International Journal of Precision Engineering and Manufacturing最新文献

This study designed a method to determine the relationship between axial preload, temperature, and dynamic characteristics. The ball-bearing dynamic model analyzed the dynamic characteristics of a ball bearing subjected to axial preload and thermal expansion based on the Hertz contact and gyroscopic moments. By increasing the temperature, the inner contact state of the ball bearing was altered, resulting in reduced vibration of the rotor system and increased stiffness of the rotor system. Simulation data indicated that at a temperature of 40 °C, the corresponding loads of both the inner and outer rings exhibit an increase of 5.2% and 5.1%, respectively. Experimental data suggested that as the temperature increased, both the peak-to-peak and root-mean-square values of the rotor vibration decreased, while the rotor stiffness exhibited a linear increase with rising temperature. This study provided a real-time temperature control method for vibration control of rotor system.

Hydraulic solenoid valves (HSVs) are widely used for fluid control in industrial applications. Like other mechanical parts, HSVs deteriorate and faults occur during their use in industrial settings. Although several prior fault-diagnosis methods have been proposed to detect these faults, previous studies have not considered the actual implementation of the methods on the controller. Thus, to ensure reliable operation of HSVs in the field, this research paper presents an innovative HSV controller design that is able to process the proposed fault-diagnosis algorithm. In the proposed work, the hardware is designed to transmit and receive signals and data for the valve-diagnosis algorithm. For the software, an architecture is proposed to enable the fault-diagnosis algorithm to be processed in a conventional microprocessor. In this research, the proposed controller design was built and tested on an actual hydraulic system. The results show that the proposed fault-diagnosis algorithm has been successfully embedded in the microcontroller.

Electromyogram (EMG) signals obtained from muscles can provide insights into the biomechanics of human movement. EMG technology finds diverse applications including enhancing human–computer interaction, enabling muscle-controlled devices for hand gesture recognition, facilitating prosthetic control for individuals with disabilities and elder care. Hand gestures are crucial for human–computer interaction, bridging the gap between human intent and machine control. Their significance has obtained considerable attention, leading to the development of advanced detection systems. These systems facilitate effective interaction between humans and computers, thereby enhancing various applications across different domains. Current research on EMG-based hand gesture classification encounters challenges such as inaccurate classification, and limited generalization ability. To encounter these problems, an automated multi-class hand gestures identification model is proposed via machine intelligence. A publicly accessible UCI2019 EMG dataset obtained from 8-channels MYO thalmic bracelet for surface EMG data acquisition is used to demonstrate the work. Initially, the multivariate EMG channels data are pre-processed and then fed to machine learning classifiers. Six classifiers are evaluated for the proposed predictive model, with ensemble bagged tree (EBT) consistently outperforming (overall highest accuracy of 98.4%) than other classification approaches. The superior performance of EBT classifier in overall classification and class wise classification are exhibited through receiver operating characteristic (ROC) analysis. Hence, this computer-assisted decision-making model minimizes the limitations of prior studies by offering gesture based human–machine interaction and smart device control for elder care. The proposed research can also offer valuable contributions to manufacturing by facilitating tasks in the industry such as remote operation, quality control, and maintenance by enabling hands-free control and reducing the physical strain on workers.

Pulsed electrochemical machining (PECM) has significant advantages for processing difficult-to-cut metal materials. Since the charge and discharge behavior of the electric double layer (EDL) in PECM greatly influences machining characteristics, an easy-to-use and time-saving method to simulate the ECM phenomenon with equivalent circuit and identify the circuit parameters (such as EDL capacitance and Faradaic resistance) is helpful for understanding the material removal rate and machining accuracy. However, identification of circuit parameters usually requires specialized equipment (such as an electrochemical workstation) to perform impedance measurements, which are cost and time consuming. In this study, an equivalent circuit model of ECM was established, and a simple method was adopted to identify the circuit parameters by analyzing time variation of the voltage and current waveforms between the workpiece and tool. The obtained circuit parameters were applied to the equivalent circuit model in the circuit simulation software, and the simulated current waveforms were compared with the experimentally obtained waveforms to verify the reliability of parameter identification. Furthermore, the established model was applied to ECM with a diode, which was usually inserted to the circuit to suppress the reverse current and reduce the tool wear in PECM. The Faradaic current and capacitive current of the workpiece anode were discussed in the simulated model, which helped understanding the material removal rate and machining accuracy in PECM.

Lumbar interbody fusion cages have been widely used to restore spine alignment. However, it has been speculated whether a single long transforaminal lumbar interbody fusion cage is as effective as a pair of short posterior lumbar interbody fusion cages in preventing excessive subsidence, and how much posterior and transforaminal lumbar interbody fusion cages subside if they are placed off the center of the endplate towards the epiphyseal ring. Thus, in this study finite element models of the lumbar interbody fusion cages and the vertebrae, including the endplate and the epiphyseal ring, were created, and finite element simulations were conducted to estimate the subsidence of a pair of posterior lumbar interbody fusion cages and a single transforaminal lumbar interbody fusion cage not only placed at the center of the endplate but also placed off-center. The finite element simulation results indicated that a long transforaminal lumbar interbody fusion cage was equally or more effective in reducing subsidence compared to a pair of posterior lumbar interbody fusion cages. Furthermore, the results also showed that both posterior and transforaminal lumbar interbody fusion cages resulted in less subsidence when placed off-center and closer to the epiphyseal ring.

The solid-state joining method known as friction stir welding has recently gained popularity because of the many benefits it provides in terms of mechanical characteristics and weld quality. Using a Grey Rational Coefficient optimization strategy to examine the effect of Titanium Carbide nanoparticles (TiCnp) reinforcement on welds of Grade AZ31 magnesium alloy. The key parameters under investigation include TiCnp content (ranging around 1.5 wt%), tilt angle (0°, 1°, and 2°), tool rotation speed (1000, 1250, and 1500 rpm), tool profile shape (Square, Cylinder, and Triangle), traverse feed rate (25, 50, and 75 mm/min), and axial load (5, 10, and 15 kN). The mechanical parameters tested in this research comprise micro hardness, tensile strength, wear rate, and impact strength. The findings reveal that when the TiCnp concentration is tuned within the prescribed range, welds with a peak tensile strength of 242 MPa significantly improve mechanical characteristics. In this study the value of 1250-rpm rotational speed, 50 mm/min traverse speed, 1° tilt angle and tool profile shape square is significantly increases the mechanical properties. Adding of TiCnp with AZ31 Mg alloy is widely increase the tensile strength up to 230 MPa, micro hardness about 70 MPa, impact strength 89.34 MPa and wear rate of 0.0046 m³/m.

The influence of process parameters on the abrasive belt wear height in abrasive belt grinding screw rotors is studied in this paper. The independently developed special grinding device is used for the experiment. The improved Aquila Optimizer (IAO) algorithm is used to optimize the BP neural network, the experimental parameters and abrasive wear height data are input into the IAO-BP neural network model for training, then establish the prediction model of the average wear height of abrasive belt particles. The prediction samples and comparison samples are obtained by multi factor grinding experiments. The prediction accuracy is compared with ANN and GA-BP neural networks. The results show that the accuracy of the prediction model is better than that of ANN and GA-BP neural networks. The single factor prediction results of abrasive belt wear height show that the wear height of abrasive belt increases with the increase of driving wheel cylinder pressure and decreases with the increase of tension cylinder pressure. The wear height increases first and then decreases with the increase of the linear speed of the abrasive belt, and increases with the increase of the axial feed speed of the abrasive belt. The improved AO algorithm to optimize BP neural network prediction model can provide a theoretical basis for selecting process parameters of screw rotors in grinding belt. Abrasion of abrasive belt can be effectively alleviated by selecting higher linear speed and feed speed during grinding, appropriately reducing the pressure of positive cylinder and increasing the pressure of tensioning cylinder.

