Search Results (108)

In recent years, much research has been devoted to the evaluation of physical phenomena and the technological effects of precise and micromilling processes. However, the available current literature lacks synthetic work covering the current state of the art regarding cutting force–tool displacement interactions in precise and micromilling manufacturing systems. Therefore, this literature review aims to fill this research gap and focuses on the critical literature review regarding the current state of the art within the prediction methods of cutting forces and machining system’s displacements/vibrations during precise and micromilling techniques. In the first part, a currently available cutting force, as well as the static and dynamic machining system displacement models applied in precise and micromilling conditions are presented. In the next stage, a relationship between the geometrical elements of cut and generated cutting forces and tool displacements are discussed, based on the recent literature. A subsequent part concerns the formulation of the generalized analytical models for a prediction of cutting forces and vibrations during precise and micromilling conditions. In the last stage, the conclusions and outlook are formulated based on the conducted analysis of the literature. In this context, this paper constitutes a synthetic work presenting current trends in the prediction of precise milling and micromilling mechanics. Full article

The objective of this study was to identify surface geometric deviations that may adversely affect the operational properties of bearings, including vibration, noise, and service life. A comprehensive prediction model is presented that combines a fundamental trend expressed by a power function with periodic oscillations, whose influence gradually diminishes with exponential decay. The model was calibrated using the experimental data obtained from 17 industrial RA-608-338 rolling bearing rings manufactured from high-carbon, low-alloy 100Cr6 steel. An excellent goodness-of-fit (R² exceeding 0.98) and minimal root-mean-square error (RMSE) were achieved. The proposed procedure provides a clear physical interpretation of the model’s subcomponents, while facilitating straightforward implementation in real production processes for continuous quality control and predictive maintenance purposes. This paper also includes a detailed description of the methodology, data processing, experimental results, comparison of multiple model variants, interactive visualization of the results on a logarithmic scale, and recommendations for practical application. Full article

Long-term vibrations from metro trains can cause liquefaction of water-rich sandy soil foundations, affecting the safety of operational tunnels. However, existing liquefaction studies mainly focus on seismic loads, and the macro-meso-mechanical mechanisms of liquefaction induced by train vibration loads remain unclear, which hinders the establishment of effective liquefaction prediction and evaluation methods. To investigate the microscopic mechanisms underlying sand liquefaction caused by train-induced vibrations, this study employs PFC3D discrete element software in conjunction with laboratory experiments to analyze the microscopic parameters of the unit cell. The findings indicate that the coordination number, mechanical coordination number, porosity, contact force chains, and strain energy all decrease with increasing vibration frequency. Conversely, the pore pressure, anisotropy, and energy exhibit opposite trends, continuing until the sample reaches a state of liquefaction failure. Notably, when the dynamic stress amplitude increases or the loading frequency decreases, the rate of reduction in coordination number, mechanical coordination number, porosity, contact force chains, and strain energy becomes more pronounced. Similarly, the rate of increase in pore pressure and anisotropy is more significant under these conditions. The research findings can provide a reference for the design of metro projects and liquefaction mitigation measures, thereby enhancing the safety and reliability of urban metro transportation systems. Full article

Aiming at the difficult problem of predicting the running state of the rotor of a Control Rod Drive Mechanism (CRDM) in a floating nuclear reactor, this paper proposes a Remaining Useful Life (RUL) prediction method based on Variational Mode Decomposition and Bidirectional Long Short-Term Memory (VMD-BiLSTM). Firstly, a bench experiment of the CRDM is carried out to collect the full operational cycle (full-stroke) vibration signals of the CRDM. Secondly, the collected data are decomposed based on the VMD, and the typical vibration signals at different stages of the experiment are used to verify this method and comprehensively mine the degradation characteristics. At the same time, the time-frequency domain feature analysis is carried out on the original vibration data, and the changing trends of the extracted features are carefully analyzed. Five feature quantities closely related to the degradation trend of the rotor of the CRDM are screened out, and the corresponding health indicators are constructed in combination with the stroke. Finally, the life prediction of the rotor of the CRDM is realized through the BiLSTM method. Then, the comparison experiments with other methods are carried out, and the experimental results show that the method proposed in this paper has high accuracy and reliability and can effectively solve the RUL prediction problem of CRDM, which provides a strong support to ensure the safe and stable operation of floating nuclear reactors. Full article

