Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools

doi:10.1007/s11465-019-0543-0

Front. Mech. Eng.

2019, Vol. 14

Issue (4) : 442-451 https://doi.org/10.1007/s11465-019-0543-0

RESEARCH ARTICLE

Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools

Jianmin HU¹, Zude ZHOU¹, Quan LIU¹, Ping LOU¹(

), Junwei YAN¹, Ruiya LI²

¹. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
². School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

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Abstract

Thermal error is one of the main factors that influence the machining accuracy of computer numerical control (CNC) machine tools. It is usually reduced by thermal error compensation. Temperature field monitoring and key temperature measurement point (TMP) selection are the bases of thermal error modeling and compensation for CNC machine tools. Compared with small- and medium-sized CNC machine tools, heavy-duty CNC machine tools require the use of more temperature sensors to measure their temperature comprehensively because of their larger size and more complex heat sources. However, the presence of many TMPs counteracts the movement of CNC machine tools due to sensor cables, and too many temperature variables may adversely influence thermal error modeling. Novel temperature sensors based on fiber Bragg grating (FBG) are developed in this study. A total of 128 FBG temperature sensors that are connected in series through a thin optical fiber are mounted on a heavy-duty CNC machine tool to monitor its temperature field. Key TMPs are selected using these large-scale FBG temperature sensors by using the density-based spatial clustering of applications with noise algorithm to reduce the calculation workload and avoid problems in the coupling of TMPs for thermal error modeling. Back propagation neural network thermal error prediction models are established to verify the performance of the proposed TMP selection method. Results show that the number of TMPs is reduced from 128 to 5, and the developed model demonstrates good prediction effects and strong robustness under different working conditions of the heavy-duty CNC machine tool.

Keywords thermal error heavy-duty CNC machine tools FBG key TMPs prediction model

Corresponding Author(s): Ping LOU

Just Accepted Date: 31 May 2019 Online First Date: 10 July 2019 Issue Date: 02 December 2019

Cite this article:

Jianmin HU,Zude ZHOU,Quan LIU, et al. Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools[J]. Front. Mech. Eng., 2019, 14(4): 442-451.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0543-0
https://academic.hep.com.cn/fme/EN/Y2019/V14/I4/442

Fig.1 Temperature field analysis results

Fig.2 Development of FBG temperature sensors

Fig.3 Distribution of FBG temperature sensors

Fig.4 Experiment setup

Fig.5 Spindle speed spectrum

Fig.6 Temperature variations of eight TMPs

Fig.7 Thermal error variations in X, Y, and Z directions

Fig.8 MinPst-distance graph

Tab.1 Clustering results of TMPs

Fig.9 Relative positions of TMPs with the clustering results

Fig.10 Temperature variations of five key TMPs

Fig.11 Prediction results of (a) Group I, (b) Group II, and (c) Group III in spring

Fig.12 Prediction results of (a) Group I, (b) Group II, and (c) Group III in autumn

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