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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 Low-carbon equivalent austempered ductile iron (LCE-ADI) exhibits high modulus of elasticity than conventional austempered ductile iron (ADI) due to less graphite content. Austempering parameters of temperature and time significantly influence the mechanical properties of LCE-ADI. In the present work, response of the material to two-step austempering in the range of 350–450°C was studied, and a comparison was made to single-step austempering. Reduction in ferrite cell size, increase in % carbon in carbon-stabilized austenite (CSA) and increase in volume fraction of CSA led to increase in tensile strength (10%) and hardness (20%), in addition to improved toughness (10%).

Abstract Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates.

Abstract In this article, the effect of heat treatment on the microstructure and mechanical behavior of medium-carbon steel wire intended for the spring mattress is investigated using scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), X-ray diffraction, Vickers hardness (Hv), and tensile strength. The results indicate that the microstructure elongation along the wire axis is observed with the bending and kinking lamellae at the deformation level of 57.81%, this change appears as a fracture in the microstructure and leads to an increase in hardness, tensile strength, and intensities of diffraction patterns. After heat treatment, we observed a redistribution in the grain, which is almost the same in the wire rod and drawn wires; indeed, this led to a decrease in hardness, tensile strength, and augmentation in intensities of peaks. The EBSD pole figures reveal the development of texture in the cementite slip plane (001).

Abstract The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance.

Abstract This article addresses the design, testing, and evaluation of rigorous and verifiable prognostic and health management (PHM) functions applied to autonomous aircraft systems. These PHM functions—many deployed as algorithms—are integrated into a holistic framework for integrity management of aircraft components and systems that are subject to both operational degradation and incipient failure modes. The designer of a comprehensive and verifiable prognostics system is faced with significant challenges. Data (both baseline and faulted) that are correlated, time stamped, and appropriately sampled are not always readily available. Quantifying uncertainty, and its propagation and management, which are inherent in prognosis, can be difficult. High-fidelity modeling of critical components/systems can consume precious resources. Data mining tools for feature extraction and selection are not easy to develop and maintain.

Abstract Prediction of the surface finish of hardened bearing steels was estimated in machining with ceramic uncoated cutting tools under various process parameters using two statistical approaches. A second-order (quadratic) regression model (MQR, multiple quantile regression) for the surface finish was developed and then compared with the artificial neural network (ANN) method based on the coefficient determination (R 2), root mean square error (RMSE), and percentage error (PE). The experimental results exhibited that cutting speed was the dominant parameter, but feed rate and depth of cut were insignificant in terms of the Pareto chart and analysis of variance (ANOVA). The optimum surface finish in machining bearing steel was achieved at 100 m/min speed, 0.1 mm/revolution (rev) feed rate, and 0.6 mm depth of cut.

Abstract In the current research, an aluminum alloy AA8090 is welded using the friction stir welding (FSW) technique. The main objective is to eliminate the chances of defects in the weld joint, which were observed in the conventional joining process. Experiments were planned according to the one factor at a time (OFAT) approach. The input process parameters involved during the present work are welding speed (WS), rotational speed (RS), tilt angle (TA), and dwell time (DT). However, the response variables investigated at different input parametric combinations are tensile strength (TS), percentage elongation (EL), microhardness (MH), and macroscopic structure. Due to the combination of both attributes of optimization (the higher the better in TS and the lower the better in EL), the multi-performance quality characteristics optimization approach, i.e., grey relational analysis (GRA), is implemented.

Abstract Reducing the friction of the internal combustion engine (ICE) is of major interest to reduce fuel consumption and greenhouse gas (GHG) emissions. A huge potential for friction reduction is seen in the piston ring-cylinder liner (PRCL) coupling. Approaching the cylindrical liner shape in the hot operation state will enhance the PRCL conformation. Recently, newly developed freeform honing techniques can help to achieve this perfect cylinder shape. This article presents a numerical study of the effect of freeform honing on the geometrical performance of the liner in the hot operation state. The freeform honed liner (TR) concept is based on the approach of reversing the local deformation of a conventional circular liner. A validated computational model for a gasoline engine is used to compare the geometrical performance of those TR cases with circular, elliptical (EL), and conical elliptical liners (NEL) at different operational points.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.

Abstract Laminated composites are extensively used in the aerospace industry. However, structures made from laminated composites are highly susceptible to delamination failures. It is therefore imperative to consider a structure tolerance to delamination during design and operation. Hybrid composites with laminas containing different fibers were used earlier in laminates to achieve certain benefits in strength, stiffness, and buckling. However, the concept of mixing laminas with different fibers was not explored by researchers to enhance delamination tolerance levels. This article examines the above aspect of hybridization by employing machine learning algorithms and proposes a reliable method of analysis to study delamination, which is crucial to ensure the safety of airframe composite panels.

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 the present study, the mechanical performances of plain carbon steel were explored based on the grain growth behavior. In the first step, the samples were normalized at different temperatures ranging from 900°C to 1100°C for 30, 60, 100, 150, and 200 min, respectively. In order to measure the grain size, the planimetric technique of Jeffries was used based on the optical micrographs taken for each sample. The mechanical properties of each grain such as hardness, elongation, yield, and tensile strength were studied, depending on the conventional methods. Experimental results showed that the increase in both heating temperature and holding time enhances grain growth, while the growth rate decreases with increasing time. The initial grain size and proportionality constant were calculated at 950°C, where K = 2.26 μm2/min and D 0 = 25.09 μm. Moreover, a significant increase in strength and hardness was observed with a decrease in grain size.

