Search

ABSTRACT The ability to assess the risk of corrosion of metallic structures in particular environments holds considerable significance in the field of automotive industry. In recent years, machine learning has evolved into a crucial tool to evaluate the complex and multidimensional corrosion phenomena. In this paper, the special case of non‐aqueous alcoholate pitting corrosion of AA1050 in ethanol‐blended fuels with water and chloride contamination is examined via supervised machine learning techniques in order to distinguish between safe and unsafe conditions. The data space was created by conducting dedicated experiments with varying ethanol–fuel–water ratios, temperatures, and surface preparations. The classifier's performance rating of 0.87 (balanced accuracy) indicates an outstanding predictive ability and highlights the model's usefulness as decision support for subsequent experiments. [ABSTRACT FROM AUTHOR]

The decarbonization of transport is a key goal facing climate change. The electrification of the powertrain for passenger cars is part of this goal to reduce carbon emissions. This involves a big change in the global supply chain, specifically in countries with a high weight of the traditional automotive sector, such as Spain, where above 10% of the GDP comes from this industry. There is a forecasted shift from the sector of traditional automotive parts to the electric sector, where batteries and electric components will be the major part of the powertrain. This work evaluates socioeconomic and environmental impacts of the changes in the car industry from the ramp‐up of the electric vehicles market in Spain, and also in the European Union and the rest of the world. To do it, we use an environmentally extended multiregional and multi‐sectoral input–output model. Our simulations include the technological change and demand shifts estimated to achieve the penetration of electric vehicles up to 2030 and 2050. The results show significant impacts on employment and economic indicators by 2050, when the share of electric vehicles is expected to increase up to a relevant level. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive evaluation of the stability of crimping of the same terminal on FLRY A cables using three different applicators. The research involved conducting pull tests on 50 samples for each applicator type, analyzing the results using Minitab software to assess the consistency and strength of the crimp connections. The findings indicate that while the Demirel applicator produces the highest average pull test values, the Tyco applicator demonstrates superior consistency, making it more suitable for applications where process stability is critical. [ABSTRACT FROM AUTHOR]

By simulating the thermomagnetization of Ba(Fe1-xScx)12O19 from approximately 100 to 750 K, the magnetocaloric effect (MCE) in Ba(Fe1-xScx)12O19 samples is approximated. Intriguingly, the thermomagnetic property of the undoped sample BaFe12O19 is described as a conventional MCE for high temperatures. However, at doping levels x of 0.128, 0.153, and 0.189, respectively, the thermomagnetic property of Sc-doped samples is defined as an inverse MCE below temperatures of 163, 239, and 318 K. In contrast, thermomagnetic property of Ba(Fe1-xScx)12O19 is described as conventional MCE above those temperatures. Additionally, Sc doping makes it possible for MCE in Ba(Fe1-xScx)12O19 to be strongly tailored. This is helpful for operating a magnetic refrigerator (MR) which occurs in various temperature ranges and results in a higher working temperature range. A promising function for Ba(Fe1-xScx)12O19 samples in MR at high-temperature applications in the automotive and food industries or aerospace. [ABSTRACT FROM AUTHOR]

This research study employs a comparative Multi-Criteria Decision-Making (MCDM) approach to select optimal thermoplastic materials for hybrid vehicle battery packs in the automotive industry, addressing the challenges posed by high-temperature environments. Through a detailed evaluation of materials based on criteria such as thermal stability, mechanical strength, chemical resistance, and environmental impact, the research identifies materials that enhance battery efficiency, longevity, and vehicle performance. Utilizing SWARA-ARAS, SWARA-EDAS, and SWARA-TOPSIS methods, the study systematically assesses and ranks various polymers, providing recommendations that prioritize safety, performance, and sustainability. The findings offer valuable insights for manufacturers in making informed material selection decisions, contributing to the advancement of sustainable automotive technologies. This research not only highlights the importance of material selection in the context of hybrid vehicle battery packs but also sets a foundation for future studies to explore emerging materials and decision-making frameworks, aiming to further enhance the efficiency and sustainability of hybrid vehicles. [ABSTRACT FROM AUTHOR]

Additive manufacturing processes allow for precise and efficient production, but it is estimated that one-third of the materials used results in waste. Further improvement in a sustainable perspective could come from the ability to manage these scraps and from the exploration of different routes for recovery and reuse. The Selective Laser Sintering process is particularly sensitive to this issue due to the waste ratio which can reach a very high quantity of not-sintered virgin powders. In this research study, recovered PA12 powders, preliminarily characterized through thermal and mechanical analysis, were mixed with 15% basalt powder to improve their aspect and thermomechanical resistance. The influence of basalt powder (BP) on mechanical properties as well as on the thermal stability of polyamide12 (PA12) powder composites was investigated. A study conducted on mechanical properties showed that polymeric composites' stiffness and hardness were influenced by adding filler, thus improving mechanical parameters. On the other hand, the application of thermogravimetric analysis allowed us to determine the composite's thermal stability. The objective is to obtain a recovered fully biobased material that could be used to substitute the petroleum-derived polymeric ones currently employed in the production of interiors and shells in the automotive sector. [ABSTRACT FROM AUTHOR]

