Measuring and calculating the computer numerical control lathe’s cutting power and total electric power consumption based on servo parameters

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 current-loop 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

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 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 consumption^12,13 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 SEC-MRR 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 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 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.

Figure 1.

Energy/power flow of CNC machine tool.

Table 1.

Definition of energy consumption components’ power.

Name	Definition	Name	Definition
P_i	Machine tool input power	P_COOL	Power of cooling system
P_SYS	CNC system, lubrication running power	P_iPSM	PSM input power
P_PSML	PSM loss power	P_oSPM	PSM output power
P_iSPM	SPM input power	P_iSVM	SVM input power
P_SPML	SPM loss power	P_SVML	SVM loss power
P_oSPM	SPM output power	P_oSVM	SVM output power
P_MoSPL	Spindle motor loss power	P_MoSVL	Feed-axis motor loss power
P_oSP	Spindle motor output power	P_oSV	Feed-axis motor output power
P_MeSPL	Spindle output mechanical loss power	P_MeSVL	Feed-axis output mechanical loss power
P_SP	Spindle output power	P_SV	Feed-axis output power

CNC: computer numerical control; PSM: power supply module; SPM: spindle amplifier module; SVM: feed-axis (servo) amplifier module.

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 P_COOL, P_SYS can be considered as constant values, and there is a strong correlation between PSM input power P_iSPM and cutting power P_SP. 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 P_SV is far less than the spindle power P_SP in low feed rate cutting, when P_iSVM << P_iSPM; so, this article considers only the energy consumed by spindle P_SP as the effective cutting power

\begin{matrix} P_{i} = P_{COOL} + P_{SYS} + P_{iPSM} \\ = P_{COOL} + P_{SYS} + P_{iPSML} + P_{oPSM} \\ = P_{COOL} + P_{SYS} + P_{iPSML} + P_{iSPM} + P_{iSVM} \end{matrix}

(1)

\begin{matrix} P_{iSPM} = P_{SPML} + P_{oSPM} = P_{SPML} + P_{MoSPL} + P_{oSP} \\ = P_{SPML} + P_{MoSPL} + P_{MeSPL} + P_{SP} \end{matrix}

(2)

\begin{matrix} P_{iSVM} = P_{SVML} + P_{oSVM} = P_{SVML} + P_{MoSVL} + P_{oSV} \\ = P_{SVML} + P_{MoSVL} + P_{MeSVL} + P_{SV} \end{matrix}

(3)

Numerical servo system parameters

For the closed-loop or semi closed-loop control CNC system, the “three-loop” control method (the current-loop, speed-loop, and position-loop) is mostly adopted. 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.

Figure 2.

The three-loop control method of FANUC CNC system.

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 αiI22/7000HV spindle motor.³⁰

Figure 3.

Relationship between output power, torque, and rotation speed of FANUC αiI22/7000HV 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 M_e. The value of TCMD can be the current value 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 P_ST 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, P_ST is calculated with equation (4) and when SPEED > 1500, with equation (5)

P_{ST} = Me * ω = \frac{2 π \cdot T_{max}}{60} TCMD \cdot SPEED

(4)

P_{ST} = P_{max} \cdot TCMD

(5)

where $T_{max}$ is spindle motor maximum torque and $P_{max}$ 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.

Table 2.

HTC-200M CNC lathe servo controllers and servo motors.

	Servo controller	Servo motor
	Model	Model	Type	Rated power (kW)	Maximum torque (N m)
Spindle	αiSP30HV	αiI22/7000HV	A06B-1511-B153	22	162
Z-axis	αiSV20/40HV	αiF12/3000HV	A06B-0245-B101	3	35
X-axis		αiF8/3000HV	A06B-0229-B401	1.6	29

CNC: computer numerical control.

Figure 4.

Schematic diagram of the measurement system.

Figure 5.

Measurement field: (a) Machine tool & instruments, (b) Part support, (c) Power measurement wiring, and (d) Power analyzer.

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 P_ST is calculated according to equation (4) or (5). A Hioki 3390-10 power analyzer is used to measure the input power P_iSPM and output power P_oSPM of the SPM module and the input power P_iSVM of SVM module. The CNC lathe input power P_i and PSM module input power P_iPSM 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 P_SP is calculated with equation (6). MATLAB software is used to sample and analyze the experiment data

P_{SP} = \frac{F_{Y} \cdot V_{C}}{60}

(6)

where F_Y (unit: N) is for the main cutting force and V_C is the cutting speed (unit: m/min).

