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This paper aims to detect railway vehicle wheel flats and polygonized wheels using an envelope spectrum analysis. First, a brief explanation of railway vehicle wheel problems is presented, focusing particularly on wheel flats and polygonal wheels. Then, three types of wheel flat profiles and three periodic out-of-roundness (OOR) harmonic order ranges for the polygonal wheels are evaluated in the simulations, along with analyses implemented using only healthy wheels for comparison. Moreover, the simulation implements track irregularity profiles modelled based on the US Federal Railroad Administration (FRA). From the numerical calculations, the dynamic responses of several strain gauges (SGs) and accelerometer sensors located on the rail between sleepers are evaluated. Regarding defective wheels, only the right wheel of the first wheelset is considered as a defective wheel, but the detection methodology works for various damaged wheels located in any position. The results from the application of the methodology show that the envelope spectrum analysis successfully distinguishes a healthy wheel from a defective one.

Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

Improving the thermal efficiency of internal combustion engines plays a crucial role in reducing fuel consumption and engine emissions. Studies have shown that the friction loss caused by the piston ring–cylinder liner pair accounts for approximately 30–40% of the engine’s total mechanical friction. The key to improving mechanical and thermal efficiency lies in reducing frictional losses through advanced solutions. However, as engine intensification increases, the growing thermal and mechanical loads lead to out-of-round deformation of the cylinder liner. This deformation reduces the sealing conformity of the piston rings, leading to increased blow-by and elevated particulate matter (PM) emissions. To address this, a dynamic–static deformation testing system for cylinder liners, combined with a multi-physics simulation for data validation, has been developed to achieve energy conservation and emission reduction in engines. Based on established strain gauge and eddy current displacement sensors, this study developed a dynamic deformation testing system, modified for a specific type of diesel engine, and analyzed the cylinder liner deformation under fired conditions. Test results show that under engine speeds ranging from 700 rpm to 1100 rpm, the overall radial out-of-roundness of the cylinder liner increased, with a maximum deformation of 49.2 μm. The second-order component of out-of-roundness also increases with speed, showing a maximum rise of 28.9 μm, while the third-order and fourth-order components exhibit relatively minor changes. These findings suggest that the overall radial deformation under fired conditions is mainly dominated by second-order out-of-roundness, with third-order and fourth-order components contributing marginally.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

The condition monitoring and faults diagnosis of wheelset are of great significance for the safe and stable operation of trains. Aiming at the axle crack, wheel flat, wheel out-of-roundness and their coupling faults in wheelset system, this paper proposes a method to diagnose these multiple faults simultaneously. Firstly, the dynamic model of a wheelset system with flat and out-of-roundness is established in SIMPACK software to analyze the fault characteristics of these two typical wheel damages. And the wheel flat and out-of-roundness are equivalent to displacement excitation and then loaded into the finite element (FE) model of the cracked wheelset. The vibration responses of the cracked wheelset with flat and out-of-roundness are solved in ABAQUS software. Then, the simulated multi-fault vibration data of wheelset are input into a Light-GBM model to realize the faults classification of axle crack, wheel flat, wheel out-of-roundness and their coupling faults. Finally, the wheelset vibration signals under the conditions of health, axle crack, wheel flat, and crack-flat coupling fault are collected from an experimental bench to verify the proposed method. This work can provide a basis for multi-fault classification and on-line health monitoring of trains.

This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with Hotelling’s T2 Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-T2 model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-T2 model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-T2 model is outperformed in 100% of the analyzed scenarios when compared to SAE-T2 in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-T2 methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE’s potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations.

