Green machining technology and application driven by digital intelligence: a review

Abstract

With the continuous advancement of science and technology, alongside the increasing significant attention within the manufacturing industry, high-performance demands are placed on advanced equipment and components because of extreme temperatures, heavy impact loads, and other challenging operating conditions. The importance of resource conservation and environmental preservation is becoming more widely recognized. This paper reviews green machining technology, driven by digital intelligence. Initially, the background of green machining powered by digital technologies is introduced, focusing on digitalization, intelligence, and sustainability as key factors for improving machining efficiency, enhancing product performance, and minimizing both energy consumption and environmental pollution. Subsequently, the paper elaborates on the current research and development in digital intelligence-driven green machining technologies, highlighting four critical areas: smart toolholders, minimal quantity lubrication (MQL), machine tool compensation, machine tool energy consumption monitoring, and intelligent carbon emission control. Lastly, the future trends and challenges in these technologies are discussed, with an outlook on the growing importance of green machining in response to technological advancements and evolving market demands.

Keywords

Digital intelligent drives / Green machining / Smart toolholder / Minimal quantity lubrication (MQL) / Machine tool compensation / Machine tool energy monitoring / Intelligent carbon emission control

References

[1] YadavS, SamadhiyaA, KumarA, et al.. 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, 2023, 197. 107094

[2] LeiMK, MiaoWL, ZhuXP, et al.. High-performance manufacturing enabling integrated design and processing of products: a case study of metal cutting. CIRP J Manuf Sci Technol, 2021, 35: 178-192.

[3] AgrawalR, KumarN, ParvezK, et al.. Optimization of cutting force via variable feed rate in dry turning lathe of AISI 304. Mater Today Proc, 2022, 64: 1182-1187.

[4] UscaÜA, UzunM, ŞapS, et al.. Determination of machinability metrics of AISI 5140 steel for gear manufacturing using different cooling/lubrication conditions. J Mater Res Technol, 2022, 21: 893-904.

[5] LiX, HuangZ, NingW. Intelligent manufacturing quality prediction model and evaluation system based on big data machine learning. Comput Electr Eng, 2023, 111. 108904

[6] ZhouW, ZhangY, LiX. Artificial intelligence, green technological progress, energy conservation, and carbon emission reduction in China: an examination based on dynamic spatial Durbin modeling. J Clean Prod, 2024, 446. 141142

[7] HopkinsC, HosseiniA. A review of developments in the fields of the design of smart cutting tools, wear monitoring, and sensor innovation. IFAC-PapersOnLine, 2019, 52(10): 352-357.

[8] ChengYN, DingY, GaiXY, et al.. Tool wear monitoring technology and its application in heavy cutting. J Harbin Univ Sci Technol, 2022, 27(1): 79-91

[9] LiuQ, ZhangHJ, LiuXL, et al.. A review of research on intelligent cutting tools. J Mech Eng, 2021, 57(21): 248-268.

[10] DengCJ, CaoHJ, ZhangJ, et al.. Research progress of intelligent turning tools for cutting condition monitoring and process control. Mech Sci Technol Aerosp Eng, 2023, 2023: 1-17

[11] PimenovDY, GuptaMK, da SilvaLRR, et al.. Application of measurement systems in tool condition monitoring of milling: a review of measurement science approach. Measurement, 2022, 199. 111503

[12] WangW, LiuW, ZhangY, et al.. Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: a review. Chin J Aeronaut, 2024, 37(4): 23-53.

[13] ChengK, NiuZC, WangRC, et al.. Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin J Mech Eng, 2017, 30(5): 1162-1176.

[14] GuoH, HuKY, YanXG, et al.. Wireless monitoring of wear of the vibration self-sensing tool. J Xi’an Jiaotong Univ, 2022, 56(11): 1-10

[15] MöhringHC, NguyenQP, KuhlmannA, et al.. Intelligent tools for predictive process control. Proced CIRP, 2016, 57: 539-544.

[16] LiY, LiL, ChenN, et al.. Design and research on tool wear status monitoring ring based on MEMS sensors. Tool Technol, 2022, 56(7): 3-7

[17] XueZ, LiL, WuY, et al.. Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring. Measurement, 2023, 223. 113787

[18] ChenQW, ChenWF, CuiRF, et al.. Design and experimental study of vibration measuring tool handle based on wireless transmission. Mach Build Autom, 2020, 49(3): 26-29

[19] ZhouCA, ZhaoY, ZanZL, et al.. Wireless vibration sensing toolholder system for milling tool condition monitoring. Tool Eng, 2020, 54(4): 28-31

[20] MauthnerG, VotrubaW, RamsauerC, et al.. Development of a CAM-in-the-loop system for cutting parameter optimization using an instrumented toolholder. Procedia CIRP, 2022, 107: 326-331.

[21] ChenX, ChengK, WangC, et al.. Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting. Manuf Lett, 2014, 2(4): 112-117.

[22] TsengLW, HuTS, HuYC, et al.. 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, 2021, 99190.

[23] WangC, RakowskiR, ChengK, et al.. Design and analysis of a piezoelectric film embedded smart cutting tool. Proc Inst Mech Eng Part B-J Eng Manuf, 2013, 227(2): 254-260.

[24] XiaoC, DingH, ChengK, et al.. Design of an innovative smart turning tool with application to real-time cutting force measurement. Proc Inst Mech Eng Part B-J Eng Manuf, 2015, 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] ChenYL, ChenF, LiZ, et al.. Three-axial cutting force measurement in micro/nano-cutting by utilizing a fast tool servo with a smart toolholder. CIRP Ann, 2021, 70(1): 33-36.

[27] HatefiS, SmithF. 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, 2023, 14101857.

[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] RizalM, GhaniJA, NuawiMZ, et al.. Development and testing of an integrated rotating dynamometer on toolholder for milling process. Mech Syst Signal Process, 2015, 52(53): 559-576.

[30] RizalM, GhaniJA, Che HaronCH, et al.. Design and construction of a strain gauge-based dynamometer for a 3-axis cutting force measurement in turning process. J Mech Eng Sci, 2018, 12(4): 4072-4087.

[31] SongW, ZhangJ, XiaoG, et al.. Accurate cutting-force measurement with smart toolholder in lathe. Sensors, 2023, 2394419.

[32] PanzeraTH, SouzaPR, RubioJCC, et al.. Development of a three-component dynamometer to measure turning force. Int J Adv Manuf Technol, 2012, 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] MaL, MelkoteSN, MorehouseJB, et al.. Thin-film PVDF sensor-based monitoring of cutting forces in peripheral end milling. J Dyn Syst Meas Control, 2012, 1345. 051014

[35] LuoM, LuoH, AxinteD, et al.. A wireless instrumented milling cutter system with embedded PVDF sensors. Mech Syst Signal Process, 2018, 110: 556-568.

[36] WangC, ChengK, MintonT, et al.. Development of a novel surface acoustic wave (SAW) based smart cutting tool in machining hybrid dissimilar material. Manuf Lett, 2014, 2(2): 21-25.

[37] KimJ, LeeS, ChunH, et al.. Compact curved-edge displacement sensor-embedded spindle system for machining process monitoring. J Manuf Process, 2021, 64: 1255-1260.

[38] LiWD, DingH, ChengK. Research on force measuring smart cutting tool based on surface acoustic wave resonator principle. Mach Build Autom, 2014, 43(5): 46-50

[39] HuangJ, PhamDT, JiC, et al.. Smart cutting tool integrated with optical fiber sensors for cutting force measurement in turning. IEEE Trans Instrum Meas, 2020, 69(4): 1720-1727.

[40] TotisG, SortinoM. Development of a modular dynamometer for triaxial cutting force measurement in turning. Int J Mach Tools Manuf, 2011, 51(1): 34-42.

[41] TotisG, WirtzG, SortinoM, et al.. Development of a dynamometer for measuring individual cutting edge forces in face milling. Mech Syst Signal Process, 2010, 24(6): 1844-1857.

[42] ShuS, ChengK, DingH, et al.. An innovative method to measure the cutting temperature in process by using an internally cooled smart cutting tool. J Manuf Sci Eng, 2013, 1356. 061014

[43] ShuSR, DingH, ChenSJ, et al.. FEM-based design and analysis of a smart cutting tool with internal cooling for cutting temperature measurement and control. Appl Mech Mater, 2012, 217(219): 1874-1879.

[44] YinZ, HaoX, ChenW, et al.. Study on a new type of cutting temperature sensing smart tool. J Mech Eng, 2023, 59(1): 242-248.

[45] SugitaN, IshiiK, FurushoT, et al.. Cutting temperature measurement by a micro-sensor array integrated on the rake face of a cutting tool. CIRP Ann, 2015, 64(1): 77-80.

[46] CuiY, WangH, CaoK, et al.. Preparation and application of nanocomposite thin-film temperature sensor during the milling process. Mater, 2022, 15207106.

[47] ZhangP, GaoD, LuY, et al.. A novel smart toolholder with embedded force sensors for milling operations. Mech Syst Signal Process, 2022, 175. 109130

[48] ZhangP, GaoD, LuY, et al.. Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network. Measurement, 2022, 199. 111520

[49] XieZ, LuY, ChenX. A multi-sensor integrated smart toolholder for cutting process monitoring. Int J Adv Manuf Technol, 2020, 110(3/4): 853-864.

[50] SchusterA, RentzschH, IhlenfeldtS. Energy self-sufficient, multi-sensory toolholder for sensitive monitoring of milling processes. Proced CIRP, 2023, 117: 80-85.

[51] ZhangJ, KangX, YeZ, et al.. Development and testing of a wireless smart toolholder with multi-sensor fusion. Front Mech Eng, 2023, 18455.

[52] ZhangJ, HuangX, KangX, et al.. Energy field-assisted high-speed dry milling green machining technology for difficult-to-machine metal materials. Front Mech Eng, 2023, 18228.

[53] AliSH, YaoY, WuB, et al.. Recent developments in MQL machining of aeronautical materials: a comparative review. Chin J Aeronaut, 2024, 381. 102918

[54] HeT, LiuN, XiaH, et al.. Progress and trend of minimum quantity lubrication (MQL): a comprehensive review. J Clean Prod, 2023, 386. 135809

[55] ArulK, MohanavelV, Raj KumarS, et al.. Investigation of machining attributes on machining of alloys under nano fluid MQL environment: a review. Mater Today Proc, 2022, 59: 1312-1318.

[56] JunankarAA, ParateSR, DethePK, et al.. A review: enhancement of turning process performance by effective utilization of hybrid nanofluid and MQL. Mater Today Proc, 2021, 38: 44-47.

[57] SwainS, PanigrahiI, Kumar SahooA, et al.. Study on machining performances during hard turning process using vibration signal under MQL environment: a review. Mater Today Proc, 2019, 18: 3539-3545.

[58] BalasuadhakarA, ThirumalaiKS, AhmedF. A review on the role of nanoparticles in MQL machining. Mater Today Proc, 2023, 72: 2828-2832.

[59] SharmaAK, TiwariAK, DixitAR. Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: a comprehensive review. J Clean Prod, 2016, 127: 1-18.

[60] BaiX, DongL, LiC, et al.. The experimental research of lubrication performance in nanofluid minimum quantity lubrication (MQL) milling. Modul Mach Tool Autom Manuf Tech, 2018, 4: 15-18

[61] WuS, LiuG, ZhangW, et al.. High-speed milling of hardened steel under minimal quantity lubrication with liquid nitrogen. J Manuf Process, 2023, 95: 351-368.

[62] UsluerE, EmiroğluU, YapanYF, et al.. Investigation on the effect of hybrid nanofluid in MQL condition in orthogonal turning and a sustainability assessment. Sustain Mater Technol, 2023, 36. e00618

[63] MakhesanaMA, PatelKM, KrolczykGM, et al.. 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, 2023, 41: 225-238.

[64] CöngerDB, YapanYF, EmiroğluU, et al.. Influence of singular and dual MQL nozzles on sustainable milling of Al6061-T651 in different machining environments. J Manuf Process, 2024, 109: 524-536.

[65] SzczotkarzN, MrugalskiR, MarudaRW, et al.. Cutting tool wear in turning 316L stainless steel in the conditions of minimized lubrication. Tribol Int, 2021, 156. 106813

[66] ŞirinE. Evaluation of tribological performance of MQL technique combined with LN₂, CO₂, N₂ ecological cooling/lubrication techniques when turning of Hastelloy C22 superalloy. Tribol Int, 2023, 188. 108786

[67] KharkaV, MujumdarS, ShuklaS. Study on helical milling of SS 304 with small diameter tools under the influence of minimum quantity lubrication (MQL). Manuf Lett, 2023, 35: 1312-1317.

