Jia et al 2021c - Revision history

Rimni: /* 3.2 Effects of different cutting parameters on the SEC */

2022-04-05T10:07:08Z

‎3.2 Effects of different cutting parameters on the SEC

Rimni at 09:58, 5 April 2022

2022-04-05T09:58:47Z

Rimni: /* 3.1. Experimental apparatus and method */

2022-04-05T09:54:54Z

‎3.1. Experimental apparatus and method

Rimni at 09:52, 5 April 2022

2022-04-05T09:52:19Z

Rimni: /* 1. Introduction */

2022-04-05T09:47:32Z

‎1. Introduction

Rimni at 09:44, 5 April 2022

2022-04-05T09:44:51Z

Rimni at 09:21, 5 April 2022

2022-04-05T09:21:36Z

Rimni at 09:20, 5 April 2022

2022-04-05T09:20:13Z

Rimni at 14:46, 4 April 2022

2022-04-04T14:46:53Z

Rimni at 13:31, 4 April 2022

2022-04-04T13:31:45Z

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 ===3.2 Effects of different cutting parameters on the SEC===
-The experimental results show that the milling power range under different cutting parameters from 86.15 to 979.78W. The material removal rate varied from 0.36 to 7.64 cm<math>^3</math>/min. The surface roughness varied from0.42 to 1.66μm. The cutting specific energy ranging from 6.194 to 14.358 J/mm<math>^3</math>. From the point of reducing the energy consumption, in the premise of ensuring the quality of processing, it is hoped to consume less energy to remove the materials, that is, the SEC can be small.
+The experimental results show that the milling power range under different cutting parameters from 86.15 to 979.78W. The material removal rate varied from 0.36 to 7.64 cm<math>^3</math>/min. The surface roughness varied from 0.42 to 1.66μm. The cutting specific energy ranging from 6.194 to 14.358 J/mm<math>^3</math>. From the point of reducing the energy consumption, in the premise of ensuring the quality of processing, it is hoped to consume less energy to remove the materials, that is, the SEC can be small.
-The influence of the cutting parameters on the cutting specific energy can be analyzed by the Taguchi method to obtain the cutting parameters that have the minimum effect. The quality loss (signal-to-noise ratio(S/N)) of <math>u(t)</math> is calculated
+The influence of the cutting parameters on the cutting specific energy can be analyzed by the Taguchi method to obtain the cutting parameters that have the minimum effect. The quality loss (signal-to-noise ratio (<math>S/N</math>)) of <math>u(t)</math> is calculated
 {| class="formulaSCP" style="width: 100%; text-align: center;"
@@ Line 368: / Line 368: @@
 |}
+To minimize the squared deviation of <math>u(t)</math> (or minimize the quality loss), the larger the <math>S/N</math> is, the better. [[#img-4|Figures 4]] to [[#img-6|6]] show the impact of speed <math>V_s</math>, milling depth <math> a_p </math> and feed per revolution <math> f_r </math> on <math>u(t)</math>.
-To minimize the squared deviation of <math>u(t)</math> (or minimize the quality loss), the larger the <math>S/N</math> is, the better. As shown in [[#img-4|Figures 4]] to [[#img-6|6]] show the impact of speed <math>V_s</math>, milling depth ap and feed per revolution fr on <math>u(t)</math>.
 <div id='img-4'></div>
@@ Line 448: / Line 447: @@
-The influence of the cutting parameters on the cutting forces can explain the influence of the cutting parameters on the S/N. In the [[#img-4|Figure 4]], S/N increase with the increase of <math>V_s</math>. But when <math>V_s >250</math>m/min, the <math>S/N</math> began to decrease. The influence of <math>V_s</math> on the cutting force is shown in [[#img-8|Figure 8]]. <math>F_x</math> decrease with the increase of <math>V_s</math>. But the slop of the curve become smaller when <math>V_s > 250</math>m/min, that means the decrease of <math>F_x</math> is not obvious. The milling force <math>F_y</math> almost does not change when the cutting speed increase from 150m/min to 200m/min, but in the process of 200m/min to 300m/min, it decreases with the increase of the cutting speed, and the slope of the curve become smaller