高能效制造研究现状及展望

doi:10.3901/JME.2025.13.142

摘要/Abstract

摘要： 制造业能源需求的不断上升导致了巨大的环境和经济问题，提升制造业的能量效率(又称能效)十分迫切。高能效制造研究已成为目前制造系统领域的热点问题，受到了广泛的关注。为了更详细的分析国内外趋势，基于作者们在高能效制造领域的研究基础，开展高能效制造理论与应用研究现状的综述性研究。首先，在制造系统能量效率内涵的基础上，从机床设备、加工工艺和制造车间三个层面构建了高能效制造研究的内容框架；然后，系统总结高能效制造内容框架下的国内外最新研究成果，并基于此归纳四点高能效制造的挑战难点；最后，提出高能效制造可参考的未来发展方向。

关键词: 高能效制造, 制造系统, 内容框架, 研究现状及展望

Abstract: The rising demand for energy in the manufacturing industry has led to huge environmental and economic problems, and it is urgent to improve the energy efficiency of the manufacturing industry. The research on energy-efficient manufacturing (EEM) has become a hot issue in the field of manufacturing systems and has attracted more attentions. To analyze the current domestic and foreign trends in more details, based on the author 's research in the field of EEM, the theory and applications research status on EEM are reviewed. Firstly, on the basis of the connotation of energy efficiency of manufacturing system, the content framework of EEM research is constructed from three levels, which are machine tool equipment, processing technology and manufacturing workshop. Then, the latest research at home and abroad under the content framework of EEM are systematically summarized, and four challenges of EEM are summarized. Finally, the future directions of EEM are proposed.

Key words: energy-efficient manufacturing, manufacturing system, content framework, research status and future prospects

中图分类号:

吴少卿, 李聪波, 曹华军, 王凌, 李新宇. 高能效制造研究现状及展望[J]. 机械工程学报, 2025, 61(13): 142-157.

WU Shaoqing, LI Congbo, CAO Huajun, WANG Ling, LI Xinyu. The Status and Future Prospects of Research on Energy-efficient Manufacturing[J]. Journal of Mechanical Engineering, 2025, 61(13): 142-157.

