基于SOM-Kmeans的机床能效等级评价方法

doi:10.3901/JME.2023.07.295

机械工程学报 ›› 2023, Vol. 59 ›› Issue (7): 295-306.doi: 10.3901/JME.2023.07.295

扫码分享

基于SOM-Kmeans的机床能效等级评价方法

崔佳斌¹, 李聪波¹, 曹华军¹, 陈行政², 王军见³, 戴涛⁴

1. 重庆大学机械传动国家重点实验室重庆 400044;
2. 西南大学工程技术学院重庆 400715;
3. 国家机床质量监督检验中心北京 101312;
4. 重庆第二机床厂有限责任公司重庆 400072

收稿日期:2022-04-11 修回日期:2022-07-29 出版日期:2023-04-05 发布日期:2023-06-16
通讯作者: 李聪波(通信作者),男,1981年出生,博士,教授,博士研究生导师。主要研究方向为绿色制造与制造系统能效。E-mail:congboli@cqu.edu.cn
作者简介:崔佳斌,男,1996年出生,硕士研究生。主要研究方向为绿色制造。E-mail:cuijiabin@cqu.edu.cn
基金资助:
国家自然科学基金(51975075、51905448)和重庆市技术创新与应用发展专项(cstc2020jscx-msxmX0221)

SOM-Kmeans Based Machine Tools Energy Efficiency Grade Evaluation

CUI Jiabin¹, LI Congbo¹, CAO Huajun¹, CHEN Xingzheng², WANG Junjian³, DAI Tao⁴

1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044;
2. College of Engineering and Technology, Southwest University, Chongqing 400715;
3. China National Machine Tool Quality Supervision and Testing, Beijing 101312;
4. Chongqing No.2 Machine Tool Works Co., Ltd., Chongqing 400072

Received:2022-04-11 Revised:2022-07-29 Online:2023-04-05 Published:2023-06-16

摘要/Abstract

摘要： 机床能效等级评价是提高能源效率的基础。现有机床能效评价方法多运用能量利用率和比能等指标评价特定加工过程能效，未考虑加工能力对机床能效评价的影响，致使无法综合评价机床能效等级。为此，从机床加工能力和能耗特性角度构建机床能效等级评价指标，提出基于自组织映射神经网络-K均值(SOM-Kmeans)的机床能效等级评价方法。首先，分析机床加工能力特性和时段能耗特性，构建机床加工能力指标和多时段能耗特性指标；其次，运用SOM-Kmeans对机床能效信息进行聚类分析，并采用主成分分析法(PCA)对机床能效等级进行综合评价；最后，通过实际案例验证了所提方法的有效性和实用性。

关键词: 机床能效等级, 加工能力, 能耗特性, 聚类分析

Abstract: The evaluation of machine tool energy efficiency grade is the basis for improving energy efficiency. Most of the existing evaluation methods mostly use energy utilization and specific energy to evaluate the energy efficiency of specific machining processes, without considering the influence of machining capacity on machine tool energy efficiency evaluation. To comprehensively evaluate machine tools energy efficiency grade, the machine tools energy efficiency grade evaluation method based on Self-organizing Map and Kmeans (SOM-Kmeans) is proposed in terms of machining capacity and energy consumption characteristics. Firstly, the energy consumption and machining capacity characteristics are analyzed, and then the multi-period energy consumption characteristics and machining capacity index are constructed. Secondly, the cluster analysis of machine tool energy efficiency information is carried out using SOM-Kmeans and the machine tool energy efficiency grade is obtained based on principal component analysis (PCA). Finally, the effectiveness and practicability of the proposed method are verified by actual cases.

Key words: machine tool energy efficiency grade, machining capacity, energy consumption characteristics, cluster analysis

中图分类号:

TG501

崔佳斌, 李聪波, 曹华军, 陈行政, 王军见, 戴涛. 基于SOM-Kmeans的机床能效等级评价方法[J]. 机械工程学报, 2023, 59(7): 295-306.

CUI Jiabin, LI Congbo, CAO Huajun, CHEN Xingzheng, WANG Junjian, DAI Tao. SOM-Kmeans Based Machine Tools Energy Efficiency Grade Evaluation[J]. Journal of Mechanical Engineering, 2023, 59(7): 295-306.

