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Objectives: With the increasing application of 5G remote ultrasound robots in healthcare, robust methods are in critical demand to assess participant satisfaction and identify its influencing factors. At present, there is limited empirical research on multi-parametric and multidimensional satisfaction evaluation of participants with 5G remote ultrasound robot examination. Previous studies have demonstrated that structural equation modeling (SEM) effectively integrates various statistical techniques to examine the relationships among multiple variables. Therefore, this study aimed to evaluate the satisfaction of participants with 5G remote ultrasound robot examination and its influencing factors using SEM. Methods: Between April and June 2022, 213 participants from Wuhan Automobile Manufacturing Company underwent remote ultrasound examinations using the MGIUS-R3 remote ultrasound robot system. After these examinations, the participants evaluated the performance of the 5G remote ultrasound robot based on their personal experiences and emotional responses. They completed a satisfaction survey using a self-developed questionnaire, which included 19 items across five dimensions: examination efficiency, examination perception, communication perception, value perception, and examination willingness. A SEM was established to assess the satisfaction of participants with the 5G remote ultrasound robot examinations and the influencing factors. Results: A total of 201 valid questionnaires were collected. The overall satisfaction of participants with the 5G remote ultrasound robot examination was 45.43 ± 11.60, with 169 participants (84%) expressing satisfaction. In the path hypothesis relationship test, the dimensions of examination efficiency, examination perception, communication perception, and value perception had positive effects on satisfaction, with standardized path coefficients of 0.168, 0.170, 0.175, and 0.191. Satisfaction had a direct positive effect on examination willingness, with a standardized path coefficient of 0.260. Significant differences were observed across different educational levels in the dimensions of examination perception, communication perception, value perception, and examination willingness. Participants with different body mass indices also showed significant differences in examination perception; all p-values were less than 0.05. Conclusion: In this study, value perception was identified as the most significant factor influencing satisfaction. It could be improved by enhancing participants' understanding of the accuracy and safety of 5G remote ultrasound robot examinations. This enhances satisfaction and the willingness to undergo examinations. Such improvements not only facilitate the widespread adoption of this technology but also promote the development of telemedicine services. [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]

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]

Forging dies, which are crucial for manufacturing automobile tripod housings, are frequently replaced owing to their extensive use under challenging conditions, leading to the disposal of many components and increasing manufacturing costs. The development of repair and remanufacturing technologies for high-hardness forging dies is a challenging. This study explores the on-site repair of tripod forging dies using wire arc directed energy deposition (arc-DED), assesses the hardness and tensile properties of remanufactured components, and compares them with the specified forging die requirements for on-site usability. A validated novel slicer is employed in remanufacturing experiments on a 9.8 % Cr die steel tripod-forging die. Results show the effectiveness of arc-DED in remanufacturing, surpassing the specified requirements by at least 111 HV and 61 MPa in hardness and tensile strength, respectively. Thus, this study presents a promising approach to reduce the manufacturing costs and environmental impact associated with the forging-die-component disposal. [ABSTRACT FROM AUTHOR]

Several large multinationals and a few local assemblers dominate the Indian automobile industry. The largest multinational, Maruti Suzuki, entered the Indian automobile industry with a policy of developing local automotive parts manufacturers to achieve high local content ratio. This study seeks to determine the extent to which foreign direct investment (FDI) in host countries contributes to the growth of automobile parts manufacturers. We use district‐level panel data from 2000 to 2008 to assess the linkage between the production of major automobile assemblers and the development of automotive parts suppliers located in the same and adjacent districts to those assemblers. Our regression results show the heterogeneity of the linkages, suggesting that the formation of industrial clusters by local parts suppliers and the expansion of their production capacity are likely to be key determinants of the success of FDI in the Indian automobile industry, rather than relying on in‐house parts production or imports. [ABSTRACT FROM AUTHOR]

As industrial Internet of things (IIoT) for Manufacturing Service Collaboration (MSC) is becoming the current trend to accelerate the upgrade iteration of manufacturing capability, developing the robust IIoT platform operation mechanism for MSC is crucial to promote the continuous and stable service collaboration in the presence of supply and demand uncertainties. This paper studies the operational robustness of the IIoT platform for MSC. Firstly, the operation performances, requirements, and challenges of the IIoT platform towards manufacturing collaboration are analysed in classified platform practices, which can provide a comprehensive cognition about platform operation for manufacturing collaboration. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The IIoT platform operation network for MSC is modelled as an interdependent network-of-network structure based on graph theory, which helps to characterise MSC performance properties under complexities. By combining manufacturing properties with network statistics, the evaluation metrics of operational robustness are established, which is done to quantise the MSC effectiveness under uncertainty effects. A case about customised manufacturing of automobiles illustrates the application of the proposed methods. Finally, future studies about robust MSC regulation are discussed. [ABSTRACT FROM AUTHOR]

