Journal of Mechatronics and Artificial Intelligence in Engineering
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Towards the efficiency research of the working process of locomotives diesel under operating conditions
Get PDFA method is proposed for quantitative assessment and justification of the criterion of the rationing indicators of external and boost air temperature factors on the qualitative component of the working process of two-stroke supercharged diesel engines under various load conditions of the traction power plant of operating diesel locomotives. The results of the study were obtained in the numerical values and graphs, as well as analytical dependencies (equations) designed to substantiate the parameters under study, including their average values under different operating mode diesel and ambient temperatures. These studies are recommended to continue with the aim of studying the intensity of the dynamics of the decrease or increase in the relative filling coefficients of the 10D100 diesel cylinders with air and developing a methodology for predicting the criterion of the influence of the rationing of boost indicators and outside (external) air on the operating process of diesel locomotives diesels
Complex fault diagnosis in wind turbine bearings: a hybrid approach combining the improved feature mode decomposition and convolutional neural networks
Get PDFThe complex noise interference and diverse fault-induced signals in vibration data from wind turbine equipment pose significant challenges for bearing fault diagnosis, including cumbersome methodologies, prolonged processing times, and compromised accuracy. To address these limitations, this study proposes a novel composite fault diagnosis framework that integrates Feature Mode Decomposition (FMD), Fast Spectral Kurtosis (FSK), and Convolutional Neural Network (CNN). While conventional Empirical Mode Decomposition (EMD) exhibits limited noise robustness and struggles to extract subtle fault signatures in composite failure scenarios, our approach employs FMD to decompose fault-related intrinsic mode functions (IMFs)and further filters the IMF components using fast spectral cliffs with enhanced feature separability. Subsequently, the Short-Time Fourier Transform (STFT) is applied to derive time-frequency representations, followed by Fast Spectral Kurtosis analysis to identify optimal demodulation bands for non-stationary signals. The energy spectrum of denoised signals is converted into grayscale images, serving as input to a tailored CNN architecture for hierarchical feature learning. Experimental validation demonstrates that this hybrid methodology achieves a fault recognition accuracy of 98 % under compound fault conditions, outperforming conventional EMD-based approaches in terms of noise immunity and diagnostic precision. Comparative analysis reveals an 8 % improvement in detection reliability over standalone deep learning models, particularly in low signal-to-noise ratio (SNR) environments. The proposed framework offers a robust solution for multi-fault identification in industrial Bearing machinery, demonstrating superior generalization capability across varying operational conditions
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
Dual-stator ultrasonic motor achieving 2-DOF linear and rotary motion with single-phase excitation
Get PDFThis study proposes a novel dual-stator linear-rotary ultrasonic motor. The piezoelectric ceramic excites both out-of-plane and in-plane vibration modes within the stator. These distinct vibration modes independently drive the slider (rotor), generating reciprocating linear and rotational motions, respectively. Finite element analysis and laser vibrometer-based vibration testing validated the motor's operational principle. The close agreement between simulated and measured resonant frequencies for both vibration modes, with mere discrepancies of 3 % and 4 %, respectively, underscores the accuracy of the stator’s vibrational characteristics. Subsequently, two stators are fabricated and assembled to the ultrasonic motor prototype. Experimental results demonstrate the motor’s impressive performance, achieving a maximum linear velocity of 265 mm/s and a peak rotational speed of 1600 rpm. Furthermore, the motor delivers a maximum thrust force of 0.18 N and a stalling torque of 1.8 mN·m
Maintenance, repair, and overhaul of robotic systems
Get PDFThis paper not only explores the fundamental aspects of but also brings new ideas for maintenance, repair, and overhaul (MRO) operations of robotic systems (RS). This synthesis is based on the limited scholarly research in this area and on information gathered from comprehensive web searches and analysis of corporate websites so that the results reflect the current views of RS developers and operators. The paper describes several crucial areas concerning RS MRO: maintenance of robotic systems, challenges and best practices for RS MRO, predictive maintenance variables and key performance indicators, data analytics, software solutions for RS MRO, and logistics/supply chain approach that should be considered. These insights provide not only a comprehensive understanding of the current state of RS MRO but also describe trends and suggestions for the future of RS MRO, emphasizing the novelty of the proposed research conducted. Key trends that organizations will need to address include the use of artificial intelligence (AI) models and the increasing importance of RS MRO logistics and supply chain management
Machining parameters optimization in high-speed milling of titanium alloy chips
Get PDFHigh-speed cutting of titanium alloy has the advantages of high processing efficiency, reducing tool wear, and obtaining good surface quality, but there is a lack of research on the influence of chip shape on machining parameter selection mechanism in the cutting process, which hinders the development of high-speed milling quality of titanium alloy. In this paper, the shape of titanium alloy (Ti6Al4V) chips at different speeds and different temperatures were simulated. With the increase of cutting speed, the tool squeezes the workpiece material, causing it to undergo elastic deformation and thereby forming a cutting layer. As the cutting process progresses, the chip gradually takes on a serrated shape, the degree of sawtooth sharpening of chips was analyzed. The formation mechanism of chips and the formation process of sawtooth chips during right-angle cutting were analyzed. The locust optimization algorithm was used to optimize the multi-objective parameters, and it was found that high performance machining effect could be achieved by using large feed speed, radial cutting depth and spindle speed