During the last decade, developing more sustainable transportation modes has become a primary objective for car manufacturers and governments around the world to mitigate environmental issues, such as climate change, the continuous increase in greenhouse gas (GHG) emissions, and energy depletion. The use of hydrogen fuel cell technology as a source of energy in electric vehicles is considered an emerging and promising technology that could contribute significantly to addressing these environmental issues. In this study, the effects of Hydrogen Fuel Cell Battery Electric Vehicles (HFCBEVs) on global GHG emissions compared to other technologies, such as BEVs, were determined based on different relevant factors, such as predicted sales for 2050 (the result of the developed prediction model), estimated daily traveling distance, estimated future average global electricity emission factors, future average Battery Electric Vehicle (BEV) emission factors, future global hydrogen production emission factors, and future average HFCBEV emission factors. As a result, the annual GHG emissions produced by passenger cars that are expected to be sold in 2050 were determined by considering BEV sales in the first scenario and HFCBEV replacement in the second scenario. The results indicate that the environmental benefits of HFCBEVs are expected to increase over time compared to those of BEVs, due to the eco-friendly methods that are expected to be used in hydrogen production in the future. For instance, in 2021, HFCBEVs could produce more GHG emissions than BEVs by 54.9% per km of travel, whereas in 2050, BEVs could produce more GHG emissions than HFCBEVs by 225% per km of travel. [ABSTRACT FROM AUTHOR]

The automotive market is very competitive and demands consistently improving the technologies used and reducing the product cost and dimensions with each product model iteration. Hence, it is important to have access to well-defined reference designs of high-quality products to evaluate new ideas and technologies. This paper provides readers with a numerical model of such a high-quality product, i.e., the IPMSM-type traction motor from the fourth generation of the Toyota Prius hybrid transaxle. The presented results also serve for a discussion regarding the design decisions of the Toyota engineers and the applicability of the linearized machine model for the approximated torque calculations. In the introductory section, a brief history of the Prius model and references to the reverse engineering reports are given. Afterward, the machine dimensions, material properties, and winding configuration are described. Then, the model is validated with the torque measurements at constant speed. The simulation results are presented in the next chapters, and the numerical source data are supplied to the reader. Finally, the design philosophy of the Toyota drive is briefly discussed in comparison with the BMWi3 drive and the results are concluded in the last section. [ABSTRACT FROM AUTHOR]

The automotive industry, known for its dynamism and innovation, relies on effectiveness of product and process requirements management. Effective collaboration with OEM (Original Equipment Manufacturers) is essential for success. The article analyses how product and process requirements evolve during different project phases, following the APQP (Advanced Product Quality Planning) approach, depending on OEM type. The study focuses on Tier 1 organizations, direct suppliers to the automotive industry, and their diverse approaches to product development. It underscores the need for Tier 1 organizations to maintain flexibility in their operations and to be prepared for changes in OEM demands. The study emphasizes the role of effective communication and coordination among all the levels of the supply chain to ensure the successful implementation of the APQP approach. [ABSTRACT FROM AUTHOR]

Ge‐rich Ge–Sb–Te (GGST) alloys are the most promising materials for phase‐change memory in embedded applications, being able to fulfill the tough data retention requirements of automotive and consumer markets. GGST alloys are sensitive to thermal budgets and spatial confinement; thus, memory device process integration and architecture can strongly impact their final electrical properties and reliability. Herein, exploiting a statistical methodology capable to extract quantitative metrics for evaluating by‐process segregation, the inhomogeneity of out‐of‐fab GGST material in function of process parameters is studied such as architecture and alloy composition. The present results with the already known source of segregation, namely the back‐end‐of‐line thermal budget, are compared providing a comprehensive description of the main modulating factors of segregation among these different process parameters. [ABSTRACT FROM AUTHOR]

