A rapid modelling method for machine tool power consumption using transfer learning

被引：2

作者：

Wang, Qi ^{[1
,2
]}

Chen, Xi ^{[1
]}

Chen, Ming ^{[2
]}

He, Yafeng ^{[1
]}

Guo, Hun ^{[1
]}

机构：

[1] Changzhou Inst Technol, Dept Aeronaut & Mech Engn, Changzhou 213032, Peoples R China

[2] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2024年 / 131卷 / 3-4期

基金：

中国国家自然科学基金;

关键词：

Machine tool; Power consumption modelling; Transfer learning; Energy efficiency; ENERGY-CONSUMPTION; PREDICTION; EFFICIENCY; MECHANICS; WEAR;

D O I：

10.1007/s00170-024-13100-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate power consumption models are the basis for improving energy efficiency of machine tools. The acquisition of energy consumption characteristics of different machine tools requires a large number of calibration experiments, which leads to low modelling efficiency. This paper proposes a rapid modelling method using transfer-learning to obtain the power consumption model of the target machine tool. After obtaining the power consumption model of the source machine tool through detailed experiments, this method only needs a few experiments to obtain the power consumption model of the target machine tool, which greatly improves the modelling efficiency, and the method is experimentally verified on different machine tools.

引用

页码：1551 / 1566

页数：16

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