State assessment based on reliability analysis plays a crucial role in optimizing maintenance decisions and has a significant impact on fault localization and machining efficiency. Existing methods for Computer Numerical Control (CNC) machine tools primarily rely on discrete data, facing two key challenges: (1) Independent subsystem modeling neglects mutual coupling relationships, resulting in assessment results with limited interpretability, and (2) Inadequate reliability modeling obscures the fault evolution process, hindering fault localization and propagation path identification. To address these challenges, this paper proposes a novel state assessment and fault evolution method for CNC machine tools, grounded in field information fusion. First, a subsystem reliability model is established by fusing field information using Bayesian theory and the Zeros-ones transformation method to enhance accuracy. Subsequently, integrating state data with probabilities, a dynamic assessment methodology and a comprehensive metric are developed to quantitatively reflect the subsystem health state. Furthermore, based on the susceptible-infected-repair-susceptible (SIRS) model and fault propagation mechanism, a fault dynamic co-evolution model is established to quantitatively analyze the propagation paths and evolution trends of faults among subsystems over time. Validated through a gantry 5-axis CNC machining center case study, the proposed method successfully identifies subsystem fault causes and locations, demonstrating its practical effectiveness in industrial applications.

In secondary assembly of machine tool linear guideways, a small assembled straightness error does not necessarily ensure stable straightness retention after a disturbance loading–unloading cycle. This study investigates how the first preassembly response affects the straightness drift error after secondary assembly. A nonlinear finite element model is developed by considering the initial geometric error, equivalent residual stress, bolt preload, and contact states at the joint interfaces. The first preassembly response is described by local vertical deformation, contact pressures at the guideway-shim and shim-bed interfaces, effective contact state, and bed rebound. To introduce this response into the assembly process, the detrended vertical displacement component is projected into a scalar correction term for the secondary support height. A contact support compatibility index is then defined to evaluate pressure nonuniformity, pressure mismatch between the two support interfaces, and effective support continuity. The results show that the assembly scheme with the smallest assembled straightness error does not necessarily produce the smallest unloading drift error. Under the baseline numerical model, the response-transfer correction scheme reduces the unloading drift from 1.44 μm for geometric shim compensation to 0.94 μm, while maintaining effective support at all 12C2/C3 support pairs. The study provides a finite element framework for evaluating response transfer, contact support compatibility, and straightness retention after unloading in secondary assembly of linear guideways.

Fault propagation analysis is pivotal for fault tracing and candidate root cause identification in CNC machine tools. However, existing research methods often overlook the challenges of insufficient observability of subsystems and the rigid integration of propagation mechanisms with state data. To address these challenges, this paper proposes a fault propagation intensity-guided graph attention network (IG-GAT) for fault tracing. First, a hierarchical topological digraph model is constructed to quantify the static fault propagation intensity (FPI) based on physical connections. To overcome the issue of missing sensor data in unobservable subsystems, a dual-channel feature extraction framework is developed. This framework utilizes uncertainty quantification theory to transform qualitative expert priors into quantitative distributions, aligning them with the causal features of observable nodes in a unified high-dimensional space. Subsequently, the FPI is embedded into the GAT network through a learnable mechanism-fusion strategy. Unlike static hybrid models, the proposed IG-GAT incorporates an adaptive factor that dynamically adjusts the attention weights based on signal complexity. It prioritizes data-driven correlations to capture the specific causal distinctness of incipient faults. Under severe and chaotic fault conditions characterized by high data uncertainty, the model integrates the static FPI into the attention coefficients via a weighted adjacency matrix, dynamically scaled by the adaptive factor. This mathematical constraint restricts the data-driven attention weights, counteracting noise and ensuring physically plausible tracking paths. Case study results show that the proposed method outperforms existing methods in both accuracy and convergence speed. Furthermore, it provides transparent and interpretable candidate tracing paths for maintenance decisions.

