Ball Screw Research Articles

Research on Global Nonsingular Fast Terminal Sliding Mode Control Strategy of Ball Screw Feed System Based on Improved Double Power Reaching Law

Research on Global Nonsingular Fast Terminal Sliding Mode Control Strategy of Ball Screw Feed System Based on Improved Double Power Reaching Law

Algorithm for extracting the normal cross-section parameters of multiple ball screw shaft ball tracks based on an optical micrometer measurement system

Abstract The accurate and efficient measurement of the normal cross-section parameters of multiple ball screw shaft spiral ball tracks are pivotal for ensuring quality control in ball track machining. Given the intricate nature of the ball screw shaft spiral ball track, balancing the accuracy and efficiency of the normal cross-section parameters measurement is a significant challenge. In this study, we present a method to calculate two core parameters, arc radius and contact angle. The method consists of four parts: the automatic axial cross-section separation method, the arc symmetric extraction method, the spiral transformation method, and the parameter algorithm based on the weighted least squares method. The experimental and simulation results validated the effectiveness of our method. Compared with the traditional axial measurement and transformation (AMT) method, our algorithm reduced the errors in arc radius and contact angle by up to 13.9 µm and 4.77°, respectively, and improved the accuracy by up to 78.34% and 85.04%. Compared with the traditional AMT methods and directly normal measurement method, the measurement time of our algorithm was reduced by up to 1565 s and 3475 s, respectively, and the efficiency was improved by up to 71.01% and 84.51%, respectively.

An Improved Displacement Sensor Accuracy Calibration Method for Ball Screw Positioning Error Compensation

An Improved Displacement Sensor Accuracy Calibration Method for Ball Screw Positioning Error Compensation

Modeling of Ball Screw–Nut Interface Stiffness With Wear (Ball Screw Wear Dynamics)

Abstract The effects of wear, preload loss, and missing balls on the dynamics of ball screw drives in machine tools are modeled and incorporated into the finite element model of the drive assembly for condition monitoring. The contacts between the ball–nut and ball–screw are modeled using Hertzian springs, whose stiffnesses vary as a function of the worn contact area. These contact stiffnesses are then transformed to the finite element nodes on the nut and screw. The frequency response functions at the motor shaft and table, which can be measured by commercial computer numerical control (CNC), are predicted for various faults at different positions of the table. The experimentally validated model demonstrates that the faults primarily affect the first coupled torsional-axial mode of the ball screw drive and can be utilized for automated condition monitoring of ball screw drives.

Robust feature design for early detection of ball screw preload loss

The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning is ensured by the addition of a preload to the ball screw system, it is crucial to detect and monitor the loss of preload at the earliest possible stage of degradation. The degradation process of the ball screw can be characterized in two stages: the initial reduction of preload without backlash, followed by a loss of preload with an emergence of backlash. To explore the change of the ball screw dynamics caused by degradation, a novel fixed cycle feature test (FCFT) is implemented in combination with multi-level mass experiments and a run-to-failure (RTF) test. The relationships of the ball screw dynamics with preload, worktable mass, and axis position are investigated, with a focus on the axial natural frequency as an indicator of preload loss. Experimental results validate the axial natural frequency’s role as a reliable early detector of preload loss for interventive use in a prognostic system.

A tribo-dynamics model of ball screws based on flexible body element

Dynamic actuation accuracy of ball screws is dramatically affected by screw flexible deformation and the behaviors of ball-groove interface such as friction, stiffness and damping. However, few dynamics studies have considered the screw flexible deformation and rubbing interface behaviors in ball screws. In this study, a dynamics model is developed by including both the screw flexible deformation and lubrication interface behavior. Discretization of the screw/nut is completed using the 6 degree-of-freedom Timoshenko beam element. The tribological properties of ball-groove interface are solved by the mixed lubrication model. Comparison with experimentally tested nut acceleration frequencies verifies the correctness of theoretical model. On this basis, the effects of nut motion, nut position and rough surface on the dynamic response are analyzed. With the increase of time, the screw axial displacement decreases with a certain slope and is accompanied by periodic vibrations. The centers of axis trajectories at different nut positions are far apart. From Hertz, smooth surface to rough surface, the interface stiffness coefficient decreases and the nut vibration amplitude grows. The novel tribo-dynamics model can provide a theoretical basis for fault diagnosis and performance optimization of ball screws.

