Automobile Manufacturing Industry Research Articles

Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.

This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL. Population based, a cross sectional study. Han, China. This study selected 3266 Han male workers from three automobile manufacturing industries. Information including personal life habits, occupational health test information and occupational exposure history were collected and predictive factors of NIHL were screened from these workers. BPNN and logistic regression models were constructed using these predictors. The input variables of BPNN model were 20, 16 and 21 important factors screened by univariate, stepwise and lasso-logistic regression. When the BPNN model was applied to the test set, it was found to have a sensitivity (TPR) of 83.33%, a specificity (TNR) of 85.92%, an accuracy (ACC) of 85.51%, a positive predictive value (PPV) of 52.85%, a negative predictive value of 96.46% and area under the receiver operating curve (AUC) is: 0.926 (95% CI: 0.891 to 0.961), which demonstrated the better overall properties than univariate-logistic regression modelling (AUC: 0.715) (95% CI: 0.652 to 0.777). The BPNN model has better predictive performance against NIHL than the stepwise-logistic and lasso-logistic regression model in terms of TPR, TNR, ACC, PPV and NPV (p<0.05); the area under the receiver operating characteristics curve of NIHL is also higher than that of the stepwise and lasso-logistic regression model (p<0.05). It was a relatively important factor in NIHL to find cumulative noise exposure, auditory system symptoms, age, listening to music or watching video with headphones, exposure to high temperature and noise exposure time in the trained BPNN model. The BPNN model was a valuable tool in dealing with the occupational risk prediction problem of NIHL. It can be used to predict the risk of an individual NIHL.

Investigation into the Hot-Forming Limit for 22MnB5 Hot-Forming Steel under a Stamping Process

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.

XPENG Equity Structure Analysis and Stock Price Forecast

The market competition for new energy vehicles is becoming increasingly fierce. Under the Internet operation mode, the automobile manufacturing industry is divided into new forces and traditional automobile enterprises, which have a profound impact on the profit model of the automobile industry, and the driving force of enterprise operation is working capital. It is necessary the manage enterprise working capital to improve the comprehensive competitiveness of enterprises. Xiaopeng (also called XPENG) is a listed company of automobile brands. It adopts a dual-class share structure to allocate the company's shares. The structure can also be used to analyze how this will affect XPENG's stock price and predict the future trend of XPENG's stock price. The paper aims to analyze how this will affect the stock price of XPENG Company through the dual-class share structure, and predict the future direction of the stock price of XPENG Company. At the same time, the paper also hopes to forecast the stock price situation of XPENG Company through a series of reports and comparisons with other tram industries.

Microstructural evolution and carbides precipitation behavior and their effects on mechanical property of Nb–Ti microalloyed FB590 steel

Ferrite-bainite dual phase steels have been widely used in automobile manufacturing industry due to their high strength and excellent hole expansion performance. In this study, the effects of coiling temperature on mechanical properties and hole expansion performance of low carbon Nb–Ti micro-alloyed FB590 ferrite-bainite steel have been studied. Additionally, the microstructure evolution and carbide precipitation behavior during hot rolling and coiling processes have been investigated. The influence mechanism of microstructures and carbides on both mechanical properties and hole expansion performance has also been discussed. The results suggest that the fraction of the retained austenite and the martensite decreases gradually with increasing coiling temperatures from 450 °C to 500 °C. The main reason for the formation of martensite and retained austenite in steel CT450 coiled at 450 °C is that the coiling temperature is near the martensitic transformation temperature Ms. Increasing the coiling temperature to 500 °C which is above the Ms leads to ferritic and bainitic structure. The carbides precipitation behavior has been studied by TEM, thermodynamic and kinetics calculation. The results show that the size of the carbides increases with increasing coiling temperatures from 450 °C to 500 °C. Most of the large size carbides above 100 nm with high percent of Ti are the undissolved particles in austenite during slab heating. The medium size particles between 50 nm and 100 nm and enriched in Ti precipitate during rough rolling at higher temperature, while the particles smaller than 50 nm enriched in Nb precipitate during finish rolling and coiling processes. The strength of the steel is effectively increased by the fine carbides and martensite formed at low coiling temperature of 450 °C. With increasing coiling temperature from 450 °C to 500 °C, the steel's strength decreases from 630 MPa to 591 MPa, while the HER are improved from 76.8 % to 119.9 % due to the reduction of martensite and retained austenite. Although the strength is a little reduced due to the coarsening of carbides and reducing of the martensite, the plasticity and hole expansion performance is improved significantly with the coiling temperature of 500 °C.