Stamping is the earliest and most important process in automobile manufacturing, significantly impacting the overall surface quality of automobiles. Cracking is the most severe quality issue in stamping, but such defects still rely on manual detection, which has low detection efficiency and a high error rate. Furthermore, the difficulty in collecting defect data for stamped parts due to mature production processes leads to challenges in performing fully-supervised defect detection. However, unsupervised detection has limited accuracy and cannot achieve ideal results. In order to address challenges in industrial production lines, this paper proposes an adaptive incremental two-stage framework. In Stage I, to address the issue of insufficient annotated data during the initial stage of project deployment, only normal samples are used for unsupervised anomaly detection to accelerate project landing. In Stage II, to address the limitations of unsupervised detection and fully utilize the defect data collected during project iteration, the framework can fully use the initially annotated seed regions generated in Stage I for weakly supervised semantic segmentation, thereby achieving higher detection accuracy. In practical production applications, the framework adopts an accumulation triggering mechanism, which can expand the training samples through detection feedback of each stage model and continuously improve the defect detection effect using an adaptive training mechanism. To validate the effectiveness of the proposed framework, we conducted extensive experiments on the stamped parts dataset collected from actual automobile production and the public dataset KolektorSDD. Our results surpassed those of state-of-the-art defect detection and localization methods. [ABSTRACT FROM AUTHOR]

Highly integrated information sharing among people, vehicles, roads, and cloud systems, along with the rapid development of autonomous driving technologies, has spurred the evolution of automobiles from simple "transportation tools" to interconnected "intelligent systems". The intelligent cockpit is a comprehensive application space for various new technologies in intelligent vehicles, encompassing the domains of driving control, riding comfort, and infotainment. It provides drivers and passengers with safety, comfort, and pleasant driving experiences, serving as the gateway for traditional automobile manufacturing to upgrade towards an intelligent automotive industry ecosystem. This is the optimal convergence point for the intelligence, connectivity, electrification, and sharing of automobiles. Currently, the form, functions, and interaction methods of the intelligent cockpit are gradually changing, transitioning from the traditional "human adapts to the vehicle" viewpoint to the "vehicle adapts to human", and evolving towards a future of natural interactive services where "humans and vehicles mutually adapt". This article reviews the definitions, intelligence levels, functional domains, and technical frameworks of intelligent automotive cockpits. Additionally, combining the core mechanisms of human–machine interactions in intelligent cockpits, this article proposes an intelligent-cockpit human–machine interaction process and summarizes the current state of key technologies in intelligent-cockpit human–machine interactions. Lastly, this article analyzes the current challenges faced in the field of intelligent cockpits and forecasts future trends in intelligent cockpit technologies. [ABSTRACT FROM AUTHOR]

Researchers have studied Stewart-Gough platforms, also known as Gough-Stewart platforms or hexapod platforms extensively for their inherent fine control characteristics. Their studies led to the potential deployment opportunities of Stewart-Gough platforms in many critical applications such as medical field, engineering machines, space research, electronic chip manufacturing, automobile manufacturing, etc. Some of these applications need micro- and nano-level movement control in 3D space for the motions to be precise, complicated, and repeatable; a Stewart-Gough platform fulfills these challenges smartly. For this, the platform must be more accurate than the specified application accuracy level and thus proper calibration for a parallel robot is crucial. Forward kinematics-based calibration for these hexapod machines becomes unnecessarily complex and inverse kinematics complete this task with much ease. To experiment with different calibration techniques, various calibration approaches were implemented by using external instruments, constraining one or more motions of the system, and using extra sensors for auto or self-calibration. This survey paid attention to those key methodologies, their outcome, and important details related to inverse kinematic-based parallel robot calibrations. It was observed during this study that the researchers focused on improving the accuracy of the platform position and orientation considering the errors contributed by one source or multiple sources. The error sources considered are mainly kinematic and structural, in some cases, environmental factors also are reviewed; however, those calibrations are done under no-load conditions. This study aims to review the present state of the art in this field and highlight the processes and errors considered for the calibration of Stewart-Gough platforms. [ABSTRACT FROM AUTHOR]

