Zhan et al 2021b - Revision history

Rimni at 12:12, 10 March 2022

2022-03-10T12:12:39Z

Rimni at 12:08, 10 March 2022

2022-03-10T12:08:34Z

Rimni at 11:59, 10 March 2022

2022-03-10T11:59:39Z

Rimni at 11:58, 10 March 2022

2022-03-10T11:58:29Z

Rimni at 11:55, 10 March 2022

2022-03-10T11:55:25Z

Rimni: /* References */

2022-03-10T11:50:08Z

‎References

Rimni at 11:46, 10 March 2022

2022-03-10T11:46:55Z

Rimni: /* References */

2022-03-10T11:46:03Z

‎References

Rimni at 11:38, 10 March 2022

2022-03-10T11:38:59Z

Rimni at 10:48, 10 March 2022

2022-03-10T10:48:25Z

@@ Line 921: / Line 921: @@
 ====4.3.1. Analysis of influencing factors====
-After the grey relational theory calculation, according to [[#tab-7|Table 7]], the main influence of each optimization index was analyzed, and the gray correlation coefficient of each index was shown in [[#img-18|Figure 18]]. From [[#tab-7|Table 7]] and [[#img-18|Figure 18]], it can be seen that for the residual stress gray correlation coefficient range difference <math>Rv_f  > Ra_p > Rv_c</math>, the change of the feed rate has the largest fluctuation amplitude at the four experimental levels, followed by the back knife amount and cutting speed. , indicating that the degree of influence on residual stress is in order of feed rate, back knife, cutting speed; for the surface roughness of the gray correlation coefficient extreme difference <math>Rv_c  > Ra_p > Rv_f</math>, the degree of influence on the surface roughness is in turn cutting Speed, back knife quantity, feed rate; for the cutting energy consumption of the gray level contact with the extreme value <math>Rv_f > Rv_c > Ra_p</math>, then the degree of influence on the cutting energy consumption is the feed speed, cutting speed, back knife. When the value of the three cutting elements is small, the minimum residual stress will be obtained; when the back eating amount and the feed speed are lower, the cutting speed will take the higher value, the lowest surface roughness will be obtained; The lower value of the cutting amount and cutting speed, and the higher the feed rate, will result in the lowest cutting energy consumption. However, the optimal range of these cutting parameters is not the same, meaning that it is impossible to obtain the best results for the three indicators at the same time. Therefore, using the multi-index optimization method proposed in this paper will help solve this problem.
+After the grey relational theory calculation, according to [[#tab-7|Table 7]], the main influence of each optimization index was analyzed, and the gray correlation coefficient of each index was shown in [[#img-18|Figure 18]]. From [[#tab-7|Table 7]] and [[#img-18|Figure 18]], it can be seen that for the residual stress gray correlation coefficient range difference <math>Rv_f  > Ra_p > Rv_c</math>, the change of the feed rate has the largest fluctuation amplitude at the four experimental levels, followed by the back knife amount and cutting speed. , indicating that the degree of influence on residual stress is in order of feed rate, back knife, cutting speed; for the surface roughness of the gray correlation coefficient extreme difference <math>Rv_c  > Ra_p > Rv_f</math>, the degree of influence on the surface roughness is in turn cutting Speed, back knife quantity, feed rate; for the cutting energy consumption of the gray level contact with the extreme value <math>Rv_f > Rv_c > Ra_p</math>, then the degree of influence on the cutting energy consumption is the feed speed, cutting speed, back knife. When the value of the three cutting elements is small, the minimum residual stress will be obtained; when the back eating amount and the feed speed are lower, the cutting speed will take the higher value, the lowest surface roughness will be obtained. The lower value of the cutting amount and cutting speed, and the higher the feed rate, will result in the lowest cutting energy consumption. However, the optimal range of these cutting parameters is not the same, meaning that it is impossible to obtain the best results for the three indicators at the same time. Therefore, using the multi-index optimization method proposed in this paper will help solve this problem.
 <div id='img-18'></div>

