Errors Of CNC Machine Tools Research Articles

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Research on thermal error compensation strategy of CNC machine tools based on full working area modeling

Abstract Traditional manufacturing equipment is developing in the direction of precision, intelligence and integration, and the precision requirements of CNC machine tools are getting higher and higher. Compensating and controlling the thermal error of the machine tool is an important part of improving machining accuracy. This paper uses a cuckoo search algorithm to optimize the BP neural network to get the CSBP neural network model and inputs the temperature value of the CNC machine tool into the CSBP model to get the thermal error prediction results. Combined with the thermal error values of each measurement point on the CNC machine table, the B-spline function is used to fit the thermal error prediction model for the entire working area. Finally, the thermal error compensation system of the CNC machine tool has been established. Simulation and empirical studies show that the fitting and prediction accuracy of the model in this paper are better, and the maximum error value of the prediction is reduced by 20.27% compared to the LSTM model. After the application of the thermal compensation system in this paper, the thermal error of the CNC machine tool is greatly reduced, and most of the offset caused by the thermal deformation of the machine tool is eliminated through compensation. In this paper, the design of a thermal error compensation system based on full work area modeling can effectively reduce the thermal error of CNC machine tools, which can be applied to the actual machining process of machine tools to improve the machining accuracy of CNC machine tools.

Analysis of the configuration characteristics of the chaotic attractor for machine tool dynamic error under the singular spectrum

To explore the chaotic characteristics of the motion error of CNC machine tools, this paper proposes a chaotic attractor feature extraction method for the early motion error of CNC machine tools. Firstly, the chaotic verification of each time series feature of machine tool motion error proves that the change of CNC machine tool motion error is a nonlinear change process with chaotic characteristics. It then establishes the multivariate error phase space and performs a singular spectrum analysis. Because the motion error is strongly disturbed by noise, it is difficult to determine the number of singular values by the traditional singular spectrum analysis method, and this paper proposes to calculate the phase space index of the reconstructed sequence by traversing the number of singular values, to determine the main feature components; Finally, under different noise environments and different signal lengths, three feature extraction methods are introduced for experimental comparison with this paper's method, which shows that the chaotic attractor reconstructed after determining the number of singular values has a more apparent geometric structure. The method proposed in this paper has advantages in machine tool motion error.

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

Improved Prediction Model of the Friction Error of CNC Machine Tools Based on the Long Short Term Memory Method

Friction is one of important factors that cause contouring errors, and the friction error is difficult to predict because of its nonlinearity. In this paper, a prediction model of the friction error of a servo system is proposed based on the Long Short-Term Memory method (LSTM). Firstly, the transfer function is used to predict the position of the servo system, and then the prediction error of the transfer function is obtained. Secondly, the nonlinear friction error is extracted and predicted by a LSTM network. Finally, the accurate tracking error can be predicted by the proposed combined model. The experimental results show that the proposed model can improve the prediction accuracy of tracking errors dramatically.

Modeling of CNC machine tools’ spatial geometric error based on two-dimensional angle error

A modeling method of CNC machine tools’ spatial geometric error is proposed based on two-dimensional angle error in this paper. Different from traditional modeling method, this method takes into account the effect of additional angle errors because the machine tool’s angle errors in the system of the lower guide rail will be passed on to slip board structure to the upper guide rail system. Firstly, after analyzing the principle of two-dimensional angle error, an optimized error compensation model of CNC machine tools’ spatial geometric error contains two-dimensional angle error is established through the traditional homogeneous coordinate transformation matrix method (HTM). Further, by applying the experiment, the compensation effect of the model is verified through comparing the body diagonal errors of CNC machine tools before and after compensation. Experiment results show that the body-diagonal accuracy after compensation based on the optimized model is 87.73% higher than that before compensation, and 54.82% higher than that of the traditional model after compensation.Article HighlightsThe highlight of the study reported here is threefold: Firstly, the principle of two-dimensional angle error is analyzed. Secondly, an optimized spatial geometric error compensation model of CNC machine tool is established based on the principle of two-dimensional angle error. Thirdly, the compensation effect of the optimized compensation model is verified by comparing the bodies diagonal precision of the machine tool.

