Variable Rotational Speed Research Articles

Effect of Rotation Speed and Feed on the Surface of ST 41 Steel on a Lathe Machine

This study discusses the effect of feed rate and machine rotation speed on the surface roughness of ST 41 steel in the flat turning process. The aim is to determine the relationship between variations in rotation speed (100 rpm, 300 rpm, and 500 rpm) and feed rate on the final surface results of the workpiece. The method involves cutting tests on ST 41 steel specimens with a diameter of 25 mm and a length of 400 mm with various rotation speed variations. The results show that at low rotation speed (100 rpm), the surface of the workpiece tends to be rough. The surface becomes better at medium speed (300 rpm), while at high speed (500 rpm), the surface results are smoother. These findings indicate that increasing rotation speed contributes to a decrease in surface roughness. Thus, higher rotation speeds can improve the quality of turning results.

Milling stability considering multiple effects and variable rotational speed milling chatter control

One of the most frequent barriers to decreasing the surface quality of workpieces during milling machining is chatter, and the accuracy of chatter prediction is directly impacted by the number of effects that are considered during the modeling of the milling process. Previous researchers have studied the impact of chatter suppression on the spindle variable rotational speed method in the milling model while considering various effects. The variable rotational speed waveforms primarily focus on the fundamental waveform or a composite of sine waveforms. The study presents a novel method for variable speed control, utilizing a waveform that combines sine and triangular waveforms. The effect of this method on chatter suppression is investigated in a milling dynamics model that considers regenerative, modal coupling, and process damping effects. This novel method overall improves milling stability significantly. The accuracy of the novel method is verified by simulation and experiment. Next, the analysis focuses on the influence of immersion rate, spindle modulation coefficients, and phase difference on stability, based on stability prediction. The choice of these parameters has a direct influence on the ability to suppress chatter.

A hybrid transformer masked time-domain denoising network for vibration signals

Abstract Health-condition-sensitive vibration information is prone to be swamped by widespread noise. Denoising is always indispensable, but existing methods still lack adaptability. Therefore, a novel intelligent denoising framework called a hybrid transformer masked time-domain denoising network (HTMTDN) is proposed. First, a dense dilation convolution block and a hybrid transformer are constructed to deal with fault impulse scale variations and unexpected noise frequency bands respectively, which greatly improves the adaptive denoising capability and relieves tough denoising parameter tuning. Further, interpretable time- and frequency-domain joint constraints are constructed to enhance the network’s optimization ability under strong noise. Finally, a novel strategy called overlapping reconstruction is introduced to recover 1D signals from 2D signal segments. Extensive experiments based on two bearing fault datasets with variable loads and rotation speeds confirm the remarkable performance of the HTMTDN under low signalnoise ratios, and present good adaptability in 15 health conditions without separate hyperparameter tuning, which shows promise for real-world applications.

Experimental Investigation of the Wear of Some Polymer Gear Wheels Produced by Three-Dimensional Printer on the Variation of Fill Percentage and Shell Thickness

FDM additive manufacturing method has recently been widely used because it is very attractive in terms of cost and material loss compared to traditional manufacturing methods such as molding and milling. In previous studies, many researches have been carried out on the engineering strength of plastic gears produced by conventional methods, but with the discovery of new plastic forms by material science and the practical application of new production methods, researchers continue the researches on additive manufacturing gears. So far, many studies have investigated the wear variation, rotational speed and torque variation with variations in gear wheels produced with three-dimensional printers. In this study, using the ability of FDM technology to produce in layers, gear wheels with several variations in outer shell thicknesses and internal filling ratios were produced. The wear behaviors of these gear wheels produced with different variations were investigated. On the other hand, PETG and ABS polymers were also produced under the same conditions in order to provide material diversity in addition to PLA polymer, which has been used so far in many studies due to its low melting temperature and good printability. Then wear behaviors were observed under the same conditions using the FZG (Forschungsstelle fur Zahnrader und Getriebebebau) test setup. Thanks to additive manufacturing technology, polymer gear types were produced with fill ratios of 100% and 50% and outer shell thicknesses of 0.4 mm and 0.8 mm. The test specimens were operated against the steel gear at a torque of 1.5 Nm and a rotational speed of 900 rpm and the mass losses as a result of wear were compared. Thus, a comparison was made about the strength of polymer types with the variation of outer shell thickness and filling percentage.

