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Performance of semi-active cab suspension system with different control methods
Get PDFIn this study, based on adaptive control methods, the semi-active suspension system of the heavy truck cab is researched and controlled to improve the ride comfort of the heavy truck. A dynamic model of the vehicle is established for simulation. Matlab/Simulink software is used to simulate and calculate the root mean square (RMS) accelerations of the driver’s seat and cab pitch angle under different operation conditions. Proportional-integral-derivate controller with its parameters optimized by the genetic algorithm (GA-PID controller) and Fuzzy logic control combined with PID (FLC-PID controller) are used to control the semi-active cab suspension system of the heavy truck. The obtained results show that the ride comfort of the vehicle using FLC-PID is better improved in comparison with using GA-PID under different operating conditions. Especially, when the vehicle moves at a speed of 72 km/h, the RMS accelerations of the driver's seat and cab pitch angle are greatly reduced by 26.45 % and 26.07 % respectively. Therefore, the FLC-PID control should be applied to the suspension system of the vehicles to improve the vehicle's ride comfort
Align MWCNT/GNPs/PDMS based nanocomposite dry ECG electrodes for ECG recordings
Get PDFThis work focuses on the fabrication and testing of highly conductive polymer electrodes to measure the biomedical signals known as an Electrocardiogram (ECG). When doing this, optimal skin contact is considered. Align Multiwalled Carbon Nanotubes (AMWCNT), Graphene Nanoplates (GNPs), and Polydimethylsiloxane (PDMS) were used to fabricate the electrodes for continuous ECG monitoring. Sonication and magnetic stirring were used to disperse the mixture. The conductance of the PDMS/GNPs/AMWCNT electrodes were measured using a digital multimeter and were compared with that of standard silver-silver chloride (Ag/AgCl) electrodes. The current study showed that real-time monitoring achieved by an internet of things (IoT) systems with microcontroller
Research on fault diagnosis method of electromechanical transmission system based on one-dimensional convolutional neural network with variable learning rate
Get PDFAs an important part of many mechanical equipment, the mechanical transmission system is very important to carry out efficient and accurate fault monitoring and diagnosis. Compared with traditional fault diagnosis techniques, such as spectrum analysis, deep learning has been widely used in the field of mechanical system fault diagnosis due to its powerful data expression ability, and has achieved certain research results. One-dimensional convolutional neural network is a widely used model for deep learning, so in this paper, the one-dimensional convolutional neural network (1D-CNN) in the deep learning theory and the vibration signal analysis method are integrated and applied to the fault identification of mechanical transmission system to achieve accurate diagnosis and classification of faults. The experiment is mainly to collect the vibration signal data of different fault states such as broken teeth, cracking, shaft unbalance, bearing wear, and excessive friction of the driven wheel of the mechanical transmission system, it was divided into training set and testing set according to an appropriate proportion, and 1D-CNN was built using Python. The deep learning model deeply analyzed the influence of different data sample sizes and different model parameters on the recognition accuracy, and obtained an ideal diagnostic model based on variable learning rate through parameter adjustment and comparative analysis. This experimental results show that the recognition method based on one-dimensional convolutional neural network can be effectively applied to the fault diagnosis of related mechanical transmission, and has a high diagnosis accuracy
Mechanism of time-delay feedback control of suspension damping with an annular vibration-absorbing structure
Get PDFWith the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system
Genetic algorithm-based error correction algorithm for CNC turning machining of mechanical parts
Get PDFThis paper discusses how to improve the machining precision in the turning of slender shaft. The main cause of dimensional error in slender shaft machining is analyzed by establishing dimensional error model and using genetic algorithm to optimize cutting parameter selection. Based on this, the proportional-integral-differential control error compensation is proposed to reduce the error in the turning process of slender shaft. Through the simulation experiment, the machining size error of slender shaft under different cutting parameters is obtained. It is found that the increase of back blowing and feed rate will aggravate the dimensional error, while the increase of CS will reduce the dimensional error. The error after the proportional-integral-differential control error compensation is much smaller than that without the error compensation. The experimental results show that the method is reliable in reducing the errors in the turning of slender shaft, and can realize the machining mode with higher precision and efficiency. This is of great significance to the development of machinery manufacturing industry
Prediction of comprehensive dynamic performance for probability screen based on AR model-box dimension
