryl-based adhesive in between. The automotive dash panels were manufactured by multi-step stamping processes for the LAS and the MAS with a similar thickness. The shaker vibration test in a sheet specimen and the impact hammer test in an automotive dash panel were conducted to measure the frequency response function (FRF) of LAS, compared with those of MAS. The results show that the frequency response function made by the LAS has less noise and fluctuation than that of the MAS in a sheet specimen and an automotive dash panel. The damping ratios in a sheet specimen and an automotive dash panel made by the LAS have higher values than those of the MAS. This proves that the LAS has better vibration damping characteristics and a larger damping effect than the MAS in a sheet specimen and an automotive dash panel.

Microstructure and tribological behavior of Al-TiC composite strips fabricated by a multi-step densification method.

This study aims to enhance the tribological properties of automotive applications by examining the effects of TiC content on the microstructure, mechanical properties, and wear behavior. This study investigates the production of Al-TiC composite strips using a novel multi-step densification process combining mechanical alloying and hot rolling with TiC concentrations ranging from 0 to 12 vol%. The novelty of this work lies in its comprehensive approach to developing and analyzing Al-TiC composite strips using a multistep densification method. This study integrates microstructural analysis, mechanical property evaluation, and detailed tribological behavior assessment under different wear loads (5-25 N). A key innovation is the application of the Abbott Firestone method to analyze worn surfaces, providing insights into optimal wear conditions. The study reveals that increasing the TiC content to 12 vol% significantly improves densification, hardness (up to 268.8% increase), and wear resistance (up to 95% improvement at a 5N load). Dry ball-on-flat sliding wear tests at loads of 5-25N demonstrate that TiC particles hindered complete delamination wear in the composite strips. The Abbott Firestone method analysis of worn surfaces indicated an optimal exploitation zone in the Al-6 vol% TiC composite at both low and high wear loads. This comprehensive approach provides valuable insights into optimizing Al-TiC composites for enhanced performance in automotive components that require improved wear resistance.

The association between insomnia and musculoskeletal problems in employees of an automotive company in Tehran, Iran.

OBJECTIVES: The purpose of this study was to examine the association between insomnia and the number of pain areas among employees of an automotive company. Secondary aim was to examine the association between depression, anxiety, and stress, and musculoskeletal pain independent of insomnia. MATERIAL AND METHODS: We performed a cross-sectional study conducted in an automobile manufacturing factory. To collect data, we used the Nordic Musculoskeletal Questionnaire, ISI (Insomnia Severity Index), and DASS-21 (Depression, Anxiety, and Stress Scale). The univariate and multiple association analyses were performed using ordinal logistic regression adjusted for gender, age, industrial/non-industrial, sport activity, pack year, educational level, years of employment, anxiety, stress, and depression. RESULT: In individuals with insomnia, the lower back was the most common area where pain was reported (59.5%). In all areas, pain symptoms were significantly more prevalent in individuals with insomnia compared to those without insomnia. Insomnia was associated with having pain with an increased odds of number of pain sites (odds ratio [OR] 2.81,95%CI 2.34-3.39). In ordinal logistic regression, there was an association between insomnia and pain, independent of the effects of depression, anxiety, and stress (OR 2.21,95%CI 1.52-3.23). DISCUSSION: Insomnia was associated with pain in all regions of the body and a higher prevalence of pain in multiple areas. The insomnia-pain association was independent of depression, anxiety, and stress.

Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

A Detailed Properties Comparison of an Automotive Sealant Nozzle Produced Using Three Metal Additive Manufacturing Technologies.

Choosing the right metal AM equipment and material is a highly intricate process that forms a crucial part of every manufacturing company's strategic plan. This study undertakes a comprehensive comparison of the performance and material properties of three Metal Additive Manufacturing (AM) technologies: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM), and Bound Metal Deposition (BMD). An automotive nozzle was selected and manufactured using all three technologies and three metallic materials to understand their respective advantages and disadvantages. The samples were then subjected to a series of tests and evaluations, including dimensional accuracy, mechanical properties, microstructure, defects, manufacturability, and cost efficiency. The nozzle combinations were PBF in aluminum, MFDM in stainless steel, and BMD in hard tool steel. The results underscore significant differences in functionality, material characteristics, product quality, lead time, and cost efficiency, all of which are crucial factors in making equipment investment decisions. The conclusions drawn in this paper aim to assist automotive industry equipment experts in making informed decisions about the technology and materials to use for parts with characteristics like these. Future studies will delve into other technologies, automotive components, and materials to further enhance our understanding of the application of metal AM in manufacturing.

SNN4Agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents.

Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for the NCARS dataset. In this manner, our SNN4Agents framework paves the way toward enabling energy-efficient embodied SNN deployments for autonomous agents.

An Experimental Performance Assessment of Temporal Convolutional Networks for Microphone Virtualization in a Car Cabin.

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions-a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented.

Novel Water Probe for High-Frequency Focused Transducer Applied to Scanning Acoustic Microscopy System: Simulation and Experimental Investigation.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

Impact of Safety Device Features Upon Motor Vehicle Collision Facial Fracture Traumas: A Five-Year Retrospective Study in the Appalachian Tri-State Area.