Multivariate Accelerated Degradation Modeling and Reliability Assessment for Ball Screw Grease Based on Fractional Brownian Motion Process Model

Multivariate Accelerated Degradation Modeling and Reliability Assessment for Ball Screw Grease Based on Fractional Brownian Motion Process Model

Evolution of Lubrication Characteristics of Double-Nut Ball Screws Based on an Efficient Surface Roughness Modeling Method

Abstract Accurate prediction of lubrication characteristics, specifically film thickness and pressure distribution, is pivotal for ensuring optimal performance in ball screws. Despite its significance, there is a notable dearth of studies investigating the evolution of lubrication characteristics resulting from the changing surface roughness over prolonged operation of ball screws. Furthermore, obtaining the surface roughness of ball screws poses a challenge due to the limited loading capacity of measurement instruments. Traditional methods involving cutting the screw for surface roughness measurement are impractical for continuous monitoring during extended operation. To address this issue, the present study introduces an efficient approach to model the surface roughness of the raceway in double-nut ball screws. A profilometer is employed to measure profile roughness along two directions (parallel and perpendicular to the rolling direction) without the need to cut the screw raceway. The 2D power spectral densities and height probability densities of profile roughness are calculated to model the surface roughness, and the synthesized data are utilized to solve the Reynolds equation. The simulation method is validated through friction torque tests, demonstrating a calculation accuracy exceeding 92%. The study further explores the evolution of film thickness and pressure distribution in double-nut ball screws during running-in and steady wear stages, revealing severe asperity contact in the two nuts. Additionally, variations in load ratio, friction coefficient, and film thickness ratio (λ) are investigated. Considering the load ratio and λ of the slave nut, it can be inferred that boundary lubrication persists in the two nuts throughout the operation.

Machine Learning-Driven RUL Prediction and Uncertainty Quantification for Ball Screw Drives in a Cloud-Ready Maintenance Framework

In today's rapidly evolving industrial landscape, efficient predictive maintenance solutions are essential for minimizing downtime and enhancing productivity. This research introduces an adaptive cloud-based model pipeline for predicting the Remaining Useful Life (RUL) of machine components, specifically ball screws. The pipeline integrates local preprocessing, edge computing, and cloud-based adaptive model training, ensuring data privacy and reducing data transmission volumes. The system classifies wear states using various machine learning mod-els and predicts RUL through regression analysis, incorporating uncertainty quantification for robust maintenance scheduling. The experimental setup includes accelerated degradation of ball screws, with data collected via a three-dimensional accelerometer. Feature extraction and data augmentation techniques are employed to enhance prediction accuracy. Random Forest and Gradient Boosting models demonstrate superior performance, with Random Forest selected for its robustness and uncertainty quantification capabilities. Empirical results indicate high prediction accuracy, with Random Forest achieving up to 91% accuracy in Phase 2. This cloud-ready predictive maintenance framework leverages scalable cloud infrastructure for efficient data processing and real-time updates, offering a practical solution for industrial applications. The proposed approach significantly advances the adoption of digital business models within the manufacturing industry, providing a reliable and efficient tool for predictive maintenance.

Design and analysis of a piezoelectric energy harvesting shock absorber for light truck applications

Vehicle suspension vibration is a prevalent form of oscillation, with approximately 22.5 % of automobile energy dissipated through suspension-induced vibration. This phenomenon suggests that recovering the energy dissipated by the suspension system holds significant potential. This paper introduces a novel piezoelectric energy harvesting shock absorber (EHSA) based on non-contact magnetic force for light truck applications. The vibration of the vehicle suspension system is transformed into the rotation of a ball screw, enabling the one-way high-speed output of the rotor through a one-way bearing and planetary gear mechanism. Based on the non-contact magnetic force, the piezoelectric patches generate frequent oscillations, and a novel energy harvesting circuit is designed that efficiently converts the output of multiple piezoelectric units into usable electricity. The damping and energy harvesting characteristics of the EHSA with different external and internal variables are explored. Under sinusoidal excitation of 40 mm amplitude and 0.4 Hz frequency, the EHSA achieves a maximum output power of 7.51 W, with an average output power of 1.89 W. Furthermore, under random action, the EHSA can successfully illuminate 824 LED lights, enabling the temperature and humidity sensor to function normally. These findings indicate that the proposed EHSA can effectively harness vehicle suspension vibration to power onboard self-powered appliances while offering a new design idea for integrating vibration energy harvesters into vehicles.