Abstract The implementation of an intelligent powertrain health management relies on robust prognostics modelling. However, prognostic capability is often limited due to unknown future operating conditions, which vary with duty cycles and individual driver behaviors. On the other hand, the growing availability of data pertaining to vehicle usage allows advanced modelling of usage patterns and driver behaviors, bringing optimization opportunities for powertrain operation and health management. This article introduces a methodology for driving behavior modelling, underpinned by Machine Learning (ML) classification algorithms, generating model-based predictive insight for intelligent powertrain health management strategies. Specifically, the aim is to learn the patterns of driving behavior and predict characteristics for the short-term future operating conditions as a basis for enhanced control strategies to optimize energy efficiency and system reliability.

Abstract Every year the demands on the electric grid increase, but the ability to deliver power where needed remains problematic because of transmission and distribution losses, vulnerabilities to natural disasters, and struggles to meet peak load requirements in an increasing number of regions. To meet these increasing demands, especially with emerging electric vehicles, it becomes ever more important to develop integrated demand and response systems. One such promising technology is the use of a micro Combined Heat and Power (mCHP)-based distributed energy system that addresses both electricity and thermal demands (i.e., electricity, hot water, and space heating demands) by using a single unit. However, one major problem with this technology at the residential level is the optimal sizing and maximizing the operational time of mCHP systems in meeting electrical and thermal demands.

Abstract As the advancement of metal additive manufacturing (AM) technology persists, so will the expansion of its capabilities and applications. In particular, the automotive industry can benefit from the advantages provided by AM, such as flexibility in design and customized products. In this avenue, one potential application of AM is in internal combustion engines (ICEs). As a first step, this effort explores the feasibility of using AM to produce working ICE components for an air-cooled engine. The cylinder head and crankcase of an 11 cm3 displacement volume Saito FG-11 engine were the components identified for metal AM. They were manufactured through Laser Powder Bed Fusion (LBPF) and post machined to achieve the necessary tolerances. Engine testing encompassed both propeller and dynamometer setups with corresponding data collection to measure and compare engine performance.

Abstract A reproducible analytical test method was developed to quantify the fouling resistance and cleanability of camera lens covers for autonomous vehicles (AVs). Reproducible fouling and cleaning cycles were achieved using a custom-built laboratory test stand. The impact of fouling/cleaning on image quality was quantified using digital image analysis. Three optically transparent, fluorine-containing functional coatings on lens covers were used to validate the test method. Accelerated weathering was employed to deliberately degrade the functional coatings. Coating degradation was characterized using water contact angle and X-ray photoelectron spectroscopy. The effect of coating degradation on cleaning performance was studied using this test method. This analysis method was able to characterize differences in coating performance and can be used as a tool to evaluate next-generation functional coatings for autonomous vehicles.

Abstract The civil aircraft nosewheel is clamped, lifted, and retained through the pick-up and holding system of the towbarless towing vehicle (TLTV), and the aircraft may be moved from the parking position to an adjacent one, the taxiway, a maintenance hangar, a location near the active runway, or conversely only with the power of the TLTV. The TLTV interfacing with the nose-landing gear of civil transport aircraft for the long-distance towing operations at a high speed could be defined as a towbarless aircraft taxiing system (TLATS). The dynamic loads induced by the system vibration may cause damage or reduce the certified safe-life limit of the nose-landing gear or the TLTV when the towing speed increases up to 40 km/h during the towing operations due to the maximum ramp weight of a heavy aircraft.

Abstract Electrolytic hydrogen production equipment has numerous hydrogen pipelines and high-pressure hydrogen storage tanks which may leak hydrogen which can lead to explosions causing damage to the nearby personnel and equipment. The present work modeled hydrogen explosions in a skid-mounted electrolytic hydrogen production unit. The model was first used to predict the area affected by an explosion without protective walls. The effects of protective walls on the flame and overpressure were then studied by modeling explosions with various protective walls at various distances from the opening on the side of the unit. The results show that the protective walls effectively reduced the damage behind the wall. However, the reflected shock waves may cause secondary damage in front of the wall if the protective wall is too close to the opening. Moreover, the protective wall blocks the hydrogen diffusion which increases the flammable gas mass.

Abstract Aluminum boron carbide (Al-B4C) composites have been a popular choice among scientists and designers for high-performance strength-to-weight ratio engineering applications. Requirements for such applications are met due to enhanced microstructure, mechanical properties, and ease of processing conditions. The performance and application of these composites are mostly dependent on certain parameters, like composition ratios of reinforcing particles, their sizes and wettability, the presence of additional phases, etc. Prominently, efforts are also being made to synthesize Al-B4C as functionally graded materials (FGMs) that have the potential to cater to the needs of advanced engineering applications and can facilitate new dimensions in the field of aluminum matrix composites (AMCs).