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 a_p: 0.2∼2.5 mm, feed rate f: 0.1–0.5 mm/r, and cut speed V_C: 170–550 m/min record the values of P_i, P_iPSM, P_iSPM, P_oSPM and calculate P_ST, P_SP according to equations (4)–(6).

Table 3.

Cylindrical longitudinal turning test process parameters.

No.	a_p (mm)	f (mm/r)	V_C (m/min)	No.	a_p (mm)	f (mm/r)	V_C (m/min)	No.	a_p (mm)	f (mm/r)	V_C (m/min)
1	0.2	0.1	370	11	0.8	0.3	193	21	1.6	0.5	170
2	0.2	0.2	475	12	0.8	0.4	217	22	2	0.2	193
3	0.2	0.3	550	13	0.8	0.5	240	23	2	0.3	170
4	0.4	0.1	475	14	1.2	0.2	217	24	2	0.4	240
5	0.4	0.2	550	15	1.2	0.3	240	25	2.25	0.225	170
6	0.4	0.3	370	16	1.2	0.4	170	26	2.25	0.3	205
7	0.6	0.1	550	17	1.2	0.5	193	27	2.25	0.375	240
8	0.6	0.2	370	18	1.6	0.2	240	28	2.5	0.225	205
9	0.6	0.3	475	19	1.6	0.3	217	29	2.5	0.3	240
10	0.8	0.2	170	20	1.6	0.4	193	30	2.5	0.375	170

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 cut-depth varies from 0.2 to 2.2 mm, and the other process parameters are show in Table 4.

Figure 6.

Variable cut-depth cutting scheme: (a) taper turning and (b) variable cut-depth turning.

Table 4.

Variable cut-depth turning test process parameters.

No.	a_p (mm)	f (mm/r)	V_C (m/min)	No.	a_p (mm)	f (mm/r)	V_C (m/min)
31	2.2–0.2	0.1	370	35	2.2–0.2	0.2	475
32	0.2–2.2	0.1	475	36	0.2–2.2	0.2	550
33	2.2–0.2	0.1	550	37	2.2–0.2	0.3	370
34	0.2–2.2	0.2	370	38	0.2–2.2	0.3	475

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 P_ST as the independent variable, and P_i, P_iPSM, P_iSPM, P_oSPM, P_SP as the dependent variables, the relational model is built in the form of f(P_ST) = p1 * P_ST + 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.

Table 5.

Cylindrical longitudinal turning test data.

No.	St.	P_i (W)	P_iPSM (W)	P_iSPM (W)	P_oSPM (W)	P_SP (W)	P_ST (W)	No.	St.	P_i (W)	P_iPSM (W)	P_iSPM (W)	P_oSPM (W)	P_SP (W)	P_ST (W)
1	A	1488	572	480	447	0	161	16	A	1287	374	290	243	0	54
	B	2066	1122	1025	907	297	619		B	5737	4682	4588	4248	3171	2804
2	A	1652	734	681	636	0	227	17	A	1367	451	346	313	0	57
	B	2875	1904	1814	1676	517	1214		B	7039	5943	5853	5483	4195	3731
3	A	1949	1027	744	643	0	250	18	A	1334	420	345	293	0	81
	B	3652	2656	2557	2402	1024	1791		B	5904	4839	4752	4426	3402	2984
4	A	1642	723	637	595	0	247	19	A	1327	412	314	271	0	65
	B	3148	2170	2068	1919	907	1391		B	6938	5836	5739	5370	4047	3643
5	A	1793	874	882	811	0	264	20	A	1292	378	297	253	0	57
	B	4253	3241	3130	2957	1736	2242		B	7721	6595	6496	6081	4587	4195
6	A	1565	648	560	529	0	132	21	A	1274	360	284	236	0	52
	B	3974	2970	2872	2673	1667	1885		B	8261	7116	7009	6553	4953	4713
7	A	1764	843	745	672	0	339	22	A	1305	391	304	260	0	67
	B	4105	3096	2991	2823	1851	2181		B	5650	4596	4486	4164	3175	2766
8	A	1512	595	502	476	0	149	23	A	1285	372	285	237	0	56
	B	4143	3135	3037	2834	1811	2002		B	6762	5669	5564	5183	4004	3504
9	A	1706	787	973	912	0	162	24	A	1363	448	386	319	0	86
	B	6063	5002	4896	4658	3332	3425		B	11,606	10,372	10,272	9743	7608	7043
10	A	1266	352	279	214	0	51	25	A	1261	347	282	213	0	51
	B	3107	2128	2051	1815	1334	1159		B	6330	5258	5129	4738	3771	3126
11	A	1292	378	292	249	0	59	26	A	1264	351	278	210	0	52
	B	3873	2873	2788	2536	1805	1654		B	6308	5226	5163	4773	3774	3160
12	A	1329	414	338	301	0	63	27	A	1311	396	314	264	0	65
	B	4959	3929	3838	3554	2497	2374		B	8366	7225	7121	6687	5439	4675
13	A	1387	472	413	391	0	77	28	A	1421	505	388	346	0	80
	B	6081	5010	4914	4601	3345	3095		B	11,218	9981	9862	9368	7760	6826
14	A	1329	415	327	287	0	78	29	A	1326	411	323	277	0	76
	B	4380	3366	3271	3013	2138	2014		B	7428	6327	6221	5840	4736	3999
15	A	1369	454	367	336	0	84	30	A	1391	476	407	370	0	72
	B	6028	4960	4870	4559	3394	3121		B	10,107	8920	8799	8360	6912	6064

