Engine Hood Research Articles

Experimental investigation on mechanical property analysis of long and short fiber glass

Experts and different companies are always attempting to use composites in place of traditional materials to build designs that are more robust, lighter, more corrosion resistant, and other necessary qualities. Composites are being utilised in a growing number of products and applications. Increasing enterprise performance requirements, such as aerospace, automobiles, and other frameworks, require broader mechanical and hands-on testing. Mechanical test equipment has been developed using many of the equipment created to provide different ways to test composites based on the properties of the material and its expected end use. This article details various strategies for testing composites of composition (0.6 wt.% cobalt and fiberglass) for application to excavator engine hoods. Long glass fiber tensile and flexural strength were available at 97.22N/mm2 and 120.28N/mm2, respectively, while tensile and flexural strength of the reinforced short fiber was combined found in 94.12 N/mm2 and 110.23N/mm2. Also, the energy absorbed by long fiber is (7.21 Kgm) which is more than the short fiber i.e. (5 Kgm). Additionally, this article offers a source of information on these test methods that enable composites to be assessed at a high degree in order to obtain satisfactory results in terms of tension and impact.

Optimization of Agricultural Tractor Engine Hood Forming Quality Based on Neural Network Genetic Algorithm Function

To improve the forming quality of agricultural tractor engine hood and effectively solve the problems of wrinkling and cracking during the drawing forming process, DYNAFORM 5.9 finite element software was used for numerical simulation of the part. Based on a comprehensive scoring method of orthogonal experiments and grey relational analysis, the critical process parameters affecting the “maximum thickening rate” and “maximum thinning rate” were determined. Latin hypercube sampling was used to randomly sample the key process parameters, and the sampled samples served as the data basis for the optimization stage of the neural network genetic algorithm function; an optimization model of the neural network genetic algorithm function was established, with the key process parameters, and the “maximum thickening rate” and “maximum thinning rate” as inputs and outputs respectively, to construct a nonlinear mapping relationship and optimize the process parameters. The study showed that the blank holder force and die clearance had the greatest impact on the “maximum thickening rate” and “maximum thinning rate”, identifying them as key process parameters; the optimal process parameter combination was the blank holder force of 462.71 kN, the die clearance of 1.09 mm, and corresponding “maximum thickening rate” and “maximum thinning rate” of 3.97% and 25.96%, respectively. Overall, this research provides a practical optimization strategy to solve the quality issues of the engine hood, offering a theoretical basis for its actual production and processing with significant practical application value.

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

Measurement of Aluminum Alloy Automotive Cover Parts Forming Parts Based on ARGUS System

This article takes the outer panel of an aluminum alloy engine hood as the research object, uses the finite element analysis software Dynaform for finite element analysis simulation, and uses the ARGUS mesh strain measurement system to conduct experimental analysis on the solid formed parts. This article mainly completes the experimental verification of parameters such as thinning rate measurement for a formed aluminum alloy automotive cover using the ARGUS grid strain measurement system. By comparing simulation results with experimental measurement results, check whether the formed part is feasible under this process parameter. To ensure the accuracy of the results, 21 points distributed in different parts of the formed part were taken and measured using the ARGUS grid strain measurement system to check whether the simulation results are close to the experimental results.

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System.

With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a "vehicle-pedestrian" interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian's head, prevent the collision between the pedestrian's head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design.

Injury predictions for an electric-two-wheeler rider in simulated collisions with a car

Based on LS-DYNA’s finite element simulation platform and the THUMS (Total Human Model for Safety) human body model, this paper mainly focuses on analyzing and predicting for injury situations of an electric-two-wheeler (E2W) rider in simulated collisions with a car. The effective car-E2W-rider simulation model has been established to conduct simulation tests of riders’ injuries. The simulation test results indicate that there is a linear relationship between car speeds and the severity of injuries to riders across different body parts when the configuration of crash is front of cars to side of E2Ws. While the correlation between E2W speeds and human injury is relatively weak. The edge of the engine hood and the A-pillar are more harmful to the human body under the same car collision speed; The head, chest and abdomen are more likely to suffer serious injuries when speeds of two participating vehicles are relatively high. Even if at the lower speed, there is also a significant risk of tibia fractures when the lower limbs collide with the front bumper.

