Automotive Manufacturing Industry Research Articles

Evaluation of Industry 4.0 strategies for digital transformation in the automotive manufacturing industry using an integrated fuzzy decision-making model

The current paper proposes a methodological frame to identify the best Industry 4.0 (I4) strategy for the automotive manufacturing industry as a roadmap for companies to make a more straightforward digital transformation. We noticed a critical research gap when we performed an extensive literature review. Although there are many studies available that assess the readiness of companies for I4, only a few deal with decision-making in terms of sustainable development, and none have applied that to the automotive industry. Practitioners can apply the proposed model to determine the strategy based on technological developments in I4. The current paper proposes an extended version of the combined compromise solution approach with the help of the picture fuzzy sets. According to the paper's results, by using the proposed model, developing a new business model based on technological improvements is the best strategy for the digital transformation of the automotive industry. Also, the lack of research and development implementations on I4 implementation is the most influential factor. Hence, practitioners in the automotive industry know the significance of research and development in reaching successful results for digital transformation. The acquired outcomes of the paper show that developing technology-based new business models is the best strategy for the automotive manufacturing industry concerning digital transformation instead of accommodating traditional business models and high technology utilization.

Optimizing scope 3 emissions in the automotive manufacturing industry: a multidisciplinary approach

The acceleration of global climate change poses enormous challenges to the automotive manufacturing industry, a key sector in reducing greenhouse gas (GHG) emissions. Particularly, Scope 3 emissions, encompassing indirect emissions, often constitute the largest carbon footprint component in this sector, yet their quantification and management remain challenging. This paper proposes an interdisciplinary approach that integrates cloud computing, text analysis, and machine learning, and systematically details its implementation, key benefits, and potential applications. Through this methodology, the paper seeks to provide the automotive industry with innovative and actionable insights to tackle the complexities of Scope 3 emissions. It focuses on enhancing the accuracy of emission quantification and optimizing supply chains, aiming to reduce the overall carbon footprint. Moreover, this paper outlines the future challenges and directions for these technologies and methodologies in sustainable development and environmental management. This work underlines the critical role of interdisciplinary approaches in resolving environmental challenges, setting the stage for the automotive industry to forge paths towards a greener future.Graphical

Mechanical, viscoelastic, and biodegradability characteristics of ramie fibre‐reinforced acrylonitrile butadiene styrene composites

AbstractBiocomposites made from natural fibers have attracted significant attention in recent years owing to the global concern regarding plastic accumulation. In this investigation, ramie fiber was employed as a natural fiber reinforcement in the acrylonitrile butadiene styrene (ABS) matrix to enhance the biodegradability of the polymer composite. ABS/ramie fiber composites were prepared by compression molding with various wt% (5, 10, 15, and 20 wt %) of ramie fibers within the ABS matrix. Subsequently, the biodegradability, water absorption, mechanical properties, viscoelastic behavior, and thermal characteristics of the prepared composites were evaluated. The biodegradation test results demonstrated that there was a positive correlation between the concentrations of fibers in neat ABS. The maximum biodegradation of the composites was 2.56% at 20 wt% ramie fibers in the ABS matrix. Moreover, an increase in the wt% of ramie fiber led to an increase in water absorption. Although the tensile strength and impact strength decreased with increasing fiber content, the tensile modulus of the ABS/ramie fiber composites initially increased up to 5 wt% fiber and then started decreasing, and the natural fibers in ABS reduced the viscoelastic properties. The objective of developing ABS/ramie fiber composites is to utilize them as environmentally sustainable materials in automotive manufacturing industry.