when <math>V_s >250</math>m/min，that is, the <math>F_y</math> decrease of is not obvious. Because of <math>P_c=F_c \cdot V_s</math>, when <math>F_y < 250</math>m/min, the impact on S/N by decrease of <math>F_y</math> and <math>F_x</math> is larger than by increase of <math>V_s</math>. When the thickness of the cutting layer increases, the average deformation decreases, and the cutting force is increased, as shown in [[#img-10|Figure 10]]. In [[#img-6|Figure 6]], when the milling depth increases, the S/N increases and then decrease. When the milling depth increases, the cutting depth does not change and the cutting layer width increases, then the cutting load on the cutting edge, that is, the deformation resistance of cutting and friction force on the rake face is proportional to the increase. From [[#img-10|Figure 10]], with the increase of <math>a_p</math>, the <math>F_y</math> increases proportionally and the <math>F_x</math> increases almost proportionally in the beginning, but when <math>a_p >0.8</math>mm, the slope of <math>F_x</math> decreases, <math>\tan^{-1}(F_y/F_x)</math> and <math>F_c</math> increase, so the <math>P_c</math> increases. In a word, the <math>S/N</math> reduces when the <math>a_p</math> increases, but S/N increases when the <math>a_p</math> is larger than a certain value as shown in [[#img-6|Figure 6]].
+The influence of the cutting parameters on the cutting forces can explain the influence of the cutting parameters on the <math>S/N</math>. In the [[#img-4|Figure 4]], <math>S/N</math> increase with the increase of <math>V_s</math>. But when <math>V_s >250</math>m/min, the <math>S/N</math> began to decrease. The influence of <math>V_s</math> on the cutting force is shown in [[#img-8|Figure 8]]. <math>F_x</math> decrease with the increase of <math>V_s</math>. But the slop of the curve become smaller when <math>V_s > 250</math>m/min, that means the decrease of <math>F_x</math> is not obvious. The milling force <math>F_y</math> almost does not change when the cutting speed increase from 150m/min to 200m/min, but in the process of 200m/min to 300m/min, it decreases with the increase of the cutting speed, and the slope of the curve become smaller when <math>V_s >250</math>m/min，that is, the <math>F_y</math> decrease of is not obvious. Because of <math>P_c=F_c \cdot V_s</math>, when <math>F_y < 250</math>m/min, the impact on <math>S/N</math> by decrease of <math>F_y</math> and <math>F_x</math> is larger than by increase of <math>V_s</math>. When the thickness of the cutting layer increases, the average deformation decreases, and the cutting force is increased, as shown in [[#img-10|Figure 10]]. In [[#img-6|Figure 6]], when the milling depth increases, the <math>S/N</math> increases and then decrease. When the milling depth increases, the cutting depth does not change and the cutting layer width increases, then the cutting load on the cutting edge, that is, the deformation resistance of cutting and friction force on the rake face is proportional to the increase. From [[#img-10|Figure 10]], with the increase of <math>a_p</math>, the <math>F_y</math> increases proportionally and the <math>F_x</math> increases almost proportionally in the beginning, but when <math>a_p >0.8</math>mm, the slope of <math>F_x</math> decreases, <math>\tan^{-1}(F_y/F_x)</math> and <math>F_c</math> increase, so the <math>P_c</math> increases. In a word, the <math>S/N</math> reduces when the <math>a_p</math> increases, but <math>S/N</math> increases when the <math>a_p</math> is larger than a certain value as shown in [[#img-6|Figure 6]].
 From the above analysis and experimental results show that the cutting power and material removal rate are not same with the different cutting parameters. From the point of view of energy consumption, in the premise of satisfying the processing quality, the energy efficiency can be evaluated by the Eq.(5), and the energy consumption of the cutting process can be evaluated by the Eq.(6).