Application of GSABO-VMD-KELM in rolling bearing fault diagnosis
Get PDFTo address the difficulties in extracting fault features of rolling bearings and the low diagnostic accuracy, a fault diagnosis method for rolling bearings is proposed. This method integrates the Golden Sine Algorithm (GSA) with the Subtraction-Average-Based Optimizer (SABO) to form a Golden Sine Improved SABO Optimization Algorithm (GSABO). The GSABO algorithm is used for parameter optimization of Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM) in the fault diagnosis process. Firstly, the chaotic mapping strategy is used to optimize the population initialization of the Subtractive Clustering-Based Adaptive Optimization (SCAO) algorithm, enhancing population diversity. Secondly, the Golden Sine Algorithm (GSA) is integrated to improve the displacement algorithm, enhancing global search capability and effectively avoiding getting trapped in local optima. Then, the GSABO-VMD (Golden Sine Algorithm-Based Optimized Variational Mode Decomposition) is employed to decompose the rolling bearing fault signals, and the envelope entropy minimum criterion is used to select the effective modal components. Finally, time-frequency domain indicators of the selected modal components are computed to form a feature matrix, which is then input into GSABO-KELM (Golden Sine Algorithm-Based Optimized Kernel Extreme Learning Machine) for fault classification and recognition. Experimental analysis shows that compared to the unmodified SABO algorithm, GSABO has significant advantages in terms of escaping local optima, convergence speed, and accuracy. When compared with other traditional algorithms, GSABO-VMD-KELM achieves recognition accuracies of 99.3333 % and 99.0476 % on bearing data from Case Western Reserve University (CWRU) and Xi'an Jiao tong University (XJTU), respectively. This demonstrates the accuracy and superiority of the algorithm and provides valuable insights for engineering applications in rolling bearing fault diagnosis
Research progress on 3D printed geopolymer materials
Get PDFThe integration of 3D printing technology with geopolymer materials offers a sustainable alternative to conventional construction methods, significantly reducing CO2 emissions. However, challenges such as rapid setting, limited workability, and weak interlayer bonding limit their broader application. This review summarizes recent progress in 3D printed geopolymer composites, focusing on materials selection, rheological optimization, buildability, and mechanical performance enhancement. Strategies including the use of rheology modifiers, fiber reinforcements, nano-additives, and process optimization have shown promise in improving printability and structural performance. Remaining challenges, such as balancing setting time and printability and enhancing interlayer adhesion, are also discussed. Future research directions are proposed to further advance the development of high-performance, low-carbon geopolymer 3D printing materials for sustainable construction
Study on the effect of suspension system friction of heavy-haul freight vehicles on the operation performance
Get PDFDuring the operational life of heavy-haul freight vehicles, the long-term wear between components can affect the suspension parameters. Suspension system wear has a significant effect on the dynamic performance and wheel wear. Experimental tests are performed to measure the changes in suspension system parameters after wear. A dynamic model and wheel wear model of the heavy-haul freight vehicles were established to analyze their dynamics and wheel wear performance. The results showed that with the wear of the suspension system, the stiffness parameters further increase. The dynamic performance of the vehicle system deteriorates after suspension system wear, with a decrease in the critical speed and an increase in safety and ride indexes. The analysis also reveals that the wheel wear increases as the stiffness parameters increase after the suspension system wear. This paper provides a basis for maintaining heavy-haul freight vehicle suspension systems
Potential of handheld laser beam welding
Get PDFSince 2023 at the latest, handheld laser beam welding systems (HLBW) gained interest by many companies. This is mainly due to two factors. Firstly, the cost of such a system has fallen considerably in recent years. Secondly, there is an economic pressure for the manufactures of welded products, partly due to the shortage of skilled workers. This publication addresses various aspects of HLBW, in particular the current state of the art and the potential applications. The higher throughput, less straightening work due to the lower heat input, and the use of less experienced personnel has to be mentioned here. However, welders still need to be qualified, especially to get informed about the hazards of laser radiation. In addition to welding, many systems for HLBW also include a cleaning function, some even a cutting function. The risks to be considered for both last mentioned are significantly greater, since on one hand, a touchdown or contact control is often omitted and on the other, the laser beam is conditioned for a longer working distance. For HLBW, the requirements of the process must be taken into account during the design phase already. This continues with edge preparation, e.g. pre-weld cleaning. HLBW is a supplement to traditional arc welding processes. Arc processes will be still used in the future as well, e.g. for small, complex geometries or in terms of accessibility. However, for longer welds, e.g. 1.5 m long 2 mm thick stainless steel sheets, HLBW sets currently the standard, especially with regard to the welding speed for manual welding