参考文献

[1] U.S. U.S. ENERGY INFORMATION ADMINISTRATION. International Energy Outlook[EB/OL]. [2025-02-20]. http://www.eia.gov/forecasts/ieo/pdf/0484.
[2] 国家统计局. 中国统计年鉴2022[M]. 北京：中国统计出版社，2022. National Bureau of Statistics. China Statistical Yearbook 2022[M]. Beijing：China Statistics Press，2022.
[3] EUROPEAN COMMISSION. The Green Deal Industrial Plan[EB/OL]. [2025-02-15]. https://commission.europa. eu/strategy-and-policy/priorities-2019-2024/european-green-deal/green-deal-industrial-plan.
[4] AMERICAN TECHNOLOGY COUNCIL. National Strategy for Advanced Manufacturing[EB/OL]. [2025-02- 15]. https://www.whitehouse.gov/wp-content/uploads/2022/ 10/National-Strategy-for-Advanced-Manufacturing-10072022.
[5] DEPARTMENT FOR BUSINESS，ENERGY& INDUSTRIAL STRATEGY. Net Zero Strategy：Build Back Greener[EB/OL]. [2024-09-15]. https://www.gov.uk/ government/publications/net-zero-strategy.
[6] 高金吉. 中国高能耗机械装备运行现状及节能对策研究[M]. 北京：科学出版社，2013. GAO Jinji. The running status and energy saving countermeasures of high-energy-consumption mechanical equipment in China[M]. Beijing：Science Press，2013.
[7] 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.
[8] International Organization for Standardization. ISO 14955-1:2017. Machine tools—Environmental evaluation of machine tools—Part 1: Design methodology for energy-efficient machine tools[S]. Geneva：ISO，2018.
[9] DENKENA B，ABELE E，BRECHER C，et al. Energy efficient machine tools[J]. CIRP Annals-Manufacturing Technology，2020，69(2)：646-667.
[10] DMG MORI. Sustainability[EB/OL]. [2025-02-20]. https://cn.dmgmori.com/company/sustainability.
[11] 刘飞，刘培基，李聪波，等. 制造系统能量效率研究的现状及难点问题[J]. 机械工程学报，2017，53(5)：1-11. LIU Fei，LIU Peiji，LI Congbo，et al. The statue and difficult problems of research on energy efficiency of manufacturing systems[J]. Journal of Mechanical Engineering，2017，53(5)：1-11.
[12] KREITLEIN S，KUPFER I，MÜHLBAUER M，et al. The relative energy efficiency as standard for evaluating the energy efficiency of production processes based on the least energy demand[J]. Applied Mechanics and Materials，2015，805：11-18.
[13] SCHUDELEIT T，ZÜST S，WEISS L，et al. The total energy efficiency index for machine tools[J]. Energy，2016，102：682-693.
[14] 陈行政. 面向广义能效的机械加工系统工艺规划技术研究[D]. 重庆：重庆大学，2018. CHEN Xingzheng. Process planning technology and support system of mechanical machining for generalized energy efficiency[D]. Chongqing：Chongqing University，2018.
[15] 李聪波，刘飞，曹华军. 机械加工制造系统能效理论与技术[M]. 北京：机械工业出版社，2022. LI Congbo，LIU Fei，CAO Huajun. Energy efficiency theory and technology of machining manufacturing system[M]. Beijing：China Machine Press，2022.
[16] WANG J J，WANG L. A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2018，50(5)：1805-1819.
[17] XIAO Q，LI C，TANG Y，et al. A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning[J]. Energy，2019，166：142-156.
[18] HACKSTEINER M，PEHERSTORFER H，BLEICHER F. Energy efficiency of state-of-the-art grinding processes[J]. Procedia Manufacturing，2018，21:717–724.
[19] ALTINTAŞ R S，KAHYA M，ÜNVER H Ö. Modelling and optimization of energy consumption for feature based milling[J]. International Journal of Advanced Manufacturing Technology，2016，86(9-12)：3345-3363.
[20] YAN J，LI L. Multi-Objective optimization of milling parameters - the trade-offs between energy，production rate and cutting quality[J]. Journal of Cleaner Production，2013，52:462–471.
[21] FONTES D，HOMAYOUNI S，GONÇALVES J. A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources[J]. European Journal of Operational Research，2023，306(3)：1140-1157.
[22] GAO K，HUANG Y，SADOLLAH A，et al. A review of energy-efficient scheduling in intelligent production systems[J]. Complex & Intelligent Systems，2020，6：237-249.
[23] TRIEBE M J，ZHAO F，SUTHERLAND J W. Modelling the effect of slide table mass on machine tool energy consumption：the role of lightweighting[J]. Journal of Manufacturing Systems，2022，62：668-680.
[24] ALSWAT H M，MATIVENGA P T. Modelling the direct and embodied energy requirements of machining[J]. Journal of Cleaner Production，2022，366：132767.
[25] WU C，LI W，WANG L，et al. Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things[J]. IEEE Transactions on Cloud Computing，2018，9(2)：641-653.
[26] CHEN X，LI C，YANG Q，et al. Toward energy footprint reduction of a machining process[J]. IEEE Transactions on Automation Science and Engineering，2021，19(2)：772-787.
[27] LV J，TANG R，JIA S，et al. Experimental study on energy consumption of computer numerical control machine tools[J]. Journal of Cleaner Production，2016，112：3864-3874.
[28] JUNGE F，HELFERT M，ABELE E，et al. Increase in energy efficiency of industrial production processes through thermal crosslinking of cutting-machine tools and cleaning machines by heat-pump technology[C]// 12th IEA Heat Pump Conference. 2017：1-10.
