Advances in Manufacturing ›› 2026, Vol. 14 ›› Issue (1): 43-102.doi: 10.1007/s40436-025-00567-8
Tai-Min Luo1,2, Jin Zhang1,2, Chen-Jie Deng1,2, Dai-Xin Luo1,2, Gui-Bao Tao1,2, Hua-Jun Cao1,2
Received:2024-06-14
Revised:2024-08-15
Published:2026-03-23
Contact:
Hua-Jun Cao Email:E-mail:hjcao@cqu.edu.cn
E-mail:hjcao@cqu.edu.cn
Supported by:Tai-Min Luo, Jin Zhang, Chen-Jie Deng, Dai-Xin Luo, Gui-Bao Tao, Hua-Jun Cao. Green machining technology and application driven by digital intelligence: a review[J]. Advances in Manufacturing, 2026, 14(1): 43-102.
| [1] Yadav S, Samadhiya A, Kumar A et al (2023) Achieving the sustainable development goals through net zero emissions: innovation-driven strategies for transitioning from incremental to radical lean, green and digital technologies. Resour Conserv Recycl 197:107094. https://doi.org/10.1016/j.resconrec.2023.107094 [2] Lei MK, Miao WL, Zhu XP et al (2021) High-performance manufacturing enabling integrated design and processing of products: a case study of metal cutting. CIRP J Manuf Sci Technol 35:178-192 [3] Agrawal R, Kumar N, Parvez K et al (2022) Optimization of cutting force via variable feed rate in dry turning lathe of AISI 304. Mater Today Proc 64:1182-1187 [4] Usca üA, Uzun M, ?ap S et al (2022) Determination of machinability metrics of AISI 5140 steel for gear manufacturing using different cooling/lubrication conditions. J Mater Res Technol 21:893-904 [5] Li X, Huang Z, Ning W (2023) Intelligent manufacturing quality prediction model and evaluation system based on big data machine learning. Comput Electr Eng 111:108904. https://doi.org/10.1016/j.compeleceng.2023.108904 [6] Zhou W, Zhang Y, Li X (2024) Artificial intelligence, green technological progress, energy conservation, and carbon emission reduction in China: an examination based on dynamic spatial Durbin modeling. J Clean Prod 446:141142. https://doi.org/10.1016/j.jclepro.2024.141142 [7] Hopkins C, Hosseini A (2019) A review of developments in the fields of the design of smart cutting tools, wear monitoring, and sensor innovation. IFAC-PapersOnLine 52(10):352-357 [8] Cheng YN, Ding Y, Gai XY et al (2022) Tool wear monitoring technology and its application in heavy cutting. J Harbin Univ Sci Technol 27(1):79-91 [9] Liu Q, Zhang HJ, Liu XL et al (2021) A review of research on intelligent cutting tools. J Mech Eng 57(21):248-268 [10] Deng CJ, Cao HJ, Zhang J et al (2023) Research progress of intelligent turning tools for cutting condition monitoring and process control. Mech Sci Technol Aerosp Eng 2023:1-17 [11] Pimenov DY, Gupta MK, da Silva LRR et al (2022) Application of measurement systems in tool condition monitoring of milling: a review of measurement science approach. Measurement 199:111503. https://doi.org/10.1016/j.measurement.2022.111503 [12] Wang W, Liu W, Zhang Y et al (2024) Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: a review. Chin J Aeronaut 37(4):23-53 [13] Cheng K, Niu ZC, Wang RC et al (2017) Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin J Mech Eng 30(5):1162-1176 [14] Guo H, Hu KY, Yan XG et al (2022) Wireless monitoring of wear of the vibration self-sensing tool. J Xi’an Jiaotong Univ 56(11):1-10 [15] M?hring HC, Nguyen QP, Kuhlmann A et al (2016) Intelligent tools for predictive process control. Proced CIRP 57:539-544 [16] Li Y, Li L, Chen N et al (2022) Design and research on tool wear status monitoring ring based on MEMS sensors. Tool Technol 56(7):3-7 [17] Xue Z, Li L, Wu Y et al (2023) Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring. Measurement 223:113787. https://doi.org/10.1016/j.measurement.2023.113787 [18] Chen QW, Chen WF, Cui RF et al (2020) Design and experimental study of vibration measuring tool handle based on wireless transmission. Mach Build