Application and comparison of three occupational health risk assessment methods in an automobile manufacturing industry

Objective: Three occupational health risk assessment methods were used to assess the occupational health risk of noise exposed posts in an automobile manufacturing enterprise. According to the results, the selection of risk assessment methods and risk management of such occupational noise enterprises were provided. Methods: Form April to November 2021, The occupational health field survey was carried out in an automobile manufacturing industry in Tianjin. The occupational health MES risk assessment method, occupational health risk index risk assessment method and Australian occupational hazard risk assessment method were used to evaluate the occupational health risk of noise-exposed posts in this enterprise, and the evaluation results of different methods were analyzed and compared. Results: The average value of L(Aeq, 8 h) in the four workshops of automobile manufacturing industry was 82.95 dB (A) , and the noise detection exceeding rate was 22.41% (26/116) . The LAeq, 8h and exceeding rate noise of welding workshop were higher than those of other workshops (χ(2)=23.56, 32.94, P<0.01) . The three occupational health risk assessment methods have the same risk assessment results for the four major workshops. The assembly and painting workshops are level 4 risk (possible risk) , and the stamping and welding workshops are level 3 risk (significant risk) . Conclusion: Occupational noise has certain potential hazards to workers in automobile manufacturing enterprises. Therefore, in the future work, corresponding organizational management measures should be taken to improve the working environment and reduce the actual exposure level of workers in order to protect the health of occupational workers.

Emission factors and source profiles of volatile organic compounds from the automobile manufacturing industry

Controlling volatile organic compounds (VOCs) emitted from the automobile manufacturing industry requires establishing VOCs emission factors (EFs) and source profiles refinedly. In this study, 41 samples involved 32 VOCs discharge links were collected from three factories. The EFs and VOCs source profiles were estimated by the material balance method and weighted average method, respectively. The ozone formation potential (OFP) of the 110 VOCs species were calculated by the maximum incremental reactivity (MIR). According to estimations, the ranges of EFs were 0.23–1.66 kg VOCs/SUV car and 2.14–14.86 g VOCs/m2 painted area. EFs of six materials were firstly estimated, which are electrophoretic primer (152.31 ± 97.39 g VOCs/SUV car, 0.97 ± 0.38 g VOCs/m2 painted area), sealant (48.39 ± 26.20 g VOCs/SUV car, 0.46 ± 0.25 g VOCs/m2 painted area), floating coat (87.40 ± 75.63 g VOCs/SUV car, 0.86 ± 0.74 g VOCs/m2 painted area), colored paint (127.24 ± 168.24 g VOCs/SUV car, 1.25 ± 1.66 g VOCs/m2 painted area), varnish (205.46 ± 218.14 g VOCs/SUV car, 2.01 ± 2.15 g VOCs/m2 painted area), and cleaning solvent (328.54 ± 404.94 g VOCs/SUV car, 3.23 ± 3.98 g VOCs/m2 painted area). OVOCs (37.40–51.60 %) and aromatics (36.40–37.00 %) were the dominant components. n-Butyl acetate, 1,2,4-trimethylbenzene, undecane, n-hexanal, acetone, 1,2,3-trimethylbenzene, 1,3,5 -trimethylbenzene, m/p/o-xylene, 3-ethylbenzene, and 4-ethylbenzene were the major VOCs species, accounting for 68 % of total VOCs in the automobile manufacturing industry. Considering the OFP values of species, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene, 1,2,3-trimethylbenzene, m/p-xylene, acetaldehyde, methyl ethyl ketone are the key active species that should be prioritized for control.

Optimization of benzene exposure risk assessment: An integrated approach utilizing internal and external concentrations with a focus on biomarkers S-PMA & t, t-MA

In the majority of occupational settings within China, the concentrations of benzene are observed to fall markedly below the demarcated detection thresholds. Employing traditional risk assessment models, the presence of exceptionally low airborne benzene exposure concentrations may infuse heightened degrees of uncertainty. Consequently, the necessity arises to investigate risk assessment methodologies more apt for the prevalent exposure environment among employees. In the present study, a pharmacokinetic model premised on urinary benzene metabolites (S-PMA and t, t-MA) was employed to ascertain a more precise daily airborne benzene exposure concentration per individual. This value was integrated into the linear multistage model as the ‘internal exposure concentration’. In conjunction with the U.S National Environmental Protection Agency's (EPA) inhalation risk assessment model predicated on the external exposure concentration, the Singapore Ministry of Manpower's (MOM) model, and the linear multistage (LMS) model, the carcinogenic and non-carcinogenic effects of benzene were evaluated for 1781 benzene-exposed employees across 76 enterprises in Jiangsu Province. Findings suggest that in the linear multilevel model assessment, the cancer risk levels based on t, t-MA and S-PMA were higher in the printing and recording media reproduction industry, automobile manufacturing industry, general equipment manufacturing industry and the furniture manufacturing industry (median 2.842 × 10−4, 2.819 × 10−4, 2.809 × 10−4, and 2.678 × 10−4), which align more consistently with the actual benzene exposure circumstances of each industry's study participants, with overall risk levels calculated by the linear multistage model exceeding those of the EPA inhalation risk assessment model and the MOM model. This implies that the linear multistage model of internal exposure, based on the reciprocal of benzene biomarkers S-PMA and t, t-MA for airborne benzene exposure, presents enhanced sensitivity and suitability for the current occupational health risk assessment of workers. Without doubt, biomarker-based benzene exposure risk assessment emerges as the optimal choice.