[29] 刘飞，刘霜. 机床服役过程机电主传动系统的时段能量模型[J]. 机械工程学报，2012，48(21)：132-140. LIU Fei，LIU Shuang. Multi-period energy model of electro-mechanical main driving system during the service process of machine tools[J]. Journal of Mechanical Engineering，2012，48(21)：132-140.
[30] 王秋莲，刘飞. 数控机床多源能量流的系统数学模型[J]. 机械工程学报，2013，49(7)：66-74. WANG Qiulian，LIU Fei. Mathematical model of multi-source energy flows for CNC machine tools[J]. Journal of Mechanical Engineering，2013，49(7)：66-74.
[31] 施金良，刘飞，许弟建，等. 变频调速数控机床主传动系统的功率平衡方程[J]. 机械工程学报. 2010. 46(03)：118-124. SHI Jinliang，LIU Fei，XU Dijian，et al. Power balance equation about the numerical control machine tool’s main driver system driven by variable voltage variable frequency[J]. Journal of Mechanical Engineering，2010，46(03)：118-124.
[32] PAWAR S S，BERA T C，SANGWAN K S. Modelling of spindle energy consumption in CNC milling[J]. Procedia CIRP，2022，105：192-197.
[33] ZHANG Y，LI L，LIU W，et al. Dynamics analysis and energy consumption modelling based on bond graph：Taking the spindle system as an example[J]. Journal of Manufacturing Systems，2022，62：539-549.
[34] ZHAO G，ZHAO Y，MENG F，et al. Prediction model of machine tool energy consumption in hard-to-process materials turning[J]. The International Journal of Advanced Manufacturing Technology，2020，106：4499-4508.
[35] ZHOU L，LI J，LI F，et al. Energy consumption model and energy efficiency of machine tools：A comprehensive literature review[J]. Journal of Cleaner Production，2016，112：3721-3734.
[36] YOON H S，SINGH E，MIN S. Empirical power consumption model for rotational axes in machine tools[J]. Journal of Cleaner Production，2018，196：370-381.
[37] LI W，LI C，WANG N，et al. Energy saving design optimization of CNC machine tool feed system：A data-model hybrid driven approach[J]. IEEE Transactions on Automation Science and Engineering，2021，19(4)：3809-3820.
[38] TRIEBE M J，ZHAO F，SUTHERLAND J W. Achieving energy efficient machine tools by mass reduction through multi-objective optimization[J]. Procedia CIRP，2019，80:73–78.
[39] SCHLEINKOFER U，LAUFER F，ZIMMERMANN M，et al. Resource-efficient manufacturing systems through lightweight construction by using a combined development approach[J]. Procedia CIRP，2018，72:856– 861.
[40] ZHOU L，LI F，WANG Y，et al. A new empirical standby power and auxiliary power model of CNC machine tools[J]. The International Journal of Advanced Manufacturing Technology，2022，120(5)：3995-4010.
[41] CHEN X，LI C，TANG Y，et al. Energy efficient cutting parameter optimization[J]. Frontiers of Mechanical Engineering，2021，16(2)：221–248.
[42] JIA S，WANG S，ZHANG N，et al. Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing[J]. Environmental Science and Pollution Research，2022：1-22.
[43] 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.
[44] VIJAYARAGHAVAN A，DORNFELD D. Automated energy monitoring of machine tools[J]. CIRP Annals- Manufacturing Technology，2010,59(1)：21-24.
[45] MOHAMMADI A，ZÜST S，MAYR J，et al. A methodology for online visualization of the energy flow in a machine tool[J]. CIRP Journal of Manufacturing Science and Technology，2017，19：138-146.
[46] 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.
[47] CAI Y，SHI X，SHAO H，et al. Energy efficiency state identification in milling processes based on information reasoning and Hidden Markov Model[J]. Journal of Cleaner Production，2018，193：397-413.
[48] LI L，LI C，TANG Y，et al. An integrated solution to minimize the energy consumption of a resource- constrained machining system[J]. IEEE Transactions on Automation Science and Engineering，2019，17(3)：1158-1175.
[49] TUO J，LIU F，LIU P，et al. Energy efficiency evaluation for machining systems through virtual part[J]. Energy，2018，159：172-183.
[50] KAUFELD M. Energieeffizienz-Eine Möglichkeit des Maschinenvergleichs[J]. Werkstatt und Betrieb，2011，144(12)：60.
[51] WANG Q，LIU F，LI C. An integrated method for assessing the energy efficiency of machining workshop[J]. Journal of Cleaner Production，2013，52：122-133.
[52] CAO W，YAN C，WU D，et al. A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing[J]. International Journal of Advanced Manufacturing Technology，2017，93(9-12)：1-12.
[53] TUO J，LIU F，LIU P. Key performance indicators for assessing inherent energy performance of machine tools in industries[J]. International Journal of Production Research，2019，57(6)：1811-1824.
[54] WEGENER K，WEISS L，GONTARZ A. Methods and tools for evaluation of energy efficiency in production[C]//International Chemnitz Manufacturing Colloquium ICMC. 2012：593-614.
[55] Japanese Standards Association. JIS TS B 0024-1:2010. Machine tools—Test methods for electric power consumption—Part 1: Machining centres[S]. Tokyo：Japanese Standards Association，2010.
