Search Results (70)

To overcome the issues of excessive cutting force, poor chip segmentation, and premature tool wear during the drilling of Ti-6Al-4V titanium alloy. This study established the cutting edge motion trajectory function and instantaneous dynamic cutting thickness equation for ultrasonic vibration-assisted drilling through kinematic analysis. Based on this, an analytical model of drilling force was formulated, integrating tool geometry, cutting radius scale effects, dynamic chip thickness, and drilling depth. In parallel, a finite element model was constructed to achieve visual simulation analysis of chip deformation and cutting force. Finally, the accuracy of the model was verified through experiments, with a comprehensive analysis performed on how cutting parameters affect thrust force. The findings indicate that the average absolute prediction errors of thrust force and torque between the analytical model and finite element simulations were 7.87% and 6.26%, respectively, confirming the model’s capability to accurately capture instantaneous force and torque variations. Compared to traditional drilling methods, the application of ultrasonic vibration assistance resulted in reductions of 40.8% in thrust force and 41.7% in torque. The drilling force exhibited nonlinear growth as the spindle speed and feed rate were elevated, while it declined with greater vibration frequency and amplitude as drilling depth increased. Furthermore, the combined effect of optimized vibration parameters enhanced chip fragmentation, producing short discontinuous chips and effectively preventing entanglement. Overall, this research provides a theoretical and practical foundation for optimizing ultrasonic vibration-assisted drilling and improving precision hole making in titanium alloys. Full article

Improving the prediction accuracy of solar power generation is a critical challenge in promoting sustainable energy solutions. While machine learning models like long short-term memory (LSTM) have gained attention, they face practical limitations such as their complex structure, long training time, and susceptibility to overfitting. Echo state networks (ESNs) have attracted attention for their small number of trainable parameters and fast training speed, but their sensitivity to hyperparameter settings makes performance improvement difficult. In this study, the key hyperparameters of an ESN (spectral radius, input noise, and leakage rate) were optimized to maximize performance. The ESN achieved a Root Mean Square Error (RMSE) of 0.0069 for power prediction, demonstrating a significant improvement in accuracy over a tuned LSTM model. ESNs are also well-suited for real-time prediction and large-scale data processing, owing to their low computational cost and fast training speed. By providing a more accurate and efficient forecasting tool, this study supports grid operators in managing the intermittency of renewable energy, thereby fostering a more stable and reliable sustainable energy infrastructure. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

Corner radius end mills (CREMs) are widely used in machining due to their unique tool geometry, which improves surface quality. Variations in cutting force during machining significantly impact machining quality. Therefore, precisely predicting cutting forces is critical for controlling machining chatter and enhancing accuracy. Traditional element force models have complex formulas and high computational demands when considering tool runout. This paper proposes a hybrid prediction model for CREMs that integrates the separate-edge-forecast method and the BP neural network. The integration approach incorporates runout effects into cutting force coefficients and addresses nonlinear effects from runout. The accuracy of the cutting force prediction model was validated through side milling on 7075 aluminum alloy. The results indicate that the maximum error between the predicted and measured forces is 9.43%, demonstrating that this model ensures high prediction accuracy while reducing computation cost. Full article

The turning process is the main machining task in brake disc production, and the PCBN tool is the most suitable type of cutting tools in the machining of brake discs made of cast iron. The edge geometric optimization of the PCBN tool is the key factor to obtain a better tool performance. In this paper, the cutting simulation for the machining of cast iron with PCBN tool of grade HNMN120712 was established, which exhibits a simulation error lower than 10.8%. The optimal turning parameters were obtained by the equal material removal rate method. The edge geometric parameters were optimized in two stages: firstly, the optimal edge radius was obtained as 30 μm by the comprehensive normalization analysis of the cutting temperature and stress, and then, the chamfer width and angle were further optimized to 0.1 mm and 15°. At finally, the optimized PCBN tool was prepared and tested in the machining of brake discs; the results indicate that the designed tool exhibits an excellent tool performance with 3.4 times the tool life of the conventional tool. Full article

The lubrication state of the feed system of a CNC machine tool will affect its positioning accuracy, repetition accuracy, and minimum movement amount. Insufficient or excessive lubrication will affect the accuracy. The primary objective of this study is to resolve issues related to the lubrication condition of the feed system, aiming to enhance its operational stability and accuracy. In this study, a measurement system based on images of Newton’s rings was developed. The relationship between the pattern of Newton’s rings and the oil film thickness was established based on the theoretical principle of Newton’s rings. Furthermore, fuzzy logic theory was applied to predict the oil film thickness. In the oil film thickness prediction model based on the radius of Newton’s rings, the average error is 6.5%. When the average feed rate increases by 2 m/min, the oil film thickness value decreases by 43%. Finally, the prediction model is compared with the results of an actual verification experiment. The trends in oil supply timing are consistent between the predicted and experimental results, and the relative error values are less than 10%. Therefore, this study solves the problem of insufficient or excessive oil supply in the feed system guideway, increasing the accuracy of CNC machine tools and contributing to green energy technology. Full article

