Articles | Volume 14, issue 1
Short communication
16 Jan 2023
Short communication |  | 16 Jan 2023

Short communication: Part contour error prediction based on LSTM neural network

Yun Zhang, Guangshun Liang, Cong Cao, Yun Zhang, and Yan Li

Related subject area

Subject: Machining and Manufacturing Processes | Techniques and Approaches: Experiment and Best Practice
Drilling performance analysis of impregnated micro bit
Dongdong Song, Zhangping Ren, Yingxin Yang, Yousheng Chen, Gao Nie, Leichuan Tan, Hao Peng, Zijie Li, Xiaojing Chen, Meng Li, Haitao Ren, and Long Zuo
Mech. Sci., 13, 867–875,,, 2022
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Short communication: Experiment study on micro-textured tool with internal cooling
Gang Yang and Wei Feng
Mech. Sci., 13, 619–623,,, 2022
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The three-point eccentric magnetorheological polishing technology for hard brittle alumina ceramics
Cheng Zheng, Bingsan Chen, Xiaoyu Yan, Yongchao Xu, and Shangchao Hung
Mech. Sci., 13, 473–483,,, 2022
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Experimental study of the tribological characteristics of a honed cylinder liner during the running-in process
Jianxiong Kang, Yanjun Lu, Bin Zhao, Cheng Jiang, Pengzhou Li, Hongbo Luo, and Yongfang Zhang
Mech. Sci., 13, 101–110,,, 2022
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Research on the smooth velocity control of a driving switching process of a new pneumatic electric hybrid actuator
Jiabin Yang and Xiaoning Li
Mech. Sci., 12, 971–982,,, 2021
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Cited articles

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Huang, Y. S., Chen, J. H., Chen, Y., and Xu, G. D.: Thermal error modelling for machine tool feed axis based on LSTM neural network considering electro-control data, Modern Manufact. Eng., 10, 25–32, 2021 (in Chinese). 
Liu, H., Miao, E. M., Wei, X. Y., and Zhuang, X.D.: Robustness modeling method for thermal error of CNC machine tools based on ridge regression algorithm, Int. J. Mach. Tool. Manufact., 113, 35–48, 2017. 
Lu, H., Cheng, Q., Zhang, X., Liu, Q., and Zhang, Y.: A novel geometric error compensation method for gantry-moving CNC machine regarding dominant errors, Processes, 8.8, 906,, 2020. 
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Short summary
A contour error prediction model based on LSTM (long short-term memory) is established. By inputting power, vibration signal, and temperature values, the contour error of parts can be predicted to adjust the feed speed in real time. In this way, we can obtain not only higher machining accuracy, but also higher machining efficiency and reduce the scrap rate. The model is compared with other models, and the optimal model is selected to obtain the optimal prediction effect.