In order to confront the challenge of efficiently evaluating interior wind noise levels in passenger vehicles during the early stages of shape design, this paper proposes a methodology for predicting interior wind noise. The methodology integrates vehicle shape features with a whale optimization Xception model (WOA-Xception). A nonlinear mapping model is constructed between the vehicle shape features and the wind noise level at the driver’s right ear. This model is constructed using key exterior parameters, which are extracted from wind tunnel test data under typical operating conditions. The exterior parameters include the front windshield, A-pillar, and roof. The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. The prediction results on the test set demonstrate that the WOA-Xception model attains mean absolute percentage error (MAPE) values of 9.78% and 9.46% and root mean square error (RMSE) values of 3.73 and 4.06, respectively, for sedan and Sports Utility Vehicle (SUV) samples, with prediction trends that align with the measured data. A comparative analysis with traditional Xception, WOA-LSTM, and Long Short-Term Memory (LSTM) models further validates the advantages of this model in terms of accuracy and stability, and it still maintains good generalization ability on an independent validation set (mean absolute percentage error of 9.45% and 9.68%, root mean square error of 3.77 and 4.15, respectively). The research findings provide an efficient and feasible technical approach for the rapid assessment of in-vehicle wind noise performance and offer a theoretical basis and engineering references for noise, vibration, and harshness (NVH) optimization design during the early shape phase of vehicle development. Full article

Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning-based models based on multi-sensor monitoring data is fundamental for accurately predicting vibration trends during bearing operation and is crucial for bearing fault diagnosis and RUL prediction. In this work, a method for constructing a health index based on vibration signal is developed to describe the performance features of rolling bearings, which mainly includes feature extraction, sensitive feature index selection, dimensionality reduction, and normalization methods. In addition, a new RUL prediction method, TCN–Transformer, is developed which can efficiently learn and integrate local and global features of vibration signals, addressing the long time series prediction problem in RUL prediction. The TCN extracts local features, while the Transformer learns global features, both of which are seamlessly integrated through a specially designed feature fusion attention module. Both the health indicator (HI) constructed from extracted time domain and frequency domain feature parameters and the RUL prediction method were rigorously validated using the IEEE PHM 2012 Data Challenge dataset for rolling bearing prognostics. By employing the proposed HI construction method, the average comprehensive bearing performance index, used to evaluate RUL prediction accuracy, is improved by 8.69% across the entire dataset compared to the original feature-based composite index. The proposed RUL prediction model can more accurately predict the RUL of rolling bearings under different conditions, reducing the RMSE and MAE by 14.62% and 9.26%, respectively, and improving the SCORE by 13.04%. These results underscore the efficacy and superiority of our approach in RUL prediction of rotating machinery across varying conditions. Full article

To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency domain features are extracted from the vibration data covering the entire lifespan of the rolling bearings and passed through the transformer encoder layer. A novel dual-layer self-attention mechanism network structure is proposed to capture global information on the lifecycle progression of rolling bearings. Next, to further extract local temporal features within the bearing’s life cycle, a multi-scale convolution module is proposed to reinforce the local information across the entire lifespan. This method fully exploits both the long-term trends and short-term dynamic variations in the health status of rolling bearings, effectively enhancing the accuracy of life predictions. Experimental results show that, even under conditions with interference features, the TransCN model outperforms mainstream advantage model in terms of prediction accuracy and generalizability. This approach offers a new solution for managing the fault risk of rotating machinery and reducing maintenance costs. Full article