Speed harmonic suppression of PMSM and its application in ball screw vibration suppression

Abstract The speed ripple of surface-mounted permanent magnet synchronous motor (SPMSM) causes vibration of the ball screw, which degrades the accuracy and stability of the ball screw system.The harmonic injection method can suppress the speed ripple by decreasing the speed harmonic of SPMSM. When the speed ripple is suppressed, the vibration of the ball screw is also reduced.
However, large amount of calculation, complex parameter adjustment, and long suppression time limit its performance in suppressing vibration of ball screw. Therefore, in this paper, we propose a speed harmonic suppression method based on the online measurement of the transfer function from inject voltage harmonic to speed harmonic. In the proposed method, the transfer function is measured after the motor reaches a stable operating condition. And then, the designed speed harmonic controller parameters are set according to the measurement results. Compared with the existing method, the method based on online measurement has less calculation, simple parameter adjustment, fast suppression speed, and good performance in suppressing the vibration of ball screw. Experimental verifications are carried out in the forward and backward condition of the ball screw.

Experimental and numerical study on a multilayer magnetic field rotary eddy current inertial damper

To improve the output damping force of the eddy current damper, a multilayer magnetic field rotary eddy current inertial damper (MMF-RECID) is proposed. The MMF-RECID is an inerter-based eddy current damper. A prototype device of the MMF-RECID is manufactured and tested, and its mechanical properties are systematically investigated. Firstly, the configuration of the MMF-RECID is described, and the calculation formulas of the axial force are derived. Secondly, an MMF-RECID prototype is designed and fabricated, and the apparent mass and energy dissipation performance under recycled axial loading are investigated. Three-dimensional transient numerical simulations of eddy current damping are conducted using the ANSYS Electronics Desktop. The results show that the eddy current damping force exhibits velocity nonlinearity, and the test results are consistent with the simulation results. Next, the study focuses on parameters influencing the maximum eddy current damping torque generated by the copper plate and the corresponding critical rotating speed. These parameters encompass the number and size of the magnets, the thickness of the copper plate, and the air gap. Finally, the earthquake-reduction effect of a six-degree-of-freedom structure equipped with MMF-RECID is analyzed. This study demonstrates that the ball screw system of MMF-RECID achieves a multiplicative effect of apparent mass and eddy current damping force, the eddy current damping force of a multi-layer magnetic field versus a single-layer magnetic field conforms to the law of linear superposition, and the hysteresis curves are stable. The eddy current damping torque can be improved by increasing the number and size of the magnets, and decreasing the air gap. The thickness of the copper plate significantly affects both the maximum damping torque and the critical speed, and the optimal thickness range is between 2 mm and 4 mm.

Performance enhanced magnetic negative stiffness eddy-current damper: Numerical simulation and experimental investigation

Passive negative stiffness dampers (NSDs) possess significant advantages and promising application prospects in the field of structural vibration control. NSDs with high energy dissipation capability are desired to satisfy the vibration mitigation requirements for large-scale engineering structures, such as high-rise buildings and long-span bridges. However, in practical applications, due to the constraints of mounting space and additional weight, the negative stiffness and damping coefficient of existing NSDs are generally not very large and with poor adjustability. To overcome these limitations, this study develops a novel magnetic negative stiffness eddy-current damper (MNSECD) with enhanced overall performance. This damper consists of a magnetic negative stiffness system (MNSS), a multi-layer-plate-type eddy-current damping system (ECDS) in parallel, and a bidirectional ball screw transmission system. Firstly, different magnetic arrays are designed to optimize the magnetic fields of the MNSECD, and to enhance its magnetic negative stiffness and eddy-current damping without adding extra dimensions and weight. Subsequently, the electromagnetic finite-element models (FEMs) of the MNSECD are established to precisely simulate its magnetic negative stiffness and eddy-current damping forces, and these models are further utilized to elucidate the enhancement mechanism of different magnetic arrays. Finally, based on the numerical simulation results, the best performing magnetic array is selected respectively for the ECDS and MNSS systems to carry out experimental studies on a small-scale prototype. Numerical and experimental results indicate that the selected magnetic array for the MNSS proves highly effective in enhancing the magnetic negative stiffness of the MNSECD, while the selected magnetic array for the ECDS also demonstrates superior effectiveness in enhancing the eddy-current damping of the damper. These findings suggest that the novel MNSECD can be readily used in the vibration control of large-scale engineering structures.