[68] DongL, LiC, ZhouF, et al.. Temperature of the 45 steel in the minimum quantity lubricant milling with different biolubricants. Int J Adv Manuf Technol, 2021, 113(9/10): 2779-2790.

[69] LiB, LiC, ZhangY, et al.. 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, 2016, 29(4): 1084-1095.

[70] MuazM, ChoudhurySK. Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel. Measurement, 2019, 138: 557-569.

[71] XuWH, LiCH, ZhangYB, et al.. Research progress and application of electrostatic atomization minimum quantity lubrication. J Mech Eng, 2023, 59(7): 110-138.

[72] ZhangHP, RenY, XueFG, et al.. Cryogenic minimum quantity lubrication processing technology. J Harbin Univ Sci Technol, 2019, 24(2): 38-44

[73] KongXY, YuanSM, ZhuGY, et al.. Optimization of process parameters of minimum quantity lubrication system based on grey relation analysis. Aeronaut Manuf Technol, 2021, 64(6): 73-81

[74] ZhangG, ChenH, XiaoG, et al.. Effect of SiC nanofluid minimum quantity lubrication on the performance of the ceramic tool in cutting hardened steel. J Manuf Process, 2022, 84: 539-554.

[75] FragelliRL, SanchezLEA, Ingraci NetoRR, et al.. Refrigeration capacity of silver nanofluids under electrohydrodynamic effect oriented to heat removal in machining process. Exp Therm Fluid Sci, 2018, 96: 11-19.

[76] EltaggazA, HegabH, DeiabI, et al.. Hybrid nano-fluid-minimum quantity lubrication strategy for machining austempered ductile iron (ADI). Int J Interact Des Manuf, 2018, 12(4): 1273-1281.

[77] RahmatiB, SarhanAAD, SayutiM. Investigating the optimum molybdenum disulfide (MoS2) nanolubrication parameters in CNC milling of Al6061-T6 alloy. Int J Adv Manuf Technol, 2014, 70(5/8): 1143-1155.

[78] DuchosalA, WerdaS, SerraR, et al.. Numerical modeling and experimental measurement of MQL impingement over an insert in a milling tool with inner channels. Int J Mach Tools Manuf, 2015, 94: 37-47.

[79] DuF, ZhouT, TianP, et al.. Cutting performance and cutting fluid infiltration characteristics into tool-chip interface during MQL milling. Measurement, 2024, 225. 113989

[80] BaiX, JiangJ, LiC, et al.. Tribological performance of different concentrations of Al₂O₃ nanofluids on minimum quantity lubrication milling. Chin J Mech Eng, 2023, 36111.

[81] ShrivastavaA, GangopadhyayS. Evaluation of adequacy of lubricants in MQL micro-drilling by a developed analytical model and experiments. J Manuf Process, 2023, 101: 1592-1604.

[82] HsuYY, WangSS. A new compensation method for geometry errors of five-axis machine tools. Int J Mach Tools Manuf, 2007, 47(2): 352-360.

[83] RameshR, MannanMA, PooAN. Error compensation in machine tools—a review part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf, 2000, 40: 1235-1256.

[84] RameshR, MannanMA, PooAN. Error compensation in machine tools—a review part II: thermal errors. Int J Mach Tools Manuf, 2000, 40: 1257-1284.

[85] ZhangZ, JiangF, LuoM, et al.. Geometric error measuring, modeling, and compensation for CNC machine tools: a review. Chin J Aeronaut, 2024, 37(2): 163-198.

[86] LiY, ZhaoW, LanS, et al.. A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf, 2015, 95: 20-38.

[87] GaoW, IbarakiS, DonmezMA, et al.. Machine tool calibration: measurement, modeling, and compensation of machine tool errors. Int J Mach Tools Manuf, 2023, 187. 104017

[88] LaghariRA, MekidS. Comprehensive approach toward IIoT based condition monitoring of machining processes. Measurement, 2023, 217. 113004

[89] QuanL, ZhaoW. A review on positioning uncertainty in motion control for machine tool feed drives. Precis Eng, 2024, 88: 428-448.

[90] XiaoQB, WanM, YangY, et al.. Pre-compensation of contour errors for five-axis machine tools through constructing a model reference adaptive control. Mech Mach Theory, 2023, 183. 105258

[91] GuoS, SiZ, SaR, et al.. Geometric error modeling and decoupling identification of rotary axis of five-axis machine tool based on spatial trajectory planning. Measurement, 2024, 236. 114887

[92] MaJ, ZhangY, JiaoF, et al.. Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6Al-4V. J Manuf Process, 2024, 124: 1519-1540.

[93] BellottiM, de Eguilior CaballeroJR, QianJ, et al.. 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, 2021, 288. 116852

[94] MalayathG, KattaS, SidparaAM, et al.. Length-wise tool wear compensation for micro electric discharge drilling of blind holes. Measurement, 2019, 134: 888-896.

[95] PanZ, WangL, FangQ, et al.. Study on tool deflection compensation method based on cutting force observer for orbital drilling of CFRP/Ti stacks. J Manuf Process, 2022, 75: 450-460.

[96] SunZ, XuS, JiaoJ, et al.. Surface deformation errors and self-adaptive compensation for microstructured surface generation of titanium alloys. Int J Mech Sci, 2022, 236. 107736

[97] LiW, WangL, YuG. Force-induced deformation prediction and flexible error compensation strategy in flank milling of thin-walled parts. J Mater Process Technol, 2021, 297. 117258

[98] SiH, WangL. Error compensation in the five-axis flank milling of thin-walled workpieces. Proc Inst Mech Eng Part B-J Eng Manuf, 2019, 233(4): 1224-1234.

[99] HuangP, WuX, ToS, et al.. 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, 2020, 154. 103556

[100] YeoWJ, ChoiHJ, JeonM, et al.. Enhancement of optical surface quality based on real-time compensation of temperature-driven thermal errors in diamond turning. J Manuf Process, 2024, 110: 424-433.

[101] IrinoN, KobayashiA, ShinbaY, et al.. Digital twin based accuracy compensation. CIRP Ann, 2023, 72(1): 345-348.

[102] ZhouW, KangM, GuoH. Tool radius compensation algorithm for slow tool servo turning on complex surface. Mech Sci Technol Aerosp Eng, 2023, 42(5): 736-746

[103] NagayamaK, YanJ. Deterministic error compensation for slow tool servo-driven diamond turning of freeform surface with nanometric form accuracy. J Manuf Process, 2021, 64: 45-57.

[104] GuoH, KangM, ZhouW. Optimization of tool compensation algorithm for slow tool servo turning. Surf Technol, 2022, 51(4): 308-316

[105] MaH, LiZ, WangY, et al.. Research on tool compensation algorithm of conic curve. Modul Mach Tool Autom Manuf Tech, 2021, 3: 26-30

[106] WuL, LiuH, ZongW. Analysis and compensation for the dominant tool error in ultra-precision diamond ball-end milling. J Mater Process Technol, 2023, 318. 118034

[107] LiangR, YuY, ChenJ, et al.. Tool path generation with a uniform residual error distribution considering tool contour error for ultra-precision diamond turning. J Manuf Process, 2024, 115: 466-480.

[108] ChecchiA, CostaGD, MerrildCH, et al.. Offline tool trajectory compensation for cutting forces induced errors in a portable machine tool. Proced CIRP, 2019, 82: 527-531.

[109] YuDP, HongGS, WongYS. Profile error compensation in fast tool servo diamond turning of micro-structured surfaces. Int J Mach Tools Manuf, 2012, 52(1): 13-23.

[110] PengS, DingH, TangJ, et al.. Collaborative machine tool settings compensation considering both tooth flank geometrical and physical performances for spiral bevel and hypoid gears. J Manuf Process, 2020, 54: 169-179.

[111] TangZ, ZhouY, WangS, et al.. 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, 2022, 169. 104664

[112] ZhangH, XiangS, WuC, et al.. Optimal proportion compensation method of key geometric errors for five-axis machine tools considering multiple-direction coupling effects. J Manuf Process, 2024, 110: 447-461.

[113] CaiA, SongR, DuJ, et al.. Method on five-axis CNC machine 3D cutter compensation and post-process. J Chang’an Univ, 2018, 38(1): 120-126

[114] LiZL, ZhuLM. Compensation of deformation errors in five-axis flank milling of thin-walled parts via tool path optimization. Precis Eng, 2019, 55: 77-87.