Autonomous technologies have developed incrementally, especially in the car industry. Countries worldwide are giving impetus to developing automated driving (AD) and autonomous vehicles (AVs). China is used as an example of an innovative development model for autonomous driving that has significant practical implications for AD's development in the global context. Germany also keeps its role as a global hub for the AVs industry, and it has released the most advanced AD laws. This research conducted a thorough comparison of related policies, regulations, and industries in China and Germany in an effort to support the development of AD in China. Further, AD's advantages, disadvantages, opportunities, and threats in China and Germany are discussed. It sought to identify any shortages of AD in China and find an efficient way to enhance AD cooperation between China and Germany. Based on the dissimilarity of the current status and the disparity in policies, a series of countermeasures and suggestions for the development of the Chinese AD are presented in this study. [ABSTRACT FROM AUTHOR]

Refill friction stir spot welding (RFSSW) has found several industrial applications, especially in the transportation and automotive sectors. However, modeling the RFSSW process has been tackled mainly with empirical approaches. At the same time, the key physical phenomena involved have been explained and predicted by a few numerical studies in the literature. This study uses a fully Lagrangian method, smoothed particle hydrodynamics (SPH), for the simulation of RFSSW. The Lagrangian particle method simulates materials undergoing large deformation, interface dynamic changes, void formations, material temperature, and strain evolution without using complex tracking schemes often required by traditional grid-based methods. As a relevant example, magnesium-to-steel welding simulation is presented by accounting for all the main thermo-mechanical phenomena involved. Temperature, stress, and strain field histories as well as material flow taking place during the process, are determined as characteristic aspects for qualification of RFSSW; the proposed computational approach is validated by comparing the predicted and experimentally measured welding temperature. The results obtained demonstrate that SPH is a reliable tool for welding design and process optimization and provides the information related to the involved physics needed to precisely evaluate the quality, the mechanical characteristics, and the material flow of the joined region. [ABSTRACT FROM AUTHOR]

The increasing manufacturing of tires, driven by the automotive sector, has led to various sustainability and waste management issues. Consequently, it is necessary to explore alternatives for the reuse of these wastes. Rubber has demonstrated properties that could make it an attractive material for use as a granular aggregate. The aim of this study is to evaluate the influence of crumb rubber (CR) on the mechanical behavior of a granular subbase (GSB) by replacing fine CR at 10% and 15%. An environmental analysis of the proposal was conducted using the SimaPro tool, and a series of laboratory tests were performed to assess physical and mechanical characteristics. The strength results indicate a decreasing trend as the CR content in the GSB increases. Specifically, in abrasion tests, the natural material showed an average result of 30.86%, while the mixes with 10% and 15% CR exceeded the maximum limit (50%), achieving results of 59.24% and 53.98%, respectively. In terms of the California Bearing Ratio (CBR), only the natural samples compacted at maximum energy exceeded the minimum requirements for low and medium traffic levels (30%). The highest CBR value for samples containing CR was 5%, whereas the natural GSB reached a maximum CBR of 33%. A similar trend is observed in environmental outcomes, with increases of up to 17% in abiotic depletion for the sample with 15% CR. Overall, the environmental analysis suggests that incorporating CR into the GSB could lead to an increase in various environmental impacts. [ABSTRACT FROM AUTHOR]

How do environmentally-driven and firm-specific factors influence business innovation and adaptation? How can the analysis of their co-evolutionary relationship help explain firm survival or failure over time? To address these key questions, this article focuses on the study regarding the 20-year performance (1997–2017) of the Fiat (later Fiat Chrysler Automobiles) group in the automobile industry. The analysis primarily deals with three historical phases: crisis (1997–2002), turnaround (2003–2007), and expansion (2008–2017). As a key contribution, the study combines elaborations from a robust dataset comprising both industry characteristics and executive team features, with historical events both at the environmental and the firm's strategy levels. Results suggest that when executive team diversity and a wide breadth of product portfolio are concurrently present, they positively affect corporate performance. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study's results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Administrative Sciences / Yonetim Bilimleri Dergisi is the property of Canakkale Onsekiz Mart Universitesi, Terzioglu Kampusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Researchers and practitioners have been paying close attention to the circular economy (CE) since it addresses both social and environmental benefits. It allows for the optimization of the manufacturing process by developing sustainable products that retain as much of their value as feasible. This study focuses on the constraints that obstruct the application of circular economy practices in the Malaysian automobile industry. Slight attention has been paid in the literature to the hurdles to CE adoption in Malaysia. Moreover, the pandemic COVID- 19 that disrupted the supply chain in automotive industries are the foundation of issue that led to this study. Governmental, economic, and social barriers are identified among the barriers that impede the implementation of CE for automotive industry in Malaysia. Expected result will suggest the most significant variable influencing barriers to the implementation of CE practices. Researchers might use the proposed new conceptual framework to conduct future research or add more variables to the study. [ABSTRACT FROM AUTHOR]

