Accuracy Of Five-axis Machine Tools Research Articles

십자형 교정체를 이용한 5축 공작기계 회전축의 기하학적 오차 추정에 관한 연구

Estimation and compensation of geometric errors for rotary axes are among methods to improve machining accuracy of five-axis machine tools. Studies have been conducted on various methodologies for estimating geometric errors for rotary axes, which are essential for improving machining accuracies of five-axis CNC machine tools. This paper presents a method for estimating geometric errors of a rotating/tilting table using a cross-shaped calibration artifact with a touch trigger probe. The proposed method includes rotary axes error estimation equations for angles of each rotary and tilt axis based on locations of probing points. Computer simulations were performed based on a MATLAB/Simulink and ADAMS cosimulation system using the probing cycle process to verify the proposed method. Computer simulation results confirmed the usefulness of the proposed method in terms of volumetric errors.

An approach to enhancing machining accuracy of five-axis machine tools based on a new sensitivity analysis method

An approach to enhancing machining accuracy of five-axis machine tools based on a new sensitivity analysis method

One-step measurement method of five-axis machine tools using a laser tracer

To improve the motion accuracy of five-axis machine tools, it is important to develop a simple methodology to calibrate all geometric errors. This paper proposes a one-step measurement method to calibrate the five-axis machine tool using a laser tracer. First, a simplified geometric error definition of the five-axis machine tool is presented, which is sufficient and necessary to determine the tool error relative to the workpiece. Subsequently, a one-step measurement method using a laser tracer is proposed, and joint movements of five motion axes are adopted to simultaneously identify all geometric errors. Besides, a measurement path generating method is presented to save the trouble of manually planning the complex measurement path. Compared with the traditional two-step measurement method, the proposed method only needs four laser tracer positions and two reflector offsets, simplifying the measurement procedure and enhancing the measurement accuracy of geometric errors of rotary axes.

Reverse identification of dynamic and static motion errors for five-axis machine based on specimen feature decomposition

The dynamic and static error motions dramatically influence the accuracy of five-axis machine tools. We propose a new approach of reverse identification for dynamic and static motion error of five-axis machine tools through specimen cutting and feature decomposition. The error mapping between the workpiece features and the corresponding errors is established, and a feature specimen is designed accordingly. By measuring the specimen with the on-machine measurement and CMM, 15 static motion errors and dynamics-induced errors can be identified and separated. The error estimates are verified by the direct measurement via the interferometer, autocollimator and ballbar tests. These errors are compensated using a volumetric error model, and the specimen accuracy after compensation is effectively increased, which confirms the feasibility and accuracy of this method.

An approach for improving machining accuracy of five-axis machine tools

An approach for improving machining accuracy of five-axis machine tools

Thermal Error Analysis of Five-Axis Machine Tools Based on Five-Point Test Method

The accuracy of five-axis machine tools is a key performance indicator. Among the various error sources of high precision five-axis machine tools, thermal and geometric errors occupy the majority. Thermal errors have become the largest error source of high precision five-axis machine tools, accounting for about 45% of the total errors. Accurate measurement of thermal errors plays a vital role in improving the accuracy of five-axis machine tools. Taking the Shenyang HTM50100 turning and milling machine tool as an example, this paper proposes a method to measure the thermal error of the machine tool spindle using the five-point test method. In the process of thermal error modeling, we select the temperature key point and analyze the collected data. Finally, we evaluate thermal error model. The method is verified by an experiment. The experiment results show that the method is highly accurate, fast, and easy to use. It provides a theoretical basis and practical method for the measurement of thermal errors on five-axis machine tools. By evaluating the method based on multiple linear regression, the predictive ability of the model is about 77%. Compared with LSTM, the prediction accuracy is improved by 5.08%.

Self-optimizing thermal error compensation models with adaptive inputs using Group-LASSO for ARX-models

Thermal errors have a significant impact on the machining accuracy of five-axis machine tools. The thermal adaptive learning control (TALC), which combines adaptable data-based models and on-machine measurements, realizes a precise and robust long-term reduction of thermal errors of machine tools. To further improve the precision and the robustness of data-based models for thermal error compensation, this publication introduces a new method to realize adaptive model inputs. This method combines the Group-LASSO (least absolute shrinkage and selection operator) for autoregressive models with exogenous inputs (ARX) and the particle swarm optimization to realize a simultaneous estimation of the optimal inputs, the model structure, and the model parameters. Additionally, the self-optimization ability of thermal error compensation models, based on the TALC, is increased by introducing error-specific action control limits to define the frequency of model updates. The newly developed methods are applied to compensate the thermal errors of a swiveling and a rotary axis of a five-axis machine tool during a long-term test series of 350 h. Randomly generated speed profiles of the linear and rotary axes as well as the spindle and changing ambient conditions ensure a high variety of thermal load cases within in the analyzed long-term test series. The results show that the prediction accuracy measured as peak-to-peak values and the robustness of the thermal error compensation models are improved by up to 36% and 58% respectively when adaptive instead of static model inputs are used. Furthermore, the compensation results of the new method outperform the previously used sequential input selection method regarding prediction accuracy and repeatability. The average peak-to-peak value of the compensated translational thermal errors is reduced by 23% and the repeatability of the corresponding compensation results is increased by 57%. Consequently, the consideration of the resulting model structure during the selection of the optimal model inputs significantly enhances the performance of the resulting data-based thermal error compensation models.