[115] DittrichMA, UhlichF. 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, 2020, 31: 224-232.

[116] ZhuWL, YangX, DuanF, et al.. Design and adaptive terminal sliding mode control of a fast tool servo system for diamond machining of freeform surfaces. IEEE Trans Ind Electron, 2019, 66(6): 4912-4922.

[117] KöhlerJ, SeibelA. FTS-based face milling of micro structures. Proced CIRP, 2015, 28: 58-63.

[118] BasovichS, ArogetiS. Identification and robust control for regenerative chatter in internal turning with simultaneous compensation of machining error. Mech Syst Signal Process, 2021, 149. 107208

[119] FanY, FanKC, HuangY. Modeling and compensation of enhanced volumetric error of machine tools containing crosstalk errors. Precis Eng, 2024, 86: 252-264.

[120] KidaniS, IrinoN, MaruyamaS, et al.. Design and analysis of a built-in yaw measurement system using dual linear scales for automatic machine tool error compensation. J Manuf Process, 2020, 56: 1286-1293.

[121] DenkenaB, BoujnahH. Feeling machines for online detection and compensation of tool deflection in milling. CIRP Ann, 2018, 67(1): 423-426.

[122] YangJ, RaoP, ChenB, et al.. Form error on-line estimation and compensation for non-circular turning process. Int J Mech Sci, 2020, 184. 105847

[123] ZhangJ, CaoY, ChangX, et al.. Intelligent toolholder system of NC machine tool based on vision positioning. Meas Control Technol, 2023, 42(8): 56-63

[124] BehrendtT, ZeinA, MinS. Development of an energy consumption monitoring procedure for machine tools. CIRP Ann, 2012, 61(1): 43-46.

[125] SihagN, SangwanKS. Development of a multi-criteria optimization model for minimizing carbon emissions and processing time during machining. Proced CIRP, 2018, 69: 300-305.

[126] BianH, MengM. Carbon emission reduction potential and reduction strategy of China’s manufacturing industry. J Clean Prod, 2023, 423. 138718

[127] HuX, TianY, WangJ, et al.. Energy index for evaluating machine tool energy performance: classification, model and application. J Clean Prod, 2024, 447. 141356

[128] SihagN, SangwanKS. A systematic literature review on machine tool energy consumption. J Clean Prod, 2020, 275. 123125

[129] ZhouL, LiJ, LiF, et al.. Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod, 2016, 112: 3721-3734.

[130] SarıkayaM, GuptaMK, TomazI, et al.. Resource savings by sustainability assessment and energy modelling methods in mechanical machining process: a critical review. J Clean Prod, 2022, 370. 133403

[131] ZhaoGY, LiuZY, HeY, et al.. Energy consumption in machining: classification, prediction, and reduction strategy. Energy, 2017, 133: 142-157.

[132] YoonHS, KimES, KimMS, et al.. Towards greener machine tools – a review on energy saving strategies and technologies. Renew Sustain Energy Rev, 2015, 48: 870-891.

[133] SunY, LiY, NingJ, et al.. Twelve pathways of carbon neutrality for industrial parks. J Clean Prod, 2024, 437. 140753

[134] FuJ, LiP, LinY, et al.. Fight for carbon neutrality with state-of-the-art negative carbon emission technologies. Eco-Environ Health, 2022, 1(4): 259-279.

[135] MaS, DingW, LiuY, et al.. Industry 4.0 and cleaner production: a comprehensive review of sustainable and intelligent manufacturing for energy-intensive manufacturing industries. J Clean Prod, 2024, 467142879.

[136] ZhaoJ, LyuY. Research on intelligent green manufacturing process monitoring based on target detection and environmental monitoring technology. Therm Sci Eng Prog, 2024, 53. 102766

[137] HouL, FanL, QiuW, et al.. Measurement and analysis of energy consumption based on operational status of CNC machine tools. Manuf Technol Mach Tool, 2015, 9: 59-62

[138] ViswanathanN, Pitchia KrishnanB, VimalaV, et al.. Experimental analysis of power consumption in CNC turning centre for various chuck diameters. Mater Today Proc, 2022, 60: 1409-1414.

