Pretightening sequence planning of anchor bolts based on structure uniform deformation for large CNC machine tools

Liu, Haibo; Wu, Jiakun; Liu, Kuo; Kuang, Kang; Luo, Qi; Liu, Zhisong; Wang, Yongqing

doi:10.1016/j.ijmachtools.2018.09.002

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…erefore, the dynamic evolution over time should be considered when conducting maintenance and geometric error compensation. e geometric accuracy also acts as a quasistatic accuracy that gradually declines over time and thus shows strong time-correlation behaviour [33]. In this paper, the geometric error of dynamic change is mapped to the time dimension and transformed into a timeindependent index.…”

Section: Accuracy Retentivity Modelmentioning

confidence: 99%

“…e longer the time is, the better the accuracy retentivity of the machine tool is, and the opposite case also holds. However, aiming at the problems of the accuracy variations of machine tools, most studies have focused on the accuracy degradation and accuracy reliability evaluation caused by the wearing of guideways, bolt relaxation, and stress relaxation-induced deformation of machine bodies [29,[31][32][33]. It is obvious that all of these factors reduce the geometric accuracy gradually, which affects the geometric accuracy retentivity in an essential way [34].…”

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: Accuracy Retentivity Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…The estimated stiffness coefficients may depend not only on the properties of the physical structures, but also on the values of pretension of the individual supports. Even the order in which those supports have been adjusted can be an important factor (Liu et al, 2019). Moreover, the number of stiffness coefficients estimated may be too high to apply analytical optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Estimation of structural stiffness with the use of Particle Swarm Optimization

Mazur

Galewski

Kaliński

2021

Lat. Am. j. solids struct.

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The paper presents the theoretical background and four applications examples of the new method for the estimation of support stiffness coefficients of complex structures modelled discretely (e.g. with the use of the Finite Element Model (FEM) method based on the modified Particle Swarm Optimization (PSO) algorithm. In real-life cases, exact values of the supports' stiffness coefficients may change for various reasons (e.g. order of fastening, state of the contact surfaces, environment changes, etc.). Because of the unknown coefficients, reliable simulations of fixed structure (i.e. mounted, assembled, not in a free-free state) are difficult to perform. The method serves as a tool to obtain good correlation between the FEM of a structure and the experimental data. Simple modal tests are required to estimate the first few modes of the fixed system. The FEM of the structure is considered in a free-free state and the support stiffness coefficients of the FEM are estimated by the proposed method. Further simulations with test-tuned and correlated complete FEM could be performed in time or frequency domain.

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“…Liu et al [3] use the ABAQUS software to simulate the bolt up process on the assembly structure. Wang and Liu et al [4,5] investigated the tightening sequence of anchor bolts for large CNC machine tools by using the simulation and experiments. Based on the simulation results, tightening sequence from middle to sides could generate uniform deformation of machine bed.…”

Section: Introductionmentioning

confidence: 99%

“…Pretension forceTightening the bolts generate the pretension force in the bolts. The pretension force or tightening force can be calculated by equation 1[5,10] force (N) T = torque (N-m) d = diameter of bolts (m)In this study, different diameter of bolts and torque are used to tight the two components in the column, linear guideway and anchor bolts. The bolts which use in the column structure is the M30 (diameter is 30 mm) and the torque is 776 N-m.…”

mentioning

confidence: 99%

The Effect of Bolt Locking Sequences and Anchor Bolt Design on Machine Tool

Tseng¹,

Wu²,

Tsai³

et al. 2020

ujme

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Machine tool is an important role in the industry because the mechanical components or products are manufactured by machine tools. The machine tools are assembled by components. The surface between two components is the combined or contact surface. Different bolt locking sequences will affect the combined surface in the machine tool. In this study, the SolidWorks software is used to create the CAD model. The finite element simulation is used the ANSYS software to simulate the influence of bolt locking methods on the combined surface of column structure and guideway and the fillet radius design on the deformation of anchor bolts. From the simulation results, the minimum deformation of the column is observed in the locking sequence from middle to both sides. The stress distribution of the combined surface is slightly difference of three locking methods. The simulation results of the guideway show that the minimum displacement of combined surface is obtained by using locking sequence from outside to the middle. The maximum displacement is obtained locking sequence from right to the left order. From the simulation results of anchor bolt, increasing the fillet radius of anchor bolt decreases the deformation of anchor bolts. The deformation of anchor bolts drastically decreases with increasing the fillet radius from R8 to R60. The simulation models and results provide the reference for the bolt locking sequence and anchor bolt fillet radius design.

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Pretightening sequence planning of anchor bolts based on structure uniform deformation for large CNC machine tools

Cited by 22 publications

References 19 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

Estimation of structural stiffness with the use of Particle Swarm Optimization

The Effect of Bolt Locking Sequences and Anchor Bolt Design on Machine Tool

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