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Abstract Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing.

Abstract In the aeronautical field, aircraft reliability is strictly dependent on propulsion systems. Indeed, a reliable propulsion system ensures the safety of pilots and passengers and the possibility of making comfortable flights. Typically, on aircraft equipped with spark ignition (SI) engines, one of the principal requirements to make them reliable is the correct balancing between the intake air mass and fuel flows. Advances in the implementation of sophisticated control and estimation strategies on SI engines allow realizing engines with improved features in terms of performance, reducing pollution emissions, and fuel consumption. Approaches based on sensor redundancy are applied to improve the reliability in measurements of the manifold air pressure (MAP) and flow (MAF) to avoid issues related to possible faults of sensors vital for the correct functioning of SI engines.

Abstract Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example.

Abstract Hydrogen refueling stations (HRSs) have been widely built in many countries to meet the requirements of the rapidly developing hydrogen-fueled vehicle industry. Safety distances are key parameters for HRS designs, but the codes and standards used for determining safety distances vary in different countries. The two main methods for determining the safety distances for HRSs are the consequence-based method and the quantitative risk assessment (QRA)-based method. This article reviews the two methods to show state-of-the-art research on determining safety distances globally. This review shows that the harm criteria in the consequence models differ greatly in the literature and the QRA-based method is a more reasonable way to determine the HRS safety distances. In addition, the QRA models lack reliable frequency data and uniform risk acceptance criteria. Future standardized QRA models should be developed with unified regulations and standards for hydrogen infrastructure.

Abstract Gasoline compression ignition (GCI) is a promising combustion technology that can help the commercial transportation sector achieve operational flexibility and meet upcoming criteria pollutant regulations. However, high-pressure fuel injection systems (>1000 bar) are needed to enable GCI and fully realize its benefits compared to conventional diesel combustion. This work is a continuation of previous durability studies that identified three key technical risks after running gasoline-like fuel through a heavy-duty, common rail injection system: (i) cavitation damage to the inlet check valve of the high-pressure pump, (ii) loss of injector fueling capacity, (iii) cavitation erosion of the injector nozzle holes. Upgraded hardware solutions were tested on a consistent 400- to 800-hour NATO durability cycle with the same gasoline-like fuel as previous studies. The upgraded pump showed no signs of abnormal wear or cavitation damage to the inlet check valve.

Abstract Halogen detector is an important halogen gas leakage detection instrument. In order to ensure that the upper and lower shells have the same quality, it is necessary to use one mold and two pieces in production. Compared with the conventional one-mold two-cavity process, it is easier to produce warpage and volume shrinkage. To solve this problem, a multi-objective injection molding process optimization method based on deep neural network (DNN) model based on stochastic weight average (SWA) method and multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed. Melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time are the six parameters and important structure parameters (gate diameter) as design variables, warpage, and volume shrinkage rate as the optimization goal. The neural network model between variable and goal was established, and the MOEA/D algorithm was used for global optimization.

Abstract The sustainability of mines is becoming ever more important to reduce the greenhouse gas footprint and keep the resources extraction economically sustainable. Despite the electrification and hybridization trend of mining equipment, diesel engines are still expected to maintain their importance as a primary source of power especially for open pit equipment, thanks to their longer operating range. However, in order to keep high efficiency and minimize fuel consumption for the entire operating life it is crucial to understand and tackle the aging effect on the engine performance. In this research a 500-h durability test was performed on a Liebherr mining engine, with the aim of better understanding how aging affects the combustion process and engine performance (power and fuel consumption), and how this effect can be compensated. Experimental results show a 1% specific fuel consumption increase, ascribable to injector aging.

Abstract Compared with traditional plastics, glass fiber-reinforced plastic (GFRP) has more outstanding performance advantages, which is more and more widely used. To improve the quality of the products manufactured by the GFRP injection molding, the injection parameters are optimized in two stages. In the first stage, the range of optimization parameters including the glass fiber content and six molding parameters is selected by the Moldflow recommendation. The warpage and shrinkage of each orthogonal experiment are obtained by the Moldflow simulation. Then, a comprehensive evaluation method called GRA-TOPSIS and the range analysis method are utilized to identify the optimal level values of all optimization parameters. According to the order of influence of each parameter, the range of these parameters is adjusted for the second stage.