As a prevalent fastening technique in mechanical system assembly, the structural rigidity of screw joints critically governs vibrational response characteristics at both component and full-system levels. In this paper, a new screw equivalent connection stiffness model is established by considering contact stiffness of connector, the meshing stiffness of the thread and the stiffness of the screw and the connected part. Employing fractal-based surface morphology characterization and considering asperity-substrate interaction, an integrated contact load-stiffness formulation for interface morphology is established. Based on the thread stress-strain relationship, the thread axial load distribution law is introduced, the thread meshing stiffness model is derived, and the equivalent model of the screw connection stiffness is established. Finally, the stiffness weakening ratio factor is proposed, and the influence of the combined partial shape parameters on the equivalent connection stiffness is revealed by numerical analysis. The results show that with fractal dimension elevation and roughness amplitude reduction, the equivalent connection stiffness of the screw increases gradually, and the stiffness weakening ratio decreases gradually.

To address the issue of poor predictability in the static and dynamic characteristics of membrane-type hydrostatic module, this study proposes a load capacity solution method, accounting for fluid-structure interaction effects of the membrane. The oil circuit flow resistance model is refined, enabling the accurate determination of oil recess pressure under arbitrary oil film thickness variations. Based on this, a static and dynamic characteristic analysis model for the hydrostatic module is established, investigating the effects of parameters such as oil film thickness and throttle gap on its static and dynamic performance. Finally, a multi-field coupled simulation model is developed to analyze the interaction between oil recess pressure and membrane deformation. Additionally, a high-precision experimental testing platform for hydrostatic modules is designed and implemented, effectively validating the proposed method. This study provides valuable guidance for the practical application of membrane-type restricting devices.

Reducing the coefficient of friction is a critical method for improving the service life and enhancing the efficiency of artificial implants.Maintaining a robust low-friction effect is essential for optimal artificial implant performance.This work utilizes the mechanism of the interaction between the interfacial charge and microviscosity to design a composite coating for titanium alloys modified with halloysite nanotubes/poly(vinylphosphonic acid)(HNT-PVPA).Compared with that of the pure PVPA coating,the coefficient of friction of the composite coating-polytetrafluoroethylene(PTFE)system stabilized at a low-friction state of approximately 0.008,with a 13.40%improvement in the load-bearing capacity.This low-friction state is maintained over a wide range of speeds and for extended periods.Furthermore,the study reveals that the electrical property differences between the inner and outer walls of halloysite nanotubes induce specific aggregation of anions and cations.These ions increase the microviscosity around the tube wall by forming hydrogen bonds with water molecules and attracting water molecules to form hydronium cations,contributing to the low-friction mechanism.The HNT-PVPA composite coatings also enhance the toughness of the coating in the body fluid environment by stabilizing the crosslinked core region against perturbations from multivalent cations.The results provide a new approach for achieving low-friction composite polymer coatings with improved frictional properties in biotribology.

Clarifying the fault propagation mechanism is one of the key methods for improving the machine tool's reliability. However, current modeling methods usually overlook the impact of spatiotemporal coupling factors on fault propagation, leading to a limited understanding of the fault propagation mechanism. Therefore, this paper proposes a fault hierarchical propagation reliability improvement method based on spatiotemporal factors coupling. Considering the coupling effects of component comprehensive importance, fault tolerance, and failure modes on the machine tool system, a spatiotemporal fault hierarchical propagation topological directed graph model was established. Based on this, an improved method for calculating fault propagation strength was proposed to identify weak links and critical fault propagation paths. The proposed method effectively addresses the critical path identification problem across CNC machine tool systems. Comparison results demonstrate that the proposed method can accurately identify critical fault propagation paths. Furthermore, the influence of various factors on these path sequences is studied in this paper. It extends the traditional modeling methods and theories to enhance the transparency of the fault propagation process within the machine tool system. This work provides theoretical support for maintenance decision-making.

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.

Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining iterative optimization and deep reinforcement learning (DRL) is proposed to address the impact of uncertain disturbances. A real-time DRL production environment model is established for the flexible job scheduling problem. Based on the DRL model, an agent training strategy and an autonomous decision-making method are proposed. An event-driven and period-driven hybrid dynamic rescheduling trigger strategy (HDRS) with four judgment mechanisms has been developed. The decision-making method and rescheduling trigger strategy solve the problem of how and when to reschedule for the dynamic scheduling problem. The data experiment results show that the trained DRL decision-making model can provide timely feedback on the adjusted scheduling arrangements for different-scale order problems. The proposed dynamic-scheduling decision-making method and rescheduling trigger strategy can achieve high responsiveness, quick feedback, high quality, and high stability for flexible manufacturing process scheduling decision making under sudden disturbance.