Intelligent interconnection and big data will be the core content of the future competition, and a unified digital platform construction of the automobile manufacturing industry will inevitably become an important support for the future development from large to strong. Using literature research and expert consultation, 14 influencing factors of digital platform construction in the automobile manufacturing industry were sorted out. this study uses ISM (Interpretative structural modelling) model to stratify the influencing factors of digital platform construction of the automobile manufacturing industry, draw a multi-layer hierarchical structure diagram of influencing factors, and uses the MICMAC (Matrix impacts cross-reference multiplication applied to a classification) method to analyze the dependence and driving force of the main influencing factors. The results show that 14 factors are more scientific and reasonable as influencing factors of digital platform construction in the automobile manufacturing industry. A1, A3, B1, B3, C3, D2, D3 are the top-level influencing factors. C1 and C2 are the bottom influencing factors, highlighting that technical factors are still the fundamental factors affecting the digital platform of the automobile manufacturing industry. C1, D1 and C2 are autonomous factors with a high driving force and play an important role in promoting digital platform construction in the automobile manufacturing industry. The research results have important reference value for accelerating the digital transformation of the automobile manufacturing industry, enhancing the core competitiveness of automobile industry enterprises, and improving the monitoring degree of operation status of the automobile industry market. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest 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.)

The study of generic knowledge extraction methods in industrial manufacturing is of great significance for real- izing the automated construction of industrial knowledge bases. Aiming at the problem that industrial ontology definition requires a large amount of labour cost and expert experience for guidance, a semi-automatic ontology construction method is designed based on the sememe analysis, and finally the definition of ontology, ontology class, cascade and relationship is completed, using automobile manufacturing fault repair data as an example. Subsequently, in order to solve the problems of relationship nesting and cascade model error propagation that exist in relationship extraction in the industrial domain, a semantic- aware relationship extraction model is designed. In the three links of potential relationship mining, subject extraction and object extraction in this model, different leading interrogatives are spliced based on reading comprehension methods to obtain sentence codes applicable to different links.In order to make use of the a priori knowledge of the subject, the attention mechanism is incorporated into the object extraction module to improve the expressiveness of the codes in this module. Three sessions of joint optimised training to improve extraction results. Experiments are conducted on the automotive manufacturing fault repair dataset, the automotive industry fault pattern extraction evaluation dataset, and the equipment manufacturing dataset, and the model in this paper achieves better results than other relational extraction baseline models. [ABSTRACT FROM AUTHOR]

Objective To analyze the current status and influencing factors of work -related musculoskeletal disorders (WMSDs) of the wrist/hand among frontline workers in an automobile manufacturing enterprise and to provide a scientific basis for the prevention of wrist/hand WMSDs and the adoption of health intervention measures to alleviate the disorders. Methods Using convenience sampling, frontline workers from the general assembly and welding workshops of an automobile manufacturing enterprise in Beijing were surveyed from August to October 2023. The prevalence of wrist/hand WMSDs in the past year was investigated by the Chinese version of the Musculoskeletal Disorders Questionnaire. The logistic regression analysis was employed to analyze the influencing factors of the occurrence of WMSDs of the wrist/hand. Results A total of 648 questionnaires were distributed in this study, and 591 valid questionnaires were returned, with a valid response rate of 91.2%. The overall prevalence of wrist/hand WMSDs among these workers was 8.1% (48/591). Multifactorial logistic regression analysis showed that the risk of WMSDs of the wrist/hand was 5.194 times higher (OR = 5.194, 95% CI: 2.239 -12.050) for workers who experienced bending, stretching, side bending, or rotation of the wrist during work, and 2.373 times higher (OR = 2.373, 95%CI: 1.266-4.448) for those for whom the tools, handheld devices, or workpieces were too large or too small to be easily held. Conclusions The automobile manufacturing enterprise should take measures to improve the adverse wrist postures of frontline workers, as well as to improve tools or handheld devices to facilitate grasping, in order to prevent the occurrence of wrist/hand WMSDs. [ABSTRACT FROM AUTHOR]

Taking 312 county units in the Yangtze River Delta region as the research objects, this paper expounds the spatial cluster characteristics and change rules of the three leading manufacturing industries with the comprehensive application of spatial cluster index, and analyzes the influencing factors by using the spatial panel data model. The main conclusions are as follows: First of all, the spatial cluster characteristics of the leading manufacturing showing a significant spatial imbalance in the Yangtze River Delta region. The cluster center is mainly located in the region composed of southern region of Jiangsu Province, Shanghai and northwestern region of Zhejiang Province, and this distribution pattern is relatively stable. The north region of Jiangsu Province and southwest region of Zhejiang Province are mainly in the sub-marginal position, and the leading industries cluster are less. Anhui Province is mainly in the marginal position, and most of the county units in Anhui Province do not have cluster advantages. The electronic information manufacturing shows the most obvious characteristics of core-periphery distribution. Although there are differences between textile industry and automobile manufacturing, it can be proved this characteristic. Secondly, the advantages of land cost and labor cost will attract leading industries to agglomeration. The expansion of local market scale has a negative effect on the spatial cluster of electronic information manufacturing. The construction of development zones above the provincial level will have different effects on the spatial cluster of textile industry and electronic information manufacturing. Thirdly, the development of urbanization and the growth of city size will promote the spatial cluster of automobile manufacturing and electronic information manufacturing, which is not conducive to the spatial cluster of textile industry. Fourthly, the globalization factors such as foreign capital entry and export increase will promote the improvement of spatial cluster of leading industries in the Yangtze River Delta region. Finally, we put forward specific countermeasures and suggestions from the aspects of promoting specialized industrial agglomeration and intra-regional industrial division of labor. [ABSTRACT FROM AUTHOR]