St. is the working status; St. A is under unloaded operation, the cutting force F_Y value measured by Kistler is 0, so the calculated cutting power P_SP is 0 according to the formula (6); St. B is under loaded operation.

Table 6.

Power correlation analysis.

	P_i	P_iPSM	P_iSPM	P_oSPM	P_SP	P_ST
P_i	1.000	1.000	1.000	1.000	0.996	0.998
P_iPSM	1.000	1.000	1.000	1.000	0.996	0.998
P_iSPM	1.000	1.000	1.000	1.000	0.996	0.998
P_oSPM	1.000	1.000	1.000	1.000	0.996	0.998
P_SP	0.996	0.996	0.996	0.996	1.000	0.993
P_ST	0.998	0.998	0.998	0.998	0.993	1.000

Table 7.

Linear regression results.

	p1	p2	R ²	AdjR²	SSE	RMSE
P_i	1.499	1266	0.9963	0.9962	1.76e + 06	115.9
P_iPSM	1.451	353.4	0.9962	0.9962	1.664e + 06	169.4
P_iSPM	1.455	274.8	0.9958	0.9957	1.842e + 06	178.2
P_oSPM	1.366	223.5	0.9966	0.9965	1.328e + 06	151.3
P_SP	1.14	−184.4	0.9869	0.9865	3.6e + 06	249.1

SSE: sum of squares due to error; RMSE: root mean squared error.

Figure 7.

Relationship between P_ST and other power.

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 P_ST 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, P_i(P), P_iPSM(P), P_iSPM(P), P_oSPM(P), P_SP(P). The powers measured by monitoring instruments are, respectively, P_i(R), P_iPSM(R), Pi_SPM(R), P_oSPM(R), P_SP(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 = \frac{P (P) - P (R)}{P (R)} \times 100 %

(7)

Figure 8.

Comparative analysis of predicted and actual values: (a) No.31: a_p = 2.2–0.2, V_C = 370 m/min, f = 0.1 mm/r and (b) No.38: a_p = 2.2~0.2, V_C = 475 m/min, f = 0.3 mm/r.

Table 8.

Prediction error analysis.

No.	P_i (%)	P_iPSM (%)	P_iSPM (%)	P_oSPM (%)	P_SP (%)	No.	P_i (%)	P_iPSM (%)	P_iSPM (%)	P_oSPM (%)	P_SP (%)
31	2.04	0.75	2.61	2.89	−5.45	35	4.49	3.63	5.38	4.79	1.54
32	2.44	0.85	2.81	2.90	−4.65	36	4.42	4.52	5.15	4.71	1.51
33	2.87	1.31	3.07	3.28	−2.84	37	5.76	5.25	6.36	5.84	3.05
34	2.92	2.19	3.58	3.80	−0.20	38	6.81	6.78	7.72	6.81	3.88

where err is the relative error, P(P) is the power predicted value and P(R) is the measured power value.

Conclusion and discussion

The longitudinal turning experimental results show that the nominal power P_ST calculated through servo controller parameters has a good correlation with the cutting power P_SP and the powers P_i, P_iPSM, P_iSPM 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 P_ST is calculated through TCMD and SPEED, which leads to P_ST which has a good linear relationship with cutting power P_SP. When the servo axis is working at low speed, the cutting force has little effect on the servo power. Therefore, P_iSVM is much smaller than P_iPSM and can be neglected. In summary, P_i, P_iPSM, P_iSPM have a good linear relationship with cutting power P_SP.

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 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.

Footnotes

Academic Editor: Ismet Baran

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.

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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