Tannin foam is a new functional material. It can be widely applied to the automobile industry, construction industry, and packaging industry due to its wide range of raw materials, renewable, easily degraded, low cost and almost no pollution. Preparing tannin foam is a very complex process that includes high temperature, two phases, mechanical agitation, and phase change. To investigate the influence of the stirring velocity and paddle shape, simulation was calculated by making use of the volume of fluid (VOF) method and multiple reference frame (MRF) method in a three-dimensional flow field of tannin-based foaming precursor resin. The gas holdup and velocity magnitude were analysed with various conditions of mechanical velocities and paddle shape in the stirring flow field. The result shows the higher the velocity, the greater the disturbance and paddle shape between the eggbeater and the Rushton turbine, obviously the paddle shape of the eggbeater with a wider range of agitation, which can entrap more air into the tannin-based foaming precursor resin in a short time. Especially when the speed is 1500 rpm, the flow field of the Rushton turbine comes out of a ditch, which decreases the efficiency of mass transfer; there is less air to mix into the tannin-based foaming precursor resin, which causes unevenness. At the same time, the eggbeater shows the marvelous capability of hybrid as it has two vortexes and multiple cycles that make a difference from the Rushton turbine, which has only one vortex and two upper and lower loops; the structure makes the flow field more stable allowed evenness of flow field tannin-based foaming precursor resin. The results reveal that it is beneficial for tannin-based foaming precursor resin to use an eggbeater with a speed of 1500 rpm to reduce the consumption of resources while obtaining a uniform flow field. [ABSTRACT FROM AUTHOR]

Al-Si alloys are vital in the aerospace and automotive industries due to their high strength-to-weight ratio, excellent ductility, and superior corrosion resistance. These properties, along with good thermal conductivity, low thermal expansion, and enhanced wear resistance due to silicon, make them ideal for lightweight, high-performance components like engine parts exposed to harsh conditions and thermal cycling. In recent years, the development of aluminium metal matrix composites using Al-Si alloys as the base material has gathered significant attention. These composites are engineered by integrating various reinforcing particles into the aluminium matrix, which results in remarkable improvements in the wear resistance, hardness, and overall mechanical performance of the material. The stir casting process, a well-established and cost-effective method, is frequently employed to ensure a uniform distribution of these reinforcing particles within the matrix. This review delves into the influence of different types of reinforcing particles on the properties of Al-Si alloy-based AMCs. The incorporation of these reinforcements has been shown to significantly enhance wear resistance, reduce friction, and improve the overall strength and toughness of the composites, making them ideal candidates for high-performance applications in the automotive and aerospace sectors. Moreover, this review highlights the challenges associated with the fabrication of these composites, such as achieving a homogeneous particle distribution and minimizing porosity. It also discusses the latest advancements in processing techniques aimed at overcoming these challenges. Additionally, this review addresses the potential environmental and economic benefits of using natural reinforcements, which not only reduce material costs but also contribute to sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market. [ABSTRACT FROM AUTHOR]

The role of information asymmetry in automobile insurance markets has been studied extensively. However, not all empirical studies found correlation between driver risk and chosen coverage. There are two explanations for this (Cohen and Siegelman 2010): (1) information asymmetry may not characterise environments in which consumers cannot act on private information; (2) most studies measure not risky driver behaviour, but ex post realisations of such behaviour, e.g. claims. In Taiwan's taxi insurance market, insurers cannot freely use rating factors. Usage density (e.g. daily distance), a direct measure of risk, therefore constitutes private information. Using ordinary least squares models, we find that usage density is positively correlated with total premiums. Using logistic models, we find high-density drivers more likely to buy additional policies. Thus, usage density is positively correlated with insurance demand, consistent with the risk-coverage hypothesis. Unlimited-mileage coverage may disproportionately attract high-risk drivers, an argument in favour of usage-based insurance. [ABSTRACT FROM AUTHOR]

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas. [ABSTRACT FROM AUTHOR]

The automobile industry is a major economic driver in Republic of Korea (Korea). However, little is known about its climate change contribution. This study estimates the direct and indirect greenhouse gases (GHG) emissions of the Korean automobile industry for the first time, by using a 2017 environmentally extended input-output (EEIO) model integrated by energy balance, GHG inventory and input-output table of 2017. The results show that the final demand of Korean automobile industry led to 8.4% of national GHG emissions in 2017, mostly because of indirect emissions embodied in the supply chain. The study also found that the Scope 1, Scope 2 and Scope 3 emissions on average accounted for 3.0%, 2.9%, and 94.7%, respectively. This highlights the contribution of the upstream supply chain such as primary metals (40.9%) and electricity (32.5%) when assessing the GHG emissions. Finally, the study underscores that carbon taxes could have a significant impact on the competitiveness of automobile export. Overall, this study provides valuable insights on countermeasures by identifying the GHG emissions characteristics of the automobile industry. The results of the study could be used to develop policies and strategies to reduce the GHG emissions and promote sustainable practices in the Korean automobile industry. [ABSTRACT FROM AUTHOR]