Integrated Optimum Design of a Torsion Spring-Compensated Automotive Engine Hood Linkage Mechanism

A torsion spring-assisted automotive hood linkage with joint friction is statically balanced for its entire range of motion. The four-bar linkage dimensions are to be synthesized. Coulomb friction at the joints can assist in the balancing. The magnitude of friction at the joints are unknown, and so are the torsion spring characteristics. All the aforementioned unknowns are determined in an integrated procedure such that the linkage dimensions, joint friction as well as the torsion spring are all designed optimally together in one go. The objective is to require the lowest force to close and to open the engine hood, with the entire design procedure to be performed in just one-step. Only three specifications are known: The mass characteristics (weight and center of gravity location) of the hood, the two acceptable regions of the hinge locations either on the engine hood or on the car body, as well as, the closed and opened positions of the engine hood. Thirty different design configurations (scenarios) are investigated and the results are discussed. The optimal results for a torsion spring-assisted hood linkage when compared to a similar tension spring-assisted linkage, show much better load compensation characteristics: The magnitudes and fluctuations of the external lifting force are smaller. Moreover, problem specification for a torsion spring system is also simpler.

Swarm intelligence hybridized with genetic search in multi-objective design optimization under constrained-Pareto dominance

The inclusion of uncertainty in structural design optimization led to more complex design optimization formulations, where uncertainty quantification has significant influence on solution methods and computing times. Designs maintaining steady levels of performance, under uncertainty, are called robust and the combination of both robustness and performance optimality leads to a methodology called Robust Design Optimization (RDO). In this work a new approach to the RDO of angle-ply composite laminate structures is proposed. The key concept of this methodology is the hybridization between the principles of Particle Swarm Optimization and Genetic Algorithms, through the evolution of multiple populations based on local and global constrain-dominance. The RDO problem is here defined as the bi-objective minimization of the structural weight (optimality) and the determinant of the variance–covariance matrix (robustness) of the system’s response functionals, subject to stress and displacement constraints. A numerical structural design problem, representing a composite laminate engine hood shell, shows the capabilities of the proposed approach. Results display the good convergence properties of the proposed hybridizations both in the search and objective spaces.

Virtual Reality in Vocational Training: A Study Demonstrating the Potential of a VR-based Vehicle Painting Simulator for Skills Acquisition in Apprenticeship Training

AbstractPrevious studies on Virtual Reality (VR)-enriched learning pointed out the advantages of immersive learning for the development of competencies. In the context of vocational education in vehicle painting, training opportunities are severely limited for many reasons. VR can be utilized to develop a comprehensive learning environment with authentic training tasks. Besides the need to train psychomotor skills, vehicle painting procedures are complex tasks requiring incremental training to develop knowledge, skills, and attitudes.This study aims to evaluate a VR training application for vehicle painting, focusing on the development of professional competencies regarding skills, knowledge, and attitudes.47 apprentices participated in the evaluation study. A VR-simulated painting booth was developed based on the 4 C/ID model by van Merriënboer, where they dealt with typical painting jobs on 3D workpieces (e.g., car wings, engine hood).Within the descriptive-inferential study, no significant differences between the types of competencies were revealed. The training application supports the acquisition of skills, knowledge, and attitudes equally. Further results regarding usability, cognitive load, etc., are promising.The essential finding of this study is that the VR training application is generally suitable for supporting craftsmanship within the field of vehicle painting. Since training opportunities for apprentices in this context are often rare, VR offers a unique solution especially for skills training if it follows a proven instructional model for the development of competencies.

Carbon/Basalt Fibers Hybrid Composites: Hybrid Design and the Application in Automobile Engine Hood.

The low-velocity impact properties and the optimal hybrid ratio range for improving the property of hybrid composites are studied, and the application of hybrid composites in automobile engine hoods is discussed in this paper. The low-velocity impact properties of the hybrid composite material are simulated under different stacking sequences and hybrid ratios by finite element simulation, and the accuracy of the finite element model (FEM) is verified through experiments. Increasing the proportion of carbon fiber (CF) in the hybrid layer and placing the basalt fiber (BF) on the compression side can improve the energy absorption capacity under low-velocity impact loads. CF/BF hybrid composite hoods are optimized based on the steel hood and the low-velocity impact performance of the hybrid composite. The BCCC layer absorbs the most energy under low-velocity impact loads. Compared with CFRP, the energy absorbed under 10 J and 20 J impact energy is increased by 26.1% and 14.2%, respectively. Through the low-velocity impact properties of hybrid composites, we found that placing BF on the side of the load and keep the ratio below 50%, while increasing the proportion of CF in the hybrid laminate can significantly improve the property of the hybrid laminate. The results show that the stiffness and modal properties of the hybrid composite can meet the design index requirements, and the pedestrian protection capability of the hood will also increase with the increase in the proportion of BF.

