Search Results (55)

Environmental protection has become one of the key challenges of our time. This has led to an increase in pro-environmental activities in the field of cosmetics and household chemicals, where manufacturers are increasingly trying to meet the expectations of consumers who are aware of the potential risks associated with the production of cosmetics and household chemistry products. This is one of the most important challenges of today’s industry, given that some of the raw materials still commonly used, such as surfactants, may be toxic to aquatic organisms. Many companies are choosing to use natural raw materials that have satisfactory performance properties but are also environmentally friendly. In addition, modern products are also characterized by reduced consumption of water, resources, and energy in production processes. These measures reduce the carbon footprint and reduce the amount of plastic packaging required. In the present study, seven formulations of environmentally friendly car shampoo concentrates were developed, based entirely on mixtures of bio-based surfactants. The developed formulations were tested for application on the car body surface, allowing the selection of the two best products. For these selected formulations, an in-depth physicochemical analysis was carried out, including pH, density, and viscosity measurements. Comparison of the results with commercial products available on the market was also performed. Additionally, using the multiple light scattering method, the foamability and foam stability were determined for the car shampoos developed. The results obtained indicate the very high application potential of the products under study, which combine high performance and environmental concerns. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

The automotive industry is a key pillar of many national economies, and automotive body manufacturing is among the most complex production processes. In the automotive body manufacturing process, quality control and safety assurance are of paramount importance, directly influencing the overall safety performance, structural reliability, and comfort of vehicles. Therefore, it is crucial to analyze the primary factors that influence quality and safety during the car body manufacturing process. The study first focuses on four key processes of car body manufacturing—stamping, welding, painting, and assembly—using the man, machine, material, method, environment (4M1E) framework to analyze the factors affecting quality and safety. Subsequently, a quality and safety early-warning indicator system is established for the automotive body manufacturing process, followed by a comprehensive analysis of the constructed system. To address the issue of subjectivity in traditional technique for order of preference by similarity to an ideal solution (TOPSIS) evaluation methods, this paper employs the coefficient of variation method for objective analysis of criterion-level indicators, the trapezoidal fuzzy number method for subjective analysis of criterion-level indicators, and establishes a model for optimizing target weight that balances subjective and objective approaches. Furthermore, a relative entropy-based method is applied to comprehensively evaluate criterion-level indicators. This approach reduces the information loss associated with separate weighting schemes and overcomes a known limitation of traditional TOPSIS—its inability to distinguish alternatives that lie equidistant from ideal solutions. Finally, an evaluation model for quality and safety influencing factors in body manufacturing is developed and validated through a case study, demonstrating its feasibility. The results show that the proposed model can effectively identify the key quality and safety influencing factors in the automobile body manufacturing process, guarantee quality control and safety assurance in the body manufacturing process, and thus ensure that the automobile production process meets the quality and safety requirements. Full article

The impetus of the present work is to propose a comprehensive methodology for the numerical evaluation of drying/curing, as one of the most complex and energy-consuming stages in the paint shop plant, to guarantee a decrease in energy costs without sacrificing the final paint film quality and manufacturability. Addressing the complexities of vehicle assembly, such as intricate geometry and multi-zoned ovens, our approach employs a sophisticated conjugate heat transfer (CHT) algorithm, developed under the OpenFOAM framework, providing efficient heat transfer with the accompaniment of the Large Eddy Simulation (LES) turbulence model, thereby delivering high-fidelity data. This algorithm accurately simulates turbulence and stress in the oven, validated through heat sink cases and closely aligning with experimental data. Applying modifications for the intake supply heated airflow rate and direction leads to optimal recirculation growth in the measured mean temperature within with the curing oven and along the car body surface, saving a significant amount of energy. Key adjustments in airflow direction improved temperature regulation and energy efficiency while enhancing fluid dynamics, such as velocity and temperature distribution. Furthermore, the study integrates machine learning to refine the oven’s heat-up region, which is crucial for preventing paint burnout. A data-based model using a variational auto-encoder (VAE) and an artificial neural network (ANN) effectively encodes temperature and velocity fields. This model achieves an impressive 98% accuracy within a 90% confidence interval, providing a reliable tool for predicting various operational conditions and ensuring optimal oven performance. Full article

In the context of modern railway engineering, the demand for lighter and more reliable vehicles has become a key objective for rolling stock manufacturers. Reducing energy consumption and minimizing environmental impact are driving the adoption of advanced materials and innovative design methodologies. This research activity focuses on a comparative analysis between aluminum and carbon fiber thin-walled structures used in railway vehicle car bodies. A high-fidelity finite element model (FEM) of a complete railway vehicle was developed to evaluate structural performance in compliance with European standards. Gaining deeper insights, one of the car body structures was isolated for a detailed dynamic analysis, enabling a comparative evaluation of the two materials. A structural dynamic size optimization process was applied to specific key components, aiming to maximize mass savings while maintaining mechanical integrity. The results exhibited an increase of approximately 10% in the first 10 car body eigenvalues, despite a mass reduction per unit of volume exceeding 30%, while largely preserving the nature of the eigenvectors. From a static perspective, both materials demonstrated good performance, with percentage differences below 20%. The optimization process highlighted significant potential for weight reduction in the analyzed structures. The findings highlight the critical role of optimization processes in streamlining design choices for lightweight structures. Moreover, they underscore the significant potential of high-performance carbon fiber materials in enhancing the efficiency and sustainability of railway vehicles. This study provides valuable insights for future research and practical applications in the field of lightweight railway vehicle design. Full article