Abstract The dimensional quality of the car body is built on quality management of form, fitment, and functional requirements. Each of these attributes reflects the final product quality and, therefore, needs to be ascertained quantitatively. Design intent and functionality conformance with specifications are paramount to performance, and thus quality. It is accomplished through optimal Geometric Dimensioning and Tolerancing of parts (GD&T), datum/Primary Locating Points (PLP) strategy, tricks/levers, and assembly design. Challenges stem from the complexity involved in the datum layout strategy and its optimization for desired deviations. Incorrect datum schemes in design prompt underconstrained fixtures, redundant datum, the sensitivity of datum layout, etc. and induce defects in later stages. The end effect is smoothing out the variation issues leading to compromise in quality.

Abstract This article presents an original methodology for the multi-objective optimization of Continuously Variable Transmission (CVT) for a wind turbine (WT). The objective functions of this optimization problem are to minimize the weight and maximize efficiency. This methodology also considers the variations of parameters caused by different factors (manufacturing tolerance, uncertainties in the operating conditions). Using a probabilistic model, the proposed algorithm combines a propagation of uncertainties and an optimization of the function objectives. The optimization is performed using the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the advantage of exploring the global design space and finding the best compromise between the objectives. In order to verify the solution obtained by this approach, results were compared to the ones obtained by a previous study.

Abstract Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions.

Abstract In today’s era, due to increasing energy demands, it is necessary to make vehicles lightweight without affecting their strength. In order to achieve this, the subassemblies of the automobile should be optimized. Optimizing the product not only saves energy consumption but also reduces the material required for manufacturing and increases the overall performance of the product. Taking the same as the base, this article focuses on optimization of a straight bevel gear pair used in automotive differential and performing finite element analysis (FEA) to validate its results. FEA is carried out on the optimized bevel gear to check its durability, and topology optimization is performed on the optimized gear to reduce the mass. Finally, the optimized gear is checked for fatigue. For design optimization, nonlinear multi-objective problem is formulated with a number of teeth and modules as the design parameters.

Abstract Today, as the spread of vehicles equipped with autonomous driving functions increases, accidents caused by autonomous vehicles are also increasing. Therefore, issues regarding safety and reliability of autonomous vehicles are emerging. Various studies have been conducted to secure the safety and reliability of autonomous vehicles, and the application of the International Organization for Standardization (ISO) 26262 standard for safety and reliability improvement and the importance of verifying the safety of autonomous vehicles are increasing. Recently, Mobileye proposed an RSS model called Responsibility Sensitive Safety, which is a mathematical model that presents the standardization of safety guarantees of the minimum requirements that all autonomous vehicles must meet. In this article, the RSS model that ensures safety and reliability was derived to be suitable for variable focus function cameras that can cover the cognitive regions of radar and lidar with a single camera.

Abstract To enhance the lubrication performance of the crankpin bearing (CB) at the elastic hydrodynamic lubrication regime (EHLR), the spherical dimples (SD) of the partial textures (PT) is designed on the EHLR of the CB. Based on a hydrodynamic model of the slider-crank mechanism (SCM) combined with the CB lubrication and a multi-objective optimization program of the genetic algorithm (MOGA), the initial design parameters of PT including the depth hsij and the diameter Dij of each SD defined as chromosomes in the MOGA are then optimized to further enhance the CB’s lubrication performance. Three indexes of the oil film pressure p, friction force F f, and friction coefficient μ of the CB are chosen as the objective functions. The research results indicate that based on the optimal approach of the MOGA with its good stability and repeatability, the CB’s lubrication performance is remarkably improved by the optimal parameters in comparison with the initial parameters of the SD.