There are many bolted structures in heavy-duty gantry machine tools, and the structural performance of these components greatly impacts the dynamic properties of the entire machine. Firstly, based on the fractal theory, a bolt preload-contact stiffness and damping model was established by characterizing the load, deformation and strain energies of the bolted joint surfaces. In order to precisely forecast the dynamic features, a dynamic response model of the tip point of the gantry machine was first proposed by adopting the multi-body system transfer matrix method. The feasibility of the contact mechanism and the dynamic response model were verified by modal tests respectively. Finally, the effect of bolt preload loosening on the dynamic response of the whole machine is analyzed. In the scaled model of the gantry machine, when the bolt preloads on the lathe bed-column and crossing beam-column joint surfaces are loosened by 30 %, the displacement response of the tool tip point increases by 41.2 % and 40.1 % respectively, which indicates that the effect of lathe bed-column interface is more significant. This paper seeks out the weak link in the gantry machine tool, which helps to carry out the design optimization of its assembly processing afterward.

Geometric and thermally-induced errors are the key factors affecting the machining accuracy of machine tools, especially the time-varying characteristics of thermally-induced errors significantly degrading accuracy reliability. To address this issue, a method for accuracy reliability analysis and enhancement of machine tools under dual geometric and thermally-induced error constraints is proposed. Firstly, incorporating both geometric and thermally-induced errors, a spatial comprehensive error model is established using extended multi-body system (MBS) theory and homogeneous transformation matrices (HTM). A reliability function is developed based on accuracy design index, and the accuracy multi-failure model is graphically illustrated. Secondly, the machining accuracy reliability analysis model via advanced first-order second moment (AFOSM) method is established. Through mathematical statistics analysis of geometric error and corresponding tolerance, tolerance-driven reliability sensitivity quantification and total manufacturing cost modeling are researched. Thirdly, with tolerance parameters as optimization variables and incorporating dynamic constraints for thermally-induced errors, an accuracy design function with dual optimization objectives: minimizing total manufacturing cost and maximizing accuracy reliability is proposed. Subsequently, this multi-objective problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II). Finally, a case study on a horizontal machining center demonstrates practical applicability, where geometric error measurement and thermal error tests, combined with intelligent optimization, reduce manufacturing costs by 13.37% while improving accuracy reliability from 80.75% to 90.01%. This method offers a valuable tool for enhancing accuracy in heat-sensitive machine tools in industrial settings.

In industrial production, a large number of different types of bolts are typically required for assembling complex products. For example, the assembly of an automobile engine requires more than 100 types and over 500 bolts. The bolt assembly process requires repeated tightening and loosening according to the assembly requirements. The number and type of stages in the tightening process varies from one type of bolt to another. Therefore, the primary challenge in bolt tightening fault diagnosis for complex products resides in establishing a generalized detection methodology applicable to a large number of different types of bolts. To address the aforementioned challenges, this paper innovatively proposes a general model for multi-stage tightening process. The model incorporates nine different types of stages, aligning with production realities; A feature construction method combining stages, start position operators, end position operators, and curve characteristic operators is proposed; A random forest classifier is chosen as the classification model, and a genetic algorithm is used to optimize the hyperparameters of the random forest classifier. Data experiments show that the fault diagnosis method proposed in this paper can achieve more than 99% accuracy. The fault diagnosis method proposed in this paper was applied in an automotive diesel engine assembly factory. It has been proven that the proposed fault diagnosis method can improve fault detection efficiency and increase the pass rate of finished products.

Abstract This study presents a simulation model for the wave generator and flexspline assembly, examining the relationship between radial and main section deformations. Adjusting the wall thickness enables interference-free meshing of various sections. A response surface model for the effect of circular spline profile parameters on flexspline stress has been established. To assess how various parameters of the involute gear profile affect the meshing stress of flexspline. Thus providing a theoretical basis for optimizing gear profile modifications.

The internal feedback hydrostatic rotary table is a precision support device, and its performance relies heavily on the oil pad. However, uncertainties in the manufacturing process are often overlooked during the stiffness optimization, affecting the reliability of the optimized results. Accordingly, this paper aims to analyze the influence of structural parameters on the stiffness performance of the internal feedback hydrostatic rotary table and to perform reliability optimization considering the uncertainties. Initially, a theoretical computational model of internal feedback hydrostatic rotary table, accounting for the oil leakage effect, is proposed. The model's accuracy is validated through comparative simulation calculations, and based on this model, the load-bearing performance of the table is further analyzed. Subsequently, focusing on the structural characteristics of the oil pad, a reliability optimization model that considers manufacturing uncertainties is proposed. To improve the optimization efficiency, a Levenberg-Marquardt Backpropagation (LM-BP) neural network is introduced as a surrogate model for theoretical calculations. The oil pad is optimized through a particle swarm optimization algorithm. Ultimately, the optimal structural size parameters of the oil pad are obtained, achieving maximal stiffness under a high level of reliability. Both the stiffness performance and the reliability level of the rotary table are substantially enhanced. The results indicate that the proposed method can significantly improve performance and reliability in practical applications.

Contact stiffness and backlash in the harmonic drive significantly impact a robot's positioning accuracy and vibration characteristics. The height of the harmonic drive tooth pair is typically less than 1 mm, making the measurement and modeling of backlash and contact stiffness inherently complex. This paper proposes a contact stiffness and backlash model by establishing a correlation between fractal parameters and tooth contact load. To obtain the fractal roughness parameters of the real machined tooth surface, a combination of a noncontact optical profiler and the RMS method is employed. Subsequently, the study explores the influence of rough tooth surface and contact force fractal parameters on contact stiffness and gear backlash. The results demonstrate the substantial impact of surface topography parameters and contact force on contact stiffness and backlash. Specifically, an increase in the fractal dimension correlates with a reduction in gear backlash and contact stiffness. Conversely, the fractal roughness parameter exhibits the opposite effect. Notably, an increase in contact force enhances contact stiffness.

This paper proposes a parameter optimization method for hydrostatic turntable with internal feedback in machine tools, considering stiffness and energy consumption. For the first time, a power consumption calculation method for oil pads with built-in restrictor is presented, and a stiffness calculation model for the oil film is established based on the Reynolds equation. A multi-objective optimization function is constructed in the form of weighted coefficients. An experimental setup for performance testing of the oil pad with internal feedback is developed, and finite element simulation analysis is combined to verify the accuracy of the theoretical method. Finally, corresponding optimization schemes for different scenarios are provided. The results show that at a speed of 50 r/min, power consumption can be reduced by 50.1

This paper provides a comprehensive exploration of the operational mechanism of an internal feedback hydrostatic turntable. It employs a self-compensating gap restrictor mechanism, resulting in heightened load capacity and stiffness. In this paper, the concept of internal flow is innovatively introduced. Subsequently, the Reynolds equation is solved using the finite difference method. This methodology offers a more precise and efficient assessment of the load-bearing performance of the oil pad within the turntable. Then this paper further investigates the influence of the internal flow coefficient and pressure ratio on oil pad performance, encompassing aspects like load capacity, stiffness, and flow rate. Ultimately, optimal parameters are selected to improve the structure of the gap restrictor, considering various operational scenarios. In conclusion, the method adopted in this study not only improves the calculation accuracy and efficiency but also improves the structure and performance of the oil pad. The hydrostatic turntable is a critical component of numerous computerized numerical control machine tools. This paper provides a more comprehensive exploration of the operational mechanism of an internal feedback hydrostatic turntable. In addition, this study proposes a more accurate and efficient method to analyze the performance of the oil pad and optimize its structure.image

This study aims to analyze the impact of uniform and eccentric load conditions on the performance of internal feedback hydrostatic thrust and journal bearing. Two distinct models are established: a three-degrees-of-freedom uniform load model and a five-degrees-of-freedom eccentric load model. The support stiffness, overturning stiffness, and flow rate for both thrust and journal bearings are calculated. Additionally, numerical analysis is conducted to examine the influence of oil film thickness, inlet pressure, and restrictor size on the operational characteristics of the bearings, revealing the interplay between an eccentric load and journal bearing speed. The validity of the theoretical algorithm is verified through finite element simulation. The research outcomes hold significant guiding implications for the design and application of internal feedback hydrostatic bearings.