Resilient wheel (RW) is gradually promoted for use in China’s subway vehicles recent years, which has been widely used in tram in Europe and China. As measured, the RWs in service on the metro trains have experienced eccentric wear and even polygonization of high order after 30,000 km running. Given that most subway lines have small curve radii, this study assesses the operation safety of vehicle passing through curves with RWs having polygonal wear, as well as their effect on vibration reduction compared to conventional wheels. A numeric simulation model is developed to model the metro vehicle-track dynamic system installed with resilient wheel, where a conventional wheel-rail contact model is modified considering the composite structure of a RW. A RW-rail contact model considering relatively independent motion of the wheel hub and rim is developed, which is integrated into a metro vehicle-track dynamic system model equipped with the RWs. Upon comparing the curving performance of vehicles equipped with conventional wheels, the findings reveal that, in scenarios of surplus-superelevation, the wheel load reduction ratio (WLRR) of conventional wheels demonstrates superior performance. While curving in the case of deficient-superelevation, the WLRR of RWs is lower. Furthermore, the safety performance of the RWs surpasses that of conventional wheels, particularly as the severity of polygonal wear on the wheel increases. These conclusions can be used as a reference for maintenance strategy of the RW applied on metro trains, which is urgently to be built considering the potentially wide promotion of the RW to the subway system.

The results of experimental study on the effect of the treatment regime on the shape accuracy of balls and the surface wear of a diamond wheel are presented for the diamond grinding of silicon nitride ceramic balls in an annular groove and with circular feed. The indicators of the shape accuracy of ground balls are the variance of ball diameters, the out-of-roundness profile shape factor, and wheel surface wear characterized by the radial working surface tilt angle and curvature coefficient. This effect is adequately described by the linear dependences of the ball diameter variance on the circular feed rate and wheel speed, the shape factor on the wheel speed, and the tilt angle and curvature coefficient on the circular feed rate. The treatment regime parameters, at which the studied ball grinding scheme is reasonable for application, are predicted.

The cylinder liner bears alternating thermal load and mechanical load, and evaluating the cylinder liner deformation is a key issue in the design stage of an engine. In this work, the shape and position tolerance of the cylinder liner to various loads were studied based on the finite element method, the simplex algorithm and the least square method. Firstly, the heat transfer boundary conditions of the cylinder liner were obtained through combustion simulation. Combined with the mechanical load, the transient deformation of the cylinder liner under the thermo-mechanical load was obtained. Subsequently, the out-of-roundness and coaxiality were selected to evaluate the shape and position changes in the cylinder liner. Finally, the transient tolerance analysis of the cylinder liner under alternating thermo-mechanical load was carried out. The results show that the maximum out-of-roundness of the cylinder liner under thermal load, mechanical load and thermos-mechanical load was 15.12, 43.40 and 51.76 μm, respectively. The maximum coaxiality under thermal load, mechanical load and thermos-mechanical load were 6.17, 80.49 and 80.22 μm. The side thrust was more likely to cause uneven deformation of the cylinder liner section, the liner coaxiality was mainly affected by the cylinder burst pressure, and the liner shape tolerance was much more sensitive to the mechanical load than the mechanical load.

Abrasive flow finishing (AFF) process is used to deburr, polish and radius surfaces and edges by flowing a semisolid viscoelastic abrasive medium over the surface to be finished. The AFF process is used in the industries especially in the case of finishing complex internal and external shapes. However, the AFF process is a time-consuming process due to its low finishing rate. Efforts are made by different researchers to improve its finishing rate. Rotational abrasive flow finishing (R-AFF) process is one of such processes where in complete workpiece tooling is externally rotated and the medium reciprocates with the help of hydraulic actuators to improve the finishing rate. In the present study, experiments are conducted on hard steel (AISI 4340) cylindrical tubes at different extrusion pressures, workpiece rotational speed, number of cycles and medium compositions for finding optimum conditions of the same for higher change in out of roundness (ΔOOR) and material removal (MR). In this study, soft styrene-butadiene- and silicone polymer-blended medium is used for finishing. Initially, these hard tubes are ground using internal grinding process. Because of grinding process, the workpiece possesses OOR of about 2.75 μm. Later, these workpieces are finished using R-AFF process and OOR improves to 1.69 μm (i.e. ≈39 % improvement). Overall, the results of R-AFF are encouraging and the experiments have shown that R-AFF has a very promising future for the industries in terms of better finishing.