Abstract The article presents results of tests performed with the use of a ship engine of the type Sulzer 6AL20/24. The goal of the tests was to create and verify an identification procedure for the analyzed object’s reliability states to be used without interfering with the object operation processes. The proposed method is based on an analysis of vibrations and noise generated during the engine operation, which are considered to be the most significant diagnostic signals. The signals of the engine vibrations and noise recorded during the engine operation on a laboratory test stand have been analyzed in the time domain. A number of the recorded signal characteristics are calculated. The characteristics are statistically analyzed in order to choose those which can provide the basis for the identification of reliability states. Next, based on the spaces of ability and inability, states are formulated.

Abstract Articulated vehicles contribute to the major portions of cargo transport through roads. Fifth wheel (FW) is an important component in these vehicles, which acts as the bridge between tractor and trailer and is often used as a parameter to adjust the axle loads. Ride and comfort studies linked to FW position exist. However, its influence on durability is often not considered seriously. In this article, three different FW positions placed at 200 mm, 400 mm, and 600 mm in front of the rear axle are studied virtually on a 4×2 tractor with three-axle semitrailer combination. To assess the risk associated with FW movement, acceleration-based pseudo-relative damage, power spectral density (PSD), and level crossing plots are analyzed for each FW position. Further, fatigue analysis is done on the cab structural components to understand the durability. Outcome shows that the FW position has an influence in determining the cab dynamics and durability of the components to a great extent.

Abstract Uncertainties in design represent a considerable industrial stake. Controlling the reliability and robustness of a mechanical system at the level of design has become necessary in order to control these uncertainties. Using the theory of probabilistic design optimization, the present work reports on the application of the concept of reliability-based robustness on minimizing the weight of a planetary gear train (PGT). The optimum combination of reliability and robustness for the minimum weight of the PGT was found using an optimization algorithm based on Particle Swarm Optimization (PSO) and Monte Carlo Simulation (MCS). The algorithm was developed by combining the propagation of uncertainties with the optimization of the function objective within a single probabilistic model. The results show that a reliability-based robust design offers a better alternative to the traditional deterministic design models.

Abstract The non-pneumatic tire (NPT) has been widely used due to its advantages of no run-flat, no need for air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications in military vehicles, earthmovers, the lunar rover, stair-climbing vehicles, etc. Recently, the Unique Puncture-Proof Tire System (UPTIS) NPT has been introduced for passenger vehicles. In this study, three different design configurations, viz., Tweel, Honeycomb, and newly developed UPTIS, have been compared. The effect of polyurethane (PU) material nonlinearity has also been introduced by applying five different nonlinear PU material properties in the spokes. The combined analysis of the PU material nonlinearity and spoke design configuration on the overall tire stiffness and spoke damage prediction is done using three-dimensional (3D) finite element modelling (FEM) simulations performed in ANSYS 16.0.

Abstract Road surface characteristics directly influence vehicle safety and performance, and its knowledge can be instrumental to road transportation system safety. This work focuses on the development of a test setup, which was utilized for real-time implementation of a road surface identification algorithm based on the acceleration response of an intelligent tire. Analysis of frequency domain data was used to leverage the tire-road contact information being relayed through the acceleration data. A signal processing algorithm was developed to separate each tire revolution, analyze it in real time, and convert it to the frequency domain in real time. In the end, the performance of the setup was validated with results from the literature, and the distinguishing signature possessed by each surface was used to categorize different terrains into the respective surface categories (Dry Asphalt, Wet Asphalt, Concrete) in real time.

Abstract Tires are one of the most important vehicle components since they significantly affect vehicle attributes such as handling stability, steering controllability, ride comfort, and structure durability. However, whether for tire competitive benchmarking or vehicle conceptual design, data insufficiency tends to restrict the development process. This article presents a procedure of establishing a database for the evaluation of tire and vehicle impact vibration. Forty-three tires with various sizes and usages were selected to build the datasets. The rigid ring model was used to characterize each individual tire sample on account of our application requests. In view of the test resources, an optimization approach to the standard parameterization method was proposed and fully validated with the measurement database. The parameter characteristics were then statistically investigated and compared between the different tire types.