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Modeling and prediction of gear flank twist for generating gear grinding

Gear flank twist has a significant effect on the load-carrying capacity. However, most prediction models of gear flank twist cannot be applicable for gears with profile modification. A new method for predicting gear flank twist for the generating grinding process is developed in this paper, taking into consideration the effect of lead modifications, profile modifications, and position errors of CNC machine tools on gear flank twist. And the influences of four gear parameters on gear flank twist were studied using orthogonal experiments, which prove that the change of module has the greatest effect on gear flank twist. And the maximal profile modification value has minimal effect on gear flank twist. The experimental results proved that the model can accurately predict the gear flank twist. Besides, this paper described a gear parameter optimization study to reduce gear flank twist based on nine well-planned orthogonal experiments.

Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

The thermal error of CNC machine tools can be reduced by compensation, where a thermal error model is required to provide compensation values. The thermal error model adaptive update method can correct the thermal error model by supplementing new data, which fundamentally solves the problem of model robustness. Certain problems associated with this method in temperature-sensitive point (TSP) selection and model update algorithms are investigated in this study. It was found that when the TSPs were selected frequently, the selection results may be different, that is, there was a variability problem in TSPs. Further, it was found that the variability of TSPs is mainly due to some problems with the TSP selection method, (1) the conflict between the collinearity among TSPs and the correlation of TSPs with thermal error is ignored, (2) the stability of the correlation is not considered. Then, a stable TSP selection method that can choose more stable TSPs with less variability was proposed. For the model update algorithm, this study proposed a novel regression algorithm which could effectively combine the new data with the old model. It has advantages for a model update, (1) fewer data are needed for the model update, (2) the model accuracy is greatly improved. The effectiveness of the proposed method was verified by 20 batches of thermal error measurement experiments in the real cutting state of the machine tool.

Robust modeling method for thermal error of CNC machine tools based on random forest algorithm

Robust modeling method for thermal error of CNC machine tools based on random forest algorithm

COMPENSATION OF MACHINE TOOL THERMAL ERROR BASED ON VIRTUAL SENSORS

Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. Thermal errors of ball screw system directly affect the positioning errors of CNC machine tools. This paper illustrates how virtual sensors for temperature measurement can support the thermal model to predict the thermal errors while reducing the temperature monitoring cost. Experimental results under different working conditions were performed and the proposed model for thermal error compensation proves to be accurate in predicting the thermal error of CNC machine tools.

Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method

Due to the errors of the servo system and the errors of the ball screw drive system, the positioning errors inevitably occur in the process of CNC machine tools. The measurement of traditional equipment is limited by a fixed measurement radius and a single degree of freedom, which can only be measured within a fixed plane. In this paper, four different positioning errors of CNC machine tools are first measured at full scale by using J-DBB (a modified double ball bar with one spherical joint connecting two bars) method. The J-DBB device uses a three-degree-of-freedom spherical joint as a connecting part, which realizes that the measurement radius can be continuously changed, and the measurement space is a spatial sphere. First, the principle of the J-DBB method is briefly introduced. Next, four typical positioning errors of CNC machine tools are analyzed and examined, which contain the uniform contraction error of ball screw and linear grating, periodic error of the ball screw and linear grating, interference of measurement devices error, and opposite clearance error. In the end, the trajectories of the CNC machine tool spindle with a single positioning error are simulated by using the J-DBB method. The results reveal that this method can be used for the positioning error of machine tools, which helps to better understand the spatial distribution of CNC machine tool errors and provides guidance for the reasonable selection of working areas to improve the machining accuracy of parts.