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 ===3.1. Experimental apparatus and method===
-Milling experiments were conducted on a DMG DMU60 5-axes machining center (maximal spindle speed, 25000rpm; maximal feed speed, 7.6m/min). The cutters were tungsten carbide 2-flute straight end mills with TiN coating. They had a diameter of 16mm, a helix angle of 35° and a rake angle of 0°. The workpiece material used for milling was a typical S136 die steel. [[#img-3|Figure 3]] is the schematic diagram for milling experiment setup. The workpiece was mounted on a piezoelectric platform dynamometer (Kistler 9257B), with which the cutting forces (<math>F_x</math>, <math>F_y</math>, <math>F_z</math>) were measured during the milling process.
+Milling experiments were conducted on a DMG DMU60 5-axes machining center (maximal spindle speed, 25000rpm; maximal feed speed, 7.6m/min). The cutters were tungsten carbide 2-flute straight end mills with TiN coating. They had a diameter of 16mm, a helix angle of 35° and a rake angle of 0°. The workpiece material used for milling was a typical S136 die steel. [[#img-3|Figure 3]] shows the schematic diagram for milling experiment setup. The workpiece was mounted on a piezoelectric platform dynamometer (Kistler 9257B), with which the cutting forces (<math>F_x</math>, <math>F_y</math>, <math>F_z</math>) were measured during the milling process.
 <div id='img-3'></div>

@@ Line 155: / Line 155: @@
 ===3.1. Experimental apparatus and method===
-Milling experiments were conducted on a DMG DMU60 5-axes machining center (maximal spindle speed, 25000rpm; maximal feed speed, 7.6m/min). The cutters were tungsten carbide 2-flute straight end mills with TiN coating. They had a diameter of 16mm, a helix angle of 35°and a rake angle of 0°. The workpiece material used for milling was a typical S136 die steel. [[#img-3|Figure 3]] is the schematic diagram for milling experiment setup. The workpiece was mounted on a piezoelectric platform dynamometer (Kistler 9257B), with which the cutting forces (<math>F_x</math>, <math>F_y</math>, <math>F_z</math>) were measured during the milling process.
+Milling experiments were conducted on a DMG DMU60 5-axes machining center (maximal spindle speed, 25000rpm; maximal feed speed, 7.6m/min). The cutters were tungsten carbide 2-flute straight end mills with TiN coating. They had a diameter of 16mm, a helix angle of 35° and a rake angle of 0°. The workpiece material used for milling was a typical S136 die steel. [[#img-3|Figure 3]] is the schematic diagram for milling experiment setup. The workpiece was mounted on a piezoelectric platform dynamometer (Kistler 9257B), with which the cutting forces (<math>F_x</math>, <math>F_y</math>, <math>F_z</math>) were measured during the milling process.
 <div id='img-3'></div>

