Jaw Functional Orthopedics and Cranoficial Growth
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The effect of the quantity and length of fibers on the mechanical properties of fiber-reinforced concrete based on polypropylene fibers
No full textIn these studies, the effect of polypropylene fibers on the mechanical properties of concrete was studied, and special attention was paid to determining their optimal amount and acceptable length. The fibers were added to the concrete composition in amounts of 0.1-0.5 % and lengths of 10, 20, 30, 40, 50 mm and tested. According to the results of the study, the highest results were recorded at a fiber content of 0.2-0.3 % and lengths of 20-30 mm, and the compressive strength of concrete increased by up to 15.9 % compared to ordinary concrete. When adding fibers in excess (≥ 0.4 %) or with a length of 50 mm, a decrease in strength was observed. The results obtained showed that it is possible to increase the quality and improve the strength of concrete by selecting the optimal parameters of polypropylene fibers
Neural network-based ANC algorithms: a review
Get PDFActive Noise Control (ANC) technology is of great value in the field of noise mitigation. Recently, traditional linear adaptive control methods, represented by the FxLMS algorithm, are structurally simple and computationally efficient but often suffer from performance degradation or even failure in practical applications due to nonlinear system factors. For this reason, neural network-based ANC methods have attracted significant research interest for their strong nonlinear processing capabilities and have gradually emerged as a focal point for addressing nonlinear ANC problems. This paper systematically reviews the research progress of neural networks in the field of nonlinear ANC, focusing on two key dimensions: network architecture and training methods. In terms of architecture design, existing studies primarily enhance performance through topology optimization, improvements to functional link artificial neural networks, and innovative hidden layer designs. Advancements in training methods focus on the optimization of loss functions, innovation in weight update algorithms, and the introduction of other training strategies. In the future, neural network-based ANC algorithms will continue to deepen, with potential development paths including the integration of advanced network architectures such as Generative Adversarial Networks (GANs), optimization of utility functions, pruning of hidden layers, improvement in loss function design, and the adoption of more efficient training strategies. These efforts will further improve algorithm performance and ultimately provide robust support for achieving more precise and efficient active noise control
Multi-mode frequency response prediction of milling robot based on feature transferring with small sample sets
Get PDFIndustrial robots are increasingly used in machining due to their cost-effectiveness and larger work envelopes. However, their relatively low structural stiffness makes them vulnerable to machining chatter, which negatively impacts both process stability and surface quality. Accurate prediction of the multi-mode frequency response function (FRF) of robotic milling systems is crucial to ensure process stability. Traditional FRF prediction approaches, however, often require extensive experimental procedures, are complex, and are time-consuming. To address these challenges, this study proposes an innovative feature-transfer-based method for multi-mode FRF prediction in milling robots, requiring only a minimal set of impact tests. The method organizes measured FRFs into second-order complex tensors, facilitating the transfer of features between different postures. Multi-mode parameters of the tool-tip FRF under the source posture are extracted using the least-squares complex exponential (LSCE) method and assembled into a label vector. A complex-kernel extreme learning machine with augmented inputs (CKELM-AI) is then trained to predict the tool-tip FRF under the target posture. Additionally, a virtual sample generation strategy based on CKELM-AI and feature augmentation, including statistical, frequency, and time-frequency features, is applied to enhance prediction accuracy. Experimental validation on a milling robot demonstrates that the proposed method significantly improves both prediction efficiency and accuracy, establishing a new, more efficient approach for predicting multi-mode FRFs without the need for extensive testing
Pattern recognition of acoustic emission signals by Q235 steel corrosion in marine environment