Designing a green-resilient closed-loop supply chain to preserve business continuity under interrelated disruptions (a case study of the automotive battery industry)

Improving profitability, implementing the recycling cycle to reduce resource consumption, and managing interrelated disturbances to maintain business continuity are important issues for supply chain management. Accordingly, this study proposes a multi-objective optimization model to design a green-resilient closed-loop supply chain considering pricing decisions under multiple interrelated disturbances. Herein, the fuzzy c-means clustering method is employed to lessen probable scenarios and cluster main suppliers and target markets to reduce mathematical complexity. The multi-objective model is solved by adopting the improved augmented ε-constraint method to achieve the minimum total cost, total environmental impact, and network non-resiliency. The mixed uncertainty is coped with by developing a novel robust optimization method. Finally, an automotive battery manufacturing industry case study is applied to the model. The results reveal that the mutual effects of disturbances intensify the deterioration of objective functions. Also, the dynamic pricing of products reduces the financial vulnerability of the supply chain.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Analysis of the delayed cracking mechanism of an industrial hot-dip galvanized DP1180GI steel coil

The hot-dip galvanized steel coil DP1180GI is extensively used in the automotive manufacturing industry owing to its excellent mechanical properties and corrosion resistance. However, in actual industrial production, the machining process of automotive parts is usually accompanied by deformation and stresses, which may pose a risk of delayed cracking. To address this industrial challenge, we analyzed the delayed cracking mechanism in the DP1180GI steel plates subjected to bending deformation using methods such as the hydrogen microprinting test, thermal desorption spectroscopy, and finite element simulations. The analysis offers effective data references for enhancing industrial production processes.

THE ROLE OF ENVIRONMENTAL HEALTH AND SAFETY PRACTICES IN THE AUTOMOTIVE MANUFACTURING INDUSTRY

The automotive manufacturing industry plays a pivotal role in global economic development, providing transportation solutions while simultaneously facing multifaceted challenges related to environmental health and safety (EHS) practices. This review investigates the indispensable role of EHS practices within the automotive manufacturing sector, highlighting their significance in mitigating environmental impact, ensuring workplace safety, and complying with regulatory standards. Effective EHS practices are integral to managing environmental sustainability within automotive manufacturing. These practices encompass waste management, emissions reduction, and resource conservation strategies aimed at minimizing the industry's ecological footprint. Additionally, the adoption of eco-friendly technologies and processes, such as renewable energy integration and material recycling, contributes to the industry's overall environmental stewardship. Furthermore, prioritizing workplace safety is imperative in the automotive manufacturing sector due to its inherently hazardous operational environments. EHS initiatives focus on risk assessment, hazard identification, and the implementation of preventive measures to safeguard employees from occupational injuries and illnesses. Moreover, fostering a culture of safety awareness through training programs and regular audits promotes a conducive working environment conducive to employee well-being and productivity. Compliance with regulatory standards is a cornerstone of EHS management in the automotive manufacturing industry. Adherence to local, national, and international regulations ensures operational legality and enhances corporate reputation. Through continuous monitoring and assessment, automotive manufacturers strive to stay abreast of evolving regulatory frameworks, thereby aligning their practices with industry best practices and societal expectations. The integration of robust EHS practices is indispensable for sustainable operations and corporate responsibility within the automotive manufacturing industry. By addressing environmental concerns, ensuring workplace safety, and meeting regulatory requirements, automotive manufacturers can uphold their commitment to environmental stewardship and social accountability while maintaining operational efficiency and competitiveness in the global market.
 Keywords: Environment, Health, Safety, Automotive, Manufacturing, Industry.

Quality costs and Industry 4.0: inspection strategy modelling and reviewing

AbstractInspection strategy (IS) is a key component impacting quality costs. Although often considered an inflexible output of initial quality plans, it may require revisions given the dynamic quality situation of the manufacturing system. It is from this background that the present study aims to model and compare different IS based on the cost of quality (CoQ) approach for a case study in the automotive manufacturing industry. While many computational inspection strategy models (ISMs) are available in the literature, most of them face application challenges and struggle to incorporate real-world data. The present study addresses this gap by developing a model that not only represents a real testing station in a manufacturing line but also uses historical production data. Additionally, in relation to model inputs, this study explores the challenges and opportunities of acquiring reliable quality cost estimates in the Industry 4.0 context. Among the main contributions of this work, the developed CoQ-based ISM can be used as a decision-making aiding tool for inspection revision and improvement, while conclusions about quality cost data collection in the industrial digitalization context can help advance the CoQ approach in practice.