@@ Line 57: / Line 57: @@
 |}
+where <math>T_1</math> is the no-load running time of machine tool, <math>T_2</math> is the cutting time, <math>T_3</math> is the time of positioning of machine tool and acceleration/deceleration of spindle, <math>P_1</math> and <math>P_3</math> are determined by the characteristics of the machine itself and is not affected by the cutting process. They are only related to the use time of the machine tool. Cutting power <math>P_2</math> changes with the cutting load, so it is important and difficult for cutting energy consumption evaluation.
-where <math>_1</math> is the no-load running time of machine tool, <math>T_2</math> is the cutting time, <math>T_3</math> is the time of positioning of machine tool and acceleration/deceleration of spindle, <math>P_1</math> and <math>P_3</math> are determined by the characteristics of the machine itself and is not affected by the cutting process. They are only related to the use time of the machine tool. Cutting power <math>P_2</math> changes with the cutting load, so it is important and difficult for cutting energy consumption evaluation.
 ===2.2. Factors affecting <math>P_2</math> of cutting power===
@@ Line 74: / Line 73: @@
 |}
-where <math>K_{Fc}</math> is the influence factor related to the workpiece material and tool material, and its value is related to the experimental conditions, <math>b_1</math> is the influence index of the milling depth ap on the cutting force, <math>b_2</math> is the influence index of cutting speed <math>V_s</math> on the cutting force, <math>b_3</math> is the influence index of the feed per tooth <math>F_z</math>(mm/z) on the cutting force, <math>b_4</math> is the influence index of the milling width ae on the cutting force. From the Eq. (2) can be known, main cutting force <math>F_c</math> is determined by cutting parameters (cutting speed <math>V_s</math>, milling depth <math>a_p</math>, feed per tooth <math>F_z</math>, milling width <math>a_e</math>). So, the cutting power is affected by the cutting parameters. However, the choice of cutting parameters is usually determined by the machining process and the quality. Taking the surface roughness as an example, it is not only related to the cutting parameters but also to the shape of the machining surface. Under normal conditions, increasing the cutting speed can reduce the surface roughness value. The cutting depth has little influence on the surface roughness. The feed rate which has a direct relation with the residual area has a great influence on the surface roughness. The relationship between the shape of the machining surface and the surface roughness is shown in [[#img-1|Figure 1]].
+where <math>K_{Fc}</math> is the influence factor related to the workpiece material and tool material, and its value is related to the experimental conditions, <math>b_1</math> is the influence index of the milling depth ap on the cutting force, <math>b_2</math> is the influence index of cutting speed <math>V_s</math> on the cutting force, <math>b_3</math> is the influence index of the feed per tooth <math>F_z</math>(mm/z) on the cutting force, <math>b_4</math> is the influence index of the milling width ae on the cutting force. From the Eq.(2) can be known, main cutting force <math>F_c</math> is determined by cutting parameters (cutting speed <math>V_s</math>, milling depth <math>a_p</math>, feed per tooth <math>F_z</math>, milling width <math>a_e</math>). So, the cutting power is affected by the cutting parameters. However, the choice of cutting parameters is usually determined by the machining process and the quality. Taking the surface roughness as an example, it is not only related to the cutting parameters but also to the shape of the machining surface. Under normal conditions, increasing the cutting speed can reduce the surface roughness value. The cutting depth has little influence on the surface roughness. The feed rate which has a direct relation with the residual area has a great influence on the surface roughness. The relationship between the shape of the machining surface and the surface roughness is shown in [[#img-1|Figure 1]].
 <div id='img-1'></div>
@@ Line 108: / Line 107: @@
 | colspan="1" style="padding:10px;"| '''Figure 2'''. Illustration of processing of different curvature
 |}
 ===2.3 Energy consumption evaluation function of complex process of variable cutting parameter===