Industrial motors generate vibration energy that can be converted into electrical energy using piezoelectric vibration energy harvesters (pVEHs). These energy harvesters can power devices or function as self-powered sensors. However, optimal electromechanical designs of pVEHs are required to improve their output performance under different vibration frequency and amplitude conditions. To address this challenge, we performed the electromechanical modeling of a multilayer pVEH that harvests vibration energy from induction electric motors at frequencies close to 30 Hz. In addition, a parametric analysis of the geometry of the multilayer piezoelectric device was conducted to optimize its deflection and output voltage, considering the substrate length, piezoelectric patch position, and dimensions of the central hole. Our analytical model predicted the deflection and first bending resonant frequency of the piezoelectric device, with good agreement with predictions from finite element method (FEM) models. The proposed piezoelectric device achieved an output voltage of 143.2 V and an output power of 3.2 mW with an optimal resistance of 6309.5 kΩ. Also, the principal stresses of the pVEH were assessed using linear trend analysis, finding a safe operating range up to an acceleration of 0.7 g. The electromechanical design of the pVEH allowed for effective synchronization with the vibration frequency of an induction electric motor. This energy harvester has a potential application in industrial electric motors to transform their vibration energy into electrical energy to power sensors. Full article

The hexagonal plastic collapse surface model has been explored as an effective approach for seismic response analysis in multi-degree-of-freedom (MDOF) structures. This study establishes the theoretical background of hexagonal analysis for multistory structures, emphasizing the consistency between experimental and analytical results. A simplified nonlinear dynamic analysis (SNDA) is introduced, integrating limit analysis with static proportional loading and a hexagonal plastic collapse surface model to define the internal safety zone within the mode restoring force space. The approach considers multiple vibration modes, which significantly impact elastic–plastic behavior in seismic conditions. To validate its effectiveness, a comparative evaluation is conducted between experimental data and SAP2000 dynamic time history analysis, showing strong alignment in deformation response trends. The results confirm that the hexagonal model accurately predicts failure mechanisms while improving computational efficiency, providing a practical framework for collapse prediction in structural engineering applications. Full article

Hydraulic sandblasting perforation guns play a critical role in well completion and productivity enhancement operations in oil and gas wells, as their performance and service life directly affect perforation efficiency, reservoir integrity, and downhole operational safety. Drawing on a comprehensive review of the existing literature, this paper systematically summarizes recent research progress on surface erosion, high-pressure leakage, and vibration-induced fatigue in perforation guns. Regarding erosion wear, we discuss the mechanisms and preventive strategies influenced by abrasive particle flow characteristics, material selection, and coating applications. In the field of high-pressure leakage, we analyze the key factors of seal failure, structural deformation, and material degradation that contribute to leakage formation, and we provide improvement measures involving seal structure optimization, enhanced material properties, and real-time monitoring technologies. Concerning vibration and fatigue, we elucidate the multi-factor coupling mechanisms of failure—encompassing fluid–solid interactions, cavitation impacts, and stress concentration—and outline mitigation strategies through structural redesign, material reinforcement, and fluid dynamic control. Furthermore, the paper anticipates the future trends of intelligent fault diagnosis and predictive maintenance, including multi-sensor data fusion, AI-driven predictive models, and digital twin technologies. Overall, the integrated application of precision design, dynamic optimization, and intelligent control across the entire service life of perforation guns is poised to guide forthcoming research and engineering practices, driving hydraulic sandblasting perforation technology toward greater efficiency, reliability, and intelligence. Full article

Rubber materials can endure substantial deformation while avoiding permanent damage or rupture, making them highly suitable for applications in the automotive industry and other sectors, particularly for noise and vibration reduction. However, rubber experiences degradation over time as defects or cracks appear and propagate under fluctuating loads. Therefore, it is of critical importance to prevent the failure of rubber components during service. As highlighted in prior literature surveys by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004, significant research has focused on the mechanics and analysis of rubber fatigue. This body of work has grown rapidly and continues to evolve. Therefore, this study aims to compile and analyze the vast body of recent research on rubber fatigue conducted over the last decade, supplementing the reviews by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004. The gathered studies were analyzed to identify current trends and emerging research gaps in the fatigue study of rubber, including advanced composite rubber materials such as magnetorheological elastomers (MREs). This review emphasizes the analysis techniques and fatigue experiments available for fatigue life prediction in rubber materials, while illustrating their practical applications in engineering analyses through specific examples. Full article

