Geometric accuracy enhancement of five-axis machine tool based on error analysis

Guo, Siu-Siu; Mei, Xuesong; Jiang, Gedong

doi:10.1007/s00170-019-04030-4

Cited by 25 publications

(16 citation statements)

References 54 publications

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“…Previous studies have focused mainly on geometric error analysis based on the method of variance analysis from the perspective of the precision design of machine tools [21,23,38]; more research on the quantitative relationship and the degree of relative importance of geometric error for determining the key geometric error terms is needed. (3) e results of sensitivity analysis and accuracy retentivity analysis are rarely directly applied in error compensation of machine tools from the view of improving compensation accuracy [39].…”

Section: Motivations and Potential Applicationsmentioning

confidence: 99%

“…Error vectors have different sensitivities to geometric errors, although the relevancy coefficient of the geometric error with respect to the error vector is the same. erefore, sensitivity evaluation should be conducted through a supplementary analysis on the basis of evaluating the relevancy and similarity between the retentivity of the geometric error terms and error vectors [39], and the relevancy, similarity of the geometric errors, and sensitivity of the error vectors corresponding to the geometric error terms should be synthetically considered. en, key geometric error terms can be identified, and a sensitivity analysis can be included within the model training process of the embedded method of feature extraction [26,45].…”

Section: Key Parameter Identification Modelling Based On Featurementioning

confidence: 99%

“…However, geometric error modelling has unique characteristics both in quantity and in the intrinsic nature of the geometric error terms for different types of machine tools [35,36]. For example, there are a total of 30 geometric error terms for a four-axis horizontal boring machine, comprising 24 PDGEs and 6 PIGEs [37]; for a five-axis machine tool with a tilting rotary table, there are 30 PDGEs and 11 PIGEs, and a five-axis machine tool with a tilting head and rotary table has 30 PDGEs and 13 PIGEs [38,39]. is means that the well-developed geometric error models for the threeand five-axis machine tools cannot describe the quantitative relations between the errors and error vectors directly for the four-axis machine tool.…”

Section: Introductionmentioning

confidence: 99%

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A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

2019

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This paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis machine tool are first modelled mathematically based on screw theory. Then, the retentivity of geometric accuracy with respect to the geometric error is defined based on the mapping between the constitutive geometric errors and the time domain. The results show that the variation in the spatial error vector is nonlinear while considering the operation time of the machine tool and the position of the motion axes. Based on this aspect, key factors are extracted that simultaneously consider the correlation, similarity, and sensitivity of the geometric error terms, and the results reveal that the effect of the position-independent geometric errors (PIGEs) on the error vectors of the position and orientation is greater than that of the position-dependent geometric errors (PDGEs) of the linear and rotary axes. Then, the fruit fly optimization algorithm (FOA) is adopted to determine the compensation values through multiobjective tradeoffs between accuracy retentivity and fluctuation in the geometric errors. Finally, an experiment on a four-axis horizontal boring machine tool is used to validate the effectiveness of the proposed approach. The experimental results show that the variations in the precision of each test piece are lower than 25.0%, and the maximum variance in the detection indexes between the finished test pieces is 0.002 mm when the optimized parameters are used for error compensation. This method not only recognizes the key geometric errors but also compensates for the geometric error of the machine tool based on the accuracy retentivity analysis results. The results show that the proposed methodology can effectively enhance the machining accuracy.

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Section: Motivations and Potential Applicationsmentioning

confidence: 99%

Section: Key Parameter Identification Modelling Based On Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

2019

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“…Geometric error has unique characteristics both in quantity and in the intrinsic nature of the geometric error terms of different types of machine tools. 12,17,18,19 This means that the geometric error measurement method developed for a five-axis machine tool with a tilting rotary table cannot be used for a five-axis machine tool with a tilting head and a rotary table. 12 In addition, few studies have measured the PDGEs of the rotary axes in a five-axis machine tool with a tilting head and a rotary table with few double ball bar (DBB) installations.…”

Section: Introductionmentioning

confidence: 99%

Highly efficient and accurate calibration method for the position-dependent geometric errors of the rotary axes of a five-axis machine tool

Guo

Tang

Jiang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper proposes a calibration method for continuous measurements with a double ball bar (DBB) used to identify the position-dependent geometric errors (PDGEs) of the rotary axes of five-axis machine tools. The different DBB installation modes for the rotary axes of the spindle and workbench are established, and the same initial DBB installation position is used for multiple tests. This approach minimizes the number of required DBB installations, which increases the measurement efficiency of the PDGEs of the rotary axes and reduces installation errors. PDGEs identification based on the adaptive least absolute shrinkage and selection operator (LASSO) method is proposed. By assigning coefficients to the PDGEs polynomial, the ill-conditioned problem of the identification process can be effectively avoided, thereby improving the identification accuracy. The measurement and identification methods proposed in this paper are verified by experiments on a five-axis machine tool. After compensation, the PDGEs are obviously reduced and the accuracy indexes of the circular trajectory tests performed under multiaxis synchronous control are obviously improved.

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“…Further-more, Tao developed a geometric error model for the turning process on an ultra-precision lathe based on MBS [6], and the machining accuracy of workpieces can be improved by crucial geometric error compensation. Guo et al [7] established a synthetic geometric error model for a five-axis machine tool with tilting head and turntable based on the MBS and the method of homogeneous coordinate transformation. Based on the theory of MBS, Chen et al [8] established an error model of angular measurement with geometric errors of a torsional characteristic measurement system based on MBS, which can effectively predict the angular measurement error.…”

Section: Introductionmentioning

confidence: 99%

A Geometric Accuracy Error Analysis Method for Turn-Milling Combined NC Machine Tool

Zhao

Luo

2020

Symmetry

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Turn-Milling Combined NC machine tool is different from traditional machine tools in structure and process realization. As an important means in the design stage, the analysis method of geometric accuracy error is also different from the traditional method. The actual errors and the error compensation values are a pair of "symmetry" data sets which are connected by the movement of machine tools. The authors try to make them more consistent through this work. The geometric error terms were firstly determined by topological structure analysis, then based on homogeneous coordinate transformation and multibody system theory, the geometric error model was established. With the interval theory, the function rule of sensitivity of geometric error sources to spatial errors was analyzed in detail, and the global maximum interval sensitivity of nine geometric error sources was extracted, providing a theoretical basis for error compensation and precision distribution. The geometric error sensitivity analysis method proposed in this paper can accurately evaluate the influence weights of each error term on the machining accuracy, and identify the important sensitive error terms with great influence on the machining accuracy from many error terms.

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Geometric accuracy enhancement of five-axis machine tool based on error analysis

Cited by 25 publications

References 54 publications

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

Highly efficient and accurate calibration method for the position-dependent geometric errors of the rotary axes of a five-axis machine tool

A Geometric Accuracy Error Analysis Method for Turn-Milling Combined NC Machine Tool

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