[56] GIACONE E，MANCO S. Energy efficiency measurement in industrial processes[J]. Energy，2012，38：331-45
[57] CAMPATELLI G，SCIPPA A，LORENZINI L，et al. Optimal workpiece orientation to reduce the energy consumption of a milling process[J]. International Journal of Precision Engineering and Manufacturing-Green Technology，2015，2(1)：5-13.
[58] SHU L，DUFLOU J，HERRMANN C，et al. Design for reduced resource consumption during the use phase of products[J]. CIRP Annals-Manufacturing Technology，2017，66(2)：635-658.
[59] MA F，ZHANG H，GONG Q，et al. A novel energy efficiency grade evaluation approach for machining systems based on inherent energy efficiency[J]. International Journal of Production Research，2021，59(19)：6022-6033.
[60] LIU P，ZHANG Z，WANG X，et al. A generalized method for the inherent energy performance modeling of machine tools[J]. Journal of Manufacturing Systems，2021，61：406-422.
[61] NEWMAN S T，NASSEHI A，IMANI-ASRAI R，et al. Energy efficient process planning for CNC machining[J]. CIRP Journal of Manufacturing Science and Technology，2012，5(2)：127-136.
[62] CUI X，GUO J. Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy，damage equivalent stress，and surface roughness considered[J]. The International Journal of Advanced Manufacturing Technology，2018，96：4281-4293.
[63] EMAMI M，SADEGHI M H，SARHAN A A D，et al. Investigating the minimum quantity lubrication in grinding of Al2O3 engineering ceramic[J]. Journal of Cleaner Production，2014，66：632-643.
[64] KUMAR R，BILGA P S，SINGH S. Multi objective optimization using different methods of assigning weights to energy consumption responses，surface roughness and material removal rate during rough turning operation[J]. Journal of Cleaner Production，2017，164：45-57.
[65] XIAO Y，JIANG Z，GU Q，et al. A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost[J]. Journal of Manufacturing Systems，2021，59：535-548.
[66] LU F，ZHOU G，ZHANG C，et al. Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning[J]. Robotics and Computer-Integrated Manufacturing，2023，81：102488.
[67] ZHAO X，LI C，TANG Y，et al. An integrated decision- making method of flexible process plan and cutting parameter considering dynamic machining resources[J]. IEEE Transactions on Automation Science and Engineering，2023，10(3)：123-135.
[68] LI W，LI B，HE S，et al. A novel milling parameter optimization method based on improved deep reinforcement learning considering machining cost[J]. Journal of Manufacturing Processes，2022，84：1362-1375.
[69] LI L，LI C，TANG Y，et al. An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining[J]. Journal of Cleaner Production，2017，162：458-473.
[70] LIU Q，LI X，GAO L. Mathematical modeling and a hybrid evolutionary algorithm for process planning[J]. Journal of Intelligent Manufacturing，2021，32：781-797.
[71] HU L，PENG C，EVANS S，et al. Minimising the machining energy consumption of a machine tool by sequencing the features of a part[J]. Energy，2017，121：292-305.
[72] FENG C，HUANG Y，WU Y，et al. Feature-based optimization method integrating sequencing and cutting parameters for minimizing energy consumption of CNC machine tools[J]. The International Journal of Advanced Manufacturing Technology，2022，121(1)：503-515.
[73] TIAN C，ZHOU G，LU F，et al. Cooperative optimization of cutting parameters，process routes，and scheduling considering carbon emissions with analytic target cascading[J]. The International Journal of Advanced Manufacturing Technology，2021，114：605-623.
[74] TIAN C，ZHOU G，LU F，et al. An integrated multi- objective optimization approach to determine the optimal feature processing sequence and cutting parameters for carbon emissions savings of CNC machining[J]. International Journal of Computer Integrated Manufacturing，2020，33(6)：609-625.
[75] WEN X，LIAN X，QIAN Y，et al. Dynamic scheduling method for integrated process planning and scheduling problem with machine fault[J]. Robotics and Computer-Integrated Manufacturing，2022，77：102334.
[76] GUPTA K，LAUBSCHER R F，PAULO DAVIM J，et al. Recent developments in sustainable manufacturing of gears：A review [J]. Journal of Cleaner Production，2016， 112：3320-3330.
[77] ZHANG J，LI C，LI Y，et al. Energy consumption modeling and optimization of a hobbing machine tool considering multi-axis coupling[J]. IEEE Transactions on Automation Science and Engineering，2023，10(3)：123-135.
[78] SARANYA K，JEGARAJ J J R，KUMAR K R，et al. Artificial intelligence-based selection of optimal cutting tool and process parameters for effective turning and milling operations[J]. Journal of the Institution of Engineers，2018，99(4)：381-392.