Dynamic positioning error analysis and modeling of CNC machine tool guideway system

The dynamic characteristics of the XY worktable are thoroughly analyzed in this study to accurately compensate the measurement errors of on-machine measurement systems caused by the dynamic and static characteristics under multiple factors. The dynamic positioning error model of the XY worktable motion components is established, and the theoretical model of the dynamic positioning error of the XY worktable is derived. The actual parameters of the experimental platform for the correlation error of CNC machine tools are substituted into the model for theoretical calculation and analysis. Results show that the motion speed of the worktable, the weight of the measured workpiece, the installation position of the workpiece, and the friction force are the main factors causing the dynamic positioning error. Dynamic positioning error analysis and verification experiments were conducted to verify the validity of the proposed theoretical model. Experimental results show that a regular change occurs between the dynamic positioning error and the movement speed of the XY worktable, and the actual measurement results are basically consistent with the theoretical calculation results. The research results can lay a good foundation for establishing the measurement error prediction and compensation model of on-machine measurement systems of CNC machine tools and improving its measurement accuracy.

Research on Application of Key Technologies of Error Compensation for CNC Machine Tools Based on Computer

The CNC Machine Tools with high efficiency, high precision and high stability is the inevitable trend of the modern manufacturing industry development.This development trend puts forward the extremely high request to the CNC machine tool error compensation technology. However, the error of CNC machine tools have a variety of reasons, these errors affect each other, which leads to the inevitable error. At the high efficiency processing, we must ensure the high stability and high precision of the machine tool. Which requires the error compensation function to maintain high stability and high compensation accuracy, when the CNC machine tool is under the action of high speed and dynamic load. The rapid development of computer technology provides us with more possibilities to solve error compensation. CNC machine tool error compensation technology based on computer is an important means to improve the machining accuracy in the manufacturing industry, which reduce the error in the production process and increase the production efficiency. This paper firstly analysis the error classification and error source percentage of CNC machine tools. And then it analysis the movement error when the CNC machine tools is working. Finally, it puts forward the key technique and application of error compensation based on computer.

Development of real-time prediction module for precision and error of CNC system finishing

High speed and high precision are the important trends in the development of CNC machine tools. In order to improve the machining accuracy of CNC machine tools, the errors of CNC machine tools must be reduced as much as possible. Generally speaking, the errors of CNC machine tools include spatial errors, thermal errors, cutting force errors, tool wear errors, etc., in which spatial errors account for a large part of CNC machine tool errors, so reasonable control of reducing spatial errors is of great significance to improve the accuracy of CNC machine tools. Methods commonly used to reduce spatial errors include error prevention methods and error compensation methods. Error prevention is a method to eliminate and reduce error sources through design, manufacturing, assembly, etc. Although this method can reduce the original error, it is limited by improving manufacturing and assembly accuracy to reduce errors, and it is economically expensive. The error compensation method is to artificially create a new error to offset the current original error to achieve the purpose of improving machining accuracy. This method is easy to implement and has low cost. Therefore, the error compensation method is the most economical and effective method to improve the accuracy of the machine tool.

The Thermal Error Modeling with Deep Transfer Learning

Thermal error of CNC machine tools is one of the main factors affecting the machining accuracy. The data-driven method for thermal error modeling is an effective and efficient, but they have some flaws, such as poor accuracy, bad robustness, and etc. because of having no quite enough data set and imbalanced data set. In this paper, a new method based on transfer learning for thermal error modeling is presented for solving the issue of imbalanced data set. The dataset of monitoring the temperature field of the machine tools includes monitoring data of three kinds of operating conditions, namely stopping, idling, and machining. When the fewer idling data is used to train a model, the larger stopping data are introduced as train aids. Transfer learning is adopted to fully learn the common characteristics of the two different working conditions, which can effectively solve the problem of imbalanced dataset. The experimental results prove that our method have better performance than other methods trained only with limited idling data.