Occupational Health Assessment and Risks in Automotive and Wood Manufacturing Industries: A Retrospective Study

Objective: This study examines workers' health status and occupational risks in the automotive manufacturing and wood manufacturing sectors based on medical examinations conducted between January 2019 and January 2020. Materials and Methods: It involves 27 individuals with an average age of 36.26. Younger workers, especially those aged 20 to 35, show a higher propensity for workplace accidents due to their engagement in hazardous tasks and limited experience. The research emphasizes prevalent health issues such as metabolic syndrome, diabetes, obesity, and cardiovascular diseases among these workers, influenced by sedentary work routines and inadequate physical activity. Workers employed for 1-10 years exhibit the highest incidence of workplace accidents, warranting careful monitoring in this demographic. Key factors observed include tobacco consumption, blood cell counts, hemoglobin, glucose, liver function indicators, and exposure to hazardous substances. The study highlights prevalent hearing impairment and lung abnormalities in these workers, aligning with global trends in occupational health. Results: It recommends regular health screenings, lung film examinations during initial employment, and periodic check-ups for early detection and treatment. While acknowledging limitations, such as study specificity and resource constraints, it underscores the role of such studies in shaping policies, guiding preventive measures, and enhancing worker health and safety in high-risk industries. Conclusion: This study underscores the need for proactive health monitoring and regular screenings to safeguard workers' health, even in industries where symptoms might not be evident.

Metallurgical Analysis in the Outer Tube Fault Area

In the automotive manufacturing industry, product failures are still found. In this study, one of the automotive components, namely the outer tube of the motorcycle front fork, was cracked. The outer tube is part of the shock absorber component which is also connected to the motorcycle steering system. Failure analysis aims to obtain some data or facts that can indicate the root cause of the failure or failure. Sample preparation which includes the process of selecting the cut area, cleaning and cutting for the fractography process. As for metallography, sample preparation is continued with the process of mounting, sanding, polishing and etching. Fractographic test showed that the fracture occurred in a brittle fracture mode, with no traces of plastic deformation. The morphology of the fracture surface also shows the presence of gas trapped in large quantities. In the metallographic test, the overall microstructure shows an eutectic phase with a uniform dendrite structure pattern, this indicates that metal solidification occurs quite quickly and can cause a lot of gas to be trapped in the metal characterized by the presence of multiple porosities. This can provide an indication of the relationship between the microstructure and the fracture surface morphology. Cracking and brittle fracture can be caused by the stress that occurs in the metal that exceeds the yield and plastic limits due to the large pressure of gas trapped in the 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.

A Deep Convolutional Neural Network-Based Method for Self-Piercing Rivet Joint Defect Detection

Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. First, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Second, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 96.3%, which is 3.9% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.

Exploring the impact of Customization Strategy, Product Variety on Operational Performance in Automotive Manufacturing Industry

This research has three big contributions to car manufacturing companies in China. Firstly, it defines how an improvement in product variety impacts operational performance. Second, it explains the impact on operational performance of customization strategies practices. Thirdly, the thesis identifies the relative differences of impact by five types of customisations of the product variety on specific aspects of operating performance. It includes a comprehensive analysis of the literature associated with the effects of product variety and customisation as well as techniques for coping with trade-offs between product variety and operating performance. By adopting a quantitative research method, a survey of 12 car manufacturing sector companies from China was conducted. The objectives and questions of this research is clearly defined and outlines the difference the research is to bring to information. This also establishes theories that take into account the relationship between product variety, customisation and operational efficiency. The project focuses on data collection methods and a questionnaire is designed to be submitted to Chinese car manufacturing companies. This gives the empirical results of this study a detailed debate. Finally, the research is concluded by providing a summary of the results of the work and of the study's theoretical contributions. Effective mass customisation and customer-driven development will offer Chinese carmakers a variety of benefits. The basic results, therefore, have significant management consequences for Chinese car producers to pursue numerous strategies under different product design profiles.

