Search Results (43)

Against the backdrop of increasingly severe global climate change, the automotive industry, as a carbon-intensive sector, has found its low-carbon transformation crucial for achieving the “double carbon” goals. This paper constructs manufacturer decision-making models under an oligopolistic market scenario for the single dual-credit policy and the “dual-credit + carbon cap-and-trade” policy, revealing the nonlinear impacts of new energy vehicle (NEV) credit trading prices, carbon trading prices, and credit ratio requirements on manufacturers’ pricing, emission reduction effort levels, and profits. The results indicate the following: (1) Under the “carbon cap-and-trade + dual-credit” policy, manufacturers can balance emission reduction costs and NEV production via the carbon trading market to maximize profits, with lower emission reduction effort levels than under the single dual-credit policy. (2) A rise in credit trading prices prompts hybrid manufacturers (producing both fuel vehicles and NEVs) to increase NEV production and reduce fuel vehicle output; higher NEV credit ratio requirements raise fuel vehicle production costs and prices, suppressing consumer demand. (3) An increase in carbon trading prices raises production costs for both fuel vehicles and NEVs, leading to decreased market demand; hybrid manufacturers reduce emission reduction efforts, while others transfer costs through price hikes to boost profits. (4) Hybrid manufacturers face high carbon emission costs due to excessive actual fuel consumption, driving them to enhance emission reduction efforts and promote low-carbon technological innovation. Full article

Over the last decade, a transformation in the automotive industry has been witnessed, as advancements in artificial intelligence and sensor technology have continued to accelerate the development of driverless vehicles. These systems are expected to significantly reduce traffic accidents and associated costs, making their integration into future transportation systems highly impactful. To explore this field in a controlled and flexible manner, scaled autonomous vehicle platforms are increasingly adopted for experimentation. In this work, we propose a set of methodologies to perform autonomous driving tasks through a software–hardware co-design approach. The developed system focuses on deploying a modular and reconfigurable software stack tailored to run efficiently on constrained embedded hardware, demonstrating a balance between real-time capability and computational resource usage. The proposed platform was implemented on a 1:10 scale vehicle that participated in the Bosch Future Mobility Challenge (BFMC) 2024. It integrates a high-performance embedded computing unit and a heterogeneous sensor suite to achieve reliable perception, decision-making, and control. The architecture is structured across four interconnected layers—Input, Perception, Control, and Output—allowing flexible module integration and reusability. The effectiveness of the system was validated throughout the competition scenarios, leading the team to secure first place. Although the platform was evaluated on a scaled vehicle, its underlying software–hardware principles are broadly applicable and scalable to larger autonomous systems. Full article

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data. Full article

Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of “low-carbon consumption and emission-reduced production”. A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry. Full article

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability. Full article

This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices. Full article

The aim of this study is to assess the level of awareness and readiness of organizations to implement the assumptions of Industry 5.0, as well as to identify the benefits and challenges associated with this process. The paper makes an original contribution by combining empirical analysis with the proposal of a practical model, enabling a better understanding of the technological and social transformation process in Polish organizations. The article presents an original model for implementing the assumptions of Industry 5.0, integrating technological, social, and organizational aspects, offering a comprehensive approach to transformation towards sustainable and human-centered development. The study was conducted among 556 Polish companies from five sectors: IT, automotive, industrial, service, and banking/financial, using a non-random sampling method and data analysis through techniques such as association rules and hierarchical clustering. The research results indicate that most organizations are familiar with the basics of the Industry 5.0 concept (25% full knowledge, 66% partial knowledge), but only a portion is engaged in the transformation process (59%), which typically takes place gradually (53%). The most commonly reported benefit of Industry 5.0 by organizations was improved product and service quality (73%), while the most frequently cited challenges included the need for staff training (58%), ensuring data and network security (53%), and modernizing infrastructure and systems (52%). Benefits such as improved product quality, increased production efficiency, and cost optimization are primarily recognized by companies in the IT and industrial sectors. At the same time, challenges such as the need to modernize infrastructure and ensure data security, as well as implementation costs, remain significant barriers, particularly for small- and medium-sized enterprises. The research findings have practical significance as they provide companies and decision-makers with guidance on effective planning and implementation of actions related to the implementation of Industry 5.0. Full article

This study addresses the emerging need for efficient and cost-effective solutions in low-volume production by exploring the mechanical performance and industrial feasibility of cutting tools that are fabricated using stereolithography apparatus (SLA) technology. SLA’s high-resolution capabilities make it suitable for creating precise cutting dies, which were tested on aluminum sheets (Al99.5, 0.3 mm, and AlMg3, 1.0 mm) under a 60-ton hydraulic press. Measurements using digital image correlation (DIC) revealed minimal wear and deformation, with tolerances consistently within IT 0.1 mm. The results demonstrated that SLA-printed tools perform comparably to conventional metal tools in cutting and bending operations, achieving similar surface quality and edge precision while significantly reducing the production time and cost. Despite some limitations in wear resistance, the findings highlight SLA technology’s potential for rapid prototyping and short-run manufacturing in the automotive and electronics sectors. This research fills a critical gap in understanding SLA-based tooling applications, offering insights into process optimization to enhance tool durability and broaden material compatibility. These advancements position SLA technology as a transformative tool-making technology for flexible manufacturing. Full article

