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Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range.

Abstract Previous works have established strategies to model artificial test lightning plasma with specific waveform parameters and use the predicted plasma behavior to estimate test specimen damage. To date no computational works have quantified the influence of varying the waveform parameters on the predicted plasma behavior and resulting specimen damage. Herein test standard Waveform B has been modelled and the waveform parameters of “waveform peak,” “rise time,” and “time to reach the post-peak value” have been varied. The plasma and specimen behaviors have been modelled using the Finite Element (FE) method (a Magnetohydrodynamic FE multiphysics model for the plasma, a FE thermal-electric model for the specimen). For the test arrangements modelled herein, it has been found that “peak current” is the key parameter influencing plasma properties and specimen damage.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract Sustainable practices are taking precedence across many industries, as evident from their shift towards the use of environmentally responsible materials, such as natural fiber-reinforced acrylated epoxidized soybean oil (NF-AESO). However, due to the lower reactivity of AESO, the curing reaction usually requires higher temperatures and longer curing time (e.g., 150°C for 6-12 h), thus making the entire process unsustainable. In this study, we demonstrate the potential power of photons towards manufacturing NF-AESO composites in a sustainable manner at room temperature (RT) within 10 min. Two photoinitiators, i.e., the 2,2-dimethoxy phenylacetophenone (DMPA) and 1-hydroxycyclohexyl phenyl ketone (HCPK), were evaluated and compared with the thermal initiator, i.e., tert-butyl perbenzoate (TBPB). Based on the mechanical performance of the AESOs, the photoinitiation system for NF-AESO was optimized.

Abstract Low-Pressure cooled Exhaust Gas Recirculation (LP-cEGR) is proven to be an effective technology for fuel efficiency improvement in turbocharged spark-ignition (SI) engines. Aiming to fully exploit the EGR benefits, new challenges are introduced that require more complex and robust control systems and strategies. One of the most important restrictions of LP-cEGR is the transient response, since long air-EGR flow paths introduce significant transport delays between the EGR valve and the cylinders. High dilution generally increases efficiency, but can lead to cycle-by-cycle combustion variation. Especially in SI engines, higher-than-requested EGR dilution may lead to combustion instabilities and misfires. Considering the long EGR evacuation period, one of the most challenging transient events is throttle tip-out, where the engine operation shifts from a high-load point with high dilution tolerance to a low-load point where EGR tolerance is significantly reduced.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract High technology expertise and strong advancement in electric vehicles and Lithium (Li)-ion battery devices and systems have increased the speed of development and application of new equipment. It is reported that Li-ion battery life reduces almost by 60 days per degree temperature rise in an operational temperature of 30°C to 40°C, which makes cooling a high priority. The current study focuses on cooling the battery system using Phase Change Material (PCM) placed as bands of different dimensions around the prismatic battery. Eight novel designs of varying dimensions were constructed for three-volume scenarios. The heat generations considered in this study are 6,855 W/m3, 12,978 W/m3, 19,100 W/m3, and 63,970 W/m3. The data obtained was trained using an artificial neural network (ANN), and an equation was attained to fit the data. The optimum placement of PCM with respect to the number of bands and dimensions was achieved through a Genetic Algorithm.

Abstract To protect ship equipment of river and sea transport, it is suggested to use polymeric protective coatings based on epoxy diane oligomer ED-20, polyethylene polyamine (PEPA) curing agent and filler, which is a departure from industrial production. Thus the purpose of the work is analysis of major dependency of the properties on the content of fillers that allowed to revealed the critical filler content (furnace black) in composites to form a protective coating with the required set of characteristics. The infrared (IR) spectral analysis was used to investigate the presence of bonds on the surface of particles of the PM-75 furnace black, which allows us to assess the degree of cross-linking of the polymer. The influence of the content of dispersed furnace black on the physicomechanical and thermophysical properties and the structure of the protective coating is investigated.

Abstract The causes of failure due to cracking in the resistance spot welding of the advanced high-strength steels dual-phase 590 (DP590) were investigated using scanning electron microscopy (SEM), optical microscopy, and the tensile-shear test. The results showed that by increasing the current amount, the formation of the melting zone occurred in the heat-affected zone, leading to the cracking in this area, reducing the tensile strength and decreasing the mechanical properties; the initiation and growth of cracking and failure in this region also happened. In the heat-affected zone, by increasing the current amount with the softening phenomenon, the recrystallized coarse grains also occurred, eventually resulting in the loss of mechanical properties. The results of the tensile-shear test also indicated that by increasing the current up to 12 kA, the strength was raised, but the ductility was reduced.