Get PDFIn order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value
Evaluation of the hardness and wear behavior of date seed powder/epoxy composites as potential biomaterials
Get PDFThe date palm, or Phoenix dactylifera, is a crop that is grown for food and is used by many people on a regular basis. The cultivation of date palms, their processing, and consumer demand all produce tons of waste. Date palm seeds can account for up to 10 % of the total weight of the fruits. A topic of interest for research is the application of date seed waste in the fields of engineering, nutrition, and cosmetics. This work aims to show that epoxy/palm seed date composites are suitable for use as biomaterials by examining their wear behavior. Give a brief introduction to a few ideas before going over the fundamental idea of wear resistance. Future wear resistance improvements in biomedical applications and other industries that might benefit from this addition will continue to be achieved through the use of date palm seed microparticles in epoxy. In this study, epoxy composites containing palm seed particles are made by the mixing procedure. The studied composites made from date palm seed powder particles has micron particle size of 150 which is mixed with epoxy as a matrix. The percentages of date palm seeds are varying from 5 % to 20 %. The 20 percent sample had the best results, losing 7.03 percent of their starting weight. The 10 percent group, which lost 9.4 percent, the 15 percent group, which lost 12.2 percent, and the 5 percent sample, which lost 19.95 percent, were the next in line. Each of the four samples had 1 kg to 5 kg of weight applied to them at five different rotation speeds. Upon analysis of all the samples under investigation, it was discovered that the date palm seeds enhanced and increased wear resistance, leading to better outcomes
Development of shock absorber with quasi-zero stiffness effect to reduce dynamic effects on pump unit
Get PDFThe article is devoted to the improvement of the vibration isolation and shockproof properties of the shock absorber used in the vibration isolation compensator system of the pumping unit (PU). The set task is to create an elastic damping system with the desired low (quasi-zero) stiffness, which makes it possible to reduce the detrimental effect of pumping unit vibrations both on human health and on the equipment itself. High internal dynamic (vibration) loads are the main causes of early failure of pumping units, which are transmitted to the equipment through pipelines and foundations, mainly due to various operational factors. A promising trend in increasing the operating reliability and efficiency of pumping and power equipment is the use of a vibration-isolating compensator system (VICS). To obtain greater efficiency of vibration isolation, it is proposed to apply a modern method of vibration damping – the use of elastic mechanical systems with quasi-zero stiffness. The damping devices with a quasi-zero stiffness effect were studied. An assessment of the operability and effectiveness of these dampers at oil and gas facilities was given. Based on the information studied, a new concept of a quasi-zero stiffness damping device is proposed. The method of mathematical analysis determined its power curve. A 3D model of the damper device was built using a simulation software. The functional check of the damper was performed with mathematical modeling carried out on the computing device with the help of a specialized Ansys software that allows performing a system analysis of an object using the finite element method. The following results were obtained: the design of the damper with the effect of quasi-zero stiffness allows to widen the range of basement load transmission coefficient reduction towards low frequencies. The low rigidity of the system at the operating point ensures low values of the frequency of natural oscillations, and, consequently, high vibration isolation qualities
Application of AI intelligent vision detection technology using deep learning algorithm
Get PDFThis study aims to design efficient and reliable artificial intelligence vision detection models to improve detection efficiency and accuracy. The study filters defect-free images by image preprocessing and region of interest detection techniques. AlexNet network is enhanced by introducing attention mechanism modules, deep separable convolutions, and more to effectively boost the network's feature extraction capacity. An area convolutional neural network is developed to rapidly identify and locate defects on steel plate surfaces, utilizing an enhanced AlexNet network for feature extraction. Results demonstrated that the algorithm attained an average detection rate of 98 % and can identify defects in a minimal time of only 0.0011 seconds. For the detection of six types of steel plate defects, the average accuracy of the optimized fast regional convolutional neural network reached more than 0.9, especially for the detection of small-size defects with excellent performance. This improved AlexNet network has a great advantage in F1 value. The conclusion of the study shows that the designed artificial intelligence vision detection model has high detection accuracy, speed, and performance stability in steel plate surface defect detection and has a wide range of application prospects
Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals
Get PDFThis study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes