Fused deposition modeling (FDM) is the most prevalent thermoplastic additive manufacturing technology. Many input parameters and their settings have a significant impact on the quality and functionality of FDM parts produced. To enhance the quality of parts, it is critical to be able to predict surface roughness distribution in advance. The development of artificial neural network (ANN) models to forecast the impact of main FDM process factors on the part quality in terms of surface roughness while utilizing ABS (Acrylonitrile butadiene styrene) material is described in this work. Taguchi L9 orthogonal array was used to plan the experiments. Different printing input parameters such as layer thickness, orientation angle, and infill angle are used in the experiments. In terms of controllable input parameters, ANN is used to construct a predictive mathematical model. The effects of various printing settings on surface roughness were investigated using analysis of variance (ANOVA), main effect plots, and contour plots. Experiment findings and regression value are used to validate the models. The model has shown to be capable of adequately predicting responses within a maximum percentage error of 4.664 percent of arithmetic roughness average (Ra), which is a good agreement.
This study presents the rail wheel contact problems under normal and tangential categories. Both analytical and numerical approaches were used for modelling, where the analytical approach assumed elliptical contact patches based on the Hertz theory. In the numerical approach, 3D finite element models were used to investigate non-elliptical contact patches. The only elastic material model was considered in the case of Hertz theory. However, in the case of finite element analysis, both elastic and elastoplastic material models were used to simulate the material's behavior under the applied load. The elastoplastic material model was used to determine the amount of stress at which the plastic deformation starts, which enables determining the rail wheel's critical load. The commercial software ABAQUS was employed for 3D modeling and contact stress analysis. The study shows maximum stress at 3 mm from the rail wheel contact surface when the maximum load of 85 kN is applied. This initiates the cracks in the subsurface and causes the portion of the rail wheel to break off in the form of spalling after a certain time.
Micro-electromechanical-systems (MEMS) based sensors are used for monitoring the state of machines in condition-based maintenance tasks. This approach is applied at tram depots for the purpose of identifying faulty wheels on trams in order to eliminate defective trams at the entry or dispatch gates. The application of MEMS-based sensors for the detection of wheel faults is the focus of this study. A method for processing of the collected sensor data is developed. It is based on assessing the energy of vibrations at different frequency bands. Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) is used for obtaining a description of the sensor data. The task of finding the energy threshold for detecting faulty wheels, frequency band and parameters of MODWPT which most distinctly distinguish the wheels is the goal of the method. The weighted difference (DW) between the extreme values of energy in a frequency band for normal and faulty wheels is proposed as the measure of the ability to distinguish the wheels. The search for the solution is formulated as a discrete optimisation problem of maximising this measure. Both the simulation and experimental results indicate that faulty wheels have greater vibration energy than normal wheels. The properties of this approach are discussed and evaluated.
The article deals with wheel-rail contact analysis at railway turnout using a finite element modelling approach. The focus is understanding the wheel-rail contact problems and finding the means of reducing these problems at railway turnouts. The main aim of the work reported in this article is to analyse fatigue life and simulate the wheel-rail contact problems for a repeated wheel loading cycle by considering the effect of normal and tangential contact force impact under different vehicle loading conditions. The study investigates the impact of tangential contact force generated due to different-angled shapes of the turnout and aims to reveal how it affects the life of contacting surfaces. The obtained results show that the maximum von-Mises equivalent alternating stress, maximal fatigue sensitivity, and maximum hysteresis loop stresses were observed under tangential contact force. These maximum stresses and hysteresis loops are responsible for rolling contact fatigue damage, and excessive deformation of the wheel-rail contact surface. At a constant rotational velocity, the tangential contact force has a significant impact on the fatigue life cycle and wheel-rail material subjected to fatigue damage at lower cycles compared to the normal contact force. The finite element modelling analysis result indicated that the contact damages and structural integrity of the wheel-rail contact surface are highly dependent on contact force type and can be affected by the track geometry parameters.
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