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error before stabilizing the production process. Therefore, to efficiently transform these inefficiencies related to time and cost, there is a need for real-time predictive technology for forming quality based on the position of drawbeads and the bead force. This study proposes a method for predicting formability in real-time, based on a digital twin framework that considers the position of drawbeads and holder force. A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. The accuracy of the machine learning models was demonstrated, achieving 100% accuracy in determining crack occurrence, with a mean squared error (MSE) of 0.141 for wrinkle prediction and 0.038 for crack prediction, thereby ensuring the accuracy of the forming prediction model based on drawbead applications. Based on these predictive models, a user-friendly GUI has been developed, which is expected to reduce design time and costs while facilitating real-time predictions of forming quality, such as wrinkles and cracks, on-site. Full article

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost. Full article

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article

The European Green Deal poses a two-pronged challenge for the automotive industry: migrating to solutions based on light structures, requiring lightweight concepts and light materials, while at the same time avoiding dependence on the importation of these advanced materials. Aluminium alloys are lightweight and cost-effective materials that can successfully meet the requirements of many structural applications; however, their production requires bauxite and other Critical Raw Materials (CRMs), such as Si and Mg. Aluminium alloys are fully recyclable, but scrap is usually contaminated and its use is related to an increment of impurities, tramp elements and undesired inclusions. Traditionally, the use of secondary alloys has been restricted to low-performance applications. The present work analyses the effect that the use of scrap has on the quantity of inclusions present in the alloy and on other properties relevant for material processing. This study was carried out using common alloys associated with three of the most common aluminium processes used in the car manufacturing industry: high-pressure die casting (HPDC) (AB-43500), extrusion (6063) and sheet metal forming (5754 and 6181). The reference alloys were mixed with different levels of scrap (0, 20, 40, 60, 80 and 100%), with an aim to keep the chemical composition as unaffected as possible. The inclusion level of the alloy was characterized using the Prefil Footprinter^® test. In addition, the obtained materials, after being cast in an open mould, were subjected to metallographic characterization. Relevant properties were measured to assess the processability of the alloys for the corresponding transforming process using the flowability test for the HPDC alloy and high-temperature compression for the extrusion alloys. The results obtained suggest that the number of inclusions present in the melt highly increase with the amount of scrap used to produce the alloy. These inclusions are also related to a significant loss of flowability, but do not have a noticeable impact on microstructure. Full article

The current exceeding of six out of nine planetary boundaries requires a significant transition of human societies towards absolute sustainability. Industrial manufacturing systems were and still are an important motor for socio-economic development but at the cost of a significant negative impact on the biosphere. Current concepts in absolute sustainability and sustainable manufacturing provide solutions for sustainability transitions in industry, but various methodological, technical and procedural challenges arise during their adaptation in industrial practice. The development and operationalization of a “zero impact factory” strategy by Volkswagen Group has identified various implementational challenges, which are discussed in this article. First, an overview of motivations for “zero impact” transformations in industry are pointed out. Second, relevant aspects for the strategic management of sustainability transitions in manufacturing companies are highlighted based on a literature analysis. Third, the strategy development process is explained based on a systematic structure, which includes design-thinking principles for sustainability transitions of large technical systems such as factories in global manufacturing systems. Fourth, the developed strategy content is presented, including (1) the strategy vision, (2) the defined quantified “zero impact” goals, (3) a system model and a prototype of a zero impact factory, (4) the developed “Impact Points” and the “Site Checklist” methods (for evaluating the environmental transformation of a factory) and (5) the definition of processes for strategic management during strategy operationalization. Finally, various organizational challenges and opportunities are pointed out, which are considered novel insights from industrial practice and relevant for the science-based strategic management within automotive companies and other global industrial manufacturing organizations, as well for advancing sustainability concepts in applied industrial science. Full article

Software-defined vehicles (SDVs) make automotive systems more intelligent and adaptable, and this transformation relies on hybrid automotive in-vehicle networks that refer to multiple protocols using automotive Ethernet (AE) or a controller area network (CAN). Numerous researchers have developed specific intrusion-detection systems (IDSs) based on ResNet18, VGG16, and Inception for AE or CANs, to improve confidentiality and integrity. Although these IDSs can be extended to hybrid automotive in-vehicle networks, these methods often overlook the requirements of real-time processing and minimizing of the false positive rate (FPR), which can lead to safety and reliability issues. Therefore, we introduced an IDS based on the Swin Transformer to bolster hybrid automotive in-vehicle network reliability and security. First, multiple messages from the traffic assembly are transformed into images and compressed via two-dimensional wavelet discrete transform (2D DWT) to minimize parameters. Second, the Swin Transformer is deployed to extract spatial and sequential features to identify anomalous patterns with its attention mechanism. To compare fairly, we re-implemented up-to-date conventional network models, including ResNet18, VGG16, and Inception. The results showed that our method could detect attacks with 99.82% accuracy and 0 FPR, which saved 14.32% in time costs and improved the accuracy by 1.60% compared to VGG16 when processing 512 messages. Full article

3D printing enables the creation of complex and sophisticated designs, offering enhanced efficiency, customizability, and cost-effectiveness compared to traditional manufacturing methods. Ceramics, known for their heat resistance, hardness, wear resistance, and electrical insulation properties, are particularly suited for aerospace, automotive, electronics, healthcare, and energy applications. The rise of 3D printing in ceramics has opened new possibilities, allowing the fabrication of complex structures and the use of diverse raw materials, overcoming the limitations of conventional fabrication methods. This review explores the transformative impact of 3D printing, or additive manufacturing, across various sectors, explicitly focusing on ceramics and the different 3D ceramics printing technologies. Furthermore, it presents several active companies in ceramics 3D printing, proving the close relation between academic research and industrial innovation. Moreover, the 3D printed ceramics market forecast shows an annual growth rate (CAGR) of more than 4% in the ceramics 3D printing market, reaching USD 3.6 billion by 2030. Full article