@@ Line 30: / Line 30: @@
 The energy yearbook published by the U.S. energy information administration in 2012 showed that industrial electricity consumption accounted for 31% of the total electricity consumption, manufacturing electricity consumption accounted for 90% of the industrial electricity consumption, and machine tools electricity consumption occupied 75% of manufacturing electricity consumption [1]. For the reason, the energy consumption of the manufacturing industry has been widely concerned by the industry and academia. The United States Department of Energy has launched an Industrial Assessment Center program to improve the energy efficiency of manufacturing processes. Gutowski et al. at the Massachusetts Institute of Technology studied the energy consumption of various manufacturing processes on the job shop from a thermodynamic point and proposed a generalized energy flow of manufacturing system, in which machinery manufacturing is one of the important contents [2]. Peng analyzed the composition of cutting energy consumption from the perspective of mechanical mechanics, and analyzed the influence of cutting parameters on cutting power from the deduced cutting power formula [3]. Wang et al. collect experimental data of surface roughness, cutting force and power through instruments, performs multi-objective optimization based on weighted gray correlation and least squares fitting methods, and establishes a multi-objective prediction model [4]. Xie et al. analyze the energy consumption characteristics at different periods of the machining process, and obtains the coefficients of the energy consumptioncutting parameter model; then constructs the univariate influence characteristic curve and multivariate influence characteristic surface of each cutting parameter according to the cutting processing conditions; finally, the processing conditions Lower cutting parameters for energy saving decisions [5-6]. Mori et al. proposed to improve some functions of machine tools, which can reduce the energy consumption of machine tool in cutting process [7]. Qiu analyzed the cutting energy consumption and proposed a cutting energy consumption prediction model based on the exponential model and the specific cutting force model [8]. Gu and Xu established a machine tool energy consumption prediction model based on improved fruit fly algorithm and neural network integration with processing parameters as input, which can more accurately and stably predict the energy consumption of machine tools during machining [9]. Sun et al. proposed a plunge milling tool path generation method which could control the radial depth to improve the cutting efficiency and cutter life [10]. Li et al. proposed cutting tools energy consumption base on material extraction, manufacturing, use, and recycling [11]. Winter et al. proposed a method for reducing cutting fluid and energy consumption [12]. Study the energy consumption of the machine tool spindle during startup and operation [13]. The International Organization drafted the standard “environmental evaluation of machine tools” in 2010 (ISO 14955-1:2014), and the International Organization for Standardization had revised the standard “environmental evaluation of machine tools” in 2017 (ISO 14955-1:2017) [14]. The energy consumption of manufacturing system or flexible manufacturing system is further studied [15-18].
-Scholars naturally study energy efficiency while investigating energy consumption. In the aspect of energy efficiency evaluation, the scholars use the physical concept “specific energy” to scale the energy efficiency of the machine, that is, specific energy consumption (SEC). It represents the power consumed for removing unit volume material. Patrik et al. apply an interdisciplinary perspective to study industrial system energy efficiency [19]. Liu et al. put forward the development trend of energy efficiency of mechanical processing system [20]. Zhang analyzed energy efficiency techniques in the domain of discrete part manufacturing by reviewing [21]. Production decisions consider energy efficiency [22]. The energy efficiency of machine tool and production system was discussed hierarchically [23]. Real time power consumption monitoring to improve energy efficiency was presented [24]. Effects of tool geometry and cutting parameters on energy efficiency during turning of ANSI 4140 steel were investigated [25]. Through the study of effects of the sawing parameters on sawing force and energy consumption, Huang proved that increasing the grain depth of cutting is conducive to improve the ratio of volume crushing, thus reduce the sawing specific energy [26]. Rodrigues and Coelho studied the relationship between the SEC and cutting speed and tool geometry angle in the condition of high speed cutting [27]. On the basis of a large amount of collected data, Gutowski et al. established averaged SEC diagrams based on the materials for a variety of technology [28].
+Scholars naturally study energy efficiency while investigating energy consumption. In the aspect of energy efficiency evaluation, the scholars use the physical concept “specific energy” to scale the energy efficiency of the machine, that is, specific energy consumption (SEC). It represents the power consumed for removing unit volume material. Patrik et al. apply an interdisciplinary perspective to study industrial system energy efficiency [19]. Liu et al. put forward the development trend of energy efficiency of mechanical processing system [20]. Zhang analyzed energy efficiency techniques in the domain of discrete part manufacturing by reviewing [21]. Production decisions consider energy efficiency [22]. The energy efficiency of machine tool and production system was discussed hierarchically [23]. Real time power consumption monitoring to improve energy efficiency was presented [24]. Effects of tool geometry and cutting parameters on energy efficiency during turning of ANSI 4140 steel were investigated [25]. Through the study of effects of the sawing parameters on sawing force and energy consumption, Huang et al. proved that increasing the grain depth of cutting is conducive to improve the ratio of volume crushing, thus reduce the sawing specific energy [26]. Rodrigues and Coelho studied the relationship between the SEC and cutting speed and tool geometry angle in the condition of high speed cutting [27]. On the basis of a large amount of collected data, Gutowski et al. established averaged SEC diagrams based on the materials for a variety of technology [28].
 The SEC mentioned above can be a factor on its impact or the average value of the whole cutting energy consumption divided by removed material. The change of SEC caused by the change of cutting parameters to guarantee the quality in the complex process is not considered. That is to say, the time characteristic of energy consumption is not considered in the cutting process. Energy consumption is a function of time in the cutting process. For the reason, in this paper, based on the change of cutting parameters with time, the energy efficiency evaluation method of cutting technology of variable cutting parameters is proposed. Based on the experimental study of the influence of the cutting parameters on the energy consumption and the cutting quality, a method for evaluating the energy consumption of complex machining is proposed.