Data imbalanced fault diagnosis of gearbox transmission system under various speeds based on dynamic dual-scale normalized fusion network

Abstract Gearboxes play a pivotal role in industrial production, and their reliability and safety are essential for production safety and efficiency. However, gearboxes frequently encounter challenges such as variable rotational speeds and unknown operating conditions. Unfortunately, most existing traditional fault diagnosis methods face the following issues: (1) They heavily rely on expert experience and pre-existing knowledge bases, making them unable to tackle fault diagnosis in unknown working conditions. (2) While addressing various speed issues, they seldom consider the problem of data imbalance in real-world industrial environments. (3) Many transfer learning methods primarily focus on global distribution alignment and knowledge transfer between source and target domains, neglecting the importance of fine-grained distribution alignment between subdomains. To address these issues, a dynamic dual-scale normalization fusion network (DSNFNet) is proposed for cross-domain fault diagnosis under variable speed and data imbalance. Firstly, the two parallel graph convolution frameworks constructed are used to extract multi-scale fault features. Subsequently, a dual-scale normalization fusion module is adopted to integrate the global and local fault feature information within the subdomains of both the source and target domains, thereby aligning their fine-grained distributions to obtain domain-invariant features. Finally, a dynamic soft threshold feedback strategy is introduced, which assigns pseudo labels to the target domain data, enabling the model to give equal attention to each class of data samples, even under data imbalance conditions, thereby improving the recognition accuracy of minority fault classes. Validating the proposed method on two real cases, our method achieved the highest accuracy compared to other advanced approaches.

A NOVEL TOOL FOR EXTRACTING FREQUENCY CURVES FROM LARGE-SCALE DATA TO DETECT ROLLING BEARING FAULTS UNDER NON-STATIONARY CONDITIONS

Investigating vibrations in mechanical transmission systems to detect and identify the causes of abnormal vibrations remains a prominent focus for researchers. Within industrial transmission systems, rolling bearings play a pivotal role. Utilizing the envelope spectrum of vibration signals to diagnose faults in rolling bearings proves highly reliable due to the bearings' mechanical properties. Under constant shaft rotational speed, most envelope analysis methods can accurately identify faults at the characteristic frequency of rolling bearings. However, numerous transmission systems operate at variable rotational speeds due to technological demands and load fluctuations. In such scenarios, the vibration signals of rolling bearings undergo frequency modulation, rendering conventional vibration analysis methods like envelope spectrum analysis ineffective. Time-frequency analysis is a valuable diagnostic tool for identifying faults in rolling bearings under these conditions. Nonetheless, its practical application, particularly with large-scale data, necessitates high-performance computers capable of processing extensive multi-dimensional matrices. To overcome this challenge, this paper introduces a novel tool based on the Synchrosqueezing Transform (SST) and other resampling techniques. The proposed method additionally incorporates advanced techniques for extracting frequency curves from non-stationary signals to detect the fault characteristic frequencies (FCFs) of rolling bearings. The effectiveness of this approach is showcased through both a simulated example and an experimental instance. Furthermore, a comprehensive comparison of the novel method with existing techniques is provided.

Cavitation Performance of a Pump With Independently Rotating Inducer and Impeller In Co-Rotation and Counter-Rotation Modes by Varying Rotor Speeds

Abstract Centrifugal pumps, consisting of an inducer and impeller, are used in various industries such as marine, aerospace, and nuclear. Typically, inducers and impellers in centrifugal pumps are fixed on the same shaft, having the same rotation direction and speed. Thanks to the use of independent rotation technology for the inducer and impeller, each can be rotated simultaneously at different speeds and in different directions. This article investigates the impact of varying the speed ratio between the inducer and impeller on the cavitation performance of centrifugal pumps equipped with inducer as an innovative concept of allowing the rotors to rotate independently. Two inducers with identical geometry with opposite rotation directions are used to study the impact of speed in both co-rotation and counter-rotation modes. The cavitation performance of each mode has been analyzed for three different flow rates at varying inducer speeds. The development and structure of different types of cavitation occurring both in the inducer and in the space between the inducer and the impeller have been analyzed using a high speed camera. The findings indicate that inducer in counter-rotation mode demonstrates enhanced cavitation performance. Increasing the speed ratio of the inducer relative to the impeller in this mode significantly improves the pump cavitation performance at different flow rates while in the co-rotation mode, increasing the speed results in the cavitation performance drop.

Effect of Magnetic Field and Slip Conditions on Flow in a Rotating Porous Channel With Viscous Dissipation

ABSTRACTThis study examines the steady flow of an electrically conducting fluid through a rotating porous channel bounded by stationary, impermeable horizontal plates at constant temperature. The primary aim is to explore the combined effects of a magnetic field, wall slip conditions, and viscous dissipation. The channel rotates at a constant angular velocity, with slip conditions applied at the walls. A pressure gradient drives the primary flow, while rotation generates the secondary flow. Analytical solutions for velocity profiles and volumetric flow rates are obtained, and the temperature distribution is calculated using MATLAB's “bvp4c” function. The research offers novel insights into the behavior of primary and secondary flow velocities under different Hartmann and Taylor numbers, emphasizing the impact of slip conditions. Additionally, the influence of the Eckert number on temperature is analyzed in conjunction with these parameters. These findings contribute valuable theoretical perspectives for enhancing cooling systems in rotating machinery using conductive fluids in porous channels. This study opens avenues for future research to investigate unsteady flow conditions and the effects of variable magnetic fields and rotational speeds on fluid behavior in rotating porous channels.

Effect of oil film clearance on dynamic characteristics of <?A3B2 pi6?>variable speed rotor with non-concentric SFD

Effect of oil film clearance on dynamic characteristics of <?A3B2 pi6?>variable speed rotor with non-concentric SFD

Joining of thin aluminum sheets by micro-friction stir welding (μFSW) using a pinless tool

Abstract In the present research, butt welding of 0.8 mm thick aluminum 6061–T6 sheets has been executed using a pinless tool with variable tool rotational speed (1000-2500 rpm), welding speed (100–250 mm min−1), and a constant plunging depth (0.1 mm). Tensile and microhardness tests have been conducted to evaluate the joint’s mechanical strength. The joint developed at 2500 rpm, and 100 mm min−1 shows a maximum strength of 277.65 MPa (88.4% of original strength), indicating that higher tool rotational speed and slower welding speed enhance joint formation through adequate heat generation and material intermixing. Small amount of defects like flash, surface galling, and irregular shoulder marks have been found in the weld region.

Biokinetic modeling approach to investigate the impact of rotational speed variations in modified rotating biological contactors for palm oil mill effluent treatment

Palm oil mill effluent (POME) contains high organic content, posing significant environmental challenges that demand effective treatment solutions. Conventional rotating biological contactor (RBC) used for POME treatment are limited by insufficient surface area for microbial growth and inefficient oxygen transfer, limiting their effectiveness. This study enhances RBC systems by incorporating bioballs to increase microbial attachment surfaces and improve treatment performance. Experiments were conducted at various rotational speeds, and biokinetic modeling was applied under unsteady-state conditions. The results showed that the RBC operated at 3 rpm achieved the highest removal efficiencies, with 67.4 % for SCOD, 92.4 % for TSS, and 73 % for NH3. The biokinetic model revealed that rotational speed did not significantly impact oxygen transfer, mainly during the aeration phase. Lower speeds optimized substrate degradation and microbial growth, while higher speeds caused biofilm detachment. The innovative use of bioballs improves POME treatment efficiency at lower speeds, offering a cost-effective solution.

