Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
Machining process simulation is a method to increase machining quality and efficiency. The right cutting tool geometry and parameters are chosen during the machining simulation process to create a variety of precision component shapes. A machining simulation for the production of polygonal shafts is presented in this paper. The quality of the parabolic shaft during manufacture will be directly impacted by the machining process, which is simulated using the proper tools and appropriate machining parameters. CAM ESPRIT TNG (x64) software is used in the simulation to simulate the turning and milling process. The machining process can be made more efficient because the simulation process demonstrates that every step of the process operates as intended....
This paper includes the laboratory and experimental methodology used to compare the effect of using mineral, semi-synthetic, and synthetic engine oil on the parts wear of a four-stroke gasoline internal composition engine (ICE). Three test platforms included three engines with identical, technical, and design specifications. They were operated under the same investment, ambient, and climatic conditions. The first engine was equipped with synthetic oil, the second with semi-synthetic oil, and the third with mineral engine oil. All of them had (SAE10W40 API: SL/CF). All test platforms were operated through three stages with variable loads for up to 1,500 operating hours (hr). Oil drain intervals (ODI) were every 100 operating hours. Used oil samples were taken to analyze the physical and chemical characteristics of viscosity, total base number TBN, flash point, metals wear Irion (Fe), copper (Cu), chromium (Cr), and wear index (WI) to investigate the effect of all oils on the wear of engine parts and by comparing the changes in wear. The used oil analysis (UOA) results were drawn that showed the superiority of the use of synthetic oil over semi-synthetic and mineral. It prolonged the technical engine’s lifetime....
The hydraulic circuit in hydraulic mechanisms may be the cause of several vibration anomalies. Flexible pipes, in particular, commonly used in test rigs, may be the source of vibration issues due to their relatively low natural frequencies altering the pump noise, vibration, and harshness (NVH) performance. The purpose of this study is to detail a methodology based on lumped parameter modeling and experiments to analyze the circuit NVH behavior. An experimental study is carried out on two pump designs to determine the outlet pressure fluctuation of various test rig configurations. Numerical simulations are also performed to simulate the actual behavior of the hydraulic system considering these different test configurations. The tests are carried out at a chosen frequency range with a hydraulic circuit configuration representing realistic layouts. In these situations, the hydraulic circuit layout can be the source of NVH anomalies. Realistic design solutions are proposed to modify the test rig NVH behavior in order to achieve a flat response throughout the desired working range....
The welding industry plays a fundamental role in manufacturing. Ensuringweld quality is critical when safety, reliability, performance, and the associated cost are taken into account. A ungsten inert gas (TIG) weld quality assessment can be a laborious and time-consuming process. The current state of the art is quite simple, with a person continuously monitoring the procedure. However, this approach has some limitations. Operator decisions can be subjective, and fatigue can affect their observations, leading to inaccuracies in the assessment. In this research project, a deep learning approach is proposed to classify weld defects using convolutional neural networks (CNNs) to automate the process. The dataset used for this project is sourced from Kaggle, provided by Bacioiu et al. The proposed CNN-based approach aims to accurately classify weld defects using the image data. This study trains the model on the welding dataset, using five convolutional layers followed by five pooling layers and, finally, three fully connected layers. The softmax activation function is employed in the output layer to categorize the input into the six weld categories. The per-class metrics, such as precision, recall, and F1-score, suggest that the model is dependable and accurate....
Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHATalgorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection....
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