Thermal error analysis, modeling and compensation of five-axis machine tools
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DOI 10.1007/s12206-020-0920-y
Journal of Mechanical Science and Technology 34 (10) 2020 Original Article DOI 10.1007/s12206-020-0920-y Keywords: · Cradle type five-axis machine tool · SSO-ANN · Thermal error compensation · Temperature sensitive points · ‘S’-shaped test piece
Correspondence to: Yongchao Liu [email protected]
Citation: Huang, Z., Liu, Y., Du, L., Yang, H. (2020). Thermal error analysis, modeling and compensation of five-axis machine tools. Journal of Mechanical Science and Technology 34 (10) (2020) 4295~4305. http://doi.org/10.1007/s12206-020-0920-y
Received August 14th, 2019 Revised
July 9th, 2020
Accepted August 4th, 2020 † Recommended by Editor Yong Tae Kang
Thermal error analysis, modeling and compensation of five-axis machine tools Zhi Huang1, Yongchao Liu1, Li Du1 and Han Yang2 1
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of 2 China, Chengdu 611731, China, Sichuan Chengfei Integration Technology Corp., Chengdu 610091, China
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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