Lignocellulosic Biomass to Automotive Manufacturing: The Adoption of Bio-Based Materials and Bio-Fuels

Review Lignocellulosic Biomass to Automotive Manufacturing: The Adoption of Bio-Based Materials and Bio-Fuels Zhiping Zhang, Yinggang Jiao, Yameng Li, Huan Zhang, Quanguo Zhang, and Bing Hu * Henan Agricultural University, Henan International Joint Laboratory of Biomass Energy and Nanomaterials, Zhengzhou 450002, PR China * Correspondence: hubing535@163.com Received: 20 July 2023 Accepted: 21 September 2023 Published: 26 September 2023 Abstract: In order to meet the demand of automobile lightweight and reduce the automotive industry's dependence on non-renewable energy sources such as petroleum, the utilization of bio-based materials and bio-fuels has become a key link to realize the sustainable development of automotive manufacturing industry. Moreover, along with the coming of the era of Carbon Neutral, the evolution of automotive manufacturing has begun. Hence, in the paper, the alternative solutions of the body materials, related component, and vehicle fuel in which bio-products can be introduced into were reviewed. The types and preparation methods for bio-based materials and bio-fuels were mentioned, especially the discussion of bio-hydrogen production and its application in the automotive industry. The summary of the correlation between biomass and automobile manufacturing industry in this article has certain guiding significance for the energy industry, automobile manufacturing industry and biomass energy utilization field.

Portfolio Optimization Analysis -- Based on Six Stocks from Different Industries in the U.S. Stock Market

As the stock market becomes more and more unstable, investors all over the world pay more attention to risk management. Creating a portfolio is an effective way to make the trade-off between risk and return rate. This paper selects six stocks from the U.S. stock market in different industries, including the real estate industry, high technology, financial services company, pharmaceutical industry, automotive manufacturing industry and gas industry. This paper simulates 1,000,000 different investment portfolios by using the Monte Carlo simulation, then drawing the efficient frontier of the portfolio and figuring out the maximum Sharpe ratio and minimum volatility portfolio. As the results showed, McKesson Corporation accounted for the max proportion in both the maximum Sharpe ratio portfolio and the minimum volatility portfolio, which are 45.7149% and 60.8960% respectively. J.P. Morgan takes up 0.1232%, which is the min proportion in the maximum Sharpe ratio portfolio. However, Amazon accounted for the min proportion in the minimum volatility portfolio, which is 0.4828%. In the last, this paper compared the cumulative return of the three portfolios with the S&P 500 Index, only the return rate of the equal-weight portfolio and minimum volatility portfolio is better than the benchmark. Hence, this result may provide suggestions for picking certain kinds of portfolio in this special period.

An integrated optimization model for planning supply chains' resilience and business continuity under interrelated disruptions: a case study

PurposeThe adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.Design/methodology/approachThis study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.FindingsThe results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.Originality/valueApplying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

Empirical analysis of success factors to implement lean-green practices in an Indian automotive manufacturing industry using ISM-MICMAC approach

Empirical analysis of success factors to implement lean-green practices in an Indian automotive manufacturing industry using ISM-MICMAC approach

Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems

The global automotive manufacturing industry's growth in downtime reductio is substantial, valued at $3272.6 billion USD with a 3.01% growth rate.This growth in downtime reduction underscores the industry's commitment to enhancing efficiency, quality, and overall productivity across its diverse range of operations. Downtime in the braking system assembly line can lead to utilization loss or technical availability loss. In this context, many proactive maintenance strategies are explored but there's limited focus on addressing error-prone machines and utilizing Machine Learning for predicting downtime. The objective is to prioritize downtime reduction through error analysis, critical machine identification, and implementing ML-based solutions.
 This comprehensive research delved into the significance of minimizing downtime in the braking system final assembly line through meticulous data analysis, visualization techniquesand targeted interventions and then identified key issues and achieved tangible improvements in operational efficiency. The analysis revealed significant findings and then employed the Pareto Principle to identify top downtime machines, illustrating their distribution through a Pareto chart of machine defects. Furthermore, Exceptions and Problem Areas were identified utilizing statistical process controls, offering insights into critical error contributors. Notably, a comprehensive exploration of the most prominent downtime machine was undertaken, evidenced by LCL and UCL charts and a Fishbone Diagram detailing causal relationships. The research leverages real-world data involving dates, machine names, and downtime durations to develop a predictive model that aids in proactively managing production disruptions.
 The application of RPN calculations before and after error correction demonstrated a substantial reduction from of 432 to 75, validating the efficacy of the corrective actions. The real-time data was used to build a model that can predict when production machines downtime might happen. This helps us be prepared and manage any possible disruptions in production.The outcome of the project highlights the intrinsic link between downtime reduction and assembly line efficiency, emphasizing the importance of data-driven interventions. This culminated in the resolution of key issues, illustrated by the mitigation of the PCBA Pressin machine and LVDT sensor errors, yielding tangible reductions in downtime and notable productivity improvements. In this direction, exploring AI-driven predictive maintenance holds immense potential for advancing downtime reduction strategies. Leveraging AI algorithms to analyse live data streams from machinery and sensors can enable the detection of patterns indicating imminent failures and can pre-emptively prevent downtime.

An Optimization Study on Resistance Spot Welding of DP600 Sheet Steel via Experiment and Statistical Analysis

Resistance Spot Welding (RSW) is widely used in many automotive, boiler, and ship manufacturing industries. Therefore, the optimization and effectiveness of the RSW process are very useful and cost-efficient processes in addition to improving weld quality. This study used various welding parameters to investigate the optimization of the RSW process of Advanced High Strength Steel (AHSS). RSW was carried out using different welding times, currents, and electrode pressure values. Microstructural analysis, nugget formation, and tensile shear test were performed on samples. This study applied the Taguchi method and ANOVA to set up the welding process and optimize its results. Microstructural characterization showed that the weld nugget microstructures had a high-volume fraction of martensite. Using the Minitab software, Taguchi (DOE) and ANOVA on the tensile shear test results showed that welding current is more effective than clamping pressure on the tensile shear load-bearing capacity (TLBC). However, clamping pressure affects the weld current effects on weld performance. Additionally, the TLBC of RSW samples increases, whereas increasing the clamping pressure leads to a decrease in TLBC and nugget dimensions. Consequently, a detailed analysis of the RSW coefficients and their effects led to optimized results.

Improving the Quality of Automotive Components through the Effective Management of Complaints in Industry 4.0

The fundamental problems with component production in the automotive industry are the low quality of the components, the high number of failures, and the subsequent number of complaints. The quality of automotive components is a prerequisite for automated production in a SMART Factory 4.0 in the context of Industry 4.0. Managing complaints is a critical area in the automotive manufacturing industry. The primary goal of this article is to indicate the significance of a CRM system and the importance of complaints management in automotive manufacturing, which improves the quality of the process of producing components while improving the quality of the inputs—the quality of materials, the quality of the human factor, and the quality of the services provided in connection with the components. (1) A Slovak company was selected as the subject of this research, which focuses on the production of automotive parts. (2) The methods of research focused on using economic analysis and instruments of quality management such as the QRQC method, FMEA method, the Ishikawa diagram, and the Pareto analysis and Lorenz curve. This article presents the results of complaints about automotive components: locksets in vehicles. (3) This research shows the basic cause of the problem: a bent pin after riveting in the process of producing the components. In the frame of this research, we suggested corrective measures to eliminate the actual causes of failure in the production of the components. The RPN index reached a level below the limit of 150 (the critical level of the customers’ requirements) after implementing the corrective measures. The total costs of complaints in this year were calculated to be EUR 13.575 and the costs will be reduced gradually after changes are implemented in the production process. (4) A new complaints information system and a new training center are beneficial for solving problems in the production of automotive components. The implementation of a CRM system and other systems are prerequisites for solving customer complaints.