Geometric error analysis and compensation for multi-axis spiral bevel gears milling machine

Xiang, Sitong; Li, Huimin; Deng, Ming; Yang, Jing

doi:10.1016/j.mechmachtheory.2017.10.014

Cited by 55 publications

(21 citation statements)

References 30 publications

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“…e geometric errors vary due to the wearing of machine components as the machine tool is running. In addition, complicated coupling exists among the geometric error parameters, resulting in a nonlinear distribution of geometric error terms [6,17]. Within those factors, the volumetric position and orientation accuracy show nonstationary time varying characteristics, and the accuracy retentivity of geometric accuracy inevitably varies with fluctuating effects at different moments.…”

Section: Geometric Error Analysis Modellingmentioning

confidence: 99%

“…Within those factors, the volumetric position and orientation accuracy show nonstationary time varying characteristics, and the accuracy retentivity of geometric accuracy inevitably varies with fluctuating effects at different moments. e measuring geometric error directly reflects the geometric accuracy: the higher the measurement error is, the lower the precision of the machine tool will be, and the geometric accuracy is one of the most important accuracy indexes [6,23]. Geometric errors can affect the shape, size tolerance, and surface roughness of the machined parts [8,42]; for instance, a parallelism error between the spindle and guide rail will lead to tapering and poor surface roughness of the workpiece.…”

Section: Geometric Error Analysis Modellingmentioning

confidence: 99%

“…Geometric error modelling is a key part of error compensation technology and also for the basis for decoupling identification [5,6]. Many beneficial explorations and studies on high-quality modelling have been conducted by researchers, and some effective methods have been proposed and applied to modelling, such as the product of exponentials [7,8], homogeneous transformation matrices (HTMs) method based on multibody system theory [9], differential transformation method [10,11], screw theory method [6,12,13], and parametric polynomial method [14]. Fu et al [15] established the rotation twist and rotation product of exponential formulas of rotary axes with a clear geometric meaning of twists to describe their positions and motions, and the corresponding POE formulas of squareness errors were established by analysing their geometric definition.…”

Section: Introductionmentioning

confidence: 99%

“…Fan et al [19] adopted a truncated Fourier series function to express the curve of a guideway surface; the mapping between the tolerance and geometric errors of the guideways of machine tools was formulated; and mapping relations between error vectors and geometric error terms were established. e analysis of geometric error terms has been an active research topic in the past 3 years due to the implementation of both offline error compensation and real-time error compensation [6,20]. e fundamental reason is that the precision of the machine tool can be further enhanced by analysing the correlation effect and nonlinear characteristics of geometric error terms [21,22].…”

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: Geometric Error Analysis Modellingmentioning

confidence: 99%

Section: Geometric Error Analysis Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2019

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“…For this kind of deviation, operators use a pneumatic micrometer to measure the deviation and then adjust the signal points of the grinding control system and manually input the compensation values. 5,6 This method of compensation lags behind the grinding process, and hence, the randomness is large. The compensation methods for workpieces of different sizes are not the same and mainly depend on the experience of the operator.…”

Section: Introductionmentioning

confidence: 99%

Online prediction and control method for compensation regulation value in grinding processes

Zheng

Liu

Tian

et al. 2019

Advances in Mechanical Engineering

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In any grinding process, compensation regulation value is a crucial factor for maintaining precision during the batch processing of workpieces. Geometric characteristics, buffing allowance, temperature, wheel speed, and workpiece speed are the main factors that affect compensation regulation value in any grinding process. In this article, a novel prediction method for compensation regulation value is proposed based on incremental support vector machine and mixed kernel function. The support vectors for the prediction model are extracted using the convex hull vertex optimization algorithm, and the speed of the operation can be increased effectively. In addition, the parameters of the model are optimized using cross-validation optimization method to improve the accuracy of the prediction model. Then, the feedback control strategy of compensation regulation value for the grinding process is also proposed. Single-factor and multifactor experiments are implemented respectively using the proposed method. The results verify the feasibility and effectiveness of the proposed method. It is also noted that the machining accuracy is improved significantly in comparison with the machining without prediction and compensation control. Moreover, by applying the prediction compensation control of compensation regulation value to the active measurement and control of the grinding process, a feedback system is formed, and then the intelligentization of the grinding system can be realized.

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A New Self-Calibration Method for the 5-DOF Hybrid Robot Based on Vision Measurement

Liu

Jia

Yan

2023

Lecture Notes in Mechanical Engineering

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Geometric error analysis and compensation for multi-axis spiral bevel gears milling machine

Cited by 55 publications

References 30 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

Online prediction and control method for compensation regulation value in grinding processes

A New Self-Calibration Method for the 5-DOF Hybrid Robot Based on Vision Measurement

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