The maglev gyroscope torque feedback orientation measurement system, equipped with abundant sampling data and high directional accuracy, plays a crucial role in underground engineering construction. However, when subjected to external instantaneous vibration interference, the gyroscope rotor signal frequently exhibits abnormal jumps, leading to significant errors in azimuth measurement results. To solve this problem, we propose a novel noise reduction algorithm that integrates Moving Average Filtering with Autoregressive Integrated Moving Average (MAF-ARIMA), based on the noise characteristics of the rotor jump signal. This algorithm initially adaptively decomposes the rotor signal, subsequently extracting the effective components of the north-seeking torque with precision and applying MAF processing to effectively filter out noise interference. Furthermore, we utilize the stable sampling trend data of the rotor signal as sample data, employing the ARIMA model to accurately predict the missing abnormal jump trend data, thereby ensuring the completeness and coherence of the rotor signal trend information. Experimental results demonstrate that, compared to the original rotor signal, the reconstructed signal processed by the MAF-ARIMA algorithm exhibits an average reduction of 70.58% in standard deviation and an average decrease of 47.31% in the absolute error of azimuth measurement results. These findings fully underscore the high efficiency and stability of the MAF-ARIMA algorithm in processing gyroscope rotor jump signals. Full article

Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an L₁ filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in L₁ filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods. Full article

A numerical tool is developed to simulate the random vibration-response-only-based fatigue delamination diagnosability in thermoplastic coupons. That is the ability to both detect damage and identify its current severity, aiming to establish a virtual framework for optimizing diagnosability methods. The numerical tool employs the FE method. It comprises two modules: a fatigue delamination module and a random vibration module. The first module implements a fatigue crack growth model based on the cohesive zone modeling method to predict delamination accumulation, while the second module uses an experimentally verified FE model of the delaminated coupon to predict its random vibration response. Delamination accumulation is evident in the ‘predicted’ FE-based power spectral densities. The model’s capability to diagnose delamination is demonstrated using seven different damage metrics based on simulated random vibration responses, enabling damage detection and severity assessment (increasing trend guides to distinguishing each fatigue state from its counterparts). Comparisons with their experimentally obtained counterparts are also used in the assessment. The procedure clearly suggests that the proposed numerical tool may be reliably used for virtually assessing the efficacy of random vibration-based fatigue damage diagnosability for any given structure and also to aid the user in selecting the method’s parameters for virtual diagnosability optimization. Full article

Torsional vibrations pose a serious challenge in drilling operations and can lead to effects such as stick-slip phenomena, tool wear, and reduced drilling efficiency. While previous research has been conducted on torsional vibrations, there is a notable gap in comparative studies that assess the scalability of downscale models to real-world drilling conditions. This study fills this gap by systematically comparing torsional vibrations in downscale and upscale drill strings under different torque conditions at three different depths, shedding light on scaling effects in drilling vibrations. Numerical simulation was carried out taking into account non-linear interactions, damping effects, and torque variations. The laboratory set-up was for a well length of 15 m and was geometrically scaled to represent an upscale well of 450 m. Certain operational parameters such as rotation speed, torque, density, and friction coefficients were modified to keep realistic dynamic behavior, and all models were run at an identical speed of rotation to enforce consistency. The results show that both the upscale and downscale models exhibited stick-slip behavior, but differences in vibration intensity and stabilization trends point out how scaling affects torsional dynamics. Notably, the upscale bit first faced higher torsional oscillation than the set rotation speed after overcoming stick-slip before stabilizing, whereas the downscale bit went through prolonged stick-slip instability before synchronization. This study enhances the understanding of scaling effects in torsional drilling vibrations, offering a foundation for optimizing experimental setups and improving predictive modeling in drilling operations. Full article