A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption

doi:10.1007/s40436-024-00526-9

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (1): 167-195.doi: 10.1007/s40436-024-00526-9

• • 上一篇

A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption

Yue Meng, Sheng-Ming Dong, Xin-Sheng Sun, Shi-Liang Wei, Xian-Li Liu

Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, People's Republic of China

收稿日期:2023-12-17 修回日期:2024-03-17 发布日期:2025-02-26
通讯作者: Yue MENG,E-mail:mengyue@hrbust.edu.cn E-mail:mengyue@hrbust.edu.cn
作者简介:Yue Meng is the lecturer and master’s supervisor of the School of mechanical engineering, Harbin University of Science and Technology. Her research interests are machine tool energy efficiency evaluation and energy consumption optimization technology.
Sheng-Ming Dong is a master’s degree candidate at the School of Mechanical Engineering, Harbin University of Science and Technology. His research interests are data-driven energy efficiency assessment and green manufacturing.
Xin-Sheng Sun is a master’s degree candidate in the School of mechanical engineering, Harbin University of Science and Technology. His research interest is high-efficiency machining.
Shi-Liang Wei is the associate professor and master supervisor of the School of mechanical engineering, Harbin University of Science and Technology. His research interests are precision machining of hard, brittle materials.
Xian-Li Liu is the professor and doctoral supervisor of the School of mechanical engineering, Harbin University of Science and Technology. His research interests are advanced cutting tools and technology, digital machining, and intelligent manufacturing technology..
基金资助:
This work was financially supported by the National Natural Science Foundation of China (Grant No. U22A20128).

A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption

Yue Meng, Sheng-Ming Dong, Xin-Sheng Sun, Shi-Liang Wei, Xian-Li Liu

Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, People's Republic of China

Received:2023-12-17 Revised:2024-03-17 Published:2025-02-26
Contact: Yue MENG,E-mail:mengyue@hrbust.edu.cn E-mail:mengyue@hrbust.edu.cn
Supported by:
This work was financially supported by the National Natural Science Foundation of China (Grant No. U22A20128).

摘要/Abstract

摘要： High-quality development in the manufacturing industry is often accompanied by high energy consumption. The accurate prediction of the energy consumption of computer numerical control (CNC) machine tools, which plays a vital role in manufacturing, is of great importance in energy conservation. However, the existing research ignores the impact of multi-factor energy losses on the performance of machine tool energy consumption prediction models. The existing models must be selected and verified several times to determine the appropriate hyperparameters. Therefore, in this study, a machine tool energy consumption prediction method based on a mechanism and data-driven model that considers multi-factor energy losses and hyperparameter dynamic self-optimization is proposed to improve the accuracy and reduce the difficulty of hyperparameter tuning. The proposed multi-factor energy-loss prediction model is based on the theoretical prediction model of machine-tool cutting energy consumption. After creating the model, a hyperparameter search space embedding a tree-structured Parzen estimator (TPE) was designed based on Hyperopt to dynamically self-optimize the hyperparameters in the deep neural network (DNN) model. Finally, two sets of experiments were designed for verification and comparison with the theoretical and data models. The results showed that the energy consumption prediction performances of the proposed hybrid model in the two sets of experiments were 99% and 97%.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00526-9

关键词: Mechanism-data hybrid-driven, Cutting energy consumption, Hyperopt, Hyperparameters tuning

Abstract: High-quality development in the manufacturing industry is often accompanied by high energy consumption. The accurate prediction of the energy consumption of computer numerical control (CNC) machine tools, which plays a vital role in manufacturing, is of great importance in energy conservation. However, the existing research ignores the impact of multi-factor energy losses on the performance of machine tool energy consumption prediction models. The existing models must be selected and verified several times to determine the appropriate hyperparameters. Therefore, in this study, a machine tool energy consumption prediction method based on a mechanism and data-driven model that considers multi-factor energy losses and hyperparameter dynamic self-optimization is proposed to improve the accuracy and reduce the difficulty of hyperparameter tuning. The proposed multi-factor energy-loss prediction model is based on the theoretical prediction model of machine-tool cutting energy consumption. After creating the model, a hyperparameter search space embedding a tree-structured Parzen estimator (TPE) was designed based on Hyperopt to dynamically self-optimize the hyperparameters in the deep neural network (DNN) model. Finally, two sets of experiments were designed for verification and comparison with the theoretical and data models. The results showed that the energy consumption prediction performances of the proposed hybrid model in the two sets of experiments were 99% and 97%.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00526-9

Key words: Mechanism-data hybrid-driven, Cutting energy consumption, Hyperopt, Hyperparameters tuning

Yue Meng, Sheng-Ming Dong, Xin-Sheng Sun, Shi-Liang Wei, Xian-Li Liu. A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption[J]. Advances in Manufacturing, 2025, 13(1): 167-195.

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A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption

A mechanism-data hybrid-driven modeling method for predicting machine tool-cutting energy consumption

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