@@ Line 541: / Line 541: @@
 [8] Qiu X.  Research on prediction method of cutting energy consumption in workpiece machining process and development of application system (in Chinese). J. Chongqing Univ., 13(2):8-14, 2016.
-[9] Gu W., Xu Z.J. Prediction of machine tool energy consumption based on neural network integration and fruit fly algorithm (in Chinese). D Huazhong University of Science and Technology, 1990.
+[9] Gu W., Xu Z.J. Prediction of machine tool energy consumption based on neural network integration and fruit fly algorithm (in Chinese). Huazhong University of Science and Technology, 1990.
 [10] Sun C., Wang Y.H., Huang N.D. A new plunge milling tool path generation method for radial depth control using medial axis transform. Int. J. Adv. Manuf. Technol., 76:1575-1582, 2015. doi: 10.1007/s00170-014-6375-5.
@@ Line 567: / Line 567: @@
 [21] Zhang Y.J.  Energy efficiency techniques in machining process: a review. Int. J. Adv. Manuf. Technol., 71:1123-1132, 2014. doi: 10.1007/s00170-013-5551-3.
-[22] Fysikopoulos A., Pastras G., Alexopoulos T., Chryssolouris G.  On a generalized approach to manufacturing energy efficiency. Int. J. Adv. Manuf. Technol., 73:1437-1452, 2014.  doi:10.1007/s00170-014-5818-3.
+[22] Fysikopoulos A., Pastras G., Alexopoulos T., Chryssolouris G.  On a generalized approach to manufacturing energy efficiency. Int. J. Adv. Manuf. Technol., 73:1437-1452, 2014.  doi: 10.1007/s00170-014-5818-3.
 [23] Neugebauer R., Wabner M., Rentzsch H., Ihlenfeldt S. Structure principles of energy efficient machine tools. CIRP J. Manuf. Sci. Technol., 4:136-147, 2011. doi: 10.1016/j.cirpj.2011.06.017.

@@ Line 97: / Line 97: @@
 |}
-where <math> f </math> (mm/min）is the feed rate, <math> T </math> is interpolation period, <math> r </math> is radius of curvature, <math> l </math> is the <math>._{l=fT}</math>.
+where <math> f </math> (mm/min）is the feed rate, <math> T </math> is the interpolation period, <math> r </math> is the radius of curvature, and <math> l </math> is the <math>._{l=fT}</math>.
 In summary, evaluating consumption is an evaluation of the complex process. In the process of A to E, the cutting parameters should be changed with the processing track. The change of cutting parameters will affect the cutting power <math>P_2</math>. Obviously, this cutting power <math>P(t)</math> is a function of cutting time. And the material removal rate (MRR) <math>M(t)</math> determined by the cutting parameters is also a function of the time. So, the evaluation of cutting energy consumption under the condition of complex process should be the evaluation of energy consumption due to the change of cutting parameters with time. In the process of cutting energy consumption evaluation under complex conditions, the dynamic change of energy consumption due to the change of cutting parameters with time should be taken into full consideration.