Autom 49(3):26-29 [19] Zhou CA, Zhao Y, Zan ZL et al (2020) Wireless vibration sensing toolholder system for milling tool condition monitoring. Tool Eng 54(4):28-31 [20] Mauthner G, Votruba W, Ramsauer C et al (2022) Development of a CAM-in-the-loop system for cutting parameter optimization using an instrumented toolholder. Procedia CIRP 107:326-331 [21] Chen X, Cheng K, Wang C et al (2014) Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting. Manuf Lett 2(4):112-117 [22] Tseng LW, Hu TS, Hu YC et al (2021) A smart toolholder calibrated by machine learning for measuring cutting force in fine turning and its application to the specific cutting force of low carbon steel S15C. Machines 9(9):190. https://doi.org/10.3390/machines9090190 [23] Wang C, Rakowski R, Cheng K et al (2013) Design and analysis of a piezoelectric film embedded smart cutting tool. Proc Inst Mech Eng Part B-J Eng Manuf 227(2):254-260 [24] Xiao C, Ding H, Cheng K et al (2015) Design of an innovative smart turning tool with application to real-time cutting force measurement. Proc Inst Mech Eng Part B-J Eng Manuf 229(3):563-568 [25] Chen GH (2021) Design of the cutting force self-sensing smart turning tool and error compensation study. Dissertation, Taiyuan University of Science and Technology [26] Chen YL, Chen F, Li Z et al (2021) Three-axial cutting force measurement in micro/nano-cutting by utilizing a fast tool servo with a smart toolholder. CIRP Ann 70(1):33-36 [27] Hatefi S, Smith F (2023) Design and analysis of ultra-precision smart cutting tool for in-process force measurement and tool nanopositioning in ultra-high-precision single-point diamond turning. Micromachines 14(10):1857. https://doi.org/10.3390/mi14101857 [28] Harmon A, Fussell BK, Jerard RB (2012) Calibration and characterization of a low-cost wireless sensor for applications in CNC end milling. In: ASME 2012 international manufacturing science and engineering conference, Salt Lake City [29] Rizal M, Ghani JA, Nuawi MZ et al (2015) Development and testing of an integrated rotating dynamometer on toolholder for milling process. Mech Syst Signal Process 52/53:559-576 [30] Rizal M, Ghani JA, Che Haron CH et al (2018) Design and construction of a strain gauge-based dynamometer for a 3-axis cutting force measurement in turning process. J Mech Eng Sci 12(4):4072-4087 [31] Song W, Zhang J, Xiao G et al (2023) Accurate cutting-force measurement with smart toolholder in lathe. Sensors 23(9):4419. https://doi.org/10.3390/s23094419 [32] Panzera TH, Souza PR, Rubio JCC et al (2012) Development of a three-component dynamometer to measure turning force. Int J Adv Manuf Technol 62(9/12):913-922 [33] Hao Y (2017) Research on performance testing for capacitive four-dimensional force-measuring toolholder. Dissertation, Harbin Institute of Technology [34] Ma L, Melkote SN, Morehouse JB et al (2012) Thin-film PVDF sensor-based monitoring of cutting forces in peripheral end milling. J Dyn Syst Meas Control 134(5):051014. https://doi.org/10.1115/1.4006366 [35] Luo M, Luo H, Axinte D et al (2018) A wireless instrumented milling cutter system with embedded PVDF sensors. Mech Syst Signal Process 110:556-568 [36] Wang C, Cheng K, Minton T et al (2014) Development of a novel surface acoustic wave (SAW) based smart cutting tool in machining hybrid dissimilar material. Manuf Lett 2(2):21-25 [37] Kim J, Lee S, Chun H et al (2021) Compact curved-edge displacement sensor-embedded spindle system for machining process monitoring. J Manuf Process 64:1255-1260 [38] Li WD, Ding H, Cheng K (2014) Research on force measuring smart cutting tool based on surface acoustic wave resonator principle. Mach Build Autom 43(5):46-50 [39] Huang J, Pham DT, Ji C et al (2020) Smart cutting tool integrated with optical fiber sensors for cutting force measurement in turning. IEEE Trans Instrum Meas 69(4):1720-1727 [40] Totis G, Sortino M (2011) Development of a modular dynamometer for triaxial cutting force measurement in turning. Int J Mach Tools Manuf 51(1):34-42 [41] Totis G, Wirtz G, Sortino M et al (2010) Development of a dynamometer for measuring individual cutting edge forces in face milling. Mech Syst Signal Process 24(6):1844-1857 [42] Shu S, Cheng K, Ding H et al (2013) An innovative method to measure the cutting temperature in process by using an internally cooled smart cutting tool. J Manuf Sci Eng 135(6):061014. https://doi.org/10.1115/1.4025742 [43] Shu SR, Ding H, Chen SJ et al (2012) FEM-based design and analysis of a smart cutting tool with internal cooling for cutting temperature measurement and control. Appl Mech Mater 217/219:1874-1879 [44] Yin Z, Hao X, Chen W et al (2023) Study on a new type of cutting temperature sensing smart tool. J Mech Eng 59(1):242-248 [45] Sugita N, Ishii K, Furusho T et al (2015) Cutting temperature measurement by a micro-sensor array integrated on the rake face of a cutting tool. CIRP Ann 64(1):77-80 [46] Cui Y, Wang H, Cao K et al (2022) Preparation and application of nanocomposite thin-film temperature sensor during the milling process. Mater 15(20):7106. https://doi.org/10.3390/ma15207106 [47] Zhang P, Gao D, Lu Y et al (2022) A novel smart toolholder with embedded force sensors for milling operations. Mech Syst Signal Process 175:109130. https://doi.org/10.1016/j.ymssp.2022.109130 [48] Zhang P, Gao D, Lu Y et al (2022) Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network. Measurement 199:111520. https://doi.org/10.1016/j.measurement.2022.111520 [49] Xie Z, Lu Y, Chen X (2020) A multi-sensor integrated smart toolholder for cutting process monitoring. Int J Adv Manuf Technol 110(3/4):853-864 [50] Schuster A, Rentzsch H, Ihlenfeldt S (2023) Energy self-sufficient, multi-sensory toolholder for sensitive monitoring of milling processes. Proced CIRP 117:80-85 [51] Zhang J, Kang X, Ye Z et al (2023) Development and testing of a wireless smart toolholder with multi-sensor fusion. Front Mech Eng 18(4):55. https://doi.org/10.1007/s11465-023-0774-y [52] Zhang J, Huang X, Kang X et al (2023) Energy field-assisted high-speed dry milling green machining technology for difficult-to-machine metal materials. Front Mech Eng 18(2):28. https://doi.org/10.1007/s11465-022-0744-9 [53] Ali SH, Yao Y, Wu B et al (2024) Recent developments in MQL machining of aeronautical materials: a comparative review. Chin J Aeronaut 38(1):102918. https://doi.org/10.1016/j.cja.2024.01.018 [54] He T, Liu N, Xia H et al (2023) Progress and trend of minimum quantity lubrication (MQL): a comprehensive review. J Clean Prod 386:135809. https://doi.org/10.1016/j.jclepro.2022.135809 [55] Arul K, Mohanavel V, Raj Kumar S et al (2022) Investigation of machining attributes on machining of alloys under nano fluid MQL environment: a review. Mater Today Proc 59:1312-1318 [56] Junankar AA, Parate SR, Dethe PK et al (2021) A review: enhancement of turning process performance by effective utilization of hybrid nanofluid and MQL. Mater Today Proc 38:44-47 [57] Swain S, Panigrahi I, Kumar Sahoo A et al (2019) Study on machining performances during hard turning process using vibration signal under MQL environment: a review. Mater Today Proc 18:3539-3545 [58] Balasuadhakar A, Thirumalai KS, Ahmed F (2023) A review on the role of nanoparticles in MQL machining. Mater Today Proc 72:2828-2832 [59] Sharma AK, Tiwari AK, Dixit AR (2016) Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: a comprehensive review. J Clean Prod 127:1-18 [60] Bai X, Dong L, Li C et al (2018) The experimental research of lubrication performance in nanofluid minimum