Research Article
Measuring and calculating the computer numerical control lathe’s cutting power and total electric power consumption based on servo parameters
Advances in Mechanical Engineering 2017, Vol. 9(9) 1–11 Ó The Author(s) 2017 DOI: 10.1177/1687814017723293 journals.sagepub.com/home/ade
Zhao-hui Liu1,2, Wei-min Zhang1,3, Liang-bin Liu1,3 and Zhong-yue Xiao1,2
Abstract Improving the energy efficiency of machine tools is the goal of sustainable development of the mechanical manufacturing industry. The key is to measure or calculate energy efficiency conveniently and accurately. This article proposes a method of calculating power by reading the computer numerical control system’s servo parameters from its currentloop and speed-loop, establishing the relationships between cutting power, total power, and servo parameters, and using these relationships for power prediction. Experiments were done on a computer numerical control lathe. The result shows that this method has high precision, does not need additional sensors, and is independent of cutting process parameters, workpiece material, heat treatment state, and tools. This method may be used for developing a cutting power management module integrated in the computer numerical control system with real-time monitoring power consumption because the computer numerical control system can read servo parameters real time. Keywords Computer numerical control lathe, cutting, power consumption, servo parameters, measurement, calculation
Date received: 12 April 2016; accepted: 10 July 2017 Academic Editor: Ismet Baran
Introduction With the crisis of global climate warming and the increasing of resource prices, the world community is facing the dual challenges of resource depletion and environmental pollution. Governments and industries are paying more and more attention to issues of sustainable development.1–4 The manufacturing industry is an important wealth creator. However, it is a major natural resource and energy consumer as well. Therefore, sustainable manufacturing has become an international focus.4–6 The energy efficiency problem of machine tools has become a major focal point of both manufacturer and customer, and also a hotspot of scientific researches.7,8 In metal removal machine tools, necessary energy is used for material removal, but additional energy loss
exists due to machine auxiliary operations, transmission parts friction, electrical components’ resistance, electromagnetic leakage, and so on.9–11 For computer numerical control (CNC) lathes, milling machines, machining centers, and other machine tools, their spindles and
1
School of Mechanical and Energy Engineering, Tongji University, Shanghai, China 2 School of Mechanical Engineering, Jinggangshan University, Ji’an, China 3 Advanced Manufacturing Technology Center (AMTC), Tongji University, Shanghai, China Corresponding author: Zhao-hui Liu, School of Mechanical and Energy Engineering, Tongji University, Shanghai 201804, China. Email:
[email protected]
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).
2 feed-axes (servo) motion are usually driven by motors, and some of auxiliary operations are driven by the compressed air generated by an electric air compressor. However, usually the energy required for compressed air is a small part of the total electrical energy consumption12,13 Therefore, in this article, energy required to generate compressed air is not considered. The total energy consumption of machine tool can be easily measured by a power meter, which meets the accuracy requirements for cutting parameters optimization.14–16 But, for machine tool improvement research, W Li et al.15 and A Zein16 propose that energy breakdown analysis should be done to measure the energy consumption for each component. It is necessary to monitor the energy consumption of machine components, especially the material removal power (cutting power) to properly perform machine tool energy efficiency research.17,18 In current machine tool’s energy consumption research, the input and output power of electrical components can be measured or calculated by the acquired signals with current, voltage, or power sensors, which can be integrated in CNC system.15,16 However, the measurement and calculation of cutting power is relatively complicated. In current research, there are four typical methods for cutting power measurement and calculation, namely, (1) calculation by the classic theoretical cutting force formula and motor speed, (2) calculation by an improved formula through material removal rate (MRR) and specific energy consumption (SEC), (3) calculation by measuring cutting force and motor speed, and (4) calculation by measuring electrical input power and transmission mechanism efficiency. In method (1), the cutting power can be calculated by multiplying cutting force and cutting speed. Several texts, including the studies of Rao19 and Shaw,20 provide a cutting force formula, which involves with the cutting process parameters (cut-depth, cutting speed, and feed rate), tool angle, workpiece material, and its heat treatment. B Wang et al.21 propose energy optimization using cutting parameters and tool angle. If the process involves variable cutting speed, the speed should also be calculated or measured. This method has a relative large error and cannot perform energy monitoring online real time. In method (2), the cutting power can be calculated by multiplying SEC and MRR. A Zein16 and Yoon et al.22 research shows that the material SECMRR curve varies with different machine tools. To obtain this curve for a material in a certain machine tool, experiments must be done to reduce errors. Due to the different MRR-SEC curves of different machine tools, this method is not suitable for real-time cutting power monitoring unless the curve is obtained. In method (3), the cutting power can be calculated by multiplying cutting force and cutting speed. The key is acquiring torque or force from a dynamometer23 or