[79] ZHOU M，ZHENG G，CHEN Z. An automated CNC programming approach to machining pocket with complex islands and boundaries by using multiple cutters in hybrid tool path patterns[J]. The International Journal of Advanced Manufacturing Technology，2016，83(1)：407-420.
[80] BAGABER S A，YUSOFF A R. Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316[J]. Journal of Cleaner Production，2017，157：30-46.
[81] XIE N，ZHOU J，ZHENG B. Selection of optimum turning parameters based on cooperative optimization of minimum energy consumption and high surface quality[J]. Procedia CIRP，2018，72：1469-1474.
[82] LV Y，LI C，TANG Y，et al. Toward energy-efficient rescheduling decision mechanisms for flexible job shop with dynamic events and alternative process plans[J]. IEEE Transactions on Automation Science and Engineering，2021，19(4)：3259-3275.
[83] BAYKASOĞLU A，MADENOĞLU F S. Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive maintenance activities in dynamic flexible job shops[J]. Soft Computing，2021，25(23)：14893-14932.
[84] GUI L，FU L，LI X，et al. Optimisation framework and method for solving the serial dual-shop collaborative scheduling problem[J]. International Journal of Production Research，2023，61(13)：4341-4357.
[85] SUN M，CAI Z，ZHANG H. A teaching-learning-based optimization with feedback for LR fuzzy flexible assembly job shop scheduling problem with batch splitting[J]. Expert Systems with Applications，2023，224：120043.
[86] SALAMA M，SRINIVAS S. Adaptive neighborhood simulated annealing for sustainability-oriented single machine scheduling with deterioration effect[J]. Applied Soft Computing，2021，110：107632.
[87] CHEN X，LI J，DU Y. A hybrid evolutionary immune algorithm for fuzzy flexible job shop scheduling problem with variable processing speeds[J]. Expert Systems with Applications，2023，233：120891.
[88] PAN Z，LEI D，WANG L. A knowledge-based two- population optimization algorithm for distributed energy- efficient parallel machines scheduling[J]. IEEE Transactions on Cybernetics，2020，52(6)：5051-5063.
[89] LI X，GAO L，PAN Q，et al. An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2018，49(10)：1933-1945.
[90] LIU Q，LI X，GAO L，et al. Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling[J]. Journal of Manufacturing Systems，2021，61：300-310.
[91] ZHANG Z，TANG R，PENG T，et al. A method for minimizing the energy consumption of machining system：integration of process planning and scheduling[J]. Journal of Cleaner Production，2016，137：1647-1662.
[92] WANG Y，WANG M，LIN S. Selection of cutting conditions for power constrained parallel machine scheduling[J]. Robotics and Computer-Integrated Manufacturing，2017，43：105-110.
[93] HE B，LIU R，LI T. Integrated carbon footprint with cutting parameters for production scheduling[J]. Journal of Cleaner Production，2023，412：137307.
[94] HUANG Z，LI H，CAO H，et al. Energy-saving control strategy for multi-sleep states of CNC machine tool considering components priority[J]. IEEE Transactions on Industrial Electronics，2023，70(8)：4500-4510.
[95] LIU C，MENG X，LIU C，et al. Carbon footprint-based optimization method for remanufacturing machining paths[J]. The International Journal of Advanced Manufacturing Technology，2022，124(10)：3391-3406.
[96] LI C，WU S，YI Q，et al. A cutting parameter energy- saving optimization method considering tool wear for multi-feature parts batch processing[J]. The International Journal of Advanced Manufacturing Technology，2022，121(7)：4941-4960.
[97] SREDANOVIC B，CICA D，BOROJEVIC S，et al. Optimization of superalloy Inconel 718 micro-milling process by combined Taguchi and multi-criteria decision making method[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering，2024，46(7)：423.
[98] ZHANG T，JI W，QIU Y. A framework of energy- consumption driven discrete manufacturing system[J]. Sustainable Energy Technologies and Assessments，2021，47：101336.
[99] SUN M，CAI Z，ZHAO N. Design of intelligent manufacturing system based on digital twin for smart shop floors[J]. International Journal of Computer Integrated Manufacturing，2023，36(4)：542-566.
[100] BOSCARIOL P，RICHIEDEI D. Energy optimal design of servo-actuated systems：A concurrent approach based on scaling rules[J]. Renewable and Sustainable Energy Reviews，2022，156：111923.
[101] SHNEOR Y. Reconfigurable machine tool：CNC machine for milling，grinding and polishing[J]. Procedia Manufacturing，2018，21：221-227.
[102] GIAMPIERI A，LING CJ，MA Z，et al. A review of the current automotive manufacturing practice from an energy perspective[J]. Applied Energy，2020，261：114074.
[103] WU S，LI C，JIN Y，et al. Design of gear hobbing machine based on analytical target cascading：Modeling, decomposition and independent optimization[J]. IEEE Transactions on Automation Science and Engineering, 2025，22：12033-12046.