Articles | Volume 14, issue 1
https://doi.org/10.5194/ms-14-15-2023
https://doi.org/10.5194/ms-14-15-2023
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
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Cited articles

Abdulshahed, A. M., Longstaff, A. P., and Fletcher, S.: The application of ANFIS prediction models for thermal error compensation on CNC machine tools, Appl. Soft Comput. 27, 158–168, 2015. 
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, https://doi.org/10.3390/pr8080906, 2020. 
Miao, E., Liu, Y., Liu, H., Gao, Z., and Li, W.: Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool, Int. Jo. Mach. Tool. Manufact., 97, 50–59, 2015. 
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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.