Accurate, efficient and stable prediction of thermal displacements generated during spindle machining is essential for improving machining quality, increasing economic efficiency and ensuring production safety. Aiming at the existing thermal displacement prediction models with low precision and poor robustness, this paper put forward a prediction model based on the Bald Eagle Search (BES) algorithm optimized Least Squares Support Vector Machine (LSSVM). Firstly, the experimental platform was built to carry out the spindle thermal deformation experiment and collect the experimental data. Then use K-means clustering method to classify the temperature measurement points, and combine with gray correlation analysis to calculate the size of the correlation between each point and thermal displacement, comprehensive analysis of the classification results and the size of the correlation, from the 10 points preferred 4 points. After that, the BES algorithm, which has strong searching ability in the global range, is chosen to optimize the internal parameters of LSSVM, and the prediction model based on BES-LSSVM is constructed by learning the nonlinear correlation characteristics between the spindle temperature and axial thermal displacement. Finally, it is compared with the prediction model using BES algorithm to optimize support vector machine and the prediction model using sparrow search algorithm to optimize LSSVM respectively. The comparison reveals that the predictions output from the BES-LSSVM model have better accuracy and stability. The results of the study can provide a certain knowledge base and technical support for the effective prediction of spindle thermal displacement changes.

Understanding and optimizing the dynamic characteristics of machine tools are essential for improving the efficiency and precision of manufacturing processes. An effective method for dynamic characteristic prediction and analysis of various tools is receptance coupling substructure analysis. Precision receptance coupling substructure analysis requires a frequency response function for the rotational degrees of freedom. Although computing the full receptance matrix, which includes rotational degrees of freedom, is possible through mathematical methods or finite element method, it is time-intensive and impractical for industrial applications due to the need for additional sensor attachments or other attachments on machinery. This study proposes a new approach for the receptance coupling substructure analysis of cutting tools and holders, aiming to efficiently predict and couple the full receptance matrix of cutting tools under free-free condition. The proposed methodology divides the cutting tool into several substructures and employs receptance coupling based on Euler–Bernoulli beam theory, thereby estimating the full receptance matrix of the subassembly. This approach also enables the prediction of dynamic characteristics of the system through inverse receptance coupling with a holder. We validated the accuracy of the methodology using the finite element method and experimental methods. The full receptance matrix of the machine tool and the estimated cutting tools were coupled and experimentally verified. In addition, the applicability of the proposed methodology is ensured by performing receptance coupling for various tool overhang lengths. This study is expected to contribute significantly to the quality improvement, design, and performance enhancement of machining equipment in the manufacturing industry. Further research is required to validate the robustness of this methodology across tools with diverse geometries and shapes.

Construction workers experience high rates of low back muscle strain due to frequent lifting of heavy objects. This study examined the effectiveness of an adjustable stiffness passive waist-assist exoskeleton (WIBS) in reducing muscle activity during lifting tasks. Ten male participants performed lifting and lowering tasks both with and without the WIBS. Muscle activity in the thoracic/lumbar erector spinae and multifidus muscles was measured using electromyography. The study also assessed the usability of the exoskeleton. The results revealed that WIBS significantly reduced low back muscle activity by up to 12.5% during lifting and 15.5% during lowering of 10 kg objects, particularly at stiffness levels 2 and 3. Notably, no significant difference was observed at the free and level 1 settings. While increasing stiffness generally decreased muscle activity, there was no significant difference between stiffness levels. The participants positively rated the exoskeleton, with an average satisfaction score of 84.6 across various aspects, including suitability, stability, safety, comfort, and effectiveness. These findings suggest that WIBS could be a valuable tool for reducing muscle strain and the risk of musculoskeletal disorders in construction workers. Longitudinal studies are required to evaluate the long-term use of this technology by construction workers and its effectiveness in a wider range of construction tasks.