In order to solve the problem of the insufficient vibration isolation performance of passenger cars in the suspension matching process, the six-degree-of-freedom (6-DOF) model, including three translational (x, y, z) and three rotational (roll, pitch, yaw) degrees of freedom, is established to comprehensively analyze the dynamic behavior of the powertrain mounting system. A 6-DOF dynamic model was established to analyze the decoupling rate and frequency distribution in its inherent characteristics, calculate the dynamic reaction of the suspension system, set the decoupling rate and the dynamic reaction of the suspension as optimization objectives, and use the NSGA II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm to optimize the stiffness of the suspension. The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. At the same time, this ensures that each suspension has enough static load support stiffness, and that its static deformation and amplitude are within the limit allowed under various working conditions, avoiding premature fatigue damage. The vibration isolation capability of the optimized system has been significantly improved, and the centroid acceleration has been significantly reduced under start–stop and road excitation conditions. The optimization method was effectively verified. Compared with existing studies focusing on single-objective optimization, the proposed NSGA II-based approach achieves a 93.4% decoupling rate in the critical Rx direction (vs. 59% pre-optimization) and reduces dynamic reaction forces by 8.3% (from 193 N to 177 N), demonstrating superior engineering applicability compared with traditional methods. Finally, the robustness analysis of the optimized stiffness met the requirements of production and manufacturing, indicating that the improvement of the decoupling rate of the suspension system and the optimization of the dynamic reaction force can effectively improve the vibration isolation performance, thereby improving the ride comfort of the vehicle. Full article

In recent years, the construction industry has faced challenges related to rising material costs, labor shortages and environmental sustainability, resulting in an increased interest in modular construction cores composed of recycled materials, such as XPS, PUR, PLW and GFRP, from waste from the truck body industry. Two resins, PUR and polyester, were used to bond these recycled composites. Physical, chemical and mechanical analyses showed that the panels formed with PUR resin had superior workability due to the higher open time of the resin, 11.3% better thermal conductivity than the commercial PLW panel (SP-PLW) and reduced porosity compared to those using polyester resin. The mechanical performance of the panels improved with higher structural reinforcement content (PLW and GFRP). Compared to a commercial panel (SP-PLW), the SP-RCM1 recycled panel showed 4% higher performance, demonstrating its potential for sustainable building applications. Thermal and microscopic characterizations showed good adhesion of the materials in the best performing formulations related to higher thermal stability. Therefore, this research aims to demonstrate the feasibility of using waste from the car industry in the manufacture of sandwich panels for modular construction to address these issues. Full article

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU’s environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver’s cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient k_d = 2.0. The maximum stresses on the model during the “bending” mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the “torsion” mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

The automotive industry uses high-strength (HS), low-alloy (HSLA) steels and advanced high-strength steels (AHSSs) to manufacture front and rear rails and safety posts, as well as the car body, suspension, and chassis components of cars. These steels can be exposed to corrosive environments, such as in countries where de-icing salts are used. This research aims to characterize the corrosion behavior of AHSSs based on electrochemical noise (EN) [dual-phase (DP) and ferrite–bainite (FB)]. At room temperature, the steels were immersed in NaCl, CaCl₂, and MgCl₂ solutions and were studied by frequency–time domain analysis using wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RPs) related to the corrosion process and noise impedance (Z_n). Optical microscopy (OM) was used to observe the microstructure of the tested samples. The results generally indicated that the main corrosion process is related to uniform corrosion. The corrosion behavior of AHSSs exposed to a NaCl solution could be related to the morphology of the phase constituents that are exposed to solutions with chlorides. The Zn results showed that DP780 presented a higher corrosion resistance with 918 Ω·cm²; meanwhile, FB780 presented 409 Ω·cm² when exposed to NaCl. Also, the corrosion mechanism of materials begins with a localized corrosion process spreading to all the surfaces, generating a uniform corrosion process after some exposition time. Full article

The steel–aluminum hybrid body closure panels can achieve a more balanced and lightweight performance. However, the differences in the physical properties of metal sheets and the complex changes in the properties of the adhesive material result in cumulative deviations in the composite-forming process. This paper proposes a deformation pre-compensation modeling method for the autobody closure panels hemming system oriented towards the process chain, in response to the problem that single-process optimization cannot obtain global optimal results. Taking the car door scaled model as an example, based on surface reconstruction and node compensation, the curing deformation amount is fed back in advance to the gluing and hemming processes. The deformation deviation is corrected through geometric parameter pre-compensation to achieve overall process shape control and optimization. Research shows that this method can significantly reduce the surface differences and gaps of hemming structures with dissimilar materials, and a single iteration can reduce the assembly surface difference by more than 90%. This provides a reference for improving the manufacturing quality of steel–aluminum hybrid body closure panels. Full article