In recent industrial settings, time-of-flight (ToF) cameras have become essential tools in various applications. These cameras provide high-performance 3D measurements without relying on ambient lighting; however, their performance can degrade due to environmental factors such as temperature, humidity, and distance to the target. This study proposes a novel method to enhance the pixel-level sensing accuracy of ToF cameras by obtaining precise depth data labels in real-world environments. By synchronizing 3D simulations with the actual ToF sensor viewpoints, accurate depth values were acquired and utilized to train AI algorithms, thereby improving ToF depth accuracy. This method was validated in industrial environments such as automobile manufacturing, where the introduction of 3D vision systems improved inspection accuracy compared to traditional 2D systems. Additionally, it was confirmed that ToF depth data can be used to correct positional errors in mobile robot manipulators. Experimental results showed that AI-based preprocessing effectively reduced noise and increased the precision of depth data compared to conventional methods. Consequently, ToF camera performance was enhanced, expanding their potential applications in industrial robotics and automated quality inspection. Future research will focus on developing real-time synchronization technology between ToF sensor data and simulation environments, as well as expanding the AI training dataset to achieve even higher accuracy. [ABSTRACT FROM AUTHOR]

The automobile industry is recognized as one of the most influential sectors shaping global economies, societies, and individual lifestyles. Therefore, fierce competition among different companies is continuously undergoing, and special attention is focused on innovations to improve competitiveness. In the past several years, additive manufacturing (AM) has emerged as an innovative technology in applications in the automobile industry with significant advantages over traditional techniques. As a result, increasing efforts have been paid to combining AM technology with the development of the automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating innovative AM techniques, and meanwhile, a lot of research progress has been achieved in order to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in the automobile industry, focusing on the available AM techniques, printable materials, and industry applications, based on which the advantages and disadvantages of each technique and material system are discussed in order to reveal the current application situation. The current research gaps and challenges are also outlined to indicate future research opportunities. Hopefully, this work can be useful to related researchers as well as game players in the industry of this field. [ABSTRACT FROM AUTHOR]

The AA5052 aluminum alloy is widely used in automobile and aerospace manufacturing, and with the development of light-weight alloys, it is required that these materials exhibit better mechanical properties. Previous studies have demonstrated that the addition of Sc to aluminum alloys can improve both the microstructure and properties of the alloys. In this study, the effect of Sc on the Fe-rich phase and properties of the AA5052 aluminum alloy was studied by adding 0%, 0.05%, 0.2%, and 0.3% Sc. The results show that with the increase of Sc, the coarse needle-like Fe-rich phase gradually transforms into Chinese-script and then nearly spherical particles, reduce the size of Fe-rich phase, and refine the grain with increase of high angle grain boundaries (HAGBs). These microstructure changes enhance the strength of the AA5052 alloy through Sc addition. The ductility of the alloy is obviously improved because the addition of a lower amount of Sc changes the morphology of Fe-rich phase from needle-like into a Chinese-script, and it is subsequently reduced as a result of significant increase in HAGBs with increasing Sc content. [ABSTRACT FROM AUTHOR]

Copyright of Foundry Technology (1000-8365) is the property of Foundry Technology 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.)

Hot-forming technology for 22MnB5 hot-forming steel (22MnB5 HF steel) plates has been widely used in the automobile manufacturing industry in recent years. Physical simulation and numerical modeling were carried out in order to determine the forming limit of a 22MnB5 steel plate for the stamping process. The deformation experiments were performed in a temperature range of 600~900 °C and a strain rate range of 0.1~10 s−1. In the uniaxial tensile tests, it was found that at the forming temperature of 600 °C, the condition of dynamic recrystallization was not fully reached, and thus the corresponding tensile strength was much larger than that at other deformation temperatures. In the numerical simulation of bulging experiments, it was found that 22MnB5 steel had good formability when the initial deformation temperature was high and the forming speed was low by using the instability criterion, combining the maximum punch force and strain path transition. The forming limit diagram of 22MnB5 steel at a temperature of 700 °C and tool speed of 25 mm/s was obtained by means of simulation and a hot stamping experiment. The establishment of the forming limit of the 22MnB5 steel plate can provide theoretical and technical guidance for the hot-forming process. [ABSTRACT FROM AUTHOR]

The automobile driving axles contain many types of transmission gears, as well as complex dimensional constraints among them. This makes it a cumbersome work for design of the whole vehicle. To improve design efficiency and reduce consumption of expert labors, it is necessary to search digital design for manufacturing optimization of automobile driving axles. As a result, this paper proposes a modular mathematical modeling method to realize smart design and manufacturing of automobile driving axles. First, with use of modular segmentation, design process of automobile driving axles can be abstracted as mathematical expressions according to their basic functions. Through solution of the developed model objective, main performance indexes of automobile driving axles under various utilization conditions can be automatically output by computers. In addition, to further analyze and optimize noise-vibration-harshness performance of designed schemes, computers-aided engineering-based simulation technology is utilized to make modal analysis and vibration noise analysis toward designed automobile driving axles. Such comprehensive evaluation works can contribute to determination of better design schemes for manufacturing optimization. [ABSTRACT FROM AUTHOR]

Considering the benefits of using robots in the manufacturing and automobile sectors, robots are currently being developed for the construction industry; however, the rate of adopting robots in this industry is still in its nascent stage. As robots are introduced to project sites, safety, productivity, ease of use, level of robot autonomy, etc. become important parameters for wide-scale adoption; however, research that analyzes the point of view of construction industry stakeholders on adopting robots, to our best knowledge, is still limited. Identifying the expectations and perceptions of different stakeholders about robotic technology is crucial to ensuring the effective utilization the human–robot teams on project sites. This research aimed to study and compare stakeholders' perceptions, levels of trust, and comfort levels in using different robotic technologies on their project sites. Based on these perceptions, this research developed a decision-making framework that will guide industry stakeholders in understanding what steps need to be followed while deciding to robotize a particular task. The research used a questionnaire to collect relevant data from various construction industry stakeholders. The results suggest that these stakeholders have a positive attitude toward the adoption of robots, with drones and layout robots receiving the most favorable response. The top factors that drive robot adoption include a robot's ability to save time by reducing rework and the ability to help in monitoring and quality control. However, factors such as high costs and lack of a skilled workforce were identified as barriers to robot adoption. This research contributes to the body of knowledge concerning how different stakeholders in the construction industry perceive the utilization of robots in the construction sector. [ABSTRACT FROM AUTHOR]

Objective To investigate the occupational health literacy level among workers in two key industries in Qingpu District of Shanghai and to identify influencing factors in order to provide references for formulating relevant occupational health measures. Methods From June to September 2022, a multi -stage cluster random sampling method was used to select 965 workers from 18 enterprises in the automobile manufacturing and electrical machinery and equipment manufacturing industries in Qingpu District. The National Key Population Occupational Health Literacy Monitoring Survey Individual Questionnaire was distributed to collect data on basic information, occupational health literacy levels, and physical and mental health status. The factors affecting workers' occupational health literacy levels were analyzed. Results The overall occupational health literacy level among these surveyed workers was 66.2%, with the levels for the four dimensions as: basic knowledge of occupational health protection (87.8%), healthy work methods and behaviors (77.8%), knowledge of occupational health laws (73.4%), and basic skills of occupational health protection (42.8%). Among the four types of physical and mental health problems, the detection rates were 64.1% for depression, 34.0% for sleep disorders, 22.3% for anxiety, and 20.9% for occupational stress. Multivariate linear regression analysis showed that compared to workers with primary school education or below, those with junior high school education had a 6.0% higher score of occupational health literacy, and the occupational health literacy score rate of workers with high school, vocational high school, technical secondary school, college, bachelor degree, and above increased by about 8% (P < 0.05). Compared to workers in small enterprises, workers in medium-sized enterprises had a 2.9% lower score of occupational health literacy (P < 0.05). The detection rates of depression, sleep disorders, and occupational stress were negatively correlated with occupational health literacy level (r = -0.115, -0.093, -0.157, P < 0.01). Conclusions The overall level of occupational health literacy among workers in these two key industries was not high, the basic skills of occupational health protection were lacking, and the incidence of depression was relatively high. The targeted intervention measures should be developed for the corresponding enterprises and populations to improve their workers' occupational health literacy levels. [ABSTRACT FROM AUTHOR]

This study employs a one-group pretest and posttest design to assess the written and oral technical communication (TC) skills of students in vocational colleges in China. Fifty-nine 1st-year students in automotive engineering participated in a 3-week implementation of a TC module. To measure learner differences, students took oral and written tests before and after the intervention, respectively. Results showed that the module effectively improved students' written and oral TC skills. In addition, findings from interviews with participants and English teachers indicated that the TC module is suitable from a pedagogical perspective. [ABSTRACT FROM AUTHOR]

In response to the challenges associated with the misassembly, omission, and low manual inspection efficiency in automobile wiring harness relay assemblies, a novel online detection system has been engineered. This system consists of a mobile-based visual imaging system and an improved YOLOv5-based detection algorithm that tracks human movement to acquire images and videos. The system is coupled with deep learning for real-time detection and recognition for error-proofing the installation process of automotive wiring harness relays. This innovation aims to facilitate error-proof inspection during the assembly process of automotive wiring harness relays. The YOLOv5s model is augmented with an Adaptive Spatial Feature Fusion (ASFF) module, enhancing multi-scale feature integration capabilities. A Global Context Network (GCNet) is incorporated into the C3 module to emphasize target information from a global perspective. Additionally, the replacement of standard Convolution (Conv) modules with Global Sparse Convolution (GSConv) modules in the Neck section effectively reduces computational costs while sustaining overall performance efficacy. The experimental results show that the detection system achieved a comprehensive accuracy rate of 99.2% and an F1 score of 99.29. The system possesses high accuracy and stability, enabling flexible and intelligent target detection applications in the automotive industry. [ABSTRACT FROM AUTHOR]

Research on international business presents 'liability of foreignness' as a key factor in a Multinational Enterprise's (MNE's) operations, but it has not addressed 'foreignness' as a complex and dynamic phenomenon. Adopting an identity work perspective, this article examines 'foreignness' as social construct, studying how a Chinese MNE manoeuvres the local economic and political contexts. We also shift the focus from 'liability of foreignness' to 'liability of origin', as elements associated with the home country can also implicate liability. We discuss the market entry of a private Chinese manufacturing company in Hungary and the Netherlands as a proxy for Europeanization, analysing regional and local strategies pursued by the company in organizing its business and representing its corporate identity when dealing with divergent European contexts. [ABSTRACT FROM AUTHOR]

Drawing on social learning theory, this study presents a moderated mediation model to examine the role of proactive behavior and conformity value in the positive relationship between creative leadership and employees' creativity. Two-wave data are collected from 230 employees and their leaders in an automobile manufacturing enterprise in China, in which employees are encouraged to be creative because they need to implement novel designs and proposals to attract consumers. Statistical analysis reveals that proactive behavior partially mediates the influence of creative leadership on employees' creativity. Conformity value moderates not only the relationship between creative leadership and proactive behavior but also the indirect effect of creative leadership on employees' creativity via proactive behavior. The relationship and the mediating mechanism are stronger in the presence of employees' higher conformity value. We discuss the theoretical and practical implications as well as future research directions of the findings. [ABSTRACT FROM AUTHOR]

Broad use of lightweight aluminium alloy parts in automobile manufacturing, aerospace, electronic communication, and rail transit is mainly formed through deep drawing process. Deep drawing friction is a key boundary condition for controlling the forming quality of aluminium alloy parts. However, due to the oxidation and adhesion tendency of aluminium alloys, the tribological situations of aluminium alloy deep drawing (AADD) system is more complicated than those of traditional deep drawing of steel sheets. Therefore, the study of AADD friction is essential for manufacturing high-performance aluminium alloy parts. Herein, aiming to provide a valuable reference for researchers in related fields, a comprehensive review of AADD friction is provided, including friction mechanism, influencing factors, friction measurement, friction model, friction simulation, and lubrication-free friction control. Finally, a brief conclusion and several current challenges were discussed. [ABSTRACT FROM AUTHOR]

With smart sensors and embedded drivers, today's automotive industry has taken a giant leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet of things and started to build data-driven decision-making strategies to compete in global smart manufacturing. This paper proposes a novel design framework that uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control in a completely automated smart automobile manufacturing industry. The proposed design introduces a novel element called Trust Threshold Limit (TTL) that helps moderate the excess usage of embedded equipment, tools, energy, and cost functions, limiting wastages in the manufacturing processes. This research highlights the use cases of AI in decentralised Blockchain with smart contracts, the company's trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. The developed model supported by real-time cases incorporated cost functions, delivery time and energy evaluations. Results spotlight the use of FAI in decision accuracy for the developed smart contract-based Automobile Assembly Model (AAM), thereby qualitatively limiting the threshold level of cost, energy and other control functions in procurement assembly and manufacturing. Customisation and graphical user interface with cloud integration are some challenges of this model. [ABSTRACT FROM AUTHOR]

Objective To explore the correlation between metabolic syndrome (MS) and noise-induced hearing loss (NIHL) in noise-exposed workers. Methods Using the convenient sampling method, a total of 850 male noise-exposed workers in an automobile manufacturing enterprise were studied. The questionnaire survey and occupational health examination were combined to collect general demographic data, physical examination data, and other information. According to the results of high-frequency hearing loss, the noise-exposed workers were divided into the hearing loss group (326 workers) and the normal hearing group (524 workers). The single-factor analysis was used to compare the above data differences between the two groups. The binary multiple-factor logistic regression model was used to analyze the influencing factors of NIHL in noise-exposed workers. Results The non-compliance rate of noise intensity in the enterprise was 39.47% (15/38), with the highest occurrences in the welding and painting posts. The morbidity of hearing loss was 38.35% (326/850), and the MS morbidity was 27.53% (234/850). The frequency of obesity, high-level triglyceride, high-level fasting blood glucose, hypertension, MS,and the contact noise level in the hearing loss group were higher than those in the normal hearing group (P < 0.05). The results of multiple-factor logistic regression showed that, compared with the workers with a noise exposure duration ≤ 5 years, the risk of NIHL in the workers with a noise exposure duration of 11 - 15 years or > 15 years increased to 1.553 and 1.727 times, respectively (P < 0.001). Compared with the workers without MS, the risk of NIHL in the workers with MS increased to 1.863 times (P < 0.001). The risk of NIHL increases by 1.567 times for each 1 dB(A) increase in noise level (P < 0.001). Conclusions There was a correlation between MS and NIHL. In addition to emphasizing control of noise level in the workplace, attention should also be paid to adopting a low-sugar, low-salt, and low-sodium diet, reducing the intake of saturated fatty acids, and strengthening physical exercise to effectively control the blood pressure, blood sugar, and blood lipid levels of workers so as to reduce the risk of NIHL. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to objectively evaluate the diet consumed in a workplace cafeteria to group Japanese workers according to vegetables and salt intake and estimate the association of these groups with changes in cardiometabolic measurements. Design: This longitudinal observational study estimated the food and nutrient intake of Japanese workers from data recorded in the cafeteria system of their workplace. The primary outcomes included cardiometabolic measures obtained via regular health check-ups conducted at the workplace. The participants were divided into four groups according to high or low vegetables and salt intake based on their respective medians, and the association of each group with cardiometabolic measurement changes was estimated using robust regression with MM-estimation. Setting: A Japanese automobile manufacturing factory. Subjects: The study included 1140 men and women workers with available cafeteria and health check-up data. Results: An inverse marginal association was observed between changes in TAG levels (mmol/L) and high vegetables and low salt intake (β : –0·11, 95 % CI: –0·23, 0·01, P : 0·065) with reference to low vegetables and high salt intake. This association was stronger in participants who used the cafeteria more frequently (>=71 d; β : –0·15, 95 % CI: –0·29, –0·02, P : 0·027). Conclusions: The participants in the higher vegetables and lower salt intake group were more likely to exhibit decreased TAG levels. These findings encourage using workplace cafeteria meals to promote the health of workers. [ABSTRACT FROM AUTHOR]

Multi-scale urban innovation networks are important channels for intra- and inter-city knowledge spillovers and play an important role in urban industrial innovation and growth. However, there is a lack of direct evidence on the impact of multi-scale urban innovation networks on industrial development. Drawing upon the "buzz-and-pipeline" model, this paper analyzes the impact of multi-scale urban innovation networks on industrial development by taking the automobile manufacturing industry in China's five urban agglomerations as an example. Firstly, based on the Form of Correlation between International Patent Classification and Industrial Classification for National Economic Activities (2018) and co-patents, we construct urban innovation networks on three different geographical scales, including intra-city innovation networks, inter-city innovation networks within urban agglomerations, and innovation networks between cities within and beyond urban agglomerations. Then, we employ the ordinary least squares model with fixed effects at the urban agglomeration level to explore the impact of urban multi-scale knowledge linkages on the development of the automobile manufacturing industry and the results showed that urban innovation networks at three different geographical scales have different impacts on industrial development. Specifically, intra-city innovation networks have a facilitating effect on industrial development, while both inter-city innovation networks within urban agglomerations and innovation networks between cities within and beyond urban agglomerations have an inverted U-shaped impact on industrial development. The interactions between urban innovation networks on three different geographical scales have a negative effect on industrial development. Simultaneously, the agglomeration level of urban industry plays a positive moderating role in the impacts of multi-scale urban innovation networks on industrial development. [ABSTRACT FROM AUTHOR]

Three-dimensional printers (3DPs), as critical parts of additive manufacturing (AM), are state-of-the-art technologies that can help practitioners with digital transformation in production processes. Three-dimensional printer performance mostly depends on good integration with artificial intelligence (AI) to outperform humans in overcoming complex tasks using 3DPs equipped with AI technology, particularly in producing an object with no smooth surface and a standard geometric shape. Hence, 3DPs also provide an opportunity to improve engineering applications in manufacturing processes. As a result, AM can create more sustainable production systems, protect the environment, and reduce external costs arising from industries' production activities. Nonetheless, practitioners do not have sufficient willingness since this kind of transformation in production processes is a crucial and irrevocable decision requiring vast knowledge and experience. Thus, presenting a methodological frame and a roadmap may help decision-makers take more responsibility for accelerating the digital transformation of production processes. The current study aims to fill the literature's critical theoretical and managerial gaps. Therefore, it suggests a powerful and efficient decision model for solving 3DP selection problems for industries. The suggested hybrid FF model combines the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF–SWARA) and the Fermatean Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (FF–RAFSI) approaches. The novel FF framework is employed to solve a critical problem encountered in the automobile manufacturing industry with the help of two related case studies. In addition, the criteria are identified and categorized regarding their influence degrees using a group decision approach based on an extended form of the Delphi with the aid of the Fermatean fuzzy sets. According to the conclusions of the analysis, the criteria "Accuracy" and "Quality" are the most effective measures. Also, the suggested hybrid model and its outcomes were tested by executing robustness and validation checks. The results of the analyses prove that the suggested integrated framework is a robust and practical decision-making tool. [ABSTRACT FROM AUTHOR]

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The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity. [ABSTRACT FROM AUTHOR]

Abstract Objectives Work-related musculoskeletal disorders (WMSDs) have become one of the major occupational health problems. Lots of auto workers in China are exposed to WMSDs. However, there are few systematic review and meta-analysis about WMSDs in this field. This study aimed to evaluate the prevalence of WMSDs among these workers. Methods This study was carried out using the Systematic Reviews and Meta-analyses method under the most up to date PRISMA guidelines. A literature search was conducted for studies on the epidemiology of WMSDs among auto workers in China from inception to August 2022, using English and Chinese databases (China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine Disc, China Science and Technology Journal Database, PubMed, and Web of Science). All statistical analyses were performed using STATA V.16.0. Results Out of the 849 references identified, 26 articles were were eligible for inclusion, of which 12 reported the overall 12-month prevalence of WMSDs, while 14 stated the 12-month prevalence of WMSDs on body regions. The overall 12-month prevalence rate of WMSDs among workers was 53.1% [95% Confidence Interval (CI) = 46.3% to 59.9%]. The lower back/waist was the body region affected most (36.5%, 95%CI = 28.5% to 44.5%). The definition on WMSDs of “Chinese version” resulted in a high prevalence of WMSDs. Obesity, high educational level, long job tenure, female, logistic workers, and foundry workers are factors that led to a high prevalence rate of WMSDs in the lower back/waist. Conclusions This study showed a high prevalence rate of WMSDs among auto workers in China. Thus, it is necessary to pay particular stress to them. Several effective measures should be taken to prevent these workers from WMSDs. Trial registration This review was registered on PROSPERO (registration number CRD42023467152).

BACKGROUND: Suicide, drug overdose, and alcohol-related liver disease (ALD) mortality have been rising in the United States. While suicide and overdose have received a great deal of attention, far less public health concern has focused on chronic ALD. To address this gap, we examine ALD mortality rates, by race, in a cohort of autoworkers to describe trends over the past 75 years, from the peak in automobile manufacturing employment through its decline. METHODS: Based on the United Autoworkers-General Motors (UAW-GM) cohort we estimated temporal trends in age-adjusted ALD mortality rates from 1941 through 2015 at three automobile manufacturing plants in Michigan. We compared these rates to county, state, and U.S. rates, directly standardized to the 2000 U.S. census, to assess the roles of race and employment on ALD mortality. RESULTS: The overall age-adjusted ALD mortality rate among 41,097 male autoworkers peaked at 46.1 per 100,000 in the 1970s, followed by a gradual decline and a recent rise. Rates were slightly higher for black than white men until early 2000s, when rates increased only for white men. ALD mortality rates in the study cohort tracked national, state, and county rates for white men until the most recent time period, but were lower throughout the study period for black men, especially in the 1970s and 1980s. CONCLUSIONS: Employment in automobile manufacturing may have offered some protection against death from ALD for black men, and loss of those manufacturing jobs may have impacted white men without a college degree more in recent decades.