Volumetric Error Model of Large CNC Machine Tool and Verification Based on Particle Swarm Optimization

2013 ◽  
Vol 579-580 ◽  
pp. 76-79
Author(s):  
Yi Lei Liu ◽  
Dong Gao ◽  
Gang Wei Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Machine Tool ◽  
Particle Swarm ◽  
Geometric Errors ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Volumetric Error ◽  
Swarm Optimization ◽  
Machine Tool Accuracy ◽  
Volumetric Positioning Error

Volumetric error has large effect on machine tool accuracy; improving CNC machine tool accuracy through error compensation has received significant attention recently. This paper intends to represent volumetric error measurement based on laser tracker. The volumetric error is modeled by homogenous transformation matrix with each coordinate corresponding to each motion axis. Based on parts of spatial points volumetric error, the geometric errors which affect volumetric positioning error are verified through particle swarm optimization with the L2 parameters as the target function. The chebyshev orthogonal polynomials are applied to approximate geometric errors.

A modal parameter identification method of machine tools based on particle swarm optimization

2019 ◽  
Vol 233 (17) ◽  
pp. 6112-6123 ◽  
Author(s):  
Mengxiang Yang ◽  
Yalan Dai ◽  
Qiang Huang ◽  
Xinyong Mao ◽  
Liangjie Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Machine Tool ◽  
Modal Analysis ◽  
Particle Swarm ◽  
Cnc Machine ◽  
Modal Parameter ◽  
Swarm Optimization ◽  
Modal Parameter Identification ◽  
Analysis Theory ◽  
Frequency Identification

The dynamic characteristics of the computerized numerical control (CNC) machine tool directly affect its machining quality, and it is necessary to carry out the working mode analysis of the CNC machine tool. The traditional manual analysis and identification process is complicated and inefficient. In the industrial environment of big data, how to use the working modal analysis method to quickly and accurately obtain the dynamic characteristic parameters of the machine tool processing from these data has become the research difficulty at the current stage. This paper proposes an optimized particle swarm optimization algorithm to solve this problem. Based on the working modal analysis theory, the semi-self-power spectrum of the output signal can replace the frequency response function for modal parameter identification. The optimized semi-self-power spectrum signal is used as the objective function of the algorithm, and the ability of the algorithm to preprocess the data is optimized, so that the improved algorithm can automatically analyze the structural mode of the machine tool during processing. Comparing the experimental results, it is found that the natural frequency identification error of the cantilever beam is less than 1%, and the natural frequency identification error of the CNC milling machine is not more than 7%. The results show that the particle swarm optimization algorithm based on modal analysis theory can be applied to the automatic analysis of modal parameters under machine tool operating conditions, and it is efficient and accurate.

scholarly journals An Analysis Methodology for Stochastic Characteristic of Volumetric Error in Multiaxis CNC Machine Tool

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Qiang Cheng ◽  
Can Wu ◽  
Peihua Gu ◽  
Wenfen Chang ◽  
Dongsheng Xuan
Keyword(s):  
Machine Tool ◽  
Geometric Error ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Geometric Errors ◽  
Cnc Machine ◽  
Analysis Model ◽  
Volumetric Error ◽  
Value Analysis ◽  
Stochastic Characteristic

Traditional approaches about error modeling and analysis of machine tool few consider the probability characteristics of the geometric error and volumetric error systematically. However, the individual geometric error measured at different points is variational and stochastic, and therefore the resultant volumetric error is aslo stochastic and uncertain. In order to address the stochastic characteristic of the volumetric error for multiaxis machine tool, a new probability analysis mathematical model of volumetric error is proposed in this paper. According to multibody system theory, a mean value analysis model for volumetric error is established with consideration of geometric errors. The probability characteristics of geometric errors are obtained by statistical analysis to the measured sample data. Based on probability statistics and stochastic process theory, the variance analysis model of volumetric error is established in matrix, which can avoid the complex mathematics operations during the direct differential. A four-axis horizontal machining center is selected as an illustration example. The analysis results can reveal the stochastic characteristic of volumetric error and are also helpful to make full use of the best workspace to reduce the random uncertainty of the volumetric error and improve the machining accuracy.

Application of Ant Colony Algorithm to Volumetric Thermal Error Modeling and Compensation of a CNC Machine Tool

2012 ◽  
Vol 170-173 ◽  
pp. 3487-3490
Author(s):  
Qian Jian Guo ◽  
Qing Wen Qu ◽  
Jian Guo Yang
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Ant Colony Algorithm ◽  
Error Modeling ◽  
Ant Colony ◽  
Machining Accuracy ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Volumetric Error ◽  
Volumetric Errors

Volumetric errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce volumetric errors. Accurate modeling of volumetric errors is a prerequisite of error compensation. In this paper, a volumetric error model was proposed by using neural networks based on ant colony algorithm. Finally, a volumetric error compensation system was developed based on the proposed model, and which has been applied to a CNC machine tool in daily production. The results show that the volumetric errors are reduced and the machining accuracy of the machine tool is improved.

Applying Multi-Objective Particle Swarm Optimization to Maintenance Scheduling for CNC Machine Tools

2014 ◽  
Vol 721 ◽  
pp. 144-148 ◽  
Author(s):  
Zhi Hui Yao ◽  
Min Zhou
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Machine Tools ◽  
Particle Swarm ◽  
Maintenance Scheduling ◽  
Cnc Machine Tools ◽  
Maintenance Cost ◽  
Cnc Machine ◽  
Swarm Optimization ◽  
Multi Objective

This paper focuses on the maintenance scheduling for CNC machine tools. A bi-objective mathematical model is built with the repair time and maintenance cost. A multi-objective particle swarm optimization (MOPSO), which combines the global best position adaptive selection and local search, is proposed to solve the mathematical model. The results show that MOPSO has a better performance than other method for solving the maintenance scheduling. They also show that MOPSO is an effective algorithm that has strong convergence.

scholarly journals GEOMETRIC ERRORS COMPENSATION OF CNC MACHINE TOOL

2016 ◽  
Vol 2016 (06) ◽  
pp. 1602-1607 ◽  
Author(s):  
Michal Holub ◽  
Petr Blecha ◽  
Frantisek Bradac ◽  
Tomas Marek ◽  
Zdenek Zak
Keyword(s):  
Machine Tool ◽  
Geometric Errors ◽  
Cnc Machine Tool ◽  
Cnc Machine
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