Analysis of the Nonlinear Complex Response of Cracked Blades at Variable Rotational Speeds

The operation of an aero-engine involves various non-stationary processes of acceleration and deceleration, with rotational speed varying in response to changing working conditions to meet different power requirements. To investigate the nonlinear dynamic behaviour of cracked blades under variable rotational speed conditions, this study constructed a rotating blade model with edge-penetrating cracks and proposes a component modal synthesis method that accounts for time-varying rotational speed. The nonlinear response behaviours of cracked blades were examined under three distinct operating conditions: spinless, steady speed, and non-constant speed. The findings indicated a competitive relationship between the effects of rotational speed fluctuations and unbalanced excitation on crack nonlinearity. Variations in rotational speed dominated when rotational speed perturbation was minimal; conversely, aerodynamic forces dominated when the effects of rotational speed were pronounced. An increase in rotational speed perturbation enhanced the super-harmonic nonlinearity induced by cracks, elevated the nonlinear damage index (NDI), and accentuated the crack breathing effect. As the perturbation coefficient increased, the super-harmonic nonlinearity of the crack intensified, resulting in a more complex vibration form and phase diagram.

The effects of tool rotational speed and post-weld heat treatment of friction stir welded AZ31-B magnesium alloy

Abstract This research article examines the effect of increasing the tool rotational speed and post-weld heat treatment behavior of friction stir-welded AZ31-B magnesium alloy. The variable tool rotational speeds of 1000 rpm, 1200 rpm, 1400 rpm, and 1600 rpm with fixed tool traverse speeds of 30 mm min−1 were chosen based on the trial welding method. Results reveal that the as-welded tensile strength values of 133 MPa, 209 MPa, 215 MPa, and 213 MPa for the corresponding tool rotation speeds and post-weld heat-treated tensile strength values of 140 MPa, 213 MPa, 223 MPa, and 227 MPa significantly increased joint strength when compared to the as-welded process. The measured grain size values are reduced when the tool rotational speed increases due to material dynamic recrystallization. Al-Mg brittle intermetallic compounds significantly reduced the mechanical strength during the increase of the tool’s rotational speed. Fractography analysis reveals all welded samples fractured at the center of the stir zone with a ductile fracture angle of 45°.

Wind turbine condition monitoring dataset of Fraunhofer LBF

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters.

Fault diagnosis of rolling bearing under complex working conditions based on time-frequency joint feature extraction-deep learning

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

Noise analysis of variable-speed rotor technology on the coaxial lift-offset helicopter

The impact of variable-speed rotor technology on the aerodynamic and aeroacoustic characteristics of a coaxial lift-offset helicopter was investigated using a numerical analysis approach. The free wake vortex lattice method was utilized to predict aerodynamic performance, while the Ffowcs-Williams Hawkings acoustic analogy was employed to evaluate the noise impact. The Harrington coaxial rotor was used as the baseline configuration. Results show that reducing the rotational speed, in combination with lift-offset, leads to a decrease in overall power required during forward flight by lowering both collective and cyclic pitch. This is primarily due to a reduction in profile power across the speed range. However, excessive rotor deceleration can lead to an expanded reverse flow region, increasing induced power at high forward speeds. From an acoustics perspective, the reduced rotor speed mitigated loading noise radiated toward the ground, especially in the region 40-60 degrees below the rotor plane, achieving a maximum 2.6 dB reduction at the evaluated observer location. This study indicates that the technology can offer advantages for both performance and noise in forward flight conditions.

Improving the blade tip clearance measurement method based on blade tip timing: Accounting for rotor speed variations and non-uniform blade-by-blade tip clearance

The Blade Tip Clearance (BTC) measurement method based on Blade Tip Timing (BTT) suffers from poor accuracy under changing speed conditions and in the determination of blade-by-blade tip clearance. To address these challenges, this paper presents the Time of Arrival Polynomial Fitting (ToA-PF) method for achieving high-precision tip clearance measurements under variable speed conditions. Additionally, a correction method is introduced to effectively mitigate measurement errors in blade-by-blade tip clearance. Experimental validation on an academic test rig demonstrates significant improvements. The ToA-PF method enhances clearance measurement accuracy by 77% compared to traditional methods under low and rapidly changing rotational speeds. Furthermore, the correction method for blade-by-blade tip clearance notably enhances measurement accuracy, particularly for the shortest blade, by over 50%. These proposed methods show promise in enhancing the accuracy and practicality of the BTC measurement method based on BTT, offering substantial value for turbine blade health monitoring and predictive maintenance applications.