Abstract Heavy commercial vehicles play an important role in creating the trade and economic balance of countries. Also, the durability and safety of heavy commercial vehicles come to the fore. Heavy commercial vehicles consist of two parts. These are the chassis area with the equipment that allows the vehicle to move and the cabin section where the driver is located. The cabin area is the most important area that ensures the highest level of driver safety. Considering that the production of trucks is increasing day by day, it is inevitable for companies to increase their R&D activities in the field of cabin and cabin suspension systems for much safer, durable, and comfortable trucks. This study aims to determine the safe torque value of the fasteners and their assembly sequence of the Cab Suspension Console, which is one of the most important connection parts in a truck and which can cause a fatal accident by breaking.

Abstract The article considers one of the possible mechanisms of loading the solidity of a cylindrical metal composite high-pressure vessel (MC HPV). This mechanism manifests itself as delamination of a thin-walled metal shell (liner) from a more rigid composite shell causing local buckling. A similar effect can be detected in the manufacturing process of MC HPV, when the composite shell is formed by winding with tension a carbon fiber-reinforced plastic tape on the liner. Pressure transfer from the composite shell to the liner is carried out by the method of temperature analogy, that is, by cooling the composite shell, thermally insulated from the liner. To solve the problem of externally confined liner local buckling an approach is proposed, which is based on three points: the introduction of local technological deviations inherent in actual structures, the determination of the general stress-strain state, and a real-time deforming.

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Abstract Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.

Abstract In this article, Al-Mg-Si-Mn alloy (AA6082) is butt joined by employing friction stir welding (FSW). The mechanical and metallurgical properties of joints are analyzed by conducting tensile and microhardness testing, respectively. To measure the temperature at different locations, eight thermocouples (L-shaped k-type) are placed at equal distance from the centerline. Least square method attempts to calculate the temperature at the centerline of joints. The process parameters are also optimized using Taguchi’s five-level experimental design. The optimum process parameters are determined, employing ultimate tensile strength (UTS) as a response parameter. A statistical test “analysis of variance” is used to check the adequacy of the model. It has been observed that rotational speed and feed rate are the predominant factors for UTS and microhardness.

Abstract Tribological properties determine the elemental factors influencing the performance of the components that are subjected to relative motion. Of late, low-density Metal Matrix Composites (MMCs) have been renowned as materials for the components that are subjected to tribological applications. This work reports an experimental study of wear and corrosion behavior of Aluminum Metal Matrix Composites (AMMCs) reinforced with in situ TiB2 particles. These composites were synthesized by a mixed salt route procedure using K2TiF6 and KBF4 at a temperature of 850°C by using the stir casting method. Dry sliding wear behavior of AA5754-TiB2 in situ composites were compared with base material for the various loads, sliding speed, and sliding distances. These parameters were analyzed using Taguchi techniques. It was found that the percentage of reinforcement and load are the most significant parameters.

Abstract Based on radial basis function (RBF) neural network (NN) theory, RBF-Proportional Integral Derivative (PID) diesel engine speed control is proposed. The algorithm has strong self-learning ability and strong adaptive ability, and is able to optimize the control parameters of the speed loop controller in real time. A series of simulations are carried out with different initial weights. Simulation results reveal that initial weights have little effect on RBF-PID control performance. A STM32 MCU-based controller is developed according to the calculation requirement. Experiments are carried out on a D6114 diesel engine generator to verify the proposed speed control algorithm. The simulation results are in good agreement with the experimental results. The results show that the influence of initial weights on RBF-PID control algorithm is smaller than that on BP-PID control algorithm. When RBF-PID control algorithm is adopted, the steady speed fluctuation rate is 0.4%.

Abstract This research involves the study of the different properties of aluminum alloy (AA) 2024 in the presence of carbon nanotubes (CNTs) and Silicon (Si) nanoparticles. Structural morphology, elemental composition, mechanical properties (density, tensile strength, elongation, and hardness), and tribological properties (wear rate and coefficient of friction) of AA 2024 in the presence of CNTs, Si, and its combinations at various proportions were evaluated using a Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Analyzer (EDX), Universal Testing Machine (UTM), Model HMV-2T Vickers hardness test machine, and pin-on-disk friction-and-wear test rig. The Hybrid Metal Matrix Composite (HMMC) material is prepared by a two-stage stir casting method. It was found that the density of the AA 2024 + 4%CNT + 2%Si is 2.22 g/cm3, ultimate tensile strength is 308 N/mm2, elongation is 15.5%, and Vickers hardness is 187.5 Vickers Hardness Number (VHN).