Research Article
Energy consumption characteristics evaluation method in turning
Advances in Mechanical Engineering 2016, Vol. 8(11) 1–8 Ó The Author(s) 2016 DOI: 10.1177/1687814016680737 aime.sagepub.com
Guoyong Zhao, Chunhong Hou, Jianfang Qiao and Xiang Cheng
Abstract The accurate energy consumption characteristics evaluation in machining is helpful to optimize processing scheme to realize low carbon manufacturing. The energy consumption characteristics evaluation method in turning is researched, which includes energy efficiency calculation and energy consumption prediction. First, the simple and accurate energy efficiency computational model in turning is put forward. Second, the energy consumption prediction empirical model and the back-propagation neural network prediction model are developed respectively. Finally, the power signal measurement system is built on computer numerical controlled lathe, and the proposed energy efficiency computational model and the energy consumption prediction methods are verified in the 45# steel semi-finish turning with hard alloy cutting tools. The results show that the proposed methods can effectively evaluate the energy consumption characteristics in turning, which can help to select the high energy efficiency processing program on the basis of ensuring processing quality. Keywords Evaluation method, energy efficiency, prediction model, cutting parameters, low carbon manufacturing
Date received: 4 September 2016; accepted: 2 November 2016 Academic Editor: Xichun Luo
Introduction The manufacturing fields consume electric power to transform the raw materials into products, at the same time produce huge greenhouse gas emissions.1 The manufacturing fields account for over 60% of the whole industrial energy consumption, and about 70% emissions caused by environmental pollution come from manufacturing fields in China.2,3 Similarly, the manufacturing sector accounts for about 90% of the whole industrial energy consumption, and the machining sector accounts for about 83% of the whole manufacturing energy consumption in the United States. Consequently, the urgent task of the manufacturing fields is how to reduce the energy consumption and environmental pollution to realize low carbon manufacturing.4–6 As the main equipment in manufacturing, computer numerical control (CNC) machine tools have many
functional parts, mainly including the spindle system, feed drive system, electrical control system, auxiliary system, cutting tools, and fixture.7,8 Among them, the spindle motor drives the tool or the workpiece rotation, the feed motors achieve linear or rotary motion, the hydraulic system provides clamping force or feed drive, the cooling and lubrication system provides coolant and lubricating oil, and the tool arm motors change cutting tools automatically.9,10 Gutowski et al.11 pointed out the processing parameters optimization;
Shandong Provincial Key Laboratory of Precision Manufacturing and Nontraditional Machining, Shandong University of Technology, Zibo, China Corresponding author: Guoyong Zhao, Shandong Provincial Key Laboratory of Precision Manufacturing and Non-traditional Machining, Shandong University of Technology, Zibo 255049, China. Email:
[email protected]
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) 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 the standby time reduction and energy efficiency improvement can help to reduce machine tools energy consumption and achieve sustainable manufacturing. The CNC machine tools’ functional components were divided into two classes by Mori et al.12 One was the spindle motor and feed motors, whose energy consumption depended on the cutting force, inertia force, and friction force to move spindle box and table mainly; the other was cooling lubrication peripheral equipments, whose energy consumption depended on working time mainly. Neglecting the power losses due to friction, bearing vibration, and heat when analyzing the spindle system no-load power, Balogun and Mativenga13 pointed that the no-load power was a piecewise linear function of the spindle motor rotation speed. Yan and Li14 studied the multi-objective processing parameters optimization method based on energy consumption, machining efficiency, and surface quality in CNC milling. Zhang and Li15 divided the energy consumption in CNC machining into cutting stage energy consumption, air cutting energy consumption, and tool change energy consumption and put forward the energy consumption computing models for each processing stage. He et al.16 explored a task-oriented energy consumption modeling method for a manufacturing system. Oda et al.17 indicated that the reduction of energy consumption can be realized by optimizing the number of coolant pumps, volume, motion frequency, and control method of hydraulic equipment. Bi and Wang18 developed the energy computing method based on kinematic and dynamic models of machine tools. It allows for parallel kinematic machine tool operations such as drilling and friction-stir welding. When the developed method is applied to optimize the machine setup for energy savings, the optimization can result in 67% energy savings for a specific drilling operation. The effective energy consumption characteristics evaluation technique in machining is helpful to select the high energy efficiency processing route and processing program on the basis of ensuring processing quality. The energy consumption characteristics evaluation method in turning is researched in the article, including energy efficiency calculation and energy consumption prediction. To begin with, the simple and accurate energy efficiency computational model is proposed. Then, the energy consumption prediction empirical model and the back-propagation (BP) neural network prediction model are set up respectively. Finally, the power signal measurement system is built on CNC lathe, and the proposed energy efficiency calculation method and the energy consumption prediction methods are verified in the 45# steel semi-finish turning with hard alloy cutting tools.