Advances in Mechanical Engineering strain gauge.20 Similar to method (1), cutting speed acquisition method must also be considered. Because the strain gauge has a short lifetime and complicated wiring, and the dynamometer is expensive, this method is not suitable for integration in the CNC machine tool system. In method (4), the cutting power can be calculated by multiplying electrical input power and transmission mechanism efficiency. S Hu et al.24 and F Liu et al.25 proposed calculating or acquiring input power using current, voltage, or power sensors and also proposed calculating the transmission efficiency by analyzing the mechanism transmission chain. From these, the cutting power can be calculated. Principles of alternating current (AC) and direct current (DC) motors and the structure of transmission mechanism must be taken into account. The error is closely related to the computational model. This is also not the most suitable method for integration in the CNC system. It is noteworthy that Kim GD and Chu CN,26 X Li et al.,27 and S Aggarwal et al.28 proposed indirect cutting force measurement by monitoring motor current. This method has good accuracy and can also be used for indirect energy measurement. With the development of technology for digital servo controllers, intelligent components integrated with internal and external sensors are widely adopted in CNC machine tools.28,29 The motor current signal and speed controlled by the digital servo can be read out and written by the CNC and PC real time. This is a novel indirect method to measuring and calculating information such as cutting power and input power. The purpose of this article is to propose an indirect method of calculation cutting power and total power using servo parameters related with the motor current and motor rotation speed. This method is independent of external sensors, cut-depth, cutting speed and feed rate, tool, workpiece material, heat treatment state, and it can be easily integrated in the CNC system.
Energy flow of the CNC machine tool and servo control parameters Energy flow of the CNC machine tool The modern CNC machine tool commonly consists of CNC system, servo control system, feedback equipment, machine bed, lubricant, cooling system, and so on. Most machine tools are driven by electrical energy, where a part of the electrical energy is used to drive servo systems, and the remaining power is used drive lubricant, cool, display information, and other auxiliary functions. For most servo systems, the AC is first converted to DC through a power supply module (PSM), then PSM supplies energy to spindle amplifier module (SPM) or the feed-axis (servo) amplifier module (SVM), respectively, driving the spindle motor rotation and feed-axis motor rotation or stalling. Taking
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Table 1. Definition of energy consumption components’ power. Name
Definition
Name
Definition
Pi PSYS PPSML PiSPM PSPML PoSPM PMoSPL PoSP PMeSPL PSP
Machine tool input power CNC system, lubrication running power PSM loss power SPM input power SPM loss power SPM output power Spindle motor loss power Spindle motor output power Spindle output mechanical loss power Spindle output power
PCOOL PiPSM PoSPM PiSVM PSVML PoSVM PMoSVL PoSV PMeSVL PSV
Power of cooling system PSM input power PSM output power SVM input power SVM loss power SVM output power Feed-axis motor loss power Feed-axis motor output power Feed-axis output mechanical loss power Feed-axis output power
CNC: computer numerical control; PSM: power supply module; SPM: spindle amplifier module; SVM: feed-axis (servo) amplifier module.
Figure 1. Energy/power flow of CNC machine tool.
the CINCINNATI HTC-200M lathe center equipped with FANUC 16i as an example, its energy (power) flow is as shown in Figure 1, and the definition of each energy consumption components’ power is shown in Table 1. The relationship of each component power in Figure 1 is shown as equations (1)–(3). A Zein16 and R Sudarsan et al.17 and the experimental results in this article show that PCOOL, PSYS can be considered as constant values, and there is a strong correlation between PSM input power PiSPM and cutting power PSP. In CNC lathe, the main function of the servo axis is to drive the servo axis’ motion or stalling. There is relatively little power used when the servo axis is stalling or moving at a low feed rate. The experimental results in this article also prove that the servo axis energy power PSV is far less than the spindle power PSP in low feed rate cutting, when PiSVM \\ PiSPM; so,
this article considers only the energy consumed by spindle PSP as the effective cutting power Pi = PCOOL + PSYS + PiPSM = PCOOL + PSYS + PiPSML + PoPSM
ð1Þ
= PCOOL + PSYS + PiPSML + PiSPM + PiSVM PiSPM = PSPML + PoSPM = PSPML + PMoSPL + PoSP = PSPML + PMoSPL + PMeSPL + PSP
ð2Þ
PiSVM = PSVML + PoSVM = PSVML + PMoSVL + PoSV ð3Þ = PSVML + PMoSVL + PMeSVL + PSV
Numerical servo system parameters For the closed-loop or semi closed-loop control CNC system, the ‘‘three-loop’’ control method (the currentloop, speed-loop, and position-loop) is mostly adopted.
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Figure 2. The three-loop control method of FANUC CNC system.
Figure 3. Relationship between output power, torque, and rotation speed of FANUC aiI22/7000HV motor.30
A typical FANUC system ‘‘three-loop’’ is shown in Figure 2.29 Current, speed, and other signals of servo motor are monitored by internal and external sensors as parameters stored in servo controller; by altering related parameters, the controller can also adjust to keep the motor’s speed and input current consistent with the set value. In a full digital servo system, the CNC system can bidirectionally communicate with the servo controller to read or write parameters to set value, and it can also communicate with a PC. Taking the FANUC 16i system as an example, the CNC system monitors the motor current and speed in real time through a servo controller, internal, and external sensors and generates the command torque (TCMD) according to those data. The servo controller adjusts its output current based on the TCMD to maintain current-loop control. In a modern CNC system, a permanent magnet synchronous motor (PMSM) is generally used for the feed-axis motor and asynchronous
induction for the spindle motor. The spindle motors adopt a constant torque and constant power control method. The feed-axis motors adopt a constant torque control method. Figure 3 shows the relationship between output power, torque, and rotation speed of FANUC aiI22/7000HV spindle motor.30 Due to the electrical impedance, electromagnetic saturation, and other factors, the relationship between output torque and motor input current appears nonlinear when the current is close to the maximum or 0 values, while it appears linear when in the other ranges. In a FANUC system, the servo parameter SPEED is for the speed value of motor detected through the internal sensor, which is of high accuracy. The unit of SPEED is revolutions per minute (r/min). The servo parameter TCMD is the current regulation command for servo motor, which has an approximately linear relationship with the motor output torque Me. The value of TCMD can be the current value
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Figure 4. Schematic diagram of the measurement system.
Table 2. HTC-200M CNC lathe servo controllers and servo motors.
Spindle Z-axis X-axis
Servo controller
Servo motor
Model
Model
Type
Rated power (kW)
Maximum torque (N m)
aiSP30HV aiSV20/40HV
aiI22/7000HV aiF12/3000HV aiF8/3000HV
A06B-1511-B153 A06B-0245-B101 A06B-0229-B401
22 3 1.6
162 35 29
CNC: computer numerical control.
or the ratio of the current to the maximum current. In this article, the TCMD value is the ratio of the current to the maximum current; its unit is %. Based on this, the nominal power of the motor PST is defined in this article, which is calculated by TCMD and SPEED. The spindle motor adopts constant torque and constant power control method as shown in Figure 3; when the motor speed SPEED 1500, PST is calculated with equation (4) and when SPEED . 1500, with equation (5) PST = Me v =
2p Tmax TCMD SPEED 60
PST = Pmax TCMD
ð4Þ ð5Þ
where Tmax is spindle motor maximum torque and Pmax is spindle motor maximum power.
Experimental research Experimental equipment The HTC-200M CINCINNATI CNC lathe with a FANUC 16i system with servo controllers is used in this experiment. The servo motors are shown in Table 2. This lathe’s spindle motor is connected to spindle through V-belt, and the transmission ratio (ratio of output speed to input speed) is 0.9. The hollow cylindrical workpiece material is ASTM 1045 (Hardness HBS:
205), with an outer diameter of 140 mm, an inner diameter of 90 mm, a length of 180 mm. The experimental scheme is shown in Figure 4, and the field wiring is shown in Figure 5. The TCMD and SPEED value of spindle servo controller are read through FANUC SERVO GUIDE software with a dedicated Personal Computer Memory Card International Association (PCMCIA) card; then, the nominal power PST is calculated according to equation (4) or (5). A Hioki 3390-10 power analyzer is used to measure the input power PiSPM and output power PoSPM of the SPM module and the input power PiSVM of SVM module. The CNC lathe input power Pi and PSM module input power PiPSM are measured using a power transmission sensor (model: CE-P31-84DS5-0.5; Shenzhen SSET Co., Ltd, Shenzhen, China) and acquired using data acquisition cards (model: USB2850; Beijing ART Technology Development Co., Ltd, Beijing, China). The cutting forces are measured using Kistler 9129A dynamometer, and the cutting power PSP is calculated with equation (6). MATLAB software is used to sample and analyze the experiment data PSP =
FY VC 60
ð6Þ
where FY (unit: N) is for the main cutting force and VC is the cutting speed (unit: m/min).
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Figure 5. Measurement field: (a) Machine tool & instruments, (b) Part support, (c) Power measurement wiring, and (d) Power analyzer. Table 3. Cylindrical longitudinal turning test process parameters. No.
ap (mm)
f (mm/r)
VC (m/min)
No.
ap (mm)
f (mm/r)
VC (m/min)
No.
ap (mm)
f (mm/r)
VC (m/min)
1 2 3 4 5 6 7 8 9 10
0.2 0.2 0.2 0.4 0.4 0.4 0.6 0.6 0.6 0.8
0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.2
370 475 550 475 550 370 550 370 475 170
11 12 13 14 15 16 17 18 19 20
0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.6 1.6 1.6
0.3 0.4 0.5 0.2 0.3 0.4 0.5 0.2 0.3 0.4
193 217 240 217 240 170 193 240 217 193
21 22 23 24 25 26 27 28 29 30
1.6 2 2 2 2.25 2.25 2.25 2.5 2.5 2.5
0.5 0.2 0.3 0.4 0.225 0.3 0.375 0.225 0.3 0.375
170 193 170 240 170 205 240 205 240 170
Experiment scheme Cylindrical longitudinal turning experiment. The hollow cylindrical workpiece is clamped with chuck and supported with a center as shown in Figure 5(b). Two kinds of tool inserts are used for the longitudinal turning experiment, where the SECO SMG4Ver1 insert is used for finish turning, and the SANDVIK CoroKey 4235 insert is used for semi-finish and rough cutting. These inserts are installed in the SANDVIK DCLNL2525M
shank. In total, 30 sets of parameters are selected for this experiment, as shown in Table 3. No. 1–9 are finish turning parameters for a Taguchi L9 orthogonal table; No. 10–25 are semi-finish turning parameters for a Taguchi L16 orthogonal table; No. 26–30 are part of rough parameters as a Taguchi L9 orthogonal table. Those parameters with cut-depth ap: 0.2;2.5 mm, feed rate f: 0.1–0.5 mm/r, and cut speed VC: 170–550 m/min
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Table 4. Variable cut-depth turning test process parameters. No.
ap (mm)
f (mm/r)
VC (m/min)
No.
ap (mm)
f (mm/r)
VC (m/min)
31 32 33 34
2.2–0.2 0.2–2.2 2.2–0.2 0.2–2.2
0.1 0.1 0.1 0.2
370 475 550 370
35 36 37 38
2.2–0.2 0.2–2.2 2.2–0.2 0.2–2.2
0.2 0.2 0.3 0.3
475 550 370 475
Figure 6. Variable cut-depth cutting scheme: (a) taper turning and (b) variable cut-depth turning.
record the values of Pi, PiPSM, PiSPM, PoSPM and calculate PST, PSP according to equations (4)–(6). Variable cut-depth cutting experiment. On the same lathe, using the SECO SMG4Ver1 insert and the hollow cylinder workpiece, the taper and variable cut-depth turning are done, respectively, as shown in Figure 6. The cutdepth varies from 0.2 to 2.2 mm, and the other process parameters are show in Table 4.
Results and analysis Correlation analysis of powers According to the cylindrical longitudinal turning experiment scheme, each power under the cutting and unloaded states was recorded, as shown in Table 5. According to the data in Table 5, the relevance can be calculated as shown in Table 6, which shows a good correlation between these power values. Linear regression analysis with PST as the independent variable, and Pi, PiPSM, PiSPM, PoSPM, PSP as the dependent variables, the relational model is built in the form of f(PST) = p1 * PST + p2. The linear equation coefficients p1 and p2, fitting coefficient R2, fit correlation coefficient AdjR2, total deviation SSE and mean square deviation RMSE, which are calculated out through the regression, are shown in Table 7, and the fitting curves are shown in Figure 7. These results indicate that the fitting results have a good fitting degree.
Prediction of cutting power and machine tool power The regression equations with coefficients calculated in section ‘‘Correlation analysis of powers’’ are verified by applying the cutting parameters in section ‘‘Variable cut-depth cutting experiment.’’ Because of the continuous variation of cut-depth, the main cutting force and cutting power are continuously changing. The nominal power PST value is calculated using the SPEED and TCMD values monitored by the SERVO GUIDE software and used to predict the value of other power. Note these predicted values, respectively, Pi(P), PiPSM(P), PiSPM(P), PoSPM(P), PSP(P). The powers measured by monitoring instruments are, respectively, Pi(R), PiPSM(R), PiSPM(R), PoSPM(R), PSP(R). Figure 8(a) and (b), respectively, shows the comparison of predicted values and the actual measured values with the No. 31 and 38 set of parameters. According to the comparative analysis of the predicted values and the actual measured values, the relative errors of the predicted values calculated with equation (7) are shown in Table 8. The results show that with the increase in cutting power, the relative error between the predicted value and the actual value will increase err =
P(P) P(R) 3 100% P(R)
ð7Þ
where err is the relative error, P(P) is the power predicted value and P(R) is the measured power value.
A B A B A B A B A B A B A B A B A B A B A B A B A B A B A B
1
1488 2066 1652 2875 1949 3652 1642 3148 1793 4253 1565 3974 1764 4105 1512 4143 1706 6063 1266 3107 1292 3873 1329 4959 1387 6081 1329 4380 1369 6028
Pi (W)
572 1122 734 1904 1027 2656 723 2170 874 3241 648 2970 843 3096 595 3135 787 5002 352 2128 378 2873 414 3929 472 5010 415 3366 454 4960
PiPSM (W) 480 1025 681 1814 744 2557 637 2068 882 3130 560 2872 745 2991 502 3037 973 4896 279 2051 292 2788 338 3838 413 4914 327 3271 367 4870
PiSPM (W) 447 907 636 1676 643 2402 595 1919 811 2957 529 2673 672 2823 476 2834 912 4658 214 1815 249 2536 301 3554 391 4601 287 3013 336 4559
PoSPM (W) 0 297 0 517 0 1024 0 907 0 1736 0 1667 0 1851 0 1811 0 3332 0 1334 0 1805 0 2497 0 3345 0 2138 0 3394
PSP (W) 161 619 227 1214 250 1791 247 1391 264 2242 132 1885 339 2181 149 2002 162 3425 51 1159 59 1654 63 2374 77 3095 78 2014 84 3121
PST (W)
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
No. A B A B A B A B A B A B A B A B A B A B A B A B A B A B A B
St. 1287 5737 1367 7039 1334 5904 1327 6938 1292 7721 1274 8261 1305 5650 1285 6762 1363 11,606 1261 6330 1264 6308 1311 8366 1421 11,218 1326 7428 1391 10,107
Pi (W) 374 4682 451 5943 420 4839 412 5836 378 6595 360 7116 391 4596 372 5669 448 10,372 347 5258 351 5226 396 7225 505 9981 411 6327 476 8920
PiPSM (W) 290 4588 346 5853 345 4752 314 5739 297 6496 284 7009 304 4486 285 5564 386 10,272 282 5129 278 5163 314 7121 388 9862 323 6221 407 8799
PiSPM (W) 243 4248 313 5483 293 4426 271 5370 253 6081 236 6553 260 4164 237 5183 319 9743 213 4738 210 4773 264 6687 346 9368 277 5840 370 8360
PoSPM (W) 0 3171 0 4195 0 3402 0 4047 0 4587 0 4953 0 3175 0 4004 0 7608 0 3771 0 3774 0 5439 0 7760 0 4736 0 6912
PSP (W) 54 2804 57 3731 81 2984 65 3643 57 4195 52 4713 67 2766 56 3504 86 7043 51 3126 52 3160 65 4675 80 6826 76 3999 72 6064
PST (W)
St. is the working status; St. A is under unloaded operation, the cutting force FY value measured by Kistler is 0, so the calculated cutting power PSP is 0 according to the formula (6); St. B is under loaded operation.
15
14
13
12
11
10
9
8
7
6
5
4
3
2
St.
No.
Table 5. Cylindrical longitudinal turning test data.
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Table 6. Power correlation analysis.
Pi PiPSM PiSPM PoSPM PSP PST
Pi
PiPSM
PiSPM
PoSPM
PSP
PST
1.000 1.000 1.000 1.000 0.996 0.998
1.000 1.000 1.000 1.000 0.996 0.998
1.000 1.000 1.000 1.000 0.996 0.998
1.000 1.000 1.000 1.000 0.996 0.998
0.996 0.996 0.996 0.996 1.000 0.993
0.998 0.998 0.998 0.998 0.993 1.000
Table 7. Linear regression results.
Pi PiPSM PiSPM PoSPM PSP
p1
p2
R2
AdjR2
SSE
RMSE
1.499 1.451 1.455 1.366 1.14
1266 353.4 274.8 223.5 2184.4
0.9963 0.9962 0.9958 0.9966 0.9869
0.9962 0.9962 0.9957 0.9965 0.9865
1.76e + 06 1.664e + 06 1.842e + 06 1.328e + 06 3.6e + 06
115.9 169.4 178.2 151.3 249.1
SSE: sum of squares due to error; RMSE: root mean squared error.
Conclusion and discussion The longitudinal turning experimental results show that the nominal power PST calculated through servo
controller parameters has a good correlation with the cutting power PSP and the powers Pi, PiPSM, PiSPM measured in other locations. In the variable cut-depth cutting experiment, the equations are used for powers prediction, which shows the result has good accuracy with relative error within 8%. The reason is that the spindle motor is a digitally controlled AC motor, so the value of TCMD parameter has an approximately proportional relationship to the motor output torque. The cutting force is relatively small, where there is no V-belt slipping phenomenon; so, the real speed of motor can be calculated accurately using SPEED parameter and mechanism transmission ratio. The nominal power PST is calculated through TCMD and SPEED, which leads to PST which has a good linear relationship with cutting power PSP. When the servo axis is working at low speed, the cutting force has little effect on the servo power. Therefore, PiSVM is much smaller than PiPSM and can be neglected. In summary, Pi, PiPSM, PiSPM have a good linear relationship with cutting power PSP. The method to predict cutting power and machine tool power through servo parameters is independent of the workpiece, tool, and cutting process parameters. The CNC system can read the servo parameters in real time, which enables the integration of the energy
Table 8. Prediction error analysis. No.
Pi (%)
PiPSM (%)
PiSPM (%)
PoSPM (%)
PSP (%)
No.
Pi (%)
PiPSM (%)
PiSPM (%)
PoSPM (%)
PSP (%)
31 32 33 34
2.04 2.44 2.87 2.92
0.75 0.85 1.31 2.19
2.61 2.81 3.07 3.58
2.89 2.90 3.28 3.80
25.45 24.65 22.84 20.20
35 36 37 38
4.49 4.42 5.76 6.81
3.63 4.52 5.25 6.78
5.38 5.15 6.36 7.72
4.79 4.71 5.84 6.81
1.54 1.51 3.05 3.88
Figure 7. Relationship between PST and other power.
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Figure 8. Comparative analysis of predicted and actual values: (a) No.31: ap = 2.2–0.2, VC = 370 m/min, f = 0.1 mm/r and (b) No.38: ap = 2.2~0.2, VC = 475 m/min, f = 0.3 mm/r.
consumption monitoring module in the CNC system, and this method will provide a reference for the machine tool or CNC system manufacturers to develop the energy consumption monitoring module. The drawbacks of this method are as follows: (1) the relationship between TCMD and actual torque is obtained by the regression of experimental data, resulting in a relatively large error; (2) when the motor rotation speed is near the high-limit or low-limit speed, the relationship between TCMD and actual torque may be more complicated, where more detailed information is needed from CNC system, servo, and motor manufacturer; (3) for high-speed machining, the feed-axis power is relatively large and cannot be neglected. Follow-up studies will focus on the relationship between the feed-axis motor power and servo
parameters, especially under conventional cutting and high-speed cutting, and the establishment of a calculation algorithm for power, energy, and efficiency of conventional cutting and high-speed cutting. Further research on improving the algorithm will improve machining energy consumption prediction and energy efficiency–based machining process parameters optimization. Eventually, the algorithm will be integrated into the CNC system, and energy consumption on-machine monitoring and manufacturing system energy consumption management based on INTERNET platforms will be realized. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Liu et al. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (grant no. 2012ZX04005031).
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