Method of Evaluating Dynamic Accuracy of Five-axis Machine Tool Based on Test Piece and Comprehensive Evaluation System

Method of Evaluating Dynamic Accuracy of Five-axis Machine Tool Based on Test Piece and Comprehensive Evaluation System

A novel method for evaluating the validity of dynamic accuracy test pieces for five-axis machine tools

The detection method for direct machining the standard test pieces, which is commonly employed to exhibit the machining precision of five-axis machine tools, can truly reflect the dynamic accuracy of five-axis machine tools in the actual machining process. Existing theories on explaining the phenomenon that the ability of S-shaped test pieces to detect dynamic accuracy for five-axis machine tools are stronger than those of NAS979 test pieces mostly used the qualitative research methods, and they cannot be quantitatively used to reveal the mathematical relationship between machine tools and test pieces. Therefore, this article makes the first attempt to investigate the quantitative evaluation method for the validity of dynamic accuracy test pieces. The dynamic error function of the servo feed system in the frequency domain was derived first by establishing the mechanical system model and control system model. The method of dipole cancellation was used to acquire a simplified transfer function for the dynamic error affected by the input. Based on the zero-order hold property of the discrete input signals, the expression of dynamic error affected by input in the time domain, which intuitively shows the mathematical relationship among the machine tool performances, the test piece characteristics and the dynamic error of the servo feed system, was obtained. Then, the novel evaluation methods of the linear combinatorial value and the combinatorial linear combinatorial value were proposed. A series of comparative analyses between an S-shaped test piece and an NAS979 test piece were carried out based on the proposed new evaluation methods. A machining experiment conducted on a five-axis machine tool is used to verify the evaluation results.

An approach for accuracy enhancement of five-axis machine tools based on quantitative interval sensitivity analysis

Error compensation is a common method to improve the machining accuracy of five-axis machine tools (FAMTs). Before that, in order to identify the key geometric errors that affect the machining accuracy, sensitivity analysis (SA) in entire machining region or a specified point is usually used. However, since the influence of geometric errors on form errors is different in each interval, the result of SA would be affected, which decreases the compensating accuracy. For this reason, a new approach for accuracy enhancement of FAMTs based on quantitative interval sensitivity analysis (QISA) is presented in this paper. First, based on multi-body system theory and homogeneous transform matrix, the volumetric error model of the FAMT is established. Then, the geometric error model of the FAMT is formulated. Second, development of form error model is involved in this paper to clarify the effect law of geometric error on form error. Moreover, the machining region of S-shaped test piece is divided into five intervals. Then, the QISA method is presented to calculate the sensitivity index of each geometric error and identify key geometric errors at different intervals. Finally, simulations and experiments are conducted to validate the feasibility and effectiveness of the presented method.

A volumetric positioning error compensation method for five-axis machine tools

Volumetric positioning error is an important index that influences the machining accuracy of five-axis machine tools. Although the available compensation methods, including hardware and software compensation, provide approaches to improve the volumetric positioning accuracy of five-axis machine tools, the cost-effect ratio of these methods is usually not acceptable for most users. Based on the volumetric error model of a five-axis machine tool and the sag error compensation function together with the table multiplication function in Siemens 840D system, a novel volumetric positioning error compensation method is proposed in this paper. The experiments show that the proposed compensation method can effectively improve the machining accuracy of the machine tool with really low cost, which shows good perspective in manufacturing industry.

Identification and compensation of position-dependent geometric errors of rotary axes on five-axis machine tools by using a touch-trigger probe and three spheres

For the machining accuracy of five-axis machine tools, it must be emphasized that not only the PIGEs but also the PDGEs of rotary axes influence the machining accuracy. However, until now there is no any commercial measurement system available for identifying the PDGEs in the rotary axes of five-axis machine tools. As a result, this study proposes a robust, efficient, and automatic measurement method to identify and compensate the position-dependent geometric errors (PDGEs) of rotary axes on five-axis machine tools. The proposed measurement method has established an on-machine measurement for the PDGEs of rotary axes by using a touch-trigger probe and three spheres installed on the spindle as well as the tilting rotary table, respectively. For each rotary axis, only a single measuring pattern is implemented to measure the PDGEs with a single setup, which delineates the advantages of efficient and automated identifying procedures in each periodical measurement. By implementing the proposed measurement method, 12 PDGEs can be numerically identified based on the measurement algorithm, which is built through a kinematic error model as well as a least square method. Finally, the proposed measurement method is experimentally conducted on a commercial five-axis machine tool. Moreover, after consequently performing the proposed measurement method, the PDGEs of the rotary axes were quantitatively compensated by the commercial controller to validate its feasibility. The experimental results have clearly delineated that the linear errors and angular errors are reduced from at most 37.81 μm and 10.23 mdeg to 0.9 μm and 0.26 mdeg, respectively. Consequently, the experimental results have demonstrated that the proposed measurement method is efficient and precise.