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Energy efficiency computational model in turning The research on energy efficiency in machining is helpful to understand the machine tools energy characteristics and adopt targeted energy saving measures. As shown in Figure 1, CNC lathe can process straight cylinder, oblique cylinder, all kinds of complicated thread, groove, and worm parts. Generally, CNC lathe includes the spindle drive system, feed drive system, electric control system, auxiliary system, cutter, and workpiece. The spindle drive system and feed drive system are defined as machine tool transmission system. The structure of CNC lathe system is shown in Figure 2. In CNC turning, the cutting elements commonly include the spindle speed, feed rate, and cutting depth. Specifically, the spindle speed is used to specify the CNC machine tool spindle rotation rate in r/min, feed rate is the moving speed of the tool relative to workpiece on the feed direction in mm/min, and cutting depth is the vertical distance between the machined surface and the unprocessed surface in mm. Generally, the power consumption of spindle is the largest in turning. The power consumption of Z axis servo motor is great
Figure 1. Turning round bar on CNC lathe.
Figure 2. The structure of CNC lathe system.
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when turning outside cylindrical profile, while the power consumption of X axis servo motor is great when turning end surface.19 The energy efficiency computational model used most frequently in turning is as follows19 h=
Pc 3 100% Pin + Prfo
Step 1. Drive the spindle to run with instruction cutting speed and measure the spindle no-load power Psu with power analyzer before cutting, drive the X axis to run with instruction feed speed and measure the X axis no-load power Pxu before cutting, and drive the Z axis to run with instruction feed speed and measure the Z axis no-load power Pzu before cutting. Step 2. On each sampling period in turning process, measure the total machine tool power Ptotal from lathe power supply line end, measure the spindle input power Psin, the X axis input power Pxin, and the Z axis input power Pzin, respectively, with power analyzer. The total power Ptotal on the sampling period consists of the spindle input power Psin, the X axis input power Pxin and the Z axis input power Pzin, the standby power Prfo, and the coolant pump power Pcool ð2Þ
Step 3. The input power of the spindle and the feed axis are divided into the no-load power and the cutting power, respectively
Pin = Pu + Pc
ð3Þ
Compute the spindle cutting power on the sampling period according to the spindle input power and noload power Psc = Psin Psu
ð4Þ
If Pxin . Pxu when X axis with cutting movement, the X axis cutting power can be obtained as Pxc = Pxin Pxu
Pzc = Pzin Pzu
ð6Þ
If Pzin Pzu when Z axis without cutting movement, let the Z axis cutting power Pzc = 0.
ð1Þ
where Pc is spindle cutting power, Pin is spindle input power, and Prfo is standby power consumption. Equation (1) does not consider the cutting power of feed axes and the coolant pump power in machining. In this article, the energy efficiency computational model in turning is put forward, which includes four steps:
Ptotal = Psin + Pxin + Pzin + Prfo + Pcool
If Pzin . Pzu when Z axis with cutting movement, the Z axis cutting power can be obtained as
ð5Þ
If Pxin Pxu when X axis without cutting movement, let the X axis cutting power Pxc = 0.
Step 4. Compute the effective cutting power according to the spindle cutting power, X axis cutting power, and Z axis cutting power on the sampling period
Pvalid = Psc + Pxc + Pzc
ð7Þ
Then, the energy efficiency in turning on the sampling period can be obtained as Pvalid 3 100% Ptotal Psc + Pxc + Pzc 3 100% = Psin + Pxin + Pzin + Prfo + Pcool
h=
ð8Þ
The computational model shown as equation (8) considers not only the spindle cutting power but also the feed axes cutting power and coolant pump power in turning. On each sampling period only four signals, including the total machine tool power Ptotal, the spindle input power Psin, the X axis input power Pxin, and the Z axis input power Pzin, are needed to measure by power analyzer. Then, calculate the energy efficiency in machining with equation (8). So, the proposed energy efficiency computational model is simple and accurate.