Research on lightweight and fatigue life of engine hood based on multi-objective particle swarm optimization

A multi-objective optimization method is presented in this paper, aiming at improving the fatigue life of the engine hood while achieving light weight. By analyzing the factors affecting the fatigue life of the engine hood, a multi-objective optimization model was established that considered five design variables including the thickness of the inner plate, the thickness of the outer plate, the stiffness of the hook, the stiffness of the sealing strip, and the height of the buffer block. Then, the multi-objective particle swarm method was used for optimization, and the optimal solution was obtained in the form of a Pareto set. The ideal compromise solution was determined from the Pareto set by using fuzzy membership functions. On this basis, the optimal solution was determined from the Pareto set by comprehensively considering the steel plate material specification, mold and cost constraints. The torsional deformation test and switch fatigue test show that for the optimized engine hood, the fatigue life is increased by 117%, the mass is reduced by 0.51 kg, and the torsional deformation of the engine hood does not increase significantly. The proposed multi-objective optimization method is proved to be feasible and effective in improving engine hood fatigue life and lightweight design.

On the assessment of the mechanical properties of additively manufactured lattice structures

Lattice structures fabricated by additive manufacturing (AM) technology have many excellent properties, such as lightweight, high strength, energy absorption, and vibration reduction, which have been extensively researched and made a breakthrough. Lattice structures have been commonly used in aviation, bioengineering, robotics, and other industrial fiber because of their outstanding properties. The first part of this article provides a short review on the assessment of mechanical properties of various lattice structures in terms of their classification, applications, materials and fabrication techniques, and complexity of designing, fabrication, and post-processing as well as some of the numerical models to predict the mechanical properties of the lattice structures. The second part of the article proposes a deep learning (DL) model for a highly accurate stress-strain behavior assessment of numerous lattice structures such as namely: the octet, face center-cubic, body-centered cubic, diamond, rhombic, cubic, truncated cube, and truncated cuboctahedron, etc, which were fabricated using many different materials via various approaches and methods. Using the proposed DL model, an accuracy in terms of R2 = 0.999 (correlation coefficient), MSE = 0.0017 (mean squared error), and MAE = 0.0312 (mean absolute error) can be achieved for the prediction of the deemed mechanical property of the lattice structures. The model contains simple, quick and precise predictability that makes it ideal for the use of lattice structures in various practical applications, including heater and heat exchangers, engine hood, biomedical implant, wings, gas turbine, vibration absorber, robotic device, etc.

Facilitating vehicle-integrated photovoltaics by considering the radius of curvature of the roof surface for solar cell coverage

By mounting photovoltaics (PV) on the roof of an electric vehicle (EV), solar energy can be used to supply a considerable portion of the energy demand of the EV. The roof is the best place for PV installation on the vehicle body because doors and engine hood have less yearly-average sunlight available and more stringent mechanical requirements. The roof of a modern passenger vehicle such as a sedan is not flat, and parts with high curvature or a significant slope do not need to be covered. However, there are no design rules available for mounting PV on a vehicle roof, such as determining the coverage ratio. In this study, the distributions of roof shapes and sizes were obtained from trace drawings of various commercially available passenger vehicles. This was then used to calculate the distributions of the mechanical (i.e., local curvature) and optical (i.e., local solar utilization rate) properties. Based on the results, general guidelines were developed, including a potential coverage ratio of 96% for a hemispherical roof with a radius of curvature of 1 m. These guidelines have already been successfully demonstrated and prototyped with a small module.

Study on mechanical properties of carbon fiber honeycomb curved sandwich structure and its application in engine hood

Honeycomb sandwich structures possess many excellent characteristics such as light weight, high stiffness, high modulus and good vibrate isolation, which have became important structures for energy saving and emission reduction. However, most of the researches are concentrated on flat honeycomb sandwich structures and few of that considers the curved structures. In this paper, double-curved honeycomb sandwich core is investigated by mechanical analysis, and equivalent mechanical model is established. Detailed models of the honeycomb sandwich structure are established. Simultaneously, the corresponding equivalent models are built on the sandwich theory and curved sandwich theory, respectively. Afterward, a double-curved sandwich hood consisting of two carbon fiber reinforced plastic (CFRP) skin and Nomex honeycomb core was designed and optimized based on equivalent model and a traditional aluminum alloy engine hood. The whole optimization progress composed of free-size optimization, size optimization and stacking sequence optimization. The optimized structure with a weight reduction by 20.1% while maintaining the original rigidity. In addition, compared with the referenced aluminium alloy engine hood structure, the weight of the optimal structure was reduced 61.8% on the condition of better stiffness performance.