@@ Line 28: / Line 28: @@
 ==1. Introduction==
-The energy yearbook published by the U.S. energy information administration in 2012 showed that industrial electricity consumption accounted for 31% of the total electricity consumption, manufacturing electricity consumption accounted for 90% of the industrial electricity consumption, and machine tools electricity consumption occupied 75% of manufacturing electricity consumption [1]. For the reason, the energy consumption of the manufacturing industry has been widely concerned by the industry and academia. The United States Department of Energy has launched an Industrial Assessment Center program to improve the energy efficiency of manufacturing processes. Gutowski et al. at the Massachusetts Institute of Technology studied the energy consumption of various manufacturing processes on the job shop from a thermodynamic point and proposed a generalized energy flow of manufacturing system, in which machinery manufacturing is one of the important contents [2]. Peng analyzed the composition of cutting energy consumption from the perspective of mechanical mechanics, and analyzed the influence of cutting parameters on cutting power from the deduced cutting power formula [3]. Wang et al. collect experimental data of surface roughness, cutting force and power through instruments, performs multi-objective optimization based on weighted gray correlation and least squares fitting methods, and establishes a multi-objective prediction model [4]. Xie et al. analyze the energy consumption characteristics at different periods of the machining process, and obtains the coefficients of the energy consumptioncutting parameter model; then constructs the univariate influence characteristic curve and multivariate influence characteristic surface of each cutting parameter according to the cutting processing conditions; finally, the processing conditions Lower cutting parameters for energy saving decisions [5-6]. Mori et al. proposed to improve some functions of machine tools, which can reduce the energy consumption of machine tool in cutting process [7]. Qiu analyzed the cutting energy consumption and proposed a cutting energy consumption prediction model based on the exponential model and the specific cutting force model [8]. Gu and Xu established a machine tool energy consumption prediction model based on improved fruit fly algorithm and neural network integration with processing parameters as input, which can more accurately and stably predict the energy consumption of machine tools during machining [9]. Sun et al. proposed a plunge milling tool path generation method which could control the radial depth to improve the cutting efficiency and cutter life [10]. Li et al. proposed cutting tools energy consumption base on material extraction, manufacturing, use, and recycling [11]. Winter et al. proposed a method for reducing cutting fluid and energy consumption [12]. Study the energy consumption of the machine tool spindle during startup and operation [13]. The International Organization drafted the standard “environmental evaluation of machine tools” in 2010(ISO14955-1, 2014), and the International Organization for Standardization had revised the standard “environmental evaluation of machine tools” in 2017 (ISO 14955-1:2017) [14]. The energy consumption of manufacturing system or flexible manufacturing system is further studied [15-18].
+The energy yearbook published by the U.S. energy information administration in 2012 showed that industrial electricity consumption accounted for 31% of the total electricity consumption, manufacturing electricity consumption accounted for 90% of the industrial electricity consumption, and machine tools electricity consumption occupied 75% of manufacturing electricity consumption [1]. For the reason, the energy consumption of the manufacturing industry has been widely concerned by the industry and academia. The United States Department of Energy has launched an Industrial Assessment Center program to improve the energy efficiency of manufacturing processes. Gutowski et al. at the Massachusetts Institute of Technology studied the energy consumption of various manufacturing processes on the job shop from a thermodynamic point and proposed a generalized energy flow of manufacturing system, in which machinery manufacturing is one of the important contents [2]. Peng analyzed the composition of cutting energy consumption from the perspective of mechanical mechanics, and analyzed the influence of cutting parameters on cutting power from the deduced cutting power formula [3]. Wang et al. collect experimental data of surface roughness, cutting force and power through instruments, performs multi-objective optimization based on weighted gray correlation and least squares fitting methods, and establishes a multi-objective prediction model [4]. Xie et al. analyze the energy consumption characteristics at different periods of the machining process, and obtains the coefficients of the energy consumptioncutting parameter model; then constructs the univariate influence characteristic curve and multivariate influence characteristic surface of each cutting parameter according to the cutting processing conditions; finally, the processing conditions Lower cutting parameters for energy saving decisions [5-6]. Mori et al. proposed to improve some functions of machine tools, which can reduce the energy consumption of machine tool in cutting process [7]. Qiu analyzed the cutting energy consumption and proposed a cutting energy consumption prediction model based on the exponential model and the specific cutting force model [8]. Gu and Xu established a machine tool energy consumption prediction model based on improved fruit fly algorithm and neural network integration with processing parameters as input, which can more accurately and stably predict the energy consumption of machine tools during machining [9]. Sun et al. proposed a plunge milling tool path generation method which could control the radial depth to improve the cutting efficiency and cutter life [10]. Li et al. proposed cutting tools energy consumption base on material extraction, manufacturing, use, and recycling [11]. Winter et al. proposed a method for reducing cutting fluid and energy consumption [12]. Study the energy consumption of the machine tool spindle during startup and operation [13]. The International Organization drafted the standard “environmental evaluation of machine tools” in 2010 (ISO 14955-1:2014), and the International Organization for Standardization had revised the standard “environmental evaluation of machine tools” in 2017 (ISO 14955-1:2017) [14]. The energy consumption of manufacturing system or flexible manufacturing system is further studied [15-18].
 Scholars naturally study energy efficiency while investigating energy consumption. In the aspect of energy efficiency evaluation, the scholars use the physical concept “specific energy” to scale the energy efficiency of the machine, that is, specific energy consumption (SEC). It represents the power consumed for removing unit volume material. Patrik et al. apply an interdisciplinary perspective to study industrial system energy efficiency [19]. Liu et al. put forward the development trend of energy efficiency of mechanical processing system [20]. Zhang analyzed energy efficiency techniques in the domain of discrete part manufacturing by reviewing [21]. Production decisions consider energy efficiency [22]. The energy efficiency of machine tool and production system was discussed hierarchically [23]. Real time power consumption monitoring to improve energy efficiency was presented [24]. Effects of tool geometry and cutting parameters on energy efficiency during turning of ANSI 4140 steel were investigated [25]. Through the study of effects of the sawing parameters on sawing force and energy consumption, Huang proved that increasing the grain depth of cutting is conducive to improve the ratio of volume crushing, thus reduce the sawing specific energy [26]. Rodrigues and Coelho studied the relationship between the SEC and cutting speed and tool geometry angle in the condition of high speed cutting [27]. On the basis of a large amount of collected data, Gutowski et al. established averaged SEC diagrams based on the materials for a variety of technology [28].