An empirical study on the factors influencing the new product development process in Indian automobile industries

Automobile manufacturing industries are looking at innovative ways to improve their company processes globally to maintain profitability in an increasingly complicated and competitive global market. There is fierce competition in the automotive business, which is evolving quickly. A crucial constituent of an organization’s success, survival, and vitality in such industries is the process of new product development. Several studies have been conducted to investigate the success factors of the new product development process in the Automobile industry. This research work tested the hypothesis that factors influence the process of new product development in the Automobile manufacturing industries of Maharashtra. The evidence was collected from higher officials of respective industries. The findings show that market orientation, market competition, and organizational culture have a significant impact on the New Product Development Process in the Automobile Manufacturing industry. The result of this study also shows that the role of governance and organization competence and resources do not impact the New Product Development Process. This suggests that management in the Automobile manufacturing industry strategy should focus on all the possible influential factors during the process of product development.

Characters of a Good Project Manager in the Automotive Industry

Project is a very basic concept, it runs through all walks of life, is the so-called "everything can be project". As the leader of the project and the main executor of the project management activities, the project manager has full control of the project-related matters, which directly determines the success or failure of the project. At the same time, different industry attributes have different characteristics for project managers. This paper attempts to discuss the characteristics of excellent project managers according to the requirements of project managers in the automobile manufacturing industry.

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Empirical Modeling and Analysis of Process Parameters in Laser Beam Cutting Process

The research has been originated based on the persuasive applications of Lasers in the field of various engineering industries, i.e., Aerospace, Automobile, Electronic and Heavy manufacturing industries to machine a variety of metals and alloys. Out of all the machining processes available, Laser Beam Machining is considered to be the best one, because of the following advantages, i.e., quick material removal, non-contact, non-wearing tool, involves highly localized heat input to the work piece, reduces distortion, and offers no tool wear, diminishes tendency of cracking. The present paper focused mainly on developing the empirical relationships between the input process parameters and the output responses in Laser beam cutting process. Laser power, Cutting Speed, Gas pressure and Focal distance are the input process parameters. The output responses considered are related to the quality of cut. Surface roughness is the first output responses considered and the second one is Burr height. Total number of experiments carried out is 27 based on the Taguchi Design of Experiments. Haste alloy C276 is the work material. Response Surface Methodology is applied for the Experimental data of 31 samples to derive the empirical relationships. Later ANOVA analysis is also carried out to check the adequacy of the derived equations. Based on the ANOVA analysis, the models are classified as significant or not significant. Further the significant models can be used for optimizing the Laser beam cutting process. Once the process is optimized, it can be automated, so that the process can be run very efficiently and economically.

Current Situation and Consequences of Environmental Information Disclosure in China's Automobile Manufacturing Industry --Taking SAIC Motor Corporation Limited as an Example

A sincere commitment to establishing a community of human destiny is made through achieving carbon peak and carbon neutrality. Following the pace of affluent nations, China has incorporated carbon peak and carbon neutrality into overall economic and social development. Environmental information disclosure is the most basic and major part of environmental accounting, following the sustainable development strategy because of daily accounting supervision and accounting. In this research, a case study of SAIC Group's environmental information disclosure in China from 2019 to 2022 is chosen, and analyzes SAIC Group's operating capacity, profitability, solvency and development capacity by combining information asymmetry theory and stakeholder theory. This paper investigates the roots of environmental information disclosure in the automobile manufacturing industry, chooses a representative SAIC Group to summarize its environmental information disclosure, assesses the current level of corporate environmental information disclosure, and provides relevant departments with pointers and relates for the development of an environmental information disclosure system for enterprises in this industry. Finally, optimization recommendations are offered for the growth of listed businesses' environmental information disclosure in China's vehicle manufacturing sector.

Comparing Fault Tree Analysis methods combined with Generalized Grey Relation Analysis: A new approach and case study in the automotive industry

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.

The Supply Chain Advantages and Challenges of BYD in the New Energy Vehicle Industry: Implications for the Future

The new energy vehicle supply chain is evolving rapidly to meet growing market demand, and innovations in battery technology, motor manufacturing, and charging infrastructure, among others, are driving progress in this sector. This paper explores the supply chain strengths of BYD, a major new energy vehicle manufacturer in Chinas automobile manufacturing industry, and related issues. Through an in-depth analysis of BYDs history, the companys current status, and its innovative initiatives in the supply chain, the paper identifies its supply chain strengths in intelligence, sustainability, and innovation. However, some of the challenges BYDs supply chain faces, such as delivery issues, data security risks and low collaboration with suppliers, are also raised. Finally, the paper offers some recommendations, including actively collaborating with high-quality suppliers, strengthening data processing and management capabilities, and adhering to supply chain innovation. These recommendations can guide BYDs development and provide useful references for peer companies supply chain optimization.

The Knowledge Spillover Effect of Multi-Scale Urban Innovation Networks on Industrial Development: Evidence from the Automobile Manufacturing Industry in China

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.

3D Printer Selection for the Sustainable Manufacturing Industry Using an Integrated Decision-Making Model Based on Dombi Operators in the Fermatean Fuzzy Environment

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.

A Review and Benchmark on State-of-the-Art Steel Defects Detection

Steel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments.

Application of DC-DC power module in electric vehicle system

DC-DC power module has been increasingly widely used in car manufacturing industry and studied by scientists due to its crucial role in electric vehicles. It has two major functions. One is to provide electric power that motivates the power steering system, air conditioning, and other auxiliary equipment; the other is to operate in a hybrid electric source system by connecting with super-capacitors in series in a circuit, which performs a function of regulating power source output, as well as stabilizing the DC voltage bus. This review aims to investigate the fundamental principle of DC-DC converter, to comprehensively review the different types of DC-DC power modules and their characteristics, as well as their advantages and limitations. In addition, to discuss the major challenges existing in this system currently, and the potential it has for future developments, which refers to the power output efficiency of the system when it is applied in electric vehicles. Finally, to demonstrate some real-life applications of this module, the representative examples of firms successfully applying this power module into production. This paper has a guiding significance for acknowledging the current state of development of DC-DC power modules in the electric vehicle system, and for studying some related research fields.

Optimisation of laser welding parameters to maximise the joint strength of laser weld stainless steel joints using a GA for geothermal plants

ABSTRACTLaser beam welding is vital in high-temperature fields such as the aerospace, automobile, and gas turbine manufacturing industries. In this article, parametric studies are carried out on Nd:YAG laser weldments that influence stainless steel 430 L weldment quality. A series of trial runs are organised to investigate input parameters such as peak power, frequency, speed, and time that impact output responses such as yield strength, ultimate tensile strength, shear strength, and elongation simultaneously. Trail runs on martensitic stainless steel AISI 430 L are conducted based on Taguchi experiments, and a regression equation is established. To yield optimum results, the process parameters are optimised by using the genetic algorithm optimisation technique. By conducting experimentation with optimum parameters, the output responses are compared with the optimum results 95% confidence level was obtained. The optimum levels for process variables are identified for quality weld peak power 160 kW, frequency 9 Hz, speed 3.33 mm/ms, and time 2.53 ms. Optical microscopy reveals that refined grains are formed in the weld zone with a grain size of 2.909 µm, and a scanning electron microscopy test is performed to study the morphology of AISI 430 L weldment specimen failure takes place at the interface of the weld metal.

Risk Management Strategies for Chip Supply Chain Disruption: Case Study of R Automotive Manufacturing Company

This article addresses the chip supply chain problems faced by the global automobile industry, takes R automotive manufacturing company as a case study, and thoroughly researches the risk of the out-of-supply of the key raw material, chips, in the automobile manufacturing industry and its impact on the production of the enterprise. The article explains the importance of supply chain management in modern enterprises and points out supply chain risks. Taking the automotive manufacturing industry as the background, the article discusses in depth the importance of micro-control units as the electronic control units of automobiles, analyses the competitive advantages of R enterprises in the new energy vehicle market, analyses the risk characteristics of chip out-of-supply as well as the challenges faced by R enterprises under the influence of the global epidemic and market uncertainty, and proposes the strategies for R enterprises to cope with the risk of chip out-of-supply, including the strengthening of supply chain cooperation, formulating a reasonable procurement mechanism, establishing a good supplier relationship, and promoting independent research and development of chips. Finally, the findings of this paper are summarized, and relevant suggestions are provided for automotive manufacturing enterprises to avoid the risk of chip supply shortage.