@@ Line 171: / Line 171: @@
 ===3.1 Analysis of cutting energy consumption===
-[[#img-3|Figure 3]] is the power profile of the boring process obtained during the cutting process of the machining center MCV-810. The time period ① is the standby machine running state of the machine tool, the time ② is the state of the machine tool no-load operation, and the time ③ is the cutting and working state of the machine tool. The cutting energy can be calculated by the power meter to detect the required cutting power and the product of the cutting time. As shown in formula (1), the <math>Ec</math> is the cutting energy (unit: J), the <math>Pc</math> is the cutting power (unit: W), and the <math>Tc</math> is the time used for boring a single hole (unit: s)
+[[#img-3|Figure 3]] is the power profile of the boring process obtained during the cutting process of the machining center MCV-810. The time period ① is the standby machine running state of the machine tool, the time ② is the state of the machine tool no-load operation, and the time ③ is the cutting and working state of the machine tool. The cutting energy can be calculated by the power meter to detect the required cutting power and the product of the cutting time. As shown in Equation (1), the <math>Ec</math> is the cutting energy (unit: J), the <math>Pc</math> is the cutting power (unit: W), and the <math>Tc</math> is the time used for boring a single hole (unit: s)
 {| class="formulaSCP" style="width: 100%; text-align: left;"
@@ Line 695: / Line 695: @@
-According to the above calculation, the weight coefficient of the surface roughness <math>R_a</math> is 51.88% and the weight coefficient of the cutting energy consumption <math>Ec</math> is 48.12%, which is shown by the formula (9) of the multi-index orthogonal optimization comprehensive score Grade
+According to the above calculation, the weight coefficient of the surface roughness <math>R_a</math> is 51.88% and the weight coefficient of the cutting energy consumption <math>Ec</math> is 48.12%, which is shown by the Equation (9) of the multi-index orthogonal optimization comprehensive score Grade
 {| class="formulaSCP" style="width: 100%; text-align: center;"

@@ Line 83: / Line 83: @@
 ====2.2 The measuring instrument of surface quality====
-The instrument used for surface quality observation is shown in [[#tab-3|Table 3]]. The surface roughness measurement uses the contour arithmetic mean deviation'' Ra'' as the surface roughness evaluation parameter. The measurement parameters are selected as follows: the sampling length is 0.8 mm, the evaluation length is 4 mm, and the measurement length is 5.6 mm. The residual stresses in the surface layer of the gray cast iron after boring were measured by X ray diffraction using the protoiXRD residual stress tester.
+The instrument used for surface quality observation is shown in [[#tab-3|Table 3]]. The surface roughness measurement uses the contour arithmetic mean deviation   <math>R_a </math> as the surface roughness evaluation parameter. The measurement parameters are selected as follows: the sampling length is 0.8 mm, the evaluation length is 4 mm, and the measurement length is 5.6 mm. The residual stresses in the surface layer of the gray cast iron after boring were measured by X ray diffraction using the protoiXRD residual stress tester.
 <div class="center" style="font-size: 75%;">'''Table 3'''. Surface quality observation instrument</div>