参考文献

[1] 国家统计局. 中国统计年鉴[M]. 北京:中国统计出版社,2021. National Bureau of Statistics. China statistical yearbook[M]. Beijing:China Statistics Press,2021.
[2] VELCHEV S,KOLEV I,IVANOV K,et al. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning[J]. Journal of Cleaner Production,2014,80:139-149.
[3] LI B,TIAN X,ZHANG M. Modeling and multi-objective optimization method of machine tool energy consumption considering tool wear[J]. International Journal of Precision Engineering and Manufacturing-Green Technology,2022,9(1):127-141.
[4] GUTOWSKI T,DAHMUS J,THIRIEZ A. Electrical energy requirements for manufacturing processes[C]. Proceedings of 13th CIRP International Conference on Life Cycle Engineering. Leuven,Belgium,2006,31(1):623-638.
[5] 国家市场监督管理总局,中国国家标准化管理委员会. GB 37479-2019,风管送风式空调机组能效限定值及能效等级[S]. 北京:中国标准出版社,2020. State Administration for Market Regulation,Inspection and Quarantine of the People's Republic of China Standardization Administration. GB 37479-2019,Minimum allowable values of energy efficiency and energy efficiency grades for ducted air conditioners[S]. Beijing:Standards Press of China,2020.
[6] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB 21519-2008,储水式电热水器能效限定值及能效等级[S]. 北京:中国标准出版社,2008. General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China Standardization Administration. GB 21519-2008,Minimum allowable values of energy efficiency and energy efficiency grades for electrical storage water heaters[S]. Beijing:Standards Press of China,2008.
[7] 国家市场监督管理总局,国家标准化管理委员会. GB 18613-2020 电动机能效限定值及能效等级[S]. 北京:中国标准出版社,2021. State Administration for Market Regulation,Standardization Administration. GB 18613-2020 Minimum allowable values of energy efficiency and values of efficiency grades for motors[S]. Beijing:Standards Press of China,2021.
[8] 谢俊,刘飞,蔡维. 机床服役过程能量效率的可预测特性及预测方法研究[J]. 机械工程学报,2019,55(17):172-184. XIE Jun,LIU Fei,CAI Wei. Research on the characteristics and methodology for predicting energy efficiency during the service process of machine tools[J]. Journal of Mechanical Engineering,2019,55(17):172-184.
[9] WESTERMANN H,KAFARA M,STEINHILPER R. Development of a reference part for the evaluation of energy efficiency in milling operations[J]. Procedia CIRP,2015,26:521-526.
[10] Japanese Standards Association. TS B 0024-1:2010,Machine tools test methods for electric power consumption-part1:Machining centres[S]. Japan:Japanese Standards Association,2010.
[11] Chen,X,Li C,TANG Y,et al. Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time[J]. Energy,2019,175:1021-1037.
[12] International Organization for Standardization. ISO 14955-3:2020,Machine tools-Environmental evaluation of machine tools-Part 3:Principles for testing metal-cutting machine tools with respect to energy efficiency[S]. ISO Technical Committee,2020.
[13] JI Q,LI C,ZHU D,et al. Structural design optimization of moving component in CNC machine tool for energy saving[J]. Journal of Cleaner Production,2020,246:118976.
[14] JEREMI W,LEONESIO M,BIANCHI G. Integrated energy analysis of cutting process and spindle subsystem in a turning machine[J]. Journal of Cleaner Production,2018,170:1459-1472.
[15] XIE J,CAI W,DU Y,et al. Modelling approach for energy efficiency of machining system based on torque model and angular velocity[J]. Journal of Cleaner Production,2021,293:126249.
[16] XIAO Q,LI C,TANG Y,et al. Energy efficiency modeling for configuration-dependent machining via machine learning:A comparative study[J]. IEEE Transactions on Automation Science and Engineering,2020,18(2):717-730.
[17] 李聪波,朱岩涛,刘飞,等. 面向能量效率的数控铣削加工参数多目标优化模型[J]. 机械工程学报,2016,52(21):120-129. LI Congbo,ZHU Yantao,LIU Fei,et al. Multi-objective NC milling parameters optimization model for energy efficiency[J]. Journal of Mechanical Engineering,2016,52(21):120-129.
[18] 庹军波,刘飞,张华,等. 机床固有能量效率的内涵及其评价方法[J]. 机械工程学报,2018,54(7):167-175. TUO Junbo,LIU Fei,ZHANG Hua,et al. Connotation and assessment method for inherent energy efficiency of machine tools[J]. Journal of Mechanical Engineering,2018,54(7):167-175.
[19] 国家市场监督管理总局,国家标准化管理委员会. GB/T 40735-2021,数控机床固有能量效率的评价方法[S]. 北京:中国标准出版社,2022. State Administration for Market Regulation,Standardization Administration. GB/T 40735-2021,Method for inherent energy efficiency evaluation of computer numerical control machine tools[S]. Beijing:Standards Press of China,2022.
[20] SCHUDELEIT T,ZÜST S,WEGENER K. Methods for evaluation of energy efficiency of machine tools[J]. Energy,2015,93:1964-1970.
[21] NAN F,LI Y,JIA X,et al. Application of improved SOM network in gene data cluster analysis[J]. Measurement,2019,145:370-378.
[22] YU J,ZHOU K,YANG S. Land use efficiency and influencing factors of urban agglomerations in China[J]. Land Use Policy,2019,88:104-143.
[23] BRO R,SMILDE A K. Principal component analysis[J]. Analytical Methods,2014,6(9):2812-2831.
[24] ZHANG X,YU T,XU P,et al. An intelligent sustainability evaluation system of micro milling[J]. Robotics and Computer-Integrated Manufacturing,2022,73:102-239.

基于SOM-Kmeans的机床能效等级评价方法

SOM-Kmeans Based Machine Tools Energy Efficiency Grade Evaluation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	王庆锋, 刘家赫, 卫炳坤, 张程. 数据驱动的聚类分析故障识别方法研究[J]. 机械工程学报, 2020, 56(18): 7-14.
[2]	王丽婕,厉虹,方市彬. 基于人工鱼群优化算法的支持向量机短期风电功率预测模型[J]. 电气工程学报, 2016, 11(10): 7-12.
[3]	李春磊莫蓉常智勇张栋梁向颖. 融入多维制造信息的产品典型工艺路线发现方法[J]. 机械工程学报, 2015, 51(15): 148-157.
[4]	李志刚;张菲菲;李玲玲. 基于元器件初始信息的寿命评估方法[J]. , 2014, 50(16): 41-46.
[5]	赵刚;江平宇. 面向大规模定制生产的e-制造单元目标层解分析优化规划模型[J]. , 2007, 43(2): 178-185.
[6]	赵荣珍;张优云. 基于Rough set知识获取的故障数据表聚类离散化方法研究[J]. , 2005, 41(1): 145-150.
[7]	申丽国;韩至骏. 选择高速钢刀具材料的专家模糊聚类算法[J]. , 1995, 31(3): 51-56.