Frontal crash tests are an essential element in assessing vehicle safety. They simulate a collision that occurs when the front of the bus hits another vehicle or an obstacle. In recent years, much attention has been paid to the frontal crash testing of city buses, especially after a series of accidents resulting in deaths and injuries. Unlike car manufacturers, most bus bodybuilders do not include deformation zones in their designs. The next two regulations are widely used to assess whether a structure can withstand impact loading: UNECE Regulation No. 29—United Nations Economic Commission for Europe (UNECE R29) and the New Car Assessment Program (NCAP), which is more typical of car crash tests. The main goal of the research is to develop an applicable methodology for a frontal impact simulation on a city bus, considering UNECE R29 requirements for the passenger’s safety and distinctive features of the low-entry body layout. Among the contributions to current knowledge are such research results as: unlike suburban and intercity buses, city buses are characterized by lower stiffness in the event of a frontal collision, and therefore, when developing new models, it is necessary to lay deformation zones (currently absent from most city buses). Maximum deformation values in the bus front part are reached earlier for R29 (137 ms) than for most impacts tested by NCAP (170–230 ms) but have higher values: 577 mm vs. 150–250 mm for the sills tested. Such a short shock absorption time and high deformations indicate a significantly lighter front part of a low-entry and low-floor bus compared with classic layouts. Furthermore, it is unjustified to use the R29 boundary conditions of trucks to attach the bus with chains behind its frontal axe both in natural tests and appropriate finite element simulation—the scheme of fixing the city bus should be accordingly adapted and normatively revised. Full article

Giga-casting, a revolutionary approach for manufacturing large, single-piece car body components from aluminium, has emerged as a potential game-changer in the automotive industry. However, these large, thin-walled castings are prone to distortions during solidification and heat treatment processes. Straightening these distortions is crucial to ensure structural integrity, facilitate downstream assembly, and maintain aesthetic qualities. This paper proposes a novel method for straightening giga-cast components using a multi-pin straightening machine. The machine’s versatility stems from its ability to adapt to various geometries through multiple strategically controlled straightening pins. This paper introduces the concept of a “straightening stroke decision algorithm” to achieve precise straightening and overcome the challenges of complex shapes. This algorithm determines the stroke length for each pin, combining a polynomial model representing the global stiffness of the component with a machine learning model that captures the stiffness changes arising from the current geometry. The effectiveness of the proposed approach is evaluated through comprehensive numerical experiments using finite element analyses. The straightening performance is assessed for the straightening algorithm with different machine learning models (deep neural network and XGBoost) and compared to a traditional optimisation method. The proposed surrogate models decided the straightening strokes so that the maximum remaining distortion became 0.02% of the largest dimension of each target geometry. The results of the numerical experiment showed that the proposed straightening method is suitable for straightening distortion in large thin-walled components. Full article

Polyester is currently the main material used for the manufacture of safety belts used in car transport for the protection of passengers and the driver. The seat belt is the main passive safety element used in vehicle engineering. In this work, the behavior of two safety belts, one with three-point fastening and the other with four-point fastening, which equip the seat of a racing car used in Formula Student for use in a frontal impact with a vertical wall. A model with finite elements is used to describe the entire car–driver shock-absorber assembly. The von Mises stresses calculated for both cases under consideration are below the tensile strength. The tensions arising in the belt and the accelerations required at various points of the driver’s body are determined by both the properties of the utilized polyester and the chosen construction variant. The obtained results justify the use of the three-point and four-point belt in the cases of both common and race cars. Full article

Sustainability emphasises the crucial need to incorporate environmentally conscious practises across the entire supply chain management process in the modern age. A great emphasis is placed on minimising environmental consequences, eliminating waste, conserving energy, and sourcing materials responsibly in the production, distribution, and disposal of electric vehicles. Electric vehicle manufacturers must prioritise sustainability to ensure that their products contribute significantly to a brighter future while also meeting the ethical and environmental demands of consumers as well as regulatory bodies. Green supply chain management (GSCM) incorporates environmentally friendly practises to reduce environmental effects. This study incorporates fuzzy TOPSIS for analysing and rating GSCM practises, assisting decision-makers in prioritising sustainability in the supply chains of electric vehicle manufacturers. We develop a multi-criteria decision-making framework to evaluate GSCM criteria while accounting for inherent uncertainty. Fuzzy TOPSIS handles linguistic problems as well as ambiguity while providing a precise GSCM representation. Real-world case studies from various sectors demonstrate the applicability and benefits of our approach to finding improvement areas and expediting GSCM assessments. This research presents a systematic, quantitative way for evaluating GSCM practises, allowing supply chain alignment with sustainability goals. This promotes environmentally sustainable practises and increases the sustainability of supply chains for electric car manufacturing. Full article