机床切削能耗预测的机构-数据混合驱动建模方法

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Yue Meng, Sheng-Ming Dong, Xin-Sheng Sun, Shi-Liang Wei, Xian-Li Liu
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引用次数: 0

摘要

制造业的高质量发展往往伴随着高能耗。数控机床在制造业中起着至关重要的作用,其能耗的准确预测在节能方面具有重要意义。然而,现有的研究忽略了多因素能量损失对机床能耗预测模型性能的影响。必须对现有模型进行多次选择和验证,以确定适当的超参数。因此,本研究提出了一种基于考虑多因素能量损失和超参数动态自优化的机制和数据驱动模型的机床能耗预测方法,以提高预测精度,降低超参数整定难度。在机床切削能耗理论预测模型的基础上,提出了多因素能量损失预测模型。在建立模型后,基于Hyperopt设计了一个嵌入树结构Parzen估计器(TPE)的超参数搜索空间,对深度神经网络(DNN)模型中的超参数进行动态自优化。最后,设计了两组实验对理论模型和数据模型进行验证和比较。结果表明,该混合模型在两组实验中的能耗预测准确率分别为99%和97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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%.

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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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