@@ Line 262: / Line 262: @@
 ====3.2.2 Analysis of surface micromorphology====
-[[#img-10|Figures 10]] to [[#img-12|12]] are observation results of the surface morphology of gray cast iron boring under different cutting parameters. The results are obtained by observing the KH-1300 three-dimensional digital microscope and scanning electron microscope TM3030. The magnification of the KH-1300 three-dimensional digital microscope is: 70 times, scanning electron microscope TM3030 magnification of 1000 times. The surface characteristics of [[#img-10|Figures 10]] to [[#img-12|12]] can be observed by means of the KH-1300 three-dimensional digital microscope. In particular, the distribution of the surface texture is very different. The surface roughness Ra is 2.450, 2.826, and 3.151μm in order of arrangement in the [[#img-10|Figures 10]] to [[#img-12|12]]. When the surface roughness is small, the surface texture is finely distributed. On the other hand, the distribution interval is large. According to the results of SEM observation, the gray cast iron materials mainly consist of brittle fractures. Graphite is detached from the surface of cast iron to form pits during cutting, and the sharp edges of graphite lamella structure of flake graphite are easy to form irregular cracks and fractures. Affects the integrity of the machined surface.
+[[#img-10|Figures 10]] to [[#img-12|12]] are observation results of the surface morphology of gray cast iron boring under different cutting parameters. The results are obtained by observing the KH-1300 three-dimensional digital microscope and scanning electron microscope TM3030. The magnification of the KH-1300 three-dimensional digital microscope is: 70 times, scanning electron microscope TM3030 magnification of 1000 times. The surface characteristics of [[#img-10|Figures 10]] to [[#img-12|12]] can be observed by means of the KH-1300 three-dimensional digital microscope. In particular, the distribution of the surface texture is very different. The surface roughness <math>R_a </math> is 2.450, 2.826, and 3.151μm in order of arrangement in the [[#img-10|Figures 10]] to [[#img-12|12]]. When the surface roughness is small, the surface texture is finely distributed. On the other hand, the distribution interval is large. According to the results of SEM observation, the gray cast iron materials mainly consist of brittle fractures. Graphite is detached from the surface of cast iron to form pits during cutting, and the sharp edges of graphite lamella structure of flake graphite are easy to form irregular cracks and fractures. Affects the integrity of the machined surface.
-[[#img-10|Figure 10]] compares the surface topography under different back-feeding amount. It can be seen from [[#img-10|Figures 10]] (a<sub>1</sub>) and (a<sub>2</sub>) that the width of the surface texture slightly increases with the increase of the back knife, and the surface roughness increases. Slightly increased. Fig.8 shows the comparison of surface topography at different cutting speeds. It can be seen from Fig.11 (a<sub>1</sub>) and (a<sub>2</sub>) that as the cutting speed increases, the surface texture is denser and more uniform, the surface roughness is reduced, and the surface quality is improved. Comparing the scanning electron microscopy image of [[#img-11|Figure 11]], [[#img-11|Figure 11]] (b<sub>1</sub>) shows that the lower cutting speed is more pronounced with pits, while [[#img-11|Figure 11]] (b<sub>2</sub>) is smoother and flatter. [[#img-12|Figure 12]] shows the comparison of the surface topography at different feed rates. In the case of a small feed, the texture of the workpiece surface is relatively narrow. It can be seen from [[#img-12|Figure 12]] (b<sub>1</sub>) that the machined surface is relatively smooth and smooth. Comparing [[#img-12|Figure 12]] (a<sub>1</sub>) and (a<sub>2</sub>), as the feed rate increases, the surface texture of the workpiece changes from shallow to deep, from narrow to wide, the surface roughness increases, and pits on the surface of the material also increase, brittle fracture characteristics are obvious.  The surface quality has deteriorated significantly.
+[[#img-10|Figure 10]] compares the surface topography under different back-feeding amount. It can be seen from [[#img-10|Figures 10]] (a<sub>1</sub>) and (a<sub>2</sub>) that the width of the surface texture slightly increases with the increase of the back knife, and the surface roughness increases. Slightly increased. [[#img-8|Figure 8]] shows the comparison of surface topography at different cutting speeds. It can be seen from [[#img-11|Figure 11]] (a<sub>1</sub>) and (a<sub>2</sub>) that as the cutting speed increases, the surface texture is denser and more uniform, the surface roughness is reduced, and the surface quality is improved. Comparing the scanning electron microscopy image of [[#img-11|Figure 11]], [[#img-11|Figure 11]] (b<sub>1</sub>) shows that the lower cutting speed is more pronounced with pits, while [[#img-11|Figure 11]] (b<sub>2</sub>) is smoother and flatter. [[#img-12|Figure 12]] shows the comparison of the surface topography at different feed rates. In the case of a small feed, the texture of the workpiece surface is relatively narrow. It can be seen from [[#img-12|Figure 12]] (b<sub>1</sub>) that the machined surface is relatively smooth and smooth. Comparing [[#img-12|Figure 12]] (a<sub>1</sub>) and (a<sub>2</sub>), as the feed rate increases, the surface texture of the workpiece changes from shallow to deep, from narrow to wide, the surface roughness increases, and pits on the surface of the material also increase, brittle fracture characteristics are obvious.  The surface quality has deteriorated significantly.
 <div id='img-10'></div>

← Older revision		Revision as of 11:55, 10 March 2022
Line 67:		Line 67:


−	The machine tool is MCV-810 machining center (produced by Taiwan Seiki Corporation), the maximum spindle speed is 8000 r / min, the maximum power of the machine tool is 20 kVA, and the system is the FANAC CNC system.	+	The machine tool is MCV-810 machining center (produced by Taiwan Seiki Corporation), the maximum spindle speed is 8000 r/min, the maximum power of the machine tool is 20 kVA, and the system is the FANAC CNC system.

	The cutting force data acquisition system is made up of a cutting force measurement system composed of Kistler9257B type piezoelectric sensor, Kistler5080 charge amplifier, Kistler5697 data collector, computer and Dyno Ware2825D software. The WT330 series power meter produced by Henghe company (YOKOGAWA) is used to measure the power of machine tools during processing.		The cutting force data acquisition system is made up of a cutting force measurement system composed of Kistler9257B type piezoelectric sensor, Kistler5080 charge amplifier, Kistler5697 data collector, computer and Dyno Ware2825D software. The WT330 series power meter produced by Henghe company (YOKOGAWA) is used to measure the power of machine tools during processing.

@@ Line 991: / Line 991: @@
 [2] Xie J., Jinghua M., Xiao L. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method. Journal of Chongqing University of Technology (Natural Science), 34(8):77-86, 2020.
-[3] Liu Xia., Yiping Y. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.
+[3] Liu X., Yiping Y. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.
-[4] Wei D., Yuyi L., Jingang L. Milling parameter optimization based on low energy consumption. Chinese Journal of Engineering Machinery, 17(5):397-400, 2019.
+[4] Deng W., Yuyi L., Jingang L. Milling parameter optimization based on low energy consumption. Chinese Journal of Engineering Machinery, 17(5):397-400, 2019.
 [5] Chandgude S.B., Pawar P.J., Sadaiah M. Process parameter optimization based on principal components analysis during machining of hardened steel. International Journal of Industrial Engineering Computations, 6(3):379-390, 2015.

@@ Line 989: / Line 989: @@
 [1] Newman S., Nassehi A., Dhokia V., et al. Energy efﬁcient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 5(2):127-136, 2012.
-[2] Jun X., Jinghua M., Xiao L. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method. Journal of Chongqing University of Technology (Natural Science), 34(8):77-86, 2020.
+[2] Xie J., Jinghua M., Xiao L. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method. Journal of Chongqing University of Technology (Natural Science), 34(8):77-86, 2020.
 [3] Xia L., Yiping Y. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.

@@ Line 991: / Line 991: @@
 [2] Jun X., Jinghua M., Xiao L. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method. Journal of Chongqing University of Technology (Natural Science), 34(8):77-86, 2020.
-[3] Xia L., Yiping Y.. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.
+[3] Xia L., Yiping Y. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.
-[4] Wei D., Yuyi L., Jingang. L. Milling parameter optimization based on low energy consumption. Chinese Journal of Engineering Machinery, 17(5):397-400, 2019.
+[4] Wei D., Yuyi L., Jingang L. Milling parameter optimization based on low energy consumption. Chinese Journal of Engineering Machinery, 17(5):397-400, 2019.
 [5] Chandgude S.B., Pawar P.J., Sadaiah M. Process parameter optimization based on principal components analysis during machining of hardened steel. International Journal of Industrial Engineering Computations, 6(3):379-390, 2015.
@@ Line 1,013: / Line 1,013: @@
 [13] Campatelli G., Lorenzini L., Scippa A. Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 66(2):309-316, 2014.
-[14] Velchev S, Kolev I, Ivanov K, et al. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning[J]. Journal of Cleaner Production, 2014, 80: 139-149.
+[14] Velchev S., Kolev I., Ivanov K., et al. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning. Journal of Cleaner Production, 80:139-149, 2014.
-[15] Kant G, Sangwan K S. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining[J]. Journal of Cleaner Production, 2014, 83: 151-164.
+[15] Kant G., Sangwan K.S. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production, 83:151-164, 2014.
-[16] Liu Z J, Sun D P, Lin C X, et al. Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs[J]. Journal of Cleaner Production, 2016, 124: 266-275.
+[16] Liu Z.J., Sun D.P., Lin C.X., et al. Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs. Journal of Cleaner Production, 124:266-275, 2016.
-[17] Zhang Y, Liu Q, Zhou Y. Integrated optimization of cutting parameters and scheduling for reducing carbon emissions[J]. Journal of Cleaner Production, 2017, 149: 886-895.
+[17] Zhang Y., Liu Q., Zhou Y. Integrated optimization of cutting parameters and scheduling for reducing carbon emissions. Journal of Cleaner Production, 149:886-895, 2017.
-[18] Zhang H, Deng Z, Fu Y, et al. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption[J]. Journal of Cleaner Production, 2017, 166: 1407-1414.
+[18] Zhang H., Deng Z., Fu Y., et al. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. Journal of Cleaner Production, 166:1407-1414, 2017.
-[19] Yan J, Li L. Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality[J]. Journal of Cleaner Production, 2013, 52(4): 462-471.
+[19] Yan J., Li L. Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52(4):462-471, 2013.
-[20] Tzeng C J, Lin Y H, Yang Y K, et al. Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis[J]. Journal of Materials Processing Technology, 2009, 209(6): 2753-2759.
+[20] Tzeng C.J., Lin Y.H., Yang Y.K., et al. Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. Journal of Materials Processing Technology, 209(6):2753-2759, 2009.

