journal article·2024-10-07Open access

A Study on the Coarse-to-Fine Error Decomposition and Compensation Method of Free-Form Surface Machining

Yueping Chen, Junchao Wang, Qingchun Tang, Jie Li

Applied Sciences· Volume 14· Issue 19

Abstract

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced machining processes and optimization · Advanced Measurement and Metrology Techniques · Advanced Surface Polishing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China

Funding

National Nature Science Foundation of China · Natural Science Foundation of Guangxi Province

References (32)

Show source-provided references
1. Free-form surface machining error compensation applying 3D CAD machining pattern model10.1016/j.cad.2014.12.003 ↗
2. Error source analysis and compensation method of CNC machine tools
3. Geometric error measuring, modeling, and compensation for CNC machine tools: A review10.1016/j.cja.2023.02.035 ↗
4. An integrated error compensation method based on on-machine measurement for thin web part machining10.1016/j.precisioneng.2020.03.002 ↗
5. Generalized actual inverse kinematic model for compensating geometric error in five-axis machine tools10.1016/j.ijmecsci.2018.07.022 ↗
6. An investigation on modeling and compensation of synthetic geometric error on large machine tools based on moving least squares method10.1177/0954405416645985 ↗
7. Compensation of machining error of B spline and C spline10.1007/s00170-018-2160-1 ↗
8. F test-based automatic modeling of single geometric error component for error compensation of five-axis machine tools10.1007/s00170-017-1143-y ↗
9. A new approach to geometric error modeling and compensation for a three-axis machine tool10.1007/s00170-018-3160-x ↗
10. Machining error decomposition and compensation of complicated surfaces by EMD method10.1016/j.measurement.2017.11.027 ↗
11. A method of geometric error identification and compensation of CNC machine tools based on volumetric diagonal error measurements10.1007/s00170-022-10484-w ↗
12. An approach for machining allowance optimization of complex part with integrated structure
13. Thermal positioning error modeling of machine tools using a bat algorithm-based back propagation neural network10.1007/s00170-018-1978-x ↗
14. Real-time thermal error compensation strategy for precision machine tools10.1016/j.matpr.2020.03.363 ↗
15. Geometric error compensation method of five-axis CNC machine tool based on improved genetic algorithm10.1088/1742-6596/2825/1/012029 ↗
16. An accuracy evolution method applied to five-axis machining of curved surfaces10.1007/s00170-023-10864-w ↗
17. Stream of variation modeling and analysis for manufacturing processes based on a semi-parametric regression model10.3901/JME.2013.15.180 ↗
18. Improving the machining accuracy of thin-walled part by online measuring and allowance compensation10.1007/s00170-017-0358-2 ↗
19. Lee, W., Lee, Y., and Wei, C.C. (2019). Automatic error compensation for free-form surfaces by using on-machine measurement data. Appl. Sci., 9.10.3390/app9153073 ↗
20. Application of spatial statistical analysis in decomposition of machining errors for free-form surfaces
21. Dynamic signature verification method based on Pearson correlation coefficient
22. The correlation coefficient: An overview10.1080/10408340500526766 ↗
23. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Routledge.
24. Variational mode decomposition10.1109/TSP.2013.2288675 ↗
25. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery10.1016/j.ymssp.2017.11.029 ↗
26. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis10.1098/rspa.1998.0193 ↗
27. Novel complete ensemble EMD with adaptive noise-based hybrid filtering for rolling bearing fault diagnosis10.21595/jve.2019.20100 ↗
28. Lu, Q., Pang, L.X., Huang, H.Q., Shen, C., Cao, H., Shi, Y., and Liu, J. (2019). High-G calibration denoising method for high-G MEMS accelerometer based on EMD and wavelet thresholding. Micromachines, 10.10.3390/mi10020134 ↗
29. Gamma spectrum denoising method based on improved wavelet thresholding10.1016/j.net.2020.01.025 ↗
30. Improved VMD-KFCM algorithm for the fault diagnosis of rolling bearing vibration signals10.1049/sil2.12026 ↗
31. Accurate compensation method for probe pre-travel error in on-machine inspections10.1007/s00170-019-03729-8 ↗
32. Efficient adaptive sampling methods based on deviation analysis for on-machine inspection10.1016/j.measurement.2021.110497 ↗