PREVIS - A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

AbstractThis study proposes a machine learning (ML) based approach for optimizing fiber orientations of variable stiffness carbon fiber reinforced plastic (CFRP) structures, where neural networks are developed to estimate the objective function and analytical sensitivities with respect to design variables as a substitute for finite element analysis (FEA). To reduce the number of training samples and improve the regression accuracy, an active learning strategy is implemented by successively supplying effective samples along with the suboptimal process. After proper training of neural networks, a quasi‐global search strategy can be applied by implementing a large number of initial designs as starting points in the optimization. In this article, a mathematical example is first presented to show the superiority of the active learning strategy. Then a benchmark design example of a CFRP plate is scrutinized to compare the proposed ML‐based with the conventional FEA‐based discrete material optimization (DMO) method. Finally, topology optimization of fiber orientations is performed for design of a CFRP engine hood, in which the structural performance generated from the proposed ML‐based approach achieves 12.62% improvement compared with that obtained from the conventional single‐initial design method. This article is anticipated to demonstrate a new alternative for design of fiber‐reinforced composite structures.

Design and Analysis of Foreward Step Automotive

Nowadays with increase in competition in automobile sector, vehicle aerodynamics plays an important role. Aerodynamics affect the performance of vehicle due to change in parameters such as lift and drag force which plays a significant role at high speeds. With improvement in computer technology, manufacturers are looking toward computational fluid dynamics instead of wind tunnel testing to reduce the testing time and keeps the cost of R&D low. In this paper, lift and drag of production vehicle are determined by the analysis of flow of air around it using Ansys 18.0. After that, analysis was done on the car with different engine hood angles. Based on Cl and Cd values, optimal model was selected. To validate steady state results, transient state analysis was done on this optimal model. By introducing this considerably reduce the drag and increase lift hence improves the performance of vehicle.

Design of a New Engine Hood based on Lattice Structure for Pedestrian Protection

This paper designs four kinds of three-dimensional negative Poisson’s ratio lattice structures, based on the two-dimensional concave hexagonal negative Poisson’s ratio honeycomb structure. Different parameters are designed for simulation analysis and experimental research, and the influence of different parameters on Poisson’s ratio is studied respectively. The main contents are as follows: Firstly, the three-dimensional solid model of lattice structure is established by modeling software, and the different structural parameters are determined, and the equivalent mechanical model is established. And according to the relevant structural parameters to establish the test scheme. Then, the finite element software is used to simulate the test core material with different parameters. According to the simulation results, the influence of different parameters on Poisson’s ratio is found out. Then the crashworthiness of the test board with different parameters is simulated and analyzed. According to the results, the most suitable structure is selected and applied to the engine hood. Finally, the simulation experiment of collision between pedestrian head and engine hood is carried out. The results of this paper compare the performance of lattice sandwich panels with different parameter structures in different tests and select the optimal combination of structural parameters. The adjustment and optimization direction of the structure is put forward according to the test results.

Rapid mechanical evaluation of the engine hood based on machine learning

Rapid mechanical evaluation of the engine hood based on machine learning

An improved multi-objective optimization algorithm with mixed variables for automobile engine hood lightweight design

Engine hood is one of the important parts of the vehicles, which has influences on the lightweight, structural safety, pedestrian protection, and aesthetics. The optimization design of engine hood is a high-dimensional, multi-objective, and mixed-variable optimization problem. In order to reduce the physical test investment in the development and improve the efficiency of optimization, this article proposes a data-driven method for optimal hood design. A newly proposed single-objective optimization algorithm is improved by several strategies for multi-objective constrained problem with mixed variables. Then the hood is optimized through the specially designed machine learning model. Finally, both the hood's weight and pedestrian injury are reduced while maintaining structural stiffness and frequency in the desired range. The comparative study and final hood optimization results prove the effectiveness of the proposed method.