Dynamic error of CNC machine tools: a state-of-the-art review

The dynamic error of CNC machine tools, which often exceeds the quasi-static error at high-speed machining, becomes the main reason affecting the machining error of the sculptured surface parts. Although much research efforts have been dedicated to dynamic error, there is a lack of systematical summaries. In this review, firstly, the dynamic error is defined as the deviation of actual displacement of effector end of axis relative to reference displacement during feed motion. Secondly, according to the mechanical and control structure of the servo feed system, the dynamic error is divided into two components: dynamic error inside the servo loop (component 1) and dynamic error outside the servo loop (component 2). Based on the two components, the causes resulting in the dynamic error are analyzed from the points of view of the servo feed system itself and its input (setpoints). Thirdly, the basic strategies for reducing the dynamic error of individual axis, as well as for reducing the trajectory dynamic error by coordinating the dynamic error of individual axis, are summarized. Finally, the problems and future research directions on dynamic error are analyzed. It is concluded that resolving the contradiction between the setpoints and the servo feed system is still a great challenge for dynamic error in high-speed machining. To achieve high dynamic accuracy at high-speed machining, the control strategies on the dynamic error outside the servo loop should be further developed and integrated into dynamic error inside the servo loop-oriented control strategies. Meanwhile, the servo feed system itself and its input need to be investigated as a whole, so that the servo feed system of each axis can adapt to the differences and changes of the setpoints, and the differences in the servo dynamics of each axis can be considered in the setpoints.

Research on Reliability of Positioning Error Measurement for NC Milling Machine

Laser interferometer is the common tool to measure the positioning error of CNC machine, and its measurement process will be affected by external factors. Therefore, it is very important to ensure the error accuracy which measured by laser interferometer for compensating the error of CNC machine tools. The positioning error of MVC850B CNC milling machine was measured by Renishaw laser interferometer. Firstly, the positioning error of it was measured in standard and actual environmental parameters, and measured results were comparatively shown in factors of temperature, air pressure and humidity. Secondly, the influence degree that in three conditions of feeding speed, interval distance and processing time was analyzed by the errors of reverse clearance and screw pitch cumulative. Finally, with the statistics of positioning errors measured in a certain period of time, this paper analyzed the reliability of positioning errors measured by laser interferometer and analyzed the variation of milling machine motion axis errors. Meantime, the possible location of machine errors can be predicted, and the positioning accuracy of CNC milling machine would be promoted.

Binocular vision-based 3D method for detecting high dynamic and wide-range contouring errors of CNC machine tools

Periodic checking for contouring error in unloading conditions can effectively evaluate the dynamic performance of machining centers. Existing measurement devices have limitations in the high accuracy three-dimensional (3D) measurement of arbitrary contouring errors (e.g. ballbar and cross-grid encoder). Thus, in this paper, a cost-effective binocular vision-based 3D method for detecting high dynamic and wide-range contouring error of computer numerical control machine tools is proposed. With this method, the strobe lighting method is first presented to suppress the blurring effect of the newly designed 1024 coded targets that were embedded in the measurement fixture. Thereafter, an encoding and decoding method based on finding the optimal initial non-encoding region is proposed to automatically identify, match, and reconstruct the time-varying targets. Then, to enhance the performance of the vision system, a calculated method based on three visible non-collinear coded targets is presented to deduce the dynamic and large-scale contouring error. Finally, contouring error detection experiments are carried out in a self-built five-axis machine tool to validate the advantages of the method. Both the proposed vision method and cross-grid encoder are used to measure contouring errors of three types of trajectories under different feed rates. By comparing the difference between the two trajectories measured by the two devices, the experimental results illustrate that the mean vision detection error at 7 m min−1 is about 4.3 µm, which verifies the accuracy and effectiveness of the proposed vision measurement method.

Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools

Error compensation technology offers a significant means for improving the geometric accuracy of CNC machine tools (MTs) as well as extending their service life. Measurement and identification are important prerequisites for error compensation. In this study, a measurement system, mainly composed of a self-developed micro-angle sensor and an L-shape standard piece, is proposed. Meanwhile, a stepwise identification method, based on an integrated error model, is established. In one measurement, four degrees-of-freedom errors, including two-dimensional displacement and two-dimensional angle of a linear guideway, can be obtained. Furthermore, in accordance with the stepwise identification method, the L-shape standard piece is placed in three different planes, so that the measurement and identification of all 21 geometric errors can be implemented. An experiment is carried out on a coordinate measuring machine (CMM) to verify the system. The residual error of the angle error, translation error and squareness error are 1.5″, 2 μm and 3.37″, respectively, and these are compared to the values detected by a Renishaw laser interferometer.