By simulating the thermomagnetization of Ba(Fe1-xScx)12O19 from approximately 100 to 750 K, the magnetocaloric effect (MCE) in Ba(Fe1-xScx)12O19 samples is approximated. Intriguingly, the thermomagnetic property of the undoped sample BaFe12O19 is described as a conventional MCE for high temperatures. However, at doping levels x of 0.128, 0.153, and 0.189, respectively, the thermomagnetic property of Sc-doped samples is defined as an inverse MCE below temperatures of 163, 239, and 318 K. In contrast, thermomagnetic property of Ba(Fe1-xScx)12O19 is described as conventional MCE above those temperatures. Additionally, Sc doping makes it possible for MCE in Ba(Fe1-xScx)12O19 to be strongly tailored. This is helpful for operating a magnetic refrigerator (MR) which occurs in various temperature ranges and results in a higher working temperature range. A promising function for Ba(Fe1-xScx)12O19 samples in MR at high-temperature applications in the automotive and food industries or aerospace. [ABSTRACT FROM AUTHOR]

The massive forming industry in Germany produces around 1.4 million parts every year, which are mainly used in safety-relevant areas such as the automotive industry. The production of these parts requires a considerable amount of energy, much of which remains unused and causes high CO2 emissions. An efficient approach to reduce these emissions and improve material utilization is cross-wedge rolling, which enables efficient material utilization but is limited by the so-called Mannesmann effect, which leads to unwanted material defects. This paper describes the development and validation of a safe design criterion for cross-wedge rolling tools in order to avoid material damage caused by the Mannesmann effect and thus increase resource efficiency in forging. Based on simulation-supported investigations and experimental tests, process maps are created for various materials. The validation is carried out both in an experimental test facility with real tools and in an industrial production facility, which leads to a significant reduction in excess material and CO2 emissions. The results show that the full resource potential of cross-wedge rolling can be exploited by optimizing process parameters and tool geometries. [ABSTRACT FROM AUTHOR]

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10 × compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a standard deviation of 0.025 mm and an accuracy of 0.026 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras. Video Link: https://www.dropbox.com/s/pateqqwh4d605t3/final_video_new.mp4?dl=0. [ABSTRACT FROM AUTHOR]

In the practical evolution of shared vehicle systems, numerous societal factors come into play, such as inadequate parking infrastructure, battery capacity, and the budget for technological improvement. This study integrates the operational realities of shared vehicle systems with governmental carbon emission reduction policies and "double control" initiatives. Considering various perturbations, the paper constructs a Stackelberg short‐term model among diverse oligopolistic shared vehicle operators with technique sharing. It also develops a long‐term repeated game framework to scrutinize the impact of pertinent parameters on operators' decision variables and profits, along with system stability and complexity. The findings, elucidated through numerical analyses, reveal that the burgeoning sensitivity of users to energy conservation augments the overall shared vehicle market. The technique sharing mechanism boosts the benefit of the whole group of shared vehicle operators while diminishing the start‐up operators' revenue. In the long term, however, excessive price adjustments by SEV operators may lead to market chaos and profit erosion. Conversely, enhancing the adjustment coefficient of technological level levels by start‐up SEV2 operators may result solely in their own decision‐making becoming unmanageable. Finally, we found that both the methods of external force feedback control and parameter control are effective in restoring the chaotic market to stability. [ABSTRACT FROM AUTHOR]

This study determined the relationship between corporate social responsibility (CSR) activities and the financial performance of automotive companies in Tuguegarao City, Cagayan, using data from 2020-2022. Descriptive statistics, financial metrics analysis, ANOVA, and Chi-Square Pearson tests were employed. Results showed that while environmental and ethical CSR initiatives were emphasized, economic community development was less prioritized. No significant differences were found in CSR activities based on firm profiles, and there was no significant link between CSR activities and financial performance. The study recommends a more balanced CSR approach, with greater emphasis on local economic development and partnerships with community and educational institutions. Due to limitations in sample size and data variety, future research should involve a broader participant base. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort. [ABSTRACT FROM AUTHOR]

