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Thermal error analysis, modeling and compensation of five-axis machine tools

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Abstract

The role of five-axis CNC machine tools (FAMT) in the manufacturing industry is becoming more and more important, but due to the large number of heat sources of FAMT, the thermal error caused by them will be more complicated. To simplify the complicated thermal error model, this paper presents a new modelling method for compensation of the thermal errors on a cradle-type FAMT. This method uses artificial neural network (ANN) and shark smell optimization (SSO) algorithm to evaluate the performance of FAMT, and developing the thermal error compensation system, the compensation model is verified by machining experiments. Generally, the thermal sensitive point screening is performed by a method in which a large number of temperature sensors are arranged randomly, it increases the workload and may cause omission of the heat sensitive point. In this paper, the thermal imager is used to screen out the temperature sensitive points of the machine tool (MT), then the temperature sensor is placed at the position of the heat sensitive point of the FAMT, and the collected thermal characteristic data is used for thermal error modeling. The C-axis heating test, spindle heating test, and the combined movement test are applied in this work, and the results show that the shark smell optimization artificial neural network (SSO-ANN) model was compared to the other two models and verified better performance than back propagation artificial neural network (BP-ANN) model and particle swarm optimization neural network (PSO) model with the same training samples. Finally, a compensation experiment is carried out. The compensation values, which was calculated by the SSO-ANN model are sent to the real-time error compensation controller. The compensation effect of the model is then tested by machining the ‘S’-shaped test piece. Test results show that the 32 % reduction in machining error is achieved after compensation, which means this method improves the accuracy and robustness of the thermal error compensation system.

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Abbreviations

um:

Unit of measurement for thermal error

°C:

Unit of temperature

mm :

Unit of measurement in three coordinates

h,min,s:

Unit of time

r/min:

Rotate speed

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Acknowledgments

This research was supported by National Major Project of China (No. 2017ZX04002001).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Zhi Huang, Yongchao Liu & Li Du

  2. Sichuan Chengfei Integration Technology Corp., Chengdu, 610091, China

    Han Yang

Authors
  1. Zhi Huang
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  2. Yongchao Liu
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  3. Li Du
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  4. Han Yang
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Corresponding author

Correspondence to Yongchao Liu.

Additional information

Zhi Huang is an Associate Professor in the School of Mechanical and Electrical Engineering of University of Electronic Science and Technology of China. His research areas include advanced grinding technology for difficult-to-machine materials, advanced numerical control equipment, and theory of detection and control technology for complex machining processes. In recent years, he has completed the National Natural Science Foundation of China, national science and technology major projects, etc.

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Huang, Z., Liu, Y., Du, L. et al. Thermal error analysis, modeling and compensation of five-axis machine tools. J Mech Sci Technol 34, 4295–4305 (2020). https://doi.org/10.1007/s12206-020-0920-y

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  • DOI: https://doi.org/10.1007/s12206-020-0920-y

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