Robust modeling method for thermal error of CNC machine tools based on random forest algorithm

Thermal error of machine tools has a huge influence on the accuracy of the workpiece. However, the nonlinearity of the thermal error limits the accuracy and robustness of the prediction model. With the rapid advancement in artificial intelligence, this paper presents a novel thermal error modeling m...

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Vydané v:	Journal of intelligent manufacturing Ročník 34; číslo 4; s. 2013 - 2026
Hlavní autori:	Zhu, Mengrui, Yang, Yun, Feng, Xiaobing, Du, Zhengchun, Yang, Jianguo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2023 Springer Nature B.V
Predmet:	Accuracy Advanced manufacturing technologies Algorithms Artificial intelligence Business and Management Control Deformation effects Errors Machine learning Machine tools Machines Manufacturing Mathematical models Mechatronics Modelling Parameters Prediction models Processes Production Robotics Robustness Time lag Workpieces Thermal error Random forest Machine tool Artificial intelligence Machine learning
ISSN:	0956-5515, 1572-8145
On-line prístup:	Získať plný text
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