Motor vehicle collisions (MVCs) represent a significant public health concern, contributing to substantial morbidity and mortality globally. Despite advancements in vehicle safety technology, the impact of safety measures on facial fractures in MVCs remains poorly understood, especially in regions with diverse road conditions like the Appalachian tri-state area. This retrospective study analyzed data from two regional level II trauma centers in Appalachia, focusing on MVC-related facial fractures and safety device usage from January 2017 to December 2021. Descriptive statistics and logistic regression models were employed to assess associations between safety devices and injury outcomes. The study comprised 85 participants, categorized into groups based on safety device usage. Results indicated no significant differences in demographic characteristics, injury severity, surgical intervention rates, or specific facial fractures among groups. These findings challenge some previous research suggesting the protective effects of safety devices on facial fractures in MVCs, underscoring the complex nature of injury prevention in this context. While limitations such as retrospective data collection and sample size constrain generalizability, this study contributes valuable insights for informing injury prevention strategies and trauma care practices in Appalachia and beyond.

On-Road Evaluation of Unobtrusive In-Car Respiration Monitoring.

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver's seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the feasibility of unobtrusive in-car measurements over long periods of time. Operation of the sensor was investigated over 12 participants sitting in the driver seat. A total of 107 min of driving in diverse conditions with overall coverage rate of 84.45% underscores the sensor potential to reliably capture physiological changes in breathing rate for fatigue and stress detection.

Concentrations of potentially endocrine disrupting chemicals in car cabin air and dust - Effect of temperature and ventilation.

Materials in car cabins contain performance-enhancing semi-volatile organic compounds (SVOCs). As these SVOCs are not chemically bound to the materials, they can emit from the materials at slow rates to the surrounding, causing human exposure. This study aimed at increasing the understanding on abundance of SVOCs in car cabins by studying 18 potential endocrine disrupting chemicals in car cabin air (gas phase and airborne particles) and dust. We also studied how levels of these chemicals varied by temperature inside the car cabin along with ventilation settings, relevant to human exposure. A positive correlation was observed between temperature and SVOC concentration in both the gas and the particle phase, where average gas phase levels at 80 °C were a factor of 18-16,000 higher than average levels at 25 °C, while average particle phase levels were a factor of 4.6-40,000 higher for the studied substances. This study also showed that levels were below the limit of detection for several SVOCs during realistic driving conditions, i.e., with the ventilation activated. To limit human exposure to SVOCs in car cabins, it is recommended to ventilate a warm car before entering and have the ventilation on during driving, as both temperature and ventilation have a significant impact on SVOC levels.

Comparing End-of-Life Vehicle (ELV) and Packaging-Based Recyclates as Components in Polypropylene-Based Compounds for Automotive Applications.

Increasing recycled plastic content in cars to 25% by 2030 is one of the key measures for decarbonizing the automotive industry defined by the European Commission. This should include the recovery of plastics from end-of-life vehicles (ELVs), but such materials are hardly used in compounds today. To close the knowledge gap, two ELV recyclate grades largely based on bumper recycling were analyzed in comparison to a packaging-based post-consumer recyclate (PCR). The composition data were used to design polypropylene (PP) compounds for automotive applications with virgin base material and mineral reinforcement, which were characterized in relation to a commercial virgin-based compound. A compound with a 40 wt.-% ELV-based bumper recyclate can exceed one with just a 25 wt.-% packaging-based recyclate in terms of stiffness/impact balance. While the virgin reference can nearly be matched regarding mechanics, the flowability is not reached by any of the PCR compounds, making further development work necessary.

Analysis of automotive paint layers on plastic substrates using chemical imaging µ-FTIR and O-PTIR microspectroscopy.

Automobile paint chips are a crucial piece of trace evidence for forensic investigators. This is because automotive paints are composed of multiple layers, including the primer, basecoat, and clearcoat, each of which has its own chemical composition that can vary by vehicle make, model, year, and manufacturing plant. Thus, Fourier-transform infrared (FTIR) spectral databases for automobile paint systems have been established to aid law enforcement in, for example, narrowing search parameters for a suspect's vehicle. Recently, car manufacturers have implemented primers on plastic substrates that are much thinner (~5 µm) than those on metal substrates, making it more difficult to manually separate for analyses. Here, we evaluated FTIR microspectroscopy (µ-FTIR) and optical photothermal infrared spectroscopy (O-PTIR) to chemically image cross sections of paint chips without manually separating the layers. For µ-FTIR, transmission and transflection modes provided the highest quality spectra compared to reflection and µ-ATR analyses. Point analysis was preferable to chemical imaging, as peaks were identified in the point (MCT) detector's lower spectral range that was below the imaging (FPA) detector's cutoff, such as those associated with titanium dioxide. Reduced spectral range can lead to a similar issue in O-PTIR analyses depending on instrument configuration. However, its complementary Raman spectra showed strong titanium dioxide peaks, providing an alternate means of identification. Both techniques are likely to become more relevant as they are non-destructive and avoid manual separation of the layers. O-PTIR is particularly well-suited for analysis of the thin primer layer due to its superior spatial resolution.

Integrated operations planning model for the automotive wiring industry.

An integrated operations planning model for automotive wiring companies is studied to improve synchronization between production activities and inventory flows. These combined factors are growing in significance as they drive the need to take proactive steps in manufacturing and distributing wiring materials within the supply chain. This involves anticipating the requirements of different automotive manufacturers and thereby guaranteeing a consistent, uninterrupted, and punctual provision of raw wiring materials. This support is vital for sustaining the ongoing manufacturing operations in the automotive sector. For this push flow system, the proposed operational model is based on integer linear programming, considering capacity and bill of materials constraints to determine production quantities, inventory levels, and machine sizing. Real-life data from the automotive wiring industry validates the effectiveness of coordinated production and inventory activities, resulting in significant lead time reductions of up to 60 %. These findings provide compelling reasons for automotive wiring partners to engage in joint operations planning.