@@ Line 985: / Line 985: @@
 ==References==
-[1] Newman S, Nassehi A, Dhokia, V, et al. Energy efﬁcient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 2012, 5 (2): 127-136.
+[1] Newman S., Nassehi A., Dhokia V., et al. Energy efﬁcient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 5(2):127-136, 2012.
-[2] Xie Jun, Ma Jinghua, Luo Xiao. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method［J］. Journal of Chongqing University of Technology (Natural Science), 2020, 34(8): 77-86.
+[2] Jun X., Jinghua M., Xiao L. Research on energy-saving optimization-oriented machine tool energy consumption model and cutting parameter decision-making method. Journal of Chongqing University of Technology (Natural Science), 34(8):77-86, 2020.
-[3] Liu Xia, Yao Yiping. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters[J]. Henan Science and Technology, 2020,05:73-75
+[3] Xia L., Yiping Y.. Research on energy consumption optimization method of CNC machine tool processing based on cutting parameters. Henan Science and Technology, 05:73-75, 2020.
-[4] Deng Wei, Luo Yuyi, Luo Jingang. Milling parameter optimization based on low energy consumption[J]. Chinese Journal of Engineering Machinery. 2019,10,17(5):397-400
+[4] Wei D., Yuyi L., Jingang. L. Milling parameter optimization based on low energy consumption. Chinese Journal of Engineering Machinery, 17(5):397-400, 2019.
-[5] Chandgude S B, Pawar P J, Sadaiah M. Process parameter optimization based on principal components analysis during machining of hardened steel[J]. International Journal of Industrial EngineeringComputations, 2015, 6(3): 379-390.
+[5] Chandgude S.B., Pawar P.J., Sadaiah M. Process parameter optimization based on principal components analysis during machining of hardened steel. International Journal of Industrial Engineering Computations, 6(3):379-390, 2015.
-[6] Jiang Z, Zhou F, Zhang H, et al. Optimization of machining parameters considering minimum cutting fluid consumption[J]. Journal of Cleaner Production, 2015, 108: 183-191.
+[6] Jiang Z., Zhou F., Zhang H., et al. Optimization of machining parameters considering minimum cutting fluid consumption. Journal of Cleaner Production, 108:183-191, 2015.
-[7] Balaji M, Murthy B S N, Rao N M. Optimization of Cutting Parameters in Drilling of AISI 304 Stainless Steel Using Taguchi and ANOVA [J]. Procedia Technology, 2016, 25: 1106-1113.
+[7] Balaji M., Murthy B.S.N., Rao N.M. Optimization of cutting parameters in drilling of AISI 304 stainless steel using Taguchi and ANOVA. Procedia Technology, 25:1106-1113, 2016.
-[8] Rajesh N, Yohan M, Venkataramaiah P, et al. Optimization of Cutting Parameters for Minimization of Cutting Temperature and Surface Roughness in Turning of Al6061 Alloy[J]. Materials Today Proceedings, 2017, 4(8): 8624-8632.
+[8] Rajesh N., Yohan M., Venkataramaiah P., et al. Optimization of cutting parameters for minimization of cutting temperature and surface roughness in turning of Al6061 Alloy. Materials Today Proceedings, 4(8):8624-8632, 2017.
-<span id='OLE_LINK101'></span><span id='OLE_LINK102'></span><span id='OLE_LINK103'></span><span id='OLE_LINK104'></span>[9] Chávez-García H, Castillo-Villar K K. Simulation-based model for the optimization of machining parameters in a metal-cutting operation[J]. Simulation Modelling Practice & Theory, 2018, 84: 204–221.
+[9] Chávez-García H., Castillo-Villar K.K. Simulation-based model for the optimization of machining parameters in a metal-cutting operation. Simulation Modelling Practice & Theory, 84:204–221, 2018.
-[10] Mativenga P T, Rajemi M F. Calculation of optimum cutting parameters based on minimum energy footprint[J]. Cirp Annals, 2011, 60(1): 149–152.
+[10] Mativenga P.T., Rajemi M.F. Calculation of optimum cutting parameters based on minimum energy footprint. Cirp. Annals, 60(1):149–152, 2011.
-[11] Kuram E, Ozcelik B, Bayramoglu M, et al. Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments[J]. Journal of Cleaner Production, 2013, 42(3): 159-166.
+[11] Kuram E., Ozcelik B., Bayramoglu M., et al. Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. Journal of Cleaner Production, 42(3):159-166, 2013.
-[12] Bhushan R K. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy Si C particle composites[J]. Journal of Cleaner Production, 2013, 39(1): 242-254.
+[12] Bhushan R.K. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy Si C particle composites. Journal of Cleaner Production, 39(1):242-254, 2013.
-[13] Campatelli G, Lorenzini L, Scippa A. Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel[J]. Journal of Cleaner Production, 2014, 66(2): 309-316.
+[13] Campatelli G., Lorenzini L., Scippa A. Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 66(2):309-316, 2014.
 [14] Velchev S, Kolev I, Ivanov K, et al. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning[J]. Journal of Cleaner Production, 2014, 80: 139-149.