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 As shown in [[#img-11|Figure 11]] of the hemisphere, when the machining allowance is 1mm, surface roughness <math>Ra \le 3.2</math> and the degree between processing point and the <math>Z  </math> axis is in 0～10º,10º～20º,20º～30º ,30～40º,40º～60º,60º～90º, different feed speed is used to ensure the processing quality. The energy consumption calculated by Eq.(10) is about 7,500 kilojoule.
-=5.Conclusions=
+==5. Conclusions==
-At present, the research on cutting process of energy still remain in the concept of static macro, such as the energy flow of manufacturing system, the proportion of the cutting energy consumption in the entire system and the composition of energy consumption of machine tool. Cutting process is a complicated process because the cutting power is determined by the load, the load is determined by the cutting parameters in the machining process system. During the cutting process, cutting parameters will change with the change of processing elements. In order to guarantee the machining quality, selection of cutting parameters should change with process and processing factors. In the process of cutting energy consumption under complex conditions, the dynamic change of energy consumption due to the change of cutting parameters with time should be taken into full consideration. Studying on machining efficiency is to seek the minimum energy consumption or the maximum material removal per unit time under the condition of quality assurance. So it is proposed to evaluate the energy consumption of the complex machining process with the specific energy u(t) = P(t)/M(t). Because the evaluation index considers the actual situation during the cutting process, it can reflect the energy consumption of complex process of variable cutting parameters which makes it possible that energy consumption becomes a factor to be considered in computer aided process design. It also provides the basis for the energy consumption to be one of the conditions for choosing the cutting parameters in the design of adaptive system for cutting machine tools.
+At present, the research on cutting process of energy still remain in the concept of static macro, such as the energy flow of manufacturing system, the proportion of the cutting energy consumption in the entire system and the composition of energy consumption of machine tool. Cutting process is a complicated process because the cutting power is determined by the load, the load is determined by the cutting parameters in the machining process system. During the cutting process, cutting parameters will change with the change of processing elements. In order to guarantee the machining quality, selection of cutting parameters should change with process and processing factors. In the process of cutting energy consumption under complex conditions, the dynamic change of energy consumption due to the change of cutting parameters with time should be taken into full consideration. Studying on machining efficiency is to seek the minimum energy consumption or the maximum material removal per unit time under the condition of quality assurance. So it is proposed to evaluate the energy consumption of the complex machining process with the specific energy <math>u(t) = P(t)/M(t)</math>. Because the evaluation index considers the actual situation during the cutting process, it can reflect the energy consumption of complex process of variable cutting parameters which makes it possible that energy consumption becomes a factor to be considered in computer aided process design. It also provides the basis for the energy consumption to be one of the conditions for choosing the cutting parameters in the design of adaptive system for cutting machine tools.
 ==Acknowledgments==
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