Incorporating Local Offset in the Global Offset Method and Optimization Process for Error Compensation in Machine Tools

Abstract Evaluation and compensation of machining accuracy of five-axis machine tools is often time consuming and requires high levels of operator expertise on the measurement instruments to achieve identification of error parameters. Faster compensation methods are necessary to reflect the current state of the machine in a manufacturing floor. The Global offset method has been developed for mass production, where the information from post-machining inspection of the workpiece on a coordinate measuring machine is utilized to determine the compensation parameters that are used in the machine tool controller to improve part quality. It makes compensation easy and convenient using part measurement data. This paper explains the incorporation of the Local offsets in the Global offset method utilizing an iterative and incremental approach to farther minimize the part feature errors. The Global-Local offset method explains not only the geometric errors but those affected by the deformation due to dynamic forces, thermos-mechanical changes and backlash (that are repeatable from part-to-part) in a machine tool, and part deformation due to cutting and/or clamping forces that are also repeatable. Using the Global-Local offset parameters, part feature error residuals were found to be less than 10% the feature tolerance.

An identifying method with considering coupling relationship of geometric errors parameters of machine tools

The geometric errors parameters are important for machining accuracy of five-axis machine tool. However, most of the error identification methods only based on the detection instruments to achieve identification of single error parameter, without considering the coupling relationship of these errors between five axes. This paper measures and identifies the position-dependent error parameters and verticality errors of non-orthogonal five-axis machine tools. First, position-dependent error parameters related to the translational axis are measured by the laser interferometer. Next, based on its identification method, the rotary axis is measured in the axial, radial and tangential measurement modes by using a double ball bar. With the measured result, 18 position-dependent errors which related to the motion of linear axis, 6 position-dependent error parameters and 2 verticality errors of axis C and 4 position-dependent error parameters and 1 verticality error of axis B are identified. Finally, the identification results are verified by the compensation of an actual process on the sample of S-shaped piece, whose precise results show that the machining precision increased by 84.2% on average after compensation. Consequently, the measurement and identification method as well as the effectiveness of the compensation method of this paper are verified.

Identification and verification of location errors of rotary axes on five-axis machine tools by using a touch-trigger probe and a sphere

As a foundation to enhance the machining accuracy of five-axis machine tools, a robust, efficient, and precise method to measure the location errors of rotary axes on five-axis machine tools has been proposed in this study. This precise identification and calibration methodology for on-machine measurement of location errors of rotary axes is achieved by using a touch-trigger probe and a precise sphere installed on a tilting rotary table. Compared to commercially available devices, such as the double ball bar and R-test, the proposed measurement method has the advantages of efficient and automated calibration procedures in each periodical measurement. This proposed calibration algorithm builds a kinematic error model and measurement equations by using a forward and inverse kinematic approach and estimates the location errors by applying the least squares method. Moreover, the proposed calibration algorithm defines the location errors of the two rotary axes so they can be estimated and separated individually to avoid coupling effects. All the location errors of the rotary axes measured using the proposed measurement method were identified after compensation to improve the accuracy of the five-axis machine tool. A simulation was implemented to inspect the influence of uncertainties on the identified location errors of the rotary axes. Finally, an experimental demonstration on a five-axis machine tool with a tilting rotary table validates the feasibility of the proposed measurement method.

Dynamic accuracy evaluation for five-axis machine tools using S trajectory deviation based on R-test measurement

Abstract This paper presents a new S trajectory kinematic measurement method to evaluate the dynamic accuracy of five-axis machine tools based on R-test measurements. The S trajectory is obtained by scaling the machining path of the S-shaped test piece to the measuring stroke of the R-test. A geometric and dynamic accuracy simulation model is established to analyze the influence of various error factors on the S trajectory test and compare with the conical kinematic test described in ISO10791.6 quantitatively. The simulation results shown that the S trajectory test is more sensitive to most geometric errors and dynamic errors than the conical test. Particularly, to reflect the dynamic error factors such as poor rigidity of machine tools, servo mismatch, reverse error and nonlinear error, the S trajectory test has obvious advantages. Meanwhile, compared with the actual S-shaped piece machining, which is under development for inclusion in ISO 10791.7, the proposed non-cutting kinematic test method can simply reflect the dynamic accuracy of five axis machine tools itself without the interference of other factors and does not require additional workpiece and testing equipment. The proposed method is verified through experiments on a five-axis machine tool.