No full textTo overcome the limitations of traditional monitoring methods, which are restricted to periodic inspections, this study proposes a real-time method for identifying metal corrosion damage patterns to monitor the condition of Q235 steel corrosion based on acoustic emission (AE). Firstly, AE technology was utilized to monitor the corrosion process of Q235 steel plates in simulated industrial marine environment in real-time. Wavelet packet energy spectrum coefficients, closely related to the damage mechanism, were extracted from the acquired signals. A feature matrix was then constructed using principal component analysis (PCA) to eliminate redundant information and enhance computational efficiency. The K-means clustering algorithm was then applied to classify the AE signals into three classifications: the signals of mode 1 correspond to bubble rupture, the signals of mode 2 to pit growth and expansion, and the signals of mode 3 to the detachment of corrosion products and oxide film rupture. A damage pattern recognition model based on a convolutional neural network (CNN) was developed, enabling the real-time recognition of other unknown AE signals generating during the corrosion process of Q235 steel, and it exhibited satisfactory performance in accurately identifying corrosion-related acoustic emission patterns
Development of methodology for monitoring of metalworking fluids quality
No full textEfficient monitoring of metal-working fluids (MWFs) is crucial to maintaining optimal machining performance and ensuring the safety and health of workers in the metalworking industries. Knowledge of the performance of cutting fluids in the machining of various workpiece materials is very important to improve the efficiency of any machining process. Metal machining companies using MWS have the opportunity to choose the best product from the wide range offered, which can differ in physical parameters as it is designed to be best for the selected process. The unique adaptation to the manufacturing process poses certain challenges in monitoring MWS quality during machining. The importance of MWS quality is crucial, which can lead to costly defects and loss of workpieces. The monitoring only by the quality lab sometimes is insufficient. This article presents the development of a sensor for the indirect monitoring of MWFs, aiming to provide a cost-effective and nonintrusive solution to assess the quality and condition of these fluids. The measurement results are compared with those of other emulsion quality control protocols. Its implementation can significantly enhance the efficiency of MWF management, leading to improved machining performance, reduced downtime, and enhanced worker safety. The sensor's nonintrusive nature eliminates the need for frequent manual sampling, reducing costs and minimizing the environmental impact associated with traditional monitoring practices. Overall, the sensor described in this article offers a viable solution for indirect monitoring of MWFs, contributing to the advancement of smart manufacturing and the optimization of metalworking processes
Analysis of a 10 kW mini pumped hydro storage plant with solar integration in Uzbekistan
No full textThis paper presents the design and performance evaluation of a 10 kW mini pumped hydro storage (PSH) system integrated with solar photovoltaic (PV) energy for rural electrification in Uzbekistan. The system stores excess solar energy during the day and generates 60 kWh electricity during evening hours at a rated power of 10 kW, with an overall efficiency of about 75 %. The optimized design includes a Cross-Flow turbine (200 mm diameter, 600 rpm), a 10 m head, and 58 solar panels of 400 W. The study demonstrates that such small PSH systems can provide a cost-effective, long-lifetime alternative to chemical batteries in rural power applications
Data analytics-based model for optimizing cationic retarder and acetic acid in polyacrylic yarn dyeing
No full textThe study aims to develop a model for optimizing the concentration of solution parameters in the dyeing process of polyacrylic yarns. Specifically, the study examines the use of cationic retaiders and acetic acid, which affect the wavelength as an indicator of yarn color aging. By optimizing these parameters, the objective is to improve the color stability and longevity of dyed polyacrylic yarns. The application of Response Surface Methodology (RSM) encompassed two distinct types of data distribution properties: linear and non-linear. The R-squared (R2) value for the non-linear RSM model was 0.96, compared to 0.86 for the linear RSM model. These results indicate that the model formed based on the non-linear RSM offers superior predictive ability in optimizing solution concentration as a parameter in the polyacrylic yarn dyeing process compared to the linear RSM-based model. In addition to providing practical implications for textile practitioners, this study contributes theoretically by emphasizing the effectiveness of statistical methods such as RSM in manufacturing process analysis
Feature extraction of rolling bearing based on adaptive variational multi-harmonic mode extraction