[139] EmecS, KrügerJ, SeligerG. Online fault-monitoring in machine tools based on energy consumption analysis and non-invasive data acquisition for improved resource-efficiency. Proced CIRP, 2016, 40: 236-243.

[140] ZhangY, LiuQ, LiuQT. Network-based energy consumption monitoring system of numerical control machine tool. Mod Electron Tech, 2017, 40(21): 124-127

[141] GuW, LiZ, LiY, et al.. Study of energy consumption monitoring system of NC machine tools based on embedded technology. Mach Build Autom, 2019, 48(6): 155-158

[142] RagaiI, AbdallaAS, AbdeltawabH, et al.. Toward smart manufacturing: analysis and classification of cutting parameters and energy consumption patterns in turning processes. J Manuf Syst, 2022, 64: 626-635.

[143] LiuP, LiuF, QiuH. A novel approach for acquiring the real-time energy efficiency of machine tools. Energy, 2017, 121: 524-532.

[144] HacksteinerM, DuerF, AyatollahiI, et al.. Automatic assessment of machine tool energy efficiency and productivity. Proced CIRP, 2017, 62: 317-322.

[145] FengC, WuY, LiW, et al.. Energy consumption optimisation for machining processes based on numerical control programs. Adv Eng Inform, 2023, 57. 102101

[146] ZhangX, YuT, DaiY, et al.. Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. Int J Mech Sci, 2020, 178. 105628

[147] HeY, WuP, WangY, et al.. An OPC UA based framework for predicting energy consumption of machine tools. Proced CIRP, 2020, 90: 568-572.

[148] MourtzisD, VlachouE, MilasN, et al.. Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks. Proced CIRP, 2016, 57: 637-642.

[149] PawanrS, GargGK, RoutroyS. A novel approach to model the energy consumption of machine tools for machining cylindrical parts. J Manuf Process, 2022, 84: 28-42.

[150] JiaS, WangS, CaiW, et al.. Energy-saving strategy and method of spindle deceleration during no-load operation of machine tools for energy lean management. Energy Convers Manag X, 2024, 22. 100566

[151] AzlanATBNN, MativengaPT, ZhuM, et al.. Industry 4.0 energy monitoring system for multiple production machines. Proced CIRP, 2023, 120: 613-618.

[152] ChengL, ZhangH, YanW, et al.. Application in carbon emission prediction of machining based on GA-WNN. Mach Des Manuf, 2018, 5: 137-140

[153] ZhangH, WangZ, YanW, et al.. Research on multidimensional-feature data-driven for carbon emission prediction of CNC turning process. Mach Des Manuf, 2022, 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] YinR, LiF. Research on low carbonization tool path and parameters of NC milling under cost constraint. Mach Des Manuf, 2022, 11: 117-121

[156] AydinK. Investigation of optimal machining Monel 400 superalloy considering carbon emissions using FEM, regression and ANN methods. J Clean Prod, 2024, 447. 141616

[157] RossNS, RaiR, AnanthMBJ, et al.. Carbon emissions and overall sustainability assessment in eco-friendly machining of Monel-400 alloy. Sustain Mater Technol, 2023, 37. e00675

[158] HuY, ManY. Energy consumption and carbon emissions forecasting for industrial processes: status, challenges and perspectives. Renew Sustain Energy Rev, 2023, 182. 113405

[159] ZhuS, JiangZ, ZhangH, et al.. A carbon efficiency evaluation method for manufacturing process chain decision-making. J Clean Prod, 2017, 148: 665-680.

[160] ZhangC, JiW. Digital twin-driven carbon emission prediction and low-carbon control of intelligent manufacturing job-shop. Proced CIRP, 2019, 83: 624-629.

[161] ShamsuzzamanM, ShamsuzzohaA, MagedA, et al.. Effective monitoring of carbon emissions from industrial sector using statistical process control. Appl Energy, 2021, 300. 117352

[162] FerreiraGDS, MateusGR, RavettiMG. Minimizing carbon emission in hybrid flow shop scheduling: a comparative analysis of flow-based and set partitioning formulations. Proced Comput Sci, 2024, 232: 2831-2840.

Funding

National Key R&D Program of China(2022YFB3206700)

Graduate Research and Innovation Foundation of Chongqing, China(CYB23017)

RIGHTS & PERMISSIONS

Shanghai University and Periodicals Agency of Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature