Research on reliability allocation technology for NC machine tool meta‐action

The frequent occurrence of early failure has long been restricting the corrective of the reliability of domestic numerical control (NC) machine tools. At present, there is a lack of a systematic and effective method to eliminate the early failure of machine tools. The difficulty in eliminating the early failure of the machine tools lies in how to calculate its early failure period accurately and design the failure closed-loop elimination program properly. So, a 4-parameter non-homogeneous Poisson process (NHPP) modeling method and a closed-loop elimination system for the early failure of the machine tools were proposed to solve those problems in this paper. The method proposed in this paper is applied to the enterprise, and its feasibility is verified. The research of this paper lays a foundation for the elimination of the early failure and the improvement of the reliability level of the domestic NC machine tools.

In view of the characteristics of energy transmission in the running processes of numerical control (NC) machine tool driven by variable voltage variable frequency (VVVF), an energy flow model of main driving system is established. The power transition characteristics of main motor and mechanical transmission system and the energy loss on every section are analyzed. Finally, the dynamic power balance equation of the whole main drive system is established. Comparing with the equation of common machine tools, this equation has three characteristics. The wide application prospect of the equation is shown by the application analysis and practices.

NC (Numerical Control) machine tools is the device which automated operation of a machine tools by numerical control, and it is greatly contributing to a trial production or manufacturing efficiency-ization at a factory. Machine operation of the NC machine tools is automated, however a human operator needs to determine a processing parameters. An inappropriate parameter causes high loading state operation or cutting vibration, and NC machine tools position accuracy become worse. Introducing force control in NC machine tools has advantages, such as reduction of machine load, and suppression of vibration. For force control in NC machine tools, it is necessary to attach a force sensor which is expensive and fragile. In this paper, force sensor-less cutting force detection is proposed. Finally, validity of proposed method is verified by experiment.

The existing methods of determining testing period do not consider the effect of working condition covariates on reliability testing period of Numerical Control (NC) machine tools, which may lead to high-cost testing or low-precision assessment. Aiming at the problem, a new method of determining the testing period considering working condition covariates is proposed. The change rate of interval estimation of Mean Time between Failures (MTBF) is used as the criterion for determining the length of testing period. The reliability model of NC machine tools is established by the Cox proportional hazards model, and the two-step estimation method is used to estimate parameters of the baseline failure rate function and the coefficients of working condition covariates. The Bootstrap resamples are obtained by the Bootstrap resampling method. And then the parameters of the baseline failure rate function and the coefficients of working condition covariates are estimated simultaneously by maximum likelihood method, and thus interval estimations of MTBF under each covariate are obtained. The change rate models of MTBF interval estimation under each covariate level are established by Power function, and the testing periods under each covariate are obtained. Case study indicates that the testing periods under each covariate obtained by the proposed method are more accurate than those obtained by the others, when the same criterion and confidence level 1-α are set.

In this paper, a new approach is proposed based on data fusing with vibration signals using time-frequency parameters, probabilistic principal component analysis (PPCA) and statistical inference, for improving the accuracy and visibility of damage identification for numerical control (NC) machine tools. Time-frequency feature principal components are put forward, which extracted from eight dimensionless parameters statistically in the time and frequency domains by PPCA. The Chi-2 statistic is established according to statistical inference principle, and the feature figure of principal components is built that can acquire damage distribution of tools by measured data. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy and visibility of the proposed approach.

The dynamic character of ball screw is the key factor that influences the machining accuracy of numerical control (NC) machine tool. To improve the dynamic characteristics of the NC machine tool, it is necessary to study the dynamic characteristics of a ball screw. In this paper, the kinematics of a ball screw mechanism (BSM) are studied to expound the dynamic process of the drive, and the load disturbance is considered to analyze the contact deformation based on the Hertzian contact theory. The velocity relationships among the ball, screw, and nut are analyzed, and the influence of the contact deformation on the dynamic characteristics is simulated and investigated experimentally. The results show that the relationships between the contact deformation, which is affected by the material characteristics, the contact angle, and the load of nut are nonlinear. The contact deformation is a factor that cannot be ignored when considering the dynamic machining error of high-speed and high-precision machine tools.

The point maximum likelihood and interval estimators of the parameters, as well as two reliability indices of the log-linear process model, including cumulative mean time between failures and reliability at given time are given. In tests for failure time trends, both the graphical methods include the mean cumulative function versus time plot and the total-time-on-test plot, and the analytical methods include the Laplace, the military handbook, and the Lewis-Robinson tests are used. A real case of failure dada with time truncation for multiple numerical control (NC) machine tools is given to illustrate the use of the proposed model.

According to the required interval estimation precision of mean time between failures (MTBF) and reliability for numerical control (NC) machine tools, where failure processes can be described by the power law process model, the minimal testing truncated time and sample size of the reliability assessment test are given using Fisher information matrix method. The results show that there are two main factors which affect the truncated time and sample size. They are the log ratio of upper bound to lower bound and confidence of interval estimation for reliability indices, respectively. Among them, the minimal sample size increases significantly as the improvement of required precision of interval estimation for reliability indices and confidence increases, but the minimal testing truncated time is less affected by these two factors.

Aim to adverse effects resulted from external disturbances, system internal parameters variations and unmodeled dynamics during the operation of gantry moving type numerical control (NC) machine tool, The paper presented that suspension air-gap of beam suspension system was controlled by using auto-disturbance-rejection control technology. Auto-disturbances-rejection controller can regard system model parameter variations and external disturbance effect of system as comprehensive disturbance, automatic online estimation and compensation. First the state variables were chosen based on the mathematical model of beam suspension system, nonlinear state equation of system was obtained; then coil voltages was regarded as controlled variable, double-loop (position loop and current loop) auto-disturbances-rejection controller of the suspension air-gap was designed, nonlinear control of the beam suspension system was realized. The simulation results show that the auto-disturbances-rejection controller can adapt well to the nonlinearity of suspension system and has strong robustness to system's parameter variations and the system's disturbances. The beam friction-free stable suspension can be realized.

In this paper are proposed two new approaches to maintenance strategies for Computer Numerical Control (CNC) machine tools. The analysis is done for different families of CNC machine tools from S.C. Elmet Bacau, a company specialized in aviation. In maintenance actions applied to CNC machine tools is very important to know the evolution of defects and critical state of electrical and mechanical components. The results of this analysis concludes that maintenance actions can be judged by the developing time period diagram, between failure appearance and interruptions in operation. It is also analyzed the financial impact, revealed from known maintenance strategies adopted on CNC machine tools, resulting in a positive approach of condition based maintenance.

In minimal repair,in order to describe the failure process of numerical control(NC) machine tools experiencing both early failures and deterioration phenomena,and operating so long that the intensity function approaches a finite asymptote as the system operating time grows,a new four-parameter non-homogeneous Poisson process model is proposed.The characteristics of the model are discussed,and the physical meaning of its parameters is given,calculating formula of point estimation for model parameters and reliability indices such as the minimum intensity value and the time of minimum intensity are all derived,then a method of model selection for reliability assessment of two NC machine tools is provided based on the likelihood ratio statistic,and the model evaluation criterion is given based on the index of goodness-of-fit test.The real failure data of two NC machine tools are analyzed by using the proposed method,the results show that this method can quantitatively evaluate the machine tools' lasting time of early failure period,it is suitable for describing the failure process of NC machine tools with bounded and bathtub shaped failure trend,and can provide a theoretical basis for eliminating as much early failures as possible before the NC machine tools leave the factory.

The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time–frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time–frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the time–frequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time–frequency characteristic quantity in real time.

Based on the nonlinear mapping function of neural network and by the adoption of Back Propagation (BP) artificial neural network with three-layer structure, the nonlinear mapping between the processing state of Numerical Control (NC) machine tools and the related parameters is realized. The processing state monitoring of NC machine tools is achieved by the neural network training. In addition, the development cycle of the program is reduced and its reliability is improved by using MATLAB Script node of LABVIEW software to call the neural network toolkit of MATLAB. Through the milling experiment of the finger milling-cutter, the capability of BP artificial neural network to accomplish the inspection of tool state and prediction of wear extent is verified.KeywordsBP Neural NetworkNC Machine ToolsProcessing Condition MonitoringTool Wear

The paper presents a new approach to the modelling and simulation of a computer numerical control (CNC) machine tool feed drive. The hybrid model of the drive incorporates a distributed load, explicit damping factors and measured non-linear effects in order to achieve a realistic dynamic performance. In this way, the shortcomings of traditional modelling methods applied to CNC machine tool axis drives are overcome. A MATLAB/SIMULINK package was used to simulate this new model for a CNC machine tool feed drive. A novel hybrid model with a distributed load, explicit damping factors, backlash and friction was developed and shown to have a similar dynamic response to the machine tool feed drive system under the same conditions. The simulated results display resonance frequencies, while the lumped-parameter models generate only the response of a second-order element.

Numerical control (NC) machine tool is typical mechatronics system with complicated failure features. System reliability may be affected by the internal properties, external factors, dynamic physical effects and the complex interdependent relationship, and it is too complicated to accurately model or analyse by conventional methods. Improving the reliability of machine tool cannot rely solely on improving unit parts reliability level, and reliability design should be performed at the system level for a better profit. For this purpose, using agent-oriented modeling method, a system failure evolution model is proposed to simulate the failure dynamic process. With the model, not only system lifetime and reliability can be analyzed, system failure mechanism and impact of the internal properties, external factors and system parameters also can be researched. Unit failure-agent model, system-level failure-agent model, hierarchical model structure and simulation control method are described separately. Finally, an example of the machine tool's feed system model is given to simulate and analyze system life, and a further analysis indicate that the model is valid and close to the real data.