Firms that sell vertically differentiated products infrequently roll out multiple products at the same time. In fact, it is often a firm already selling a well‐established product that decides to expand up‐ or downwards when such an opportunity arises. A critical decision in this scenario is whether to introduce the new product under an existing brand. In this paper, we develop a game‐theoretic model in which firms expand their product line to cater to a different customer segment, choosing their branding strategy, new product quality, and prices. We find that the firm's optimal branding strategy depends on both the vertical direction of the expansion and the level of competition, and identify a novel interaction effect between these factors. In particular, firms engaged in direct competition employ branding as a commitment device to soften quality competition. When these firms extend their product line upwards, this creates a misalignment between firms' actions and consumer preferences. We also derive conditions under which firms, against conventional wisdom, choose to differentiate their products more when selling them under the same brand. Finally, we characterize the welfare effects of branding in this setting, and argue that our findings are consistent with observations from the car industry. [ABSTRACT FROM AUTHOR]

The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion batteries under compression load, using Finite Element Method (FEM) techniques to improve computational efficiency. A novel approach is proposed, involving the selective application of compression loads solely to the Jelly Roll and its casing, achieved by cutting the battery heads. Through this method, distinct mechanical behaviors are identified within the battery force displacement curve: an elastic region, a zone characterized by plastic deformation, and a segment exhibiting densification. By delineating these regions, our study facilitates a comprehensive understanding of the battery's mechanical response under compression. Two battery models were employed in this study: one representing the battery as a solid volume, and another featuring the jelly roll as a solid volume enclosed by a shell representing the casing. The material utilized was LS Dyna MAT24, chosen for its piecewise characteristics' definition, and its validation was primarily conducted through the curve fitting method applied to the force–displacement curve, taking in account the three regions of the compression force behavior. This approach not only optimizes computational resources but also offers insights crucial for enhancing the mechanical stability of Li-ion batteries in automotive applications. [ABSTRACT FROM AUTHOR]

Purpose: This study explored how COVID-19 moderated the relationship between organizational learning capabilities (OLCs), technological innovation (TI), supply chain management (SMC) processes and enterprise performance (EP). It aimed to give ideas on how organizations could change and do well during big disruptions. Design/methodology/approach: Design: A structured questionnaire served as the data collection tool, employing a stratified sampling technique. Partial least squares (PLS) was utilized for data processing. Information was gathered from the automobile industry in Xian, China, providing an in-depth understanding of how COVID-19 moderated the variables under examination. Findings: The study discovered that COVID-19 changed how organizational learning, TI, SCM and EP interacted. Some organizations had trouble keeping up with learning and innovation, but others used them to make their SCM stronger, leading to better performance. Also, different effects of COVID-19 were seen in various industries and organizations. Practical implications: This study provided practical implications for managers, policymakers and practitioners. It emphasized fostering OLCs and TI as crucial for resilience during disruptions like COVID-19. Strategic investments in SCM were highlighted to mitigate disruptions and seize opportunities. Additionally, context-specific approaches were underscored for navigating pandemic-induced challenges. Originality/value: This study enhanced existing literature by analyzing how COVID-19 moderated the link between organizational learning, TI, SCM and EP. Through diverse methodologies and organizational contexts, it offered fresh insights into dynamic organizational responses to disruptions, advancing both theoretical understanding and practical knowledge in the field. [ABSTRACT FROM AUTHOR]

To promote the improvement and development of China's automotive industry policy, this study conducts a systematic review and hierarchical analysis of both domestic and international automotive industry policies. First, the automotive industry policies of the United States, Germany, South Korea, and Japan are summarized in terms of their industrial structure adjustment, technological innovation, taxes and financial subsidies, and infrastructure construction. Benchmarking the policies of international automotive industry powers provides a reference and basis for integrating their experiences in the formulation of China's automotive industry policy. Second, beginning with China's "Automotive Industry Policy" issued in 1994, the key automotive industry policies at different points in history and under different strategies are analyzed. Through policy adjustment and target setting, the changing focus through the historical development of China's automotive industry is determined, along with the development of the industry and technological progress. Then, from the perspectives of strategic planning, promotion and application, credit management, infrastructure, management standards, and standard systems, the advantages and disadvantages of current development policies for new energy and intelligent connected vehicles are clarified. The challenges and limitations of China's automotive industry policy are summarized, including a lack of investment in technology research and development, low level of infrastructure support, and insufficient consumer protection. China's automotive industry policy is then into three different historical stages: i) 1994 to 2008, ii) 2009 to 2013, and iii) 2014 to present; and the characteristics of each stage are summarized. Finally, based on China's national conditions and considering the future development trends of the international automotive industry, the study concludes that under the strong drivers of carbon peak and neutrality goals, China's future automotive industry policy should feature energy saving and emission reduction as the main goals. Independent innovation should be the main enabler, strengthening and supplementing the supply chain through intelligent connectivity. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper initially explores the current state of hypersonic vehicle development and deployment, as well as the propulsion technologies involved. At next, two additional test cases, used for the evaluation of DSMC code SPARTA are analyzed: a Mach 12.4 flow over a flared cylinder and a Mach 15.6 flow over a 25/55-degree biconic. These (2D-axisymmetric) test cases have been selected as they are tailored for the assessment of flow and heat transfer characteristics of present and future hypersonic vehicles, for both their external and internal aerodynamics. These test cases exhibit (in a larger range compared to the test cases presented in Part A of this work) shock–boundary and shock–shock interactions, which can provide a fair assessment of the SPARTA DSMC solver accuracy, in flow conditions which characterize hypersonic flight and can adequately test its ability to qualitatively and quantitatively capture the complicated physics behind such demanding flows. This validation campaign of SPARTA provided valuable experience for the correct tuning of the various parameters of the solver, especially for the use of adequate computational grids, thus enabling its subsequent application to more complicated three-dimensional test cases of hypersonic vehicles. [ABSTRACT FROM AUTHOR]

Flax reinforced composites are becoming popular in automotive and civil industries due to their green production and recycling, and for good specific strength. To manufacture composites, firstly a multi-layer of flax preforms undergo compressive pressure before resin impregnation that causes nesting, wherein, fibres of one layer fit into the adjacent layers. This debulking of the preforms under compression is an important feature that determines the fibre volume fraction of composites. In this study, four flax structures such as: nonwoven tapes, unidirectional fabric, hopsack fabric, and nonwoven tape with glass veils were investigated for compaction behaviour under pressures between 1 and 10 bars, in single and multi-layer states, in dry and wet states, under different loading cycles, and in different ply orientations (0°/0° and 0°/90°). Nesting has been calculated for single- and multi-layer stacks. It was observed that the nonwoven structures shown greater thickness reduction compared to woven structures. Nesting factor was found to be higher than 1 for the nonwoven structures under compaction, indicating lower nesting, compared to the woven structures. In terms of thickness under repeated compaction, the reduction was the highest during first compressions, compared to the 2nd and 3rd compressions, for all the structures. When wettability was examined, thickness reduction for wet plies was higher for all the structures, compared to the dry phase. Finally, a comparative study was shown to evaluate fibre volume fractions of the composites. [ABSTRACT FROM AUTHOR]

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors. [ABSTRACT FROM AUTHOR]

Defects such as bubbles and rubber shortages occasionally occur during vehicle tire production. Tiny defects in the crown gradually expand under load, ultimately causing tire failure. However, machine-vision-based crown defect detection faces challenges like limited datasets, varying groove patterns, low colour contrast, and small defect sizes. Therefore, this article integrates principal component analysis (PCA) and deep learning techniques for the visual detection of tire crown defects. A dataset of tire crown defects suitable for deep learning was constructed using an improved-PCA method and composite sampling. We propose the ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model, which includes an improved convolutional block attention module (CBAM) in a shallow stacked structure. And we propose an efficient and accurate method called Improved Selective Search (ISS) to detect and locate tire crown defective regions. The ISS pre-selected box generation method, enhanced with a priori knowledge filtering module, was used alongside a classification model to achieve online inference detection of large-size tire crown images. The experimental results show that the performance indices of the proposed ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model significantly improve compared to the original ConvNeXt-T. The defect detection and localisation method based on ISS meets the online inspection requirements of tire production. [ABSTRACT FROM AUTHOR]

In China, new-energy vehicles are viewed as the ultimate goal for the automobile industry, given the current focus on the "dual-carbon" target. Therefore, it is important to promote the sustainable development of this new-energy market and ensure a smooth transition from fuel-driven vehicles to new-energy vehicles. This study constructs a tripartite evolutionary game model involving vehicle enterprises, consumers, and the government. It improves the tripartite evolutionary game through the mechanisms of dynamic and static rewards and punishments, respectively, using real-world data. The results show the following. (1) A fluctuation is present in the sales of new-energy vehicles by enterprises and the active promotional behavior of the government. This fluctuation leads to instability, and the behavior is difficult to accurately predict, which is not conducive new-energy vehicles' promotion and sales. (2) A static reward and punishment mechanism can change the fluctuation threshold or peak value. Nevertheless, the stability of the system's strategy is not the main reason that the government has been actively promoting it for a long time. However, enterprises are still wavering between new-energy and fuel vehicles. (3) The linear dynamic reward and punishment mechanism also has its defects. Although they are considered the stability control strategy of the system, they are still not conducive to stability. (4) The nonlinear dynamic reward and punishment mechanism can help the system to achieve the ideal stabilization strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights Carbon fiber reinforced polymer (CFRP) composites exhibit excellent characteristics such as light weight, high specific strength, specific stiffness, and chemical stability, making them customizable to meet the specific demands of various sectors such as the automotive, aerospace, defense, biomedical, and energy industries. However, the inert surface of carbon fibers (CFs) results in a poor interface compatibility with polymer matrices, leading to numerous interfacial defects and pores in prepared CFRP composites. These drawbacks significantly limit the application of CFRP composites in high‐end fields. The higher surface area and smaller size of nanomaterials provide multiple advantages for high‐performance CFRP composites that enhance the mechanical properties, impact resistance and interface adhesion between the fiber and the matrix. Hence, this review firstly summarizes the interfacial behavior and interface enhancement mechanisms for CFRP composites. Subsequently, we comprehensively review the recent advances in various nanomaterials‐modified CFRP composites, including carbon‐based nanoparticles, silicon‐based nanomaterials and metal nanomaterials, et al. Besides, we also present the applications of CFRP in emerging fields, such as military, aerospace, automotive, sports equipment, and medical, etc. Finally, we also prospected the challenges and future development trends of CFRP composites, aiming to provide new ideas and insights for future research on nanomaterial modifications and promote the development of high‐performance CFRP composites. The interface reinforced theory of CFRP is comprehensively summarized. Different types of nanoparticles that can be used for reinforcement in CFRP composites and nanomaterials modification methods are reviewed. Application of CFRP composites in various fields is presented. Challenges and future development directions of preparation of high‐performance CFRP composites are proposed. [ABSTRACT FROM AUTHOR]