Energy consumption prediction empirical model in turning The energy consumption prediction of CNC lathe is very complicated. The existing energy consumption prediction and calculation methods were mainly based on machine tools functional components.20,21 Li and Kara22 developed an effective empirical model for energy consumption calculation. They divided the machine tool power into standby power and cutting power Ptotal = Prfo + k0 MRR
ð9Þ
where MRR is material removal rate in mm3/s, k0 is empirical coefficient, and Prfo is standby power. After divided by MRR, equation (9) is changed into SEC =
Ptotal C1 = C0 + MRR MRR
ð10Þ
where SEC is the specific energy consumption predicted in J/mm3 and C0 and C1 are empirical
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coefficients. Suppose the total material removal volume be Q, then the total machine tool energy consumption can be obtained as E = SEC Q
ð11Þ
On the basis of empirical equation (10), considering not only standby power Prfo and cutting power Pcut but also spindle no-load power Psu and coolant pump power Pcool in machining, the energy consumption prediction empirical model in machining is built up in the article. The machine tool power equation is developed as Ptotal = Prfo + Pcut + Pcool + Psu = Prfo + k0 MRR + Pcool + S n + C
ð12Þ
where the spindle no-load power Psu is looked as piecewise linear function of the spindle rotation speed n. S and C are constants. So, SEC can be predicted with equation (13) Prfo + k0 MRR + Pcool + S n + C Ptotal = MRR MRR Prfo + Pcool + C S n + = k0 + MRR MRR k1 k2 + n ð13Þ = k0 + MRR MRR
SEC =
where k0, k1, and k2 are empirical coefficients. k1 depends on whether the coolant is used or not in machining and k2 depends on the spindle no-load characteristic. Compared to equation (10), equation (13) also considers the spindle no-load power consumption and coolant pump power in SEC prediction.
Energy consumption prediction BP neural network model BP network is an artificial neural network based on error back-propagation algorithm. The energy consumption prediction method in turning based on BP neural network is built up in the article, which includes three steps: Step 1. Predict the SEC in J/mm3 according to the processing parameters in turning. As shown in Figure 3, the SEC prediction model is a single hidden layer feedforward network including input layer, hidden layer, and output layer. In the network, the input layer includes three neurons, expressing cutting speed in m/min, feed rate in mm/r, and depth of cut in mm, respectively; the output layer includes one neuron only, expressing the SEC in J/mm3.
Figure 3. The network model to predict specific energy consumption.
The hidden layer comprises six neurons. And the hidden layer neuron adopts S type tangent function Tansig as transfer function; while the output layer neuron adopts S type logarithmic function Logsig as transfer function. After training many times, the weights and thresholds parameters corresponding to the minimum error of the network can be obtained. Then, the SEC in turning can be predicted with the trained network, according to the specific processing parameter. Step 2. Compute the total material removal volume in mm3 according to the machining feature and processing parameters. For instance, the material removal volume can be obtained when turning outside cylindrical profile on round bar parts " 2 # d 2 d ap L=1000 Q=p 2 2
ð14Þ
where Q is the total material removal volume in mm3, d is the round bar diameter in mm, ap is the depth of cut in mm, and L is the cutting length in mm. The material removal volume can be calculated when turning cone profile on round bar parts Q=
2 2 d p L=1000 3 2
ð15Þ
Step 3. Compute machine tools energy consumption in turning with equation (11) according to the SEC predicted in Step 1 and the material removal volume obtained in Step 2.
Energy consumption characteristics evaluation experiments in turning The power measurement system for CNC lathe The power analyzer WT1800 and shunts are used to measure power and energy consumption signals in CNC turning, shown in Figure 4. The machine tool large current signals are converted into small voltage
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5 Table 1. The processing parameters and specific energy consumption measured in turning.
Figure 4. The machine tool power measurement system.
signals by shunt sensors to input power analyzer. The voltage signals and current signals are measured from the lathe power input lines. The power analyzer and personal computer (PC) obtain communication through the USB interface. The power signals and energy consumption signals in turning are sampled real time in PC. As high-quality carbon structural steel, the 45# steel is used widely in the connecting rod, bolts, gears, template, and guide column machining. The semi-finish turning experiments are carried out on the NL2500MC/ 700 CNC lathe with hard alloy YT15 A320 external turning cutter under emulsion cooling condition. The round bar material is 45# steel, with diameter 50 mm and processing length 120 mm. The processing parameters are shown in Table 1, and all of the cutting experiments are in the same working environment. In the cutting experiments, tool wear is not serious, so the effect of tool wear on energy consumption is not considered. In each experiment, measure the machine energy consumption with power analyzer, calculate the material removal volume with equation (14), and obtain the SEC with equation (11). The first 16 sets of data in Table 1 are used to build up energy consumption prediction models, and the 17th group data are used to verify the accuracy of the prediction models.
No.
Cutting speed (m/min)
Feed speed (mm/r)
Depth of cut (mm)
SEC (J/mm3)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 185
0.05 0.07 0.06 0.05 0.07 0.06 0.05 0.07 0.06 0.05 0.07 0.06 0.05 0.07 0.06 0.05 0.06
1 0.5 0.5 0.5 1 1 1 0.5 0.5 0.5 1 1 1 0.5 0.5 0.5 0.5
96.1 115.8 124.3 143.5 46.3 54.7 59.7 75.9 82.7 90.2 35.3 37.9 43.2 57.1 63.3 73.4 78.7
Figure 5. Energy efficiency curve in turning.