Analysis of precision loss of double-nut ball screw considering dimensional error and preload degradation

Abstract A ball screw is a critical drive component capable of converting force into motion, with widespread applications in the high-precision mechanisms of various machine tools. The degradation in precision attributed to ball screw wear significantly impacts machine tool accuracy. Current ball screw wear models lack consideration for the ramifications of radial forces and ball size errors.This study introduces a pioneering approach by formulating a coupled model that integrates screw wear, ball wear, and preload degradation. Through rigorous analysis, we discern the multifaceted influences of axial force, radial force, preload adjustments, and ball size precision on the positional accuracy of ball screws.By elucidating the intricate interplay among these parameters, this study offers crucial theoretical insights into the selection and optimization of ball screws, thereby fostering advancements in machine tool design and performance.

Active prediction compensation strategy: A new positioning compensation technique for improving the accuracy of the dual ball screw feed mechanism under load

The dual ball screw feed mechanism (DBSFM) is commonly employed in high-end machining applications. However, its accuracy is challenging to enhance due to variations in the axial stiffness of the dual actuators under load, making geometric error compensation (GEC) difficult. To address this, our research establishes a comprehensive error model that integrates geometric error and load-induced error (LIE) to improve feeding accuracy. The axial stiffness matrix of the DBSFM is identified using motor current information, and an active prediction error matrix is defined based on the geometric error model, milling force model, and axial stiffness matrix. We propose and analyze the active prediction compensation (APC) strategy as a new method to enhance accuracy under load. Experimental validation using a gantry-type machine tool demonstrates that the APC strategy can reduce the positioning error of the DBSFM to ±2 μm under load, representing significant improvements over the established GEC method. Specifically, the APC strategy achieves a 53.07% and 57.04% enhancement in accuracy along the two axes. This work illustrates the effectiveness of the APC strategy in reducing positioning errors and improving feeding accuracy in DBSFM machining processes.

Research on thermal characteristics of double-nut ball screw

Abstract Preload, external load, and feed speed have significant effects on thermal characteristics of ball screws. In this paper, ground on friction heat generation theory and Hertz contact theory, a thermodynamic modeling of the double-nut ball screw is established, heat generation under different preload forces and feed speeds is analyzed, and temperature field is estimated based on finite difference method. The results show that preload and feed speed have a obvious impact on screw nuts temperature. The finite difference method makes numerical calculation of temperature field more convenient.

Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm

In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.

Investigations on contact characteristics of ball screw considering flexible deformation of screw and nut

Investigations on contact characteristics of ball screw considering flexible deformation of screw and nut

Analysis and Experiment of Thermal Field Distribution and Thermal Deformation of Nut Rotary Ball Screw Transmission Mechanism

This study designs a differential dual-drive micro-feed mechanism, superposing the two “macro feed motions” (“motor drive screw” and “motor drive nut”) using the same transmission of “the nut rotary ball screw pair” structure. These two motions are almost equal in terms of speed and turning direction, thus the “micro feed” can be obtained. (1) Background: Thermal deformation is the primary factor that can restrict the high-precision micro-feed mechanism and the distribution of heat sources differs from that of the conventional screw single-drive system owing to the structure and motion features of the transmission components. (2) Discussion: This study explores the thermal field distribution and thermal deformation of the differentially driven micro-feed mechanism when two driving motors are combined at different speeds. (3) Methods: Based on the theory of heat transfer, the differential dual-drive system can be used as the research object. The thermal equilibrium equations of the micro-feed transmission system are established using the thermal resistance network method, and a thermal field distribution model is obtained. (4) Results: Combined with the mechanism of thermal deformation theory, the established thermal field model is used to predict the axial thermal deformation of the differential dual-drive ball screw. (5) Conclusions: Under the dual-drive condition, the steady-state thermal error of the nut-rotating ball screw transmission mechanism increases with the increase in nut speed and composite speed and is greater than the steady-state thermal error under the single screw drive condition. After reaching the thermal steady state, the measured thermal elongation at the end of the screw in the experiment is approximately 10.5 μm and the simulation result is 11.98 μm. The experimental measurement result demonstrates the accuracy of the theoretical analysis model for thermal error at the end of the screw.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.