Segmentalized amplitude normalization in feature extraction technique for diagnostics enhancement of bearing deterioration under varying speeds

This research investigated the feasibility of applying hardware order-tracking (HOT) and segmentalized amplitude normalization (SAN) to enhance the diagnosis of multiple bearing defects at different levels under varying rotation speeds. The vibration of operating bearings may present an energy variation phenomenon due to different levels of bearing defects, while the fluctuation of vibration amplitude may be attributable to changes in rotation speeds. These two factors inevitably interfere with each other when diagnosing bearing defects at multiple levels and classes under varying rotation speeds. In this paper, the research focuses on conducting an in-depth analysis of signal signatures, followed by providing a physical insight into feature extraction. Consequently, it enables the application of simple machine learning methods to accurately diagnose various bearing defects, even when dealing with significantly different patterns in training and testing data due to varying rotation speeds. To verify the effectiveness of the proposed SAN method for cases involving varying rotation speeds, the training and testing sets used datasets (vibration measurements) corresponding to different rotation speed profiles. The experimental and analytical results revealed that the proposed SAN method can normalize datasets with disparate vibration patterns, and alleviate the coupling of vibration energy variation and shaft rotation speed. This enhancement resulted in approximately 18.6% increase in the accuracy of bearing diagnosis for cases involving varying rotation speeds.

Experimental optimization of the FSW parameters to weld the dissimilar Al- and Cu-alloys by using additives at their joints

Friction stir welding (FSW) stands as a promising technique for joining materials, offering advantages over conventional methods. This study investigates the influence of tool tilt angle, rotational speed and profile on welded joint characteristics using a design of experiment (DOE) approach. The research objective aims to determine optimal parameter combinations for improved mechanical properties. Methodology entails the systematic variation of tilt angle, rotational speed and tool profile in a DOE setup, with subsequent FSW of samples. Mechanical testing, including tensile, bend and hardness tests, is conducted on 24 samples, with scanning electron microscope (SEM) and microstructure analysis on two specimens. Results reveal that higher tilt angles, rotational speeds and threaded profiles yield increased tensile strength but decreased ductility. Similarly, bend tests show higher fracture loads but reduced deflection before fracture under these conditions. Hardness tests indicate elevated hardness in welded areas, with variations based on input parameters. Main findings highlight the importance of parameter optimization for desired mechanical properties. Lower tilt angles and rotational speeds promote smoother material flow and uniform heating, resulting in consistent microstructure and hardness distribution. In conclusion, this study provides valuable insights for optimizing FSW parameters to achieve desired mechanical properties in welded joints. The combination of a 2° tilt angle, threaded tool profile and 1500 rpm rotational speed demonstrated the highest tensile strength, while a 1° tilt angle, cylindrical tool profile and 1000 rpm rotational speed yielded highest ductility.

Real-time Wells turbine simulation on an oscillating-water-column wave energy converter physical model

Due to its simplicity and low cost, the Wells turbine is the most common choice for driving oscillating-water-column (OWC) wave energy converters (WECs) power take-off system. This turbine is characterized by a flow rate that is a linear function of the pressure head and inversely proportional to the rotational speed before the onset of hard stall conditions. The Wells turbine has been simulated in wave tanks using porous plugs, where the flow rate exhibits linear behaviour. However, numerical and experimental results have shown that rotational speed variations significantly influence the performance of OWC WECs. This work aims to develop a novel real-time simulator of a Wells turbine for use in physical models of OWC power plants. The proposed simulator consists of a diaphragm whose diameter is adjusted in real-time as a function of the pressure head measured in the laboratory and the rotational speed calculated by a hardware-in-the-loop model. In this way, it is possible to reproduce the full behaviour of the Wells turbine before and after a hard stall. Experimental results demonstrate the effectiveness of the simulator. A performance analysis was conducted to understand the device’s potential and compare the damping that Wells and impulse turbines introduce.