quantity lubrication (MQL) milling. Modul Mach Tool Autom Manuf Tech 4:15-18 [61] Wu S, Liu G, Zhang W et al (2023) High-speed milling of hardened steel under minimal quantity lubrication with liquid nitrogen. J Manuf Process 95:351-368 [62] Usluer E, Emiro?lu U, Yapan YF et al (2023) Investigation on the effect of hybrid nanofluid in MQL condition in orthogonal turning and a sustainability assessment. Sustain Mater Technol 36:e00618. https://doi.org/10.1016/j.susmat.2023.e00618 [63] Makhesana MA, Patel KM, Krolczyk GM et al (2023) Influence of MoS2 and graphite-reinforced nanofluid-MQL on surface roughness, tool wear, cutting temperature and microhardness in machining of Inconel 625. CIRP J Manuf Sci Technol 41:225-238 [64] C?nger DB, Yapan YF, Emiro?lu U et al (2024) Influence of singular and dual MQL nozzles on sustainable milling of Al6061-T651 in different machining environments. J Manuf Process 109:524-536 [65] Szczotkarz N, Mrugalski R, Maruda RW et al (2021) Cutting tool wear in turning 316L stainless steel in the conditions of minimized lubrication. Tribol Int 156:106813. https://doi.org/10.1016/j.triboint.2020.106813 [66] ?irin E (2023) Evaluation of tribological performance of MQL technique combined with LN2, CO2, N2 ecological cooling/lubrication techniques when turning of Hastelloy C22 superalloy. Tribol Int 188:108786. https://doi.org/10.1016/j.triboint.2023.108786 [67] Kharka V, Mujumdar S, Shukla S (2023) Study on helical milling of SS 304 with small diameter tools under the influence of minimum quantity lubrication (MQL). Manuf Lett 35:1312-1317 [68] Dong L, Li C, Zhou F et al (2021) Temperature of the 45 steel in the minimum quantity lubricant milling with different biolubricants. Int J Adv Manuf Technol 113(9/10):2779-2790 [69] Li B, Li C, Zhang Y et al (2016) Grinding temperature and energy ratio coefficient in MQL grinding of high-temperature nickel-base alloy by using different vegetable oils as base oil. Chin J Aeronaut 29(4):1084-1095 [70] Muaz M, Choudhury SK (2019) Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel. Measurement 138:557-569 [71] Xu WH, Li CH, Zhang YB et al (2023) Research progress and application of electrostatic atomization minimum quantity lubrication. J Mech Eng 59(7):110-138 [72] Zhang HP, Ren Y, Xue FG et al (2019) Cryogenic minimum quantity lubrication processing technology. J Harbin Univ Sci Technol 24(2):38-44 [73] Kong XY, Yuan SM, Zhu GY et al (2021) Optimization of process parameters of minimum quantity lubrication system based on grey relation analysis. Aeronaut Manuf Technol 64(6):73-81 [74] Zhang G, Chen H, Xiao G et al (2022) Effect of SiC nanofluid minimum quantity lubrication on the performance of the ceramic tool in cutting hardened steel. J Manuf Process 84:539-554 [75] Fragelli RL, Sanchez LEA, Ingraci Neto RR et al (2018) Refrigeration capacity of silver nanofluids under electrohydrodynamic effect oriented to heat removal in machining process. Exp Therm Fluid Sci 96:11-19 [76] Eltaggaz A, Hegab H, Deiab I et al (2018) Hybrid nano-fluid-minimum quantity lubrication strategy for machining austempered ductile iron (ADI). Int J Interact Des Manuf 12(4):1273-1281 [77] Rahmati B, Sarhan AAD, Sayuti M (2014) Investigating the optimum molybdenum disulfide (MoS2) nanolubrication parameters in CNC milling of Al6061-T6 alloy. Int J Adv Manuf Technol 70(5/8):1143-1155 [78] Duchosal A, Werda S, Serra R et al (2015) Numerical modeling and experimental measurement of MQL impingement over an insert in a milling tool with inner channels. Int J Mach Tools Manuf 94:37-47 [79] Du F, Zhou T, Tian P et al (2024) Cutting performance and cutting fluid infiltration characteristics into tool-chip interface during MQL milling. Measurement 225:113989. https://doi.org/10.1016/j.measurement.2023.113989 [80] Bai X, Jiang J, Li C et al (2023) Tribological performance of different concentrations of Al2O3 nanofluids on minimum quantity lubrication milling. Chin J Mech Eng 36(1):11. https://doi.org/10.1186/s10033-022-00830-0 [81] Shrivastava A, Gangopadhyay S (2023) Evaluation of adequacy of lubricants in MQL micro-drilling by a developed analytical model and experiments. J Manuf Process 101:1592-1604 [82] Hsu YY, Wang SS (2007) A new compensation method for geometry errors of five-axis machine tools. Int J Mach Tools Manuf 47(2):352-360 [83] Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40:1235-1256 [84] Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review part II: thermal errors. Int J Mach Tools Manuf 40:1257-1284 [85] Zhang Z, Jiang F, Luo M et al (2024) Geometric error measuring, modeling, and compensation for CNC machine tools: a review. Chin J Aeronaut 37(2):163-198 [86] Li Y, Zhao W, Lan S et al (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20-38 [87] Gao W, Ibaraki S, Donmez MA et al (2023) Machine tool calibration: measurement, modeling, and compensation of machine tool errors. Int J Mach Tools Manuf 187:104017. https://doi.org/10.1016/j.ijmachtools.2023.104017 [88] Laghari RA, Mekid S (2023) Comprehensive approach toward IIoT based condition monitoring of machining processes. Measurement 217:113004. https://doi.org/10.1016/j.measurement.2023.113004 [89] Quan L, Zhao W (2024) A review on positioning uncertainty in motion control for machine tool feed drives. Precis Eng 88:428-448 [90] Xiao QB, Wan M, Yang Y et al (2023) Pre-compensation of contour errors for five-axis machine tools through constructing a model reference adaptive control. Mech Mach Theory 183:105258. https://doi.org/10.1016/j.mechmachtheory.2023.105258 [91] Guo S, Si Z, Sa R et al (2024) Geometric error modeling and decoupling identification of rotary axis of five-axis machine tool based on spatial trajectory planning. Measurement 236:114887. https://doi.org/10.1016/j.measurement.2024.114887 [92] Ma J, Zhang Y, Jiao F et al (2024) Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6Al-4V. J Manuf Process 124:1519-1540 [93] Bellotti M, de Eguilior Caballero JR, Qian J et al (2021) Effects of partial tool engagement in micro-EDM milling and adaptive tool wear compensation strategy for efficient milling of inclined surfaces. J Mater Process Technol 288:116852. https://doi.org/10.1016/j.jmatprotec.2020.116852 [94] Malayath G, Katta S, Sidpara AM et al (2019) Length-wise tool wear compensation for micro electric discharge drilling of blind holes. Measurement 134:888-896 [95] Pan Z, Wang L, Fang Q et al (2022) Study on tool deflection compensation method based on cutting force observer for orbital drilling of CFRP/Ti stacks. J Manuf Process 75:450-460 [96] Sun Z, Xu S, Jiao J et al (2022) Surface deformation errors and self-adaptive compensation for microstructured surface generation of titanium alloys. Int J Mech Sci 236:107736. https://doi.org/10.1016/j.ijmecsci.2022.107736 [97] Li W, Wang L, Yu G (2021) Force-induced deformation prediction and flexible error compensation strategy in flank milling of thin-walled parts. J Mater Process Technol 297:117258. https://doi.org/10.1016/j.jmatprotec.2021.117258 [98] Si H, Wang L (2019) Error compensation in the five-axis flank milling of thin-walled workpieces. Proc Inst Mech Eng Part B-J Eng Manuf 233(4):1224-1234 [99] Huang P, Wu X, To S et al (2020) Deterioration of form accuracy induced by servo dynamics errors and real-time compensation for slow tool servo diamond turning of complex-shaped optics. Int J Mach Tools Manuf 154:103556. https://doi.org/10.1016/j.ijmachtools.2020.103556 [100] Yeo WJ, Choi HJ, Jeon M et al (2024) Enhancement of optical surface quality based on real-time compensation of temperature-driven thermal errors in diamond turning. J Manuf Process 110:424-433 [101] Irino N, Kobayashi A, Shinba Y et al (2023) Digital twin based accuracy compensation. CIRP Ann 72(1):345-348 [102] Zhou W, Kang M, Guo H (2023) Tool radius compensation algorithm for slow tool servo turning on complex surface. Mech Sci Technol Aerosp Eng 42(5):736-746 [103] Nagayama K, Yan J (2021) Deterministic error compensation for slow tool servo-driven diamond turning of freeform surface with nanometric form accuracy. J Manuf Process 64:45-57 [104] Guo H, Kang M, Zhou W (2022) Optimization of tool compensation algorithm for slow tool servo turning. Surf Technol 51(4):308-316 [105] Ma H, Li Z, Wang Y et al (2021) Research on tool compensation algorithm of conic curve. Modul Mach Tool Autom Manuf Tech 3:26-30 [106] Wu L, Liu H, Zong W (2023) Analysis and compensation for the dominant tool error in ultra-precision diamond ball-end milling. J Mater Process Technol 318:118034. https://doi.org/10.1016/j.jmatprotec.2023.118034 [107] Liang R, Yu Y, Chen J et al (2024) Tool path generation with a uniform residual error distribution considering tool contour error for ultra-precision diamond turning. J Manuf Process 115:466-480 [108] Checchi A, Costa GD, Merrild CH et al (2019) Offline tool trajectory compensation for cutting forces induced errors in a portable machine tool. Proced CIRP 82:527-531 [109] Yu DP, Hong GS, Wong YS (2012) Profile error compensation in fast tool servo diamond turning of micro-structured surfaces. Int J Mach Tools Manuf 52(1):13-23 [110] Peng S, Ding H, Tang J et al (2020) Collaborative machine tool settings compensation considering both tooth flank geometrical and physical performances for spiral bevel and hypoid gears. J Manuf Process 54:169-179 [111] Tang Z, Zhou Y, Wang S et al (2022) An innovative geometric error compensation of the multi-axis CNC machine tools with non-rotary cutters to the accurate worm grinding of spur face gears. Mech Mach Theory 169:104664. https://doi.org/10.1016/j.mechmachtheory.2021.104664 [112] Zhang H, Xiang S, Wu C et al (2024) Optimal proportion compensation method of key geometric errors for five-axis machine tools considering multiple-direction coupling effects. J Manuf Process 110:447-461 [113] Cai A, Song R, Du J et al (2018) Method on five-axis CNC machine 3D cutter compensation and post-process. J Chang’an Univ 38(1):120-126 [114] Li ZL, Zhu LM (2019) Compensation of deformation errors in five-axis flank milling of thin-walled parts via tool path optimization. Precis Eng 55:77-87 [115] Dittrich MA, Uhlich F (2020) Self-optimizing compensation of surface deviations in 5-axis ball-end milling based on an enhanced description of cutting conditions. CIRP J Manuf Sci Technol 31:224-232 [116] Zhu WL, Yang X, Duan F et al (2019) Design and adaptive terminal sliding mode control of a fast tool servo system for diamond machining of freeform surfaces. IEEE Trans Ind Electron 66(6):4912-4922 [117] K?hler J, Seibel A (2015) FTS-based face milling of micro structures. Proced CIRP 28:58-63 [118] Basovich S, Arogeti S (2021) Identification and robust control for regenerative chatter in internal turning with simultaneous compensation of machining error. Mech Syst Signal Process 149:107208. https://doi.org/10.1016/j.ymssp.2020.107208 [119] Fan Y, Fan KC, Huang Y (2024) Modeling and compensation of enhanced volumetric error of machine tools containing crosstalk errors. Precis Eng 86:252-264 [120] Kidani S, Irino N, Maruyama S et al (2020) Design and analysis of a built-in yaw measurement system using dual linear scales for automatic machine tool error compensation. J Manuf Process 56:1286-1293 [121] Denkena B, Boujnah H (2018) Feeling machines for online detection and compensation of tool deflection in milling. CIRP Ann 67(1):423-426 [122] Yang J, Rao P, Chen B et al (2020) Form error on-line estimation and compensation for non-circular turning process. Int J Mech Sci 184:105847. https://doi.org/10.1016/j.ijmecsci.2020.105847 [123] Zhang J, Cao Y, Chang X et al (2023) Intelligent toolholder system of NC machine tool based on vision positioning. Meas Control Technol 42(8):56-63 [124] Behrendt T, Zein A, Min S (2012) Development of an energy consumption monitoring procedure for machine tools. CIRP Ann 61(1):43-46 [125] Sihag N, Sangwan KS (2018) Development of a multi-criteria optimization model for minimizing carbon emissions and processing time during machining. Proced CIRP 69:300-305 [126] Bian H, Meng M (2023) Carbon emission reduction potential and reduction strategy of China’s manufacturing industry. J Clean Prod 423:138718. https://doi.org/10.1016/j.jclepro.2023.138718 [127] Hu X, Tian Y, Wang J et al (2024) Energy index for evaluating machine tool energy performance: classification, model and application. J Clean Prod 447:141356. https://doi.org/10.1016/j.jclepro.2024.141356 [128] Sihag N, Sangwan KS (2020) A systematic literature review on machine tool energy consumption. J Clean Prod 275:123125. https://doi.org/10.1016/j.jclepro.2020.123125 [129] Zhou L, Li J, Li F et al (2016) Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod 112:3721-3734 [130] Sar?kaya M, Gupta MK, Tomaz I et al (2022) Resource savings by sustainability assessment and energy modelling methods in mechanical machining process: a critical review. J Clean Prod 370:133403. https://doi.org/10.1016/j.jclepro.2022.133403 [131] Zhao GY, Liu ZY, He Y et al (2017) Energy consumption in machining: classification, prediction, and reduction strategy. Energy 133:142-157 [132] Yoon HS, Kim ES, Kim MS et al (2015) Towards greener machine tools-a review on energy saving strategies and technologies. Renew Sustain Energy Rev 48:870-891 [133] Sun Y, Li Y, Ning J et al (2024) Twelve pathways of carbon neutrality for industrial parks. J Clean Prod 437:140753. https://doi.org/10.1016/j.jclepro.2024.140753 [134] Fu J, Li P, Lin Y et al (2022) Fight for carbon neutrality with state-of-the-art negative carbon emission technologies. Eco-Environ Health 1(4):259-279 [135] Ma S, Ding W, Liu Y et al (2024) Industry 4.0 and cleaner production: a comprehensive review of sustainable and intelligent manufacturing for energy-intensive manufacturing industries. J Clean Prod 467:142879. https://doi.org/10.1016/j.jclepro.2024.142879 [136] Zhao J, Lyu Y (2024) Research on intelligent green manufacturing process monitoring based on target detection and environmental monitoring technology. Therm Sci Eng Prog 53:102766. https://doi.org/10.1016/j.tsep.2024.102766 [137] Hou L, Fan L, Qiu W et al (2015) Measurement and analysis of energy consumption based on operational status of CNC machine tools. Manuf Technol Mach Tool 9:59-62 [138] Viswanathan N, Pitchia Krishnan B, Vimala V et al (2022) Experimental analysis of power consumption in CNC turning centre for various chuck diameters. Mater Today Proc 60:1409-1414 [139] Emec S, Krüger J, Seliger G (2016) Online fault-monitoring in machine tools based on energy consumption analysis and non-invasive data acquisition for improved resource-efficiency. Proced CIRP 40:236-243 [140] Zhang Y, Liu Q, Liu QT (2017) Network-based energy consumption monitoring system of numerical control machine tool. Mod Electron Tech 40(21):124-127 [141] Gu W, Li Z, Li Y et al (2019) Study of energy consumption monitoring system of NC machine tools based on embedded technology. Mach Build Autom 48(6):155-158 [142] Ragai I, Abdalla AS, Abdeltawab H et al (2022) Toward smart manufacturing: analysis and classification of cutting parameters and energy consumption patterns in turning processes. J Manuf Syst 64:626-635 [143] Liu P, Liu F, Qiu H (2017) A novel approach for acquiring the real-time energy efficiency of machine tools. Energy 121:524-532 [144] Hacksteiner M, Duer F, Ayatollahi I et al (2017) Automatic assessment of machine tool energy efficiency and productivity. Proced CIRP 62:317-322 [145] Feng C, Wu Y, Li W et al (2023) Energy consumption optimisation for machining processes based on numerical control programs. Adv Eng Inform 57:102101. https://doi.org/10.1016/j.aei.2023.102101 [146] Zhang X, Yu T, Dai Y et al (2020) Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. Int J Mech Sci 178:105628. https://doi.org/10.1016/j.ijmecsci.2020.105628 [147] He Y, Wu P, Wang Y et al (2020) An OPC UA based framework for predicting energy consumption of machine tools. Proced CIRP 90:568-572 [148] Mourtzis D, Vlachou E, Milas N et al (2016) Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks. Proced CIRP 57:637-642 [149] Pawanr S, Garg GK, Routroy S (2022) A novel approach to model the energy consumption of machine tools for machining cylindrical parts. J Manuf Process 84:28-42 [150] Jia S, Wang S, Cai W et al (2024) Energy-saving strategy and method of spindle deceleration during no-load operation of machine tools for energy lean management. Energy Convers Manag X 22:100566. https://doi.org/10.1016/j.ecmx.2024.100566 [151] Azlan ATBNN, Mativenga PT, Zhu M et al (2023) Industry 4.0 energy monitoring system for multiple production machines. Proced CIRP 120:613-618 [152] Cheng L, Zhang H, Yan W et al (2018) Application in carbon emission prediction of machining based on GA-WNN. Mach Des Manuf 5:137-140 [153] Zhang H, Wang Z, Yan W et al (2022) Research on multidimensional-feature data-driven for carbon emission prediction of CNC turning process. Mach Des Manuf 11:22-26 [154] Jiang Z (2022) Research on carbon emission optimization and greenness evaluation method in CNC machining process. Dissertation, Harbin Institute of Technology [155] Yin R, Li F (2022) Research on low carbonization tool path and parameters of NC milling under cost constraint. Mach Des Manuf 11:117-121 [156] Aydin K (2024) Investigation of optimal machining Monel 400 superalloy considering carbon emissions using FEM, regression and ANN methods. J Clean Prod 447:141616. https://doi.org/10.1016/j.jclepro.2024.141616 [157] Ross NS, Rai R, Ananth MBJ et al (2023) Carbon emissions and overall sustainability assessment in eco-friendly machining of Monel-400 alloy. Sustain Mater Technol 37:e00675. https://doi.org/10.1016/j.susmat.2023.e00675 [158] Hu Y, Man Y (2023) Energy consumption and carbon emissions forecasting for industrial processes: status, challenges and perspectives. Renew Sustain Energy Rev 182:113405. https://doi.org/10.1016/j.rser.2023.113405 [159] Zhu S, Jiang Z, Zhang H et al (2017) A carbon efficiency evaluation method for manufacturing process chain decision-making. J Clean Prod 148:665-680 [160] Zhang C, Ji W (2019) Digital twin-driven carbon emission prediction and low-carbon control of intelligent manufacturing job-shop. Proced CIRP 83:624-629 [161] Shamsuzzaman M, Shamsuzzoha A, Maged A et al (2021) Effective monitoring of carbon emissions from industrial sector using statistical process control. Appl Energy 300:117352. https://doi.org/10.1016/j.apenergy.2021.117352 [162] Ferreira GDS, Mateus GR, Ravetti MG (2024) Minimizing carbon emission in hybrid flow shop scheduling: a comparative analysis of flow-based and set partitioning formulations. Proced Comput Sci 232:2831-2840 |
| No related articles found! |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
Tel: 86-21-66135510
Fax: 86-21-66132736
E-mail: aim@oa.shu.edu.cn