Energy efficiency calculation in turning The energy efficiency in turning can be obtained on each sampling period with the proposed energy efficiency computational model. For example, the energy efficiency curve is shown in Figure 5 when adopting the 11 group cutting parameters in Table 1. That is to say, when the cutting speed is 190 m/min, the feed speed is 0.07 mm/r, the depth of cut is 1 mm, and the average energy efficiency in turning is 13.12%, as shown in Figure 5. The surface hardness and shape of workpiece cannot be completely consistent, and the various electromagnetic disturbances are inevitable, so the energy efficiency curve is slightly fluctuating in machining. The auxiliary system and peripheral equipments consume almost 87% of the total energy consumption. The calculation results indicate the direction of energy saving in turning.
Energy consumption prediction in turning Energy consumption prediction with empirical model. The MRR can be calculated according to cutting speed, feed speed, and depth of cut in Table 1. After linear regression fitting with the first 16 sets of data in Table 1, the energy consumption prediction empirical model adopting the developed equation (13) can be obtained SEC = 3:4 +
6583 6:1 + n MRR MRR
ð16Þ
The relation curve among SEC, MRR, and spindle rotation speed is shown in Figure 6. As the MRR increases, the SEC reduces rapidly. As the spindle rotation speed increases, the SEC increases gradually.
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Table 2. Prediction accuracy comparison. Method
SEC measured ( J/mm3)
SEC predicted ( J/mm3)
Energy consumption predicted (kJ)
Prediction accuracy
1 2 3
78.7 78.7 78.7
76.58 75.82 80.52
714.5 707.4 751.3
97.29% 96.32% 97.70%
Figure 6. The relation curve among SEC, MRR, and n.
For the 17th group cutting parameters, the predicted SEC with equation (16) is 76.58 J/mm3. The energy consumption can be obtained according to the material removal volume and SEC predicted E = SEC Q = 76:58 9330=1000 = 714:5 kJ
ð17Þ
After linear regression fitting with the first 16 sets of data in Table 1, the energy consumption prediction empirical model can be obtained when adopting the existing equation (10) SEC = 3:7 +
6605 MRR
ð19Þ
Energy consumption prediction with BP neural network prediction model. Train the network shown in Figure 3 with the first 16 groups data in Table 1 and test the trained network with the 17th group data. The predicted SEC with the trained network is 80.52 J/mm3, and the corresponding energy consumption in turning can be obtained as E = SEC Q = 80:52 9330=1000 = 751:3 kJ
The energy consumption prediction results when adopting the above different methods are compared shown in Figure 7 and Table 2. The first method adopts empirical model shown in equation (13), and the prediction accuracy is 97.29%; the second method adopts empirical model shown in equation (10), and the prediction accuracy is 96.32%; the third method adopts BP neural network model shown in Figure 3, and the prediction accuracy is 97.70%.
ð18Þ
For the 17th group cutting parameters, the predicted SEC with equation (18) is 75.82 J/mm3. So, the predicted energy consumption in turning can be obtained as E = SEC Q = 75:82 9330=1000 = 707:4 kJ
Figure 7. Comparison between the predicted and measured energy consumption.
ð20Þ
Conclusion The effective energy consumption characteristics evaluation in machining is helpful to select the high energy efficiency processing route and processing program on the basis of ensuring processing quality. The energy consumption characteristics evaluation method including energy efficiency calculation and energy consumption prediction in turning is studied in detail in the article. The conclusions are as follows: 1.
The developed energy efficiency computational model in turning is simple and accurate. The effective cutting power and total power consumption are calculated according to the machine tool standby power, the input power and no-load power of the spindle and feed axis,
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2.
3.
4.
5.
and the coolant pump power. The method can be used to calculate the proportion of noncutting operation in machine tools energy consumption and show the direction of energy saving in turning. The proposed energy consumption prediction empirical model and BP neural network prediction model are effective. The energy consumption prediction experiment results show that the empirical model shown in equation (13) and the BP neural network model can obtain satisfactory prediction accuracy. The prediction accuracy of BP neural network model is the best among the three prediction methods, but the calculation process is the most complex. The energy consumption prediction empirical model shown in equation (13) has simple calculation process and better prediction accuracy, which considers not only standby power and cutting power but also spindle no-load power and coolant pump power in machining. Large depth of cut and large feed speed within allowable range will help to reduce SEC and machine tool energy consumption in turning. The serious tool wear will lead to the rapid increase in SEC and machine tool energy consumption in turning. So, the degree of tool wear should be controlled throughout the work process. The intelligent turning scheme for hard brittle material will be studied in order to reduce energy consumption on the basis of ensuring surface quality in future research.
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: This work was supported by the Project of Shandong Province Natural Science Foundation of China (No. ZR2016EEM29).
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