Investigation of sensitivity analysis and compensation parameter optimization of geometric error for five-axis machine tool

To improve the accuracy of five-axis machine tool with a swiveling head, an approach for optimizing compensation values taking into account the sensitivity of position-independent geometric error is proposed in this paper. At first, the synthetic volumetric error model of five-axis machine tool has been established with homogeneous transformation method and multibody system theory, which describes the effect of geometric error components on position and orientation error vector intuitively. Second, the probability distributions of geometric errors in workspace of machine tool are sufficiently taken into account in order to overcome the defects of the analysis at certain locations, and then, global quantitative sensitivity analysis is introduced for determining the effect of each geometric error on precision of machine tool. Next, the optimum values are obtained by multiobjective quality loss and precision robustness trade-offs based on archive-based microgenetic algorithm (AMGA) for geometric error compensation. Finally, the geometric error compensation experiments were carried out, and the results show that the accuracy of measuring trajectories are improved significantly, which reaches 73.7% after error compensation with optimum values based on sensitivity analysis. Hence, the proposed methodology of analysis and compensation are effective for analyzing the effect of geometric errors and improving the precision of machine tool.

Volumetric accuracy evaluation for five-axis machine tools by modeling spherical deviation based on double ball-bar kinematic test

Abstract This paper presents a new spherical deviation measurement method to evaluate the volumetric accuracy of five-axis machine tools based on double ball-bar measurements. Nine circular test paths with three kinds of tool axis directions in XY, YZ and ZX planes are planned to construct a typical sphere. The radial deviations measured by ball-bar are used to calculate the spherical deviation, which is defined as the maximum radial range of deviations around a least-squares sphere. Compared to the circular deviation described in ISO10791.6, which could only reflect the errors in one test path with one kinds of tool axis direction separately, the proposed method can reflect not only each test path's error, but also the offset errors between different test paths. And it could comprehensively quantify the effect of accuracies of the five axes together in three-dimensional space. The proposed method for five-axis machine tool can not only reflect the tool center position errors caused by three linear axes, but also indicate the tool axis direction errors caused by two rotary axes. The proposed method is verified through simulations and experiments on a five-axis machine tool.

五轴联动加工中局部轨迹优化方法

在五轴联动过程中，刀路轨迹的好坏直接影响到零件的加工效果。由于目前CNC系统普遍采用线性插补技术，刀具轨迹采用线性线段描述，这导致在节点处切向不连续，影响刀具的运动姿态，从而降低了曲面的制造精度。本文的目的是优化局部刀具轨迹，使刀具在线段连接部位平滑过渡，具体实施方法是：通过三次方B样条曲线在相邻线段构造新的控制点，这样能够满足刀具在拐角处2阶连续，保证刀具平滑过渡。实验证明，该方法可以提高五轴机床在复杂曲面上的制造精度。 In the process of the five-axis linkage, the path of the tool directly affects the machining effect of the parts. Since the linear interpolation technique is widely used in the CNC system, the tool path can be described by a linear segment, which causes the cutting direction of the nodes to be incontinuous at the nodes. The purpose of this paper is to optimize the local tool path so that the cutting tool in the line segment connecting part can transit smoothly, and the specific implementation of the method is to construct a new control point by the Cubic B-spline curve in the adjacent segment. This can meet the 2-order continuousness in the corner of the cutting tool to ensure the smooth transition of the tool. Experiments show that this method can improve the accuracy of five-axis machine tools on complex surface.

Verifying the accuracy of five-axis machine tool focused on kinematic ISO tests using a torus-shaped test work piece

In the aerospace industry the application of five-axis machines is increasing and becoming common practice. The determination of the accuracy of such five-axis machine however is often difficult because linear and rotary axes are used combined resulting in complex motions. Typical five-axis tests are specified in ISO10791-6 (e.g. AK4, BK4, CK4) and a modified BK4 test is used in this paper to determine the machine’s accuracy first employing five machine axes simultaneously.A new test work piece is derived that can be milled while imitating the same machine movements of the respective BK4 test. For this a torus-shape is cut in the top edge of a cylindrical work piece with a spherical radius mill enabling five-axis movements while cutting. After milling this test work piece the accuracy of the torus shape is measured using a CMM to verify the machine’s accuracy in five-axis mode.Finally the machine deviations measured during the BK4 measurement are compared with the CMM results revealing the same amplitude. A Q-value is introduced and proposed in this paper to express this amplitude of machine deviations as a single value parameter, indicating the machine’s accuracy when using five axes.