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model's constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications. [ABSTRACT FROM AUTHOR]

The classical theory of folded developables suggests that curved crease folding can be applied to sheet metal of high strength to manufacture parts with complex geometries. However, its application has been limited to sheet metals with low strength and requires grooving or perforation of the material at the fold lines. Applying the geometrical concept otherwise remains unexplored despite its potential in sheet metal-intensive industries like automotive, transportation, and construction. The current study introduces a novel sheet metal forming technique called developable forming. The theory of folded developables is applied to design a die tool for flange drawing a developable-shaped steel sheet. A developable connection is produced from high-strength material without resorting to thickness accommodation techniques. Experimental findings establish that the novel process does not follow the premises of the theory of folded developables. New shape effects are observed and the terms curving and coning are introduced to describe these. Additionally, significant redundant strains are identified in the flange, highlighting the different deformation modes in the developable forming process. A higher formability is achieved for a material with limited uniaxial elongation highlighting the need for further investigation on the mechanisms involved in the novel process. [ABSTRACT FROM AUTHOR]

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry. • Formulated a Markov forecasting model for autonomous vehicle market penetration. • Leveraged empirical vehicle data for robust model validation and calibration. • Explored strategic impacts for the automotive market's intelligence transition. [ABSTRACT FROM AUTHOR]

Due to their excellent properties and low manufacturing costs, SiC particle-reinforced Al matrix composites (SiCp/Al) find extensive application in automobile manufacturing and aerospace. However, SiCp/Al is less machinable. The ultrasonic-assisted drilling (UAD) decreases tool wear and enhances the quality of drilled holes when compared to conventional machining techniques. This paper investigates the effect of drilling temperature variations induced by processing conditions on the degree of softening of the base material during UAD of SiCp/Al. It also analyses the mechanism of how drilling temperature affects the quality of the holes made from the perspective of matrix softening. The study demonstrates a regularity in the impact of spindle speed, ultrasonic supply voltage(ultrasonic vibration amplitude), and feed rate as a function of drilling temperature. The effects of temperature changes caused by each of these factors on import/export edge defects, surface scratches on the hole wall, and tool machinability vary. [ABSTRACT FROM AUTHOR]

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method. [ABSTRACT FROM AUTHOR]

This study aimed to strengthen the security of autonomous vehicles by analyzing the current status of autonomous vehicle security, such as autonomous vehicle features, security threats, and compliance, and deriving security-level check items. Based on this, the relative importance could be obtained by applying it to the AHP (Analytic Hierarchy Process) model. The results of the empirical analysis showed that the order of priority was the establishment/implementation of a cybersecurity management system, encryption, and risk assessment. The significance of this study is that by deriving security-level check items related to autonomous vehicles and verifying the research model, we can reduce cyber security accidents that can cause loss of life and improve the level of autonomous vehicle security management of related companies. Additionally, by applying AHP evaluated by security experts to the autonomous vehicle field for the first time, it will contribute to the market expansion of the autonomous vehicle industry, which is concerned with security. Furthermore, major automobile companies have to manage the security levels of numerous tier companies due to the nature of the industry. Therefore, if they perform a Quick Check (QC) considering the relative importance of the autonomous vehicle security-level check items presented in this paper, they will be able to effectively identify the security levels of tier companies early. [ABSTRACT FROM AUTHOR]