Plasmonic nanostructures have been widely studied for their unique optical properties, which are useful in sensing, photonics, and energy. However, the efficient design of these structures, considering the complex relationship between geometry, material, and optical response, remains a challenge. In this study, we propose a machine learning-based approach to address the inverse design problem in nanostructures, using data generated by numerical simulations via the Finite Element Method (FEM). We used a dataset of over 140,000 entries to train the regression models CatBoost, Random Forest, and Extra Trees, capable of predicting physical parameters, such as the radius of the nanocylinder, based on the simulated optical response. The CatBoost model achieved the best performance, with a Mean Absolute Error below 0.3 nm on unseen data. In parallel, we applied a direct design approach to experimental data of metallic nanoparticles, focusing on the optical absorption prediction from particle size. In this case, Random Forest presented the best performance, with a lower risk of overfitting. The results indicate that machine learning models are promising tools for optimizing the design and characterization of plasmonic nanostructures, thus reducing the need for costly experimental techniques. Full article

Gaze tracking and estimation are essential for understanding human behavior and enhancing human–computer interactions. This study introduces an innovative, cost-effective solution for real-time gaze tracking using a standard webcam, providing a practical alternative to conventional methods that rely on expensive infrared (IR) cameras. Traditional approaches, such as Pupil Center Corneal Reflection (PCCR), require IR cameras to capture corneal reflections and iris glints, demanding high-resolution images and controlled environments. In contrast, the proposed method utilizes a convolutional neural network (CNN) trained on webcam-captured images to achieve precise gaze estimation. The developed deep learning model achieves a mean squared error (MSE) of 0.0112 and an accuracy of 90.98% through a novel trajectory-based accuracy evaluation system. This system involves an animation of a ball moving across the screen, with the user’s gaze following the ball’s motion. Accuracy is determined by calculating the proportion of gaze points falling within a predefined threshold based on the ball’s radius, ensuring a comprehensive evaluation of the system’s performance across all screen regions. Data collection is both simplified and effective, capturing images of the user’s right eye while they focus on the screen. Additionally, the system includes advanced gaze analysis tools, such as heat maps, gaze fixation tracking, and blink rate monitoring, which are all integrated into an intuitive user interface. The robustness of this approach is further enhanced by incorporating Google’s Mediapipe model for facial landmark detection, improving accuracy and reliability. The evaluation results demonstrate that the proposed method delivers high-accuracy gaze prediction without the need for expensive equipment, making it a practical and accessible solution for diverse applications in human–computer interactions and behavioral research. Full article

Robot calibration and modelling measurements are commonly performed using a laser tracker. To capture three-dimensional positions, a SMR is attached to the robot. While some researchers employ adhesive bonds for this purpose, such methods often result in inaccurate, unstable and non-repeatable SMR positioning, adversely affecting measurement precision and the traceability of research outcomes. To address these challenges, we investigated alternative methods for attaching an SMR to a robot’s flange to achieve both accuracy and repeatability. Additionally, we analysed measurement errors introduced when using a tool to attach the SMR to the flange. As a solution, we developed a 3D-printed mount designed for attachment to the flange. The mount’s accuracy was evaluated by assessing its eccentricity and the repeatability of the SMR placement. Experimental results demonstrated that the mount achieved an eccentricity radius of 0.35 mm and repeatability inaccuracies of $X=0.075\mathrm{mm}$, $Y=0.328\mathrm{mm}$, and $Z=0.485\mathrm{mm}$. These values indicate that the mount provides sufficient accuracy to support calibration processes, ensures research traceability, and serves as a viable replacement for adhesive bonds. Full article

Background: Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. Methods: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder–decoder structure with attention gates for segmentation and a slight convolutional network for classification. Results: With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. Findings: Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients. Full article

Over the past 30 years, researchers have developed X-ray-focusing telescopes by employing the principle of total reflection in thin metal films. The Wolter-I focusing mirror with variable-curvature surfaces demands high precision. However, there has been limited investigation into the removal mechanisms for variable-curvature X-ray mandrels, which are crucial for achieving the desired surface roughness and form accuracy, especially in reducing mid-spatial frequency (MSF) errors. It is essential to incorporate flexible control in deterministic small-tool polishing to improve the tool’s adaptability to curvature variations and achieve stable, Gaussian-like tool influence functions (TIFs). In this paper, we introduce a curvature-adaptive prediction model for compliance figuring, based on the Preston hypothesis, using a compliant shaping tool with high slurry absorption and retention capabilities. This model predicts the compliance figuring process of variable-curvature symmetrical mandrels for X-ray grazing incidence mirrors by utilizing planar tool influence functions. Initially, a variable-curvature pressure model was developed to account for the parabolic and hyperbolic optical surfaces’ curvature characteristics. By introducing time-varying removal functions for material removal, the model establishes a variable-curvature factor function, which correlates actual downward pressure with parameters such as contact radius and contact angle, thus linking the variable-curvature surface with a planar reference. Subsequently, through analysis of the residence time distribution across different TIF models, hierarchical filtering, and PSD distribution, real-time correction of the TIFs was achieved to enable customized variable-curvature polishing. Furthermore, by applying a time-varying deconvolution algorithm, multiple rounds of flexible polishing iterations were conducted on the mandrels of a rotationally symmetric variable-curvature optical component, and the experimental results demonstrate a significant improvement in form accuracy, surface quality, and the optical performance of the mirror. Full article

