Thermal-Induced Errors Prediction and Compensation for a Coordinate Boring Machine Based on Time Series Analysis

To improve the CNC machine tools precision, a thermal error modeling for the motorized spindle was proposed based on time series analysis, considering the length of cutting tools and thermal declined angles, and the real-time error compensation was implemented. A five-point method was applied to mea...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (2014), p.1-13
Main Authors: Mei, Xuesong, Feng, Bin, Zhang, Dongsheng, Yang, Jun, Hu, Zhenbang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Autocorrelation functions
Boring machines
Boring tools
Compensation
Cutting tools
Eddy current testing
Eddy currents
Error analysis
Error compensation
Errors
Finite element analysis
Fuzzy logic
Machine tool industry
Machine tools
Machining
Mathematical models
Measurement methods
Modelling
Motorized spindles
Neural networks
Parameter identification
Real time
Sequences
Time measurement
Time series
Time series analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To improve the CNC machine tools precision, a thermal error modeling for the motorized spindle was proposed based on time series analysis, considering the length of cutting tools and thermal declined angles, and the real-time error compensation was implemented. A five-point method was applied to measure radial thermal declinations and axial expansion of the spindle with eddy current sensors, solving the problem that the three-point measurement cannot obtain the radial thermal angle errors. Then the stationarity of the thermal error sequences was determined by the Augmented Dickey-Fuller Test Algorithm, and the autocorrelation/partial autocorrelation function was applied to identify the model pattern. By combining both Yule-Walker equations and information criteria, the order and parameters of the models were solved effectively, which improved the prediction accuracy and generalization ability. The results indicated that the prediction accuracy of the time series model could reach up to 90%. In addition, the axial maximum error decreased from 39.6 μm to 7 μm after error compensation, and the machining accuracy was improved by 89.7%. Moreover, the X / Y -direction accuracy can reach up to 77.4% and 86%, respectively, which demonstrated that the proposed methods of measurement, modeling, and compensation were effective.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/784218