No full textVariable multi-harmonic mode extraction (VMHME) not only has the advantages of high computational efficiency and extraction accuracy similar to variational mode extraction (VME), but also could extract the multi-harmonic components of periodic narrowband impulse signals in frequency band as wide as possible, making it very suitable for feature extraction in the event of rolling bearing failure. VMHME needs to accurately estimate the fault characteristic frequency of rolling bearing as its prior parameter, and small errors in estimating the fault characteristic frequency will cause significant deviations in the target extraction components. At present, the theoretical fault characteristic frequency of rolling bearings is commonly used as the estimated fault characteristic frequency. However, due to the installation deformation of rolling bearings and the random sliding between the rolling elements and the raceway during operation, it can cause a deviation between the actual fault characteristic frequency and the theoretical fault characteristic frequency. The most scientific and effective method is to enable VMHME to adaptively obtain the fault characteristic frequency based on the characteristics of the analyzed signal itself. Therefore, this paper introduces the envelope harmonic product spectrum (EHPS) theory into VMHME and proposes an adaptive VMHME (AVMHME) method to effectively extract the multi harmonic components of the periodic narrowband impulse signal when rolling bearings fail. Feasibility of the proposed method is verified through simulation and rolling bearing’ early weak fault experiment, and its superiority is also verified through comparative analysis
Research on coupling dynamic characteristics and parameter influence of TBM cutterhead system
No full textAs an important system of TBM, the host system bears the impact of unstable load from itself and the strong load of the rock in the geological layer during operation, which causes irregular vibration of the host system, resulting in low tunneling efficiency, and is more likely to cause cutterhead cracking and component damage. To this end, with the help of analysis software such as Matlab and Ansys, the intrinsic characteristics and vibration response of the host system are studied, and the specific parameters of the vibration influencing factors are discussed. The results show that the axial displacement of the center block of the cutterhead is the largest, reaching 0.85 mm, and the longitudinal displacement value is about 2-3 times of the transverse displacement; in the design stage, the mass of the central block should be controlled in the range of 50 %-55 %, and the rest of the cutterhead should be controlled in the range of 12.5 %-13.5 %; the vibration is the smallest under the uniform layout of the gear, the fluctuation of the solid short shaft connection of the motor is relatively stable, and the maximum vibration value does not exceed 3.5e-2 mm
Current research status on the occupational hazards of hand-transmitted vibration: a case study in China
No full textThe hazards arising from long-term hand-transmitted vibration operations can cause significant damage to the human body. As China is a populous country, understanding the current situation of vibration exposure among workers in various related fields in China holds significant reference value. To this end, this paper analyzes data from core journal literature in China from the 1980s to the present based on the keywords hand-arm vibration disease, hand-transmitted vibration, and occupational exposure. This paper provides an overview of the current status of hand-transmitted vibration hazards, including the distribution characteristics, hazards, and diagnostic methods of hand-arm vibration disease, as well as the deficiencies in these diagnostic methods. It also integrates data on the vibration intensity, frequency, and prevalence of vibration tools to analyze the relationship between the prevalence of hand-arm vibration disease and the vibration intensity and frequency of vibration tools. The results indicate that the vibration tools causing occupational hand-arm vibration disease are primarily found in the mining and manufacturing industries, with rock drilling jobs and positions being dominant in the mining industry and grinding jobs and positions being dominant in the manufacturing industry. The A(4) values of grinding tools, jobs, or positions are significantly higher than China’s limit value of 5 m/s2 for hand-transmitted vibration. The A(4) distribution of rock drilling tools is more concentrated, while the A(4) distribution of grinding tools is broader. The current diagnostic methods have poor specificity and sensitivity. There is insufficient awareness of the hazards of hand-transmitted vibration. There is no significant correlation between A(4) and the prevalence of vibration white finger (P>0.05), and A(4) alone is insufficient to reflect the extent of harm caused by hand-transmitted vibration operations to the human body. Both low-frequency and high-frequency vibrations may be harmful to the human body, and there may be a positive correlation between the fundamental frequency of vibration tools and the prevalence of disease (r>0,P<0.05)