In this paper, the finite element method (FEM) is integrated with orthogonal polynomial approximation in high-dimensional spaces to innovatively model the Moon’s surface gravity anomaly. The aim is to approximate solutions to Laplace’s classical differential equations of gravity, employing classical Chebyshev polynomials as basis functions. Using classical Chebyshev polynomials as basis functions, the least-squares approximation was used to approximate discrete samples of the approximation function. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. The first application of this project is to substitute the globally valid classical spherical harmonic series of approximations with locally valid series of orthogonal polynomial approximations (i.e., using the FEM approach). With an error tolerance set at ${10}^{-9}$$\mathrm{m}{\mathrm{s}}^{-2}$, this method is used to adapt the gravity model radially upwards from the lunar surface. The results showcase a need for a higher degree of approximation on and near the lunar surface, with the necessity decreasing as the radius increases. Notably, this method achieves a computational speedup of five orders of magnitude when applying the method to radial adaptation. More intrinsically, the second application involves using the methodology as an effective tool in solving boundary value problems. Specifically, this approach is implemented to solve classical differential equations involved with high-precision, long-term orbit propagation. This application provides a four-order-of-magnitude speedup in computational time while maintaining an error within the ${10}^{-10}$$\mathrm{m}{\mathrm{s}}^{-2}$ error range for various orbit propagation tests. Alongside the advancements in orthogonal approximation theory, the FEM enables revolutionary speedups in orbit propagation without compromising accuracy. Full article

Metal mirrors will be widely used in the coming decades. Therefore, as one of the enabling technologies for metal optical freeform surface manufacturing, ultra-precision (UP) diamond turning error compensation has become a research hotspot. However, for the tool offset error and tool radius error, which are the main errors in UP diamond turning, no precise and efficient calculation method has been found in the literature. In this study, a more precise and efficient algorithm was developed and validated in three ways using on-machine measurement data and profilometer measurement data. After one compensation, the tool offset error can be reduced to below 0.1 μm, and the tool radius error can be reduced to below 1 micrometer, which will significantly improve the UP turning accuracy and efficiency of optical parts. Full article

The discrete element method (DEM) has emerged as an essential computational tool in geotechnical engineering for the simulation of granular materials, offering significant advantages over traditional continuum-based methods such as the finite element method (FEM) and the finite difference method (FDM). The DEM’s ability to model particle-level interactions, including contact forces, rotations, and particle breakage, allows for a more precise understanding of granular media behavior under various loading conditions. However, accurate DEM simulations require meticulous calibration of input parameters, such as particle density, stiffness, and friction, to effectively replicate real-world behavior. This study proposes a simplified calibration procedure, intended to be conducted prior to any granular material flow DEM modeling, based on three fundamental physical tests: bulk density, surface friction, and angle of repose. The ability of these tests, conducted on dry quartz sand, to accurately determine DEM micromechanical parameters, was validated through numerical simulation of cylinder tests with varying height-to-radius ratios. The results demonstrated that this calibration approach effectively reduced computational complexity while maintaining high accuracy, with validation errors of 0% to 12%. This research underscores the efficacy of simplified DEM calibration methods in enhancing the predictive reliability of simulations, particularly for sand modeling in geotechnical applications. Full article

Magnetorheological finishing (MRF) of aspherical optical elements usually requires the coordination between the translational axes and the oscillating axes of the machine tool to realize the processing. For aspheric optical elements whose steepness exceeds the machining stroke of the equipment, there is still no better method to achieve high-precision and high-efficiency error convergence. To solve this problem, an MRF method combining virtual-axis technology and a spiral scanning path is proposed in this paper. Firstly, the distribution law of the magnetic induction intensity inside the polishing wheel is analyzed by simulation, the stability of the removal efficiency of the removal function within the ±7^∘ angle of the normal angle of the polishing wheel is determined, and MRF is expanded from traditional single-point processing to circular arc segment processing. Secondly, the spiral scanning path is proposed for aspherical rotational symmetric optical elements, which can reduce the requirements of the number of machine tool axes and the dynamic performance of machine tools. Finally, an aspherical fused silica optical element with a curvature radius of 400 mm, K value of −1, and aperture of 100 mm is processed. The PV value of this optical element converges from 189.2 nm to 24.85 nm, and the RMS value converges from 24.85 nm to 5.74 nm. The experimental results show that the proposed combined process has the ability to modify curved optical elements and can be applied to ultra-precision machining of high-steepness optical elements. Full article