Energy efficient cutting parameter optimization

doi:10.1007/s11465-020-0627-x

Front. Mech. Eng.

2021, Vol. 16

Issue (2) : 221-248 https://doi.org/10.1007/s11465-020-0627-x

REVIEW ARTICLE

Energy efficient cutting parameter optimization

Xingzheng CHEN¹, Congbo LI²(

), Ying TANG³, Li LI¹, Hongcheng LI⁴

¹. College of Engineering and Technology, Southwest University, Chongqing 400715, China
². State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
³. Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA
⁴. College of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Download: PDF(4153 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

Keywords energy efficiency cutting parameter optimization machining process

Corresponding Author(s): Congbo LI

Just Accepted Date: 29 March 2021 Online First Date: 14 May 2021 Issue Date: 15 June 2021

Cite this article:

Xingzheng CHEN,Congbo LI,Ying TANG, et al. Energy efficient cutting parameter optimization[J]. Front. Mech. Eng., 2021, 16(2): 221-248.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-020-0627-x
https://academic.hep.com.cn/fme/EN/Y2021/V16/I2/221

Fig.1 Energy boundary of the machining process.

Fig.2 Energy characteristic analysis of a machining process.

Fig.3 Flowchart of cutting parameter optimization by using experimental design. S/N: Signal-to-noise ratio; GRA: Gray relational analysis; DFA: Desirability function analysis.

Fig.4 Flowchart of cutting parameter optimization by using energy models.

Category	References	Machining type	Workpiece material	Tool insert material	Lubrication	Optimization method(s)	Optimization objective(s)			Most influential factors on energy/power consumption	Minimum energy consumption can be achieved with	Minimum power consumption can be achieved with
Category	References	Machining type	Workpiece material	Tool insert material	Lubrication	Optimization method(s)	Power consumption	Energy consumption	Multiobjective optimization with other objective(s)	Most influential factors on energy/power consumption	Minimum energy consumption can be achieved with	Minimum power consumption can be achieved with
Cutting power reduction (single-objective optimization)	Fratila and Caizar [34]	Face milling	AlMg₃	HSS	Dry, MQL, and wet	Taguchi method	Cutting power	×	×	v_c and a_p	N/A	v_c?↓, f↓, a_p?↓
	Bhattacharya et al. [37]	Turning	AISI 1045 steel	Coated carbide insert	Dry	Taguchi method	Cutting power	×	×	v_c	N/A	v_c?↓, f→, a_p→
Cutting power reduction (multiobjective optimization)	Hanafi et al. [28]	Turning	PEEK-CF30	TiN	Dry	Taguchi method and GRA	Cutting power	×	Surface roughness	a_p	N/A	v_c↓, f↓, a_p?↓
Cutting power reduction (multiobjective optimization)	Kant and Sangwan [38]	Turning	AISI 1045 steel	Carbide insert	Dry	Taguchi method, GRA and PCA	Cutting power	×	Surface roughness	f	N/A	v_c↑, f↓, a_p?↓
Energy saving (single-objective optimization)	Camposeco-Negrete [9]	Turning	AISI 6061 T6 aluminum	Carbide insert	Wet	Taguchi method	Cutting power	Air cutting energy and cutting energy	×	a_p (power consumption), f (energy consumption)	v_c↓, f↑, a_p↓	v_c↓, f↓, a_p↓
	Emami et al. [27]	Grinding	Al₂O₃ceramic	Diamond insert	MQL	Taguchi method	×	Cutting energy	×	f	f↑, a_p↑	N/A
	Campatelli et al. [36]	End milling	AISI 1050 carbon steel	Coated, carbide insert	Dry	RSM	×	Standby energy, air cutting energy, cutting energy	×	N/A	N/A	N/A
	Zhang et al. [39]	Turning	0Cr18Ni9 steel	N/A	Dry, MQL and wet	Taguchi method	×	Cutting energy	×	f and a_p	v_c?↑, f↑, a_p?↑	N/A
	Camposeco-Negrete et al. [40]	Turning	AISI 1018 steel	N/A	Dry and wet	Taguchi method	×	Cutting energy	×	f and a_p	v_c↓, f↑, a_p↓	N/A
	Bilga et al. [41]	Turning	EN 353 alloy steel	Carbide insert	N/A	Taguchi method	×	Cutting energy	×	f	v_c↑, f↑, a_p↓	N/A
Energy saving (single-objective optimization)	Altıntaş et al. [42]	Milling	AISI 304 steel	HSS	N/A	RSM	×	Cutting energy	×	f	v_c↓, f↑, a_p↓	N/A
Energy saving (multiobjective optimization)	Bhushan [43]	Turning	7075 Al alloy	Carbide insert	Dry, wet and cryogenic	RSM and DFA	×	Cutting energy	Tool life	v_c	v_c↓, f↓, a_p↓	N/A
	Yan and Li [44]	Face milling	medium-carbon steel (C45)	Carbide insert	Dry	RSM, GRA, and SQP	×	Cutting energy	MRR surface roughness	a_e	v_c↓, f↑, a_p↑, a_e↑	N/A
	Arriaza et al. [45]	Milling	Aluminum 7075	N/A	N/A	RSM and DFA	×	Cutting energy	Machining time	v_c	N/A	N/A
	Bagaber and Yusoff [46]	Turning	AISI 316 steel	Carbide insert	Dry	RSM and DFA	×	Cutting energy	Surface roughness, tool wear	f	v_c↓, f↑, a_p↑	N/A
	Suneesh and Sivapragash [47]	Turning	Mg/Al₂O₃hybrid composite	Carbide insert	Dry and MQL	Taguchi and GRA, Taguchi, and TOPSIS (for contrast)	×	Cutting energy	Surface roughness, cutting force, cutting temperature	f	v_c↓, f↓, a_p↓	N/A

Tab.1 Studies on energy efficient cutting parameter optimization by using experimental design

Category	References	Machining/model type	Energy models	Multiobjective optimization with other objective(s)	Main conclusions
Cutting parameter optimization by using experimental design and mathematical models, such as ANN, RSM or Kriging model	Jang et al. [48]	Milling, ANN-based energy model	$SEC = P cutting / MRR$	N/A	PSO was used to find the optimal cutting parameters for minimizing specific cutting energy. Minimum energy consumption can be achieved with a large feed rate and cutting depth
	Li et al. [35]	Milling, RSM-based energy model	$SEC = (E standby + E air + E cutting + E tool-changing) / MRV = 229.21 − 41.48 v c − 48.58 f − 61.30 a p − 91.66 a e + 27.82 v c 2 + 35.17 a e 2 + 31.06 v c f + 22.48 f a p + 24.01 f a e + 25.52 a p a e$	Machining time	A trade-off is found between SEC and machining time. Cutting width is observed to be the major factor affecting SEC, followed by cutting depth, feed rate, and cutting velocity. Minimum energy consumption can be achieved with a large cutting velocity, feed rate, cutting depth, and cutting width
	Moreira et al. [14]	Milling, RSM-based energy model	$SEC = E cutting / MRV$	MRR, power load	Feed rate is found to be the most significant factor on SEC. A large feed rate is recommended for energy efficient machining
	Nguyen [15]	Milling, Kriging-based energy model	$SEC = F cutting / (f a p)$	Surface roughness, production rate	Cutting depth is the most influential parameter on SEC. Large cutting parameters can decrease SEC
Cutting parameter optimization by using empirical models	Rajemi et al. [13]	Turning (EMMBMS)	$E footpr int = E standby + E cutting + E tool-changing + E tool-embodied$	×	Optimal cutting parameters vary with energy boundaries. The optimal cutting parameters for minimum cost does not necessarily satisfy the minimum energy criterion
	Arif et al. [16]	Turning (EMMBMS)	$E footpr int = E cutting + E standby + E tool-changing + E tool-embodied$	×	Influence of cutting parameters on energy consumption is different in roughing pass and finishing pass
	Li et al. [49]	Milling (EMMBMS)	$E cutting = E material + E loss-motor + E loss-moving + E idle-auxiliary$	Surface roughness	Increasing feed rate but decreasing spindle speed can reduce energy consumption and improve production rate
	Velchev et al. [50]	Milling (EMMBMS)	$E electrical = SEC · MRR · t cutting + P standby t insert-changing z t cutting T tool$	×	Low energy consumption can be achieved with maximum possible values of feed rate and cutting depth
	Wang et al. [33]	Turning (EMMBMS)	$E footprint = E startup + E cutting + E tool-changing + E tool-embodied + E fluid-embodied$	Machining cost, surface roughness	Cutting parameter optimization is significant to energy reduction but optimization effect on surface roughness is limited. Cutting parameters are ranged in relatively reasonable ranges before optimization
	Albertelli et al. [51]	Milling (EMMBMTC)	$E electrical = E functional-modules + E standby + E material$	×	The optimal cutting parameters for minimum energy consumption is different from that of minimum machining time. Proper selection of cutting parameters can reduce both energy consumption and the machining time
	Ma et al. [52]	Millling (EMMBMS)	$E electrical = ∫ 0 t cutting (P material + P air) d t$	×	Increment of cutting velocity leads to a decrement of energy consumption
	Xiong et al. [53]	Milling (EMMBMS)	$E cutting = π D milling F cutting MRR 3.672 × 106 f z a p a e η m + 0.746 P rated-compressed γ compressed$	Milling dimensional accuracy, machining time, machining cost	Multiobjective cutting parameter optimization obtained a more reasonable results even each objective is not the absolute optimal
	Deng et al. [54]	Milling (EMMBMS)	$E cutting = ∫ 0 t cutting (P s tan dby + P unload-feed + P unload-spindle + P material + P auxiliary) d t$	Machining time	Cutting specific energy consumption first decreased and then increased with the increase of cutting velocity, while it always decreased with the increase of feed rate, cutting depth and cutting width
	He et al. [55]	Milling and turning (EMMBMTC)	$E electrical = (P standby t standby-preparation + P spraying-cooling t spraying-cooling + P unload-spindle t cutting + P unload-feed t cutting + P feed-fast t feed-fast + P material t cutting) 60$	Machining time, cutting force	Different algorithms can be selected for different machining conditions and demands of specific objective problem
	Li et al. [25]	Milling (EMMBMS)	$E electrical = E startup + E standby + ∑ i = 1 m (E air i + E cutting i) + E tool-changing$	Machining cost	Specific energy consumption first decreases with the increase in cutting velocity and then increases. It always decreases with the increase in feed rate and cutting depth
	Lu et al. [17]	Turning (EMMBMS)	$E footpr int = E s tan dby + E tool-changing + E tool-embodied + E fluid-embodied + E cutting$	Machining precision	A balance is found between minimum energy consumption and maximum machining precision. MOBSA outperforms NSGA-II, MOPSO, multiobjective evolutionary algorithm based on decomposition (MOEA/D), and MOHS
	Zhang et al. [56]	Milling (EMMBMS)	$E electrical = E startup + ∑ i = 1 m E s tan dby i + ∑ i = 1 m E approaching i + ∑ i = 1 m E cutting i + E tool-changing$	Machining time, carbon emission	Energy consumption can be reduced with a large cutting velocity, feed rate, cutting depth, and cutting width. The balance of machining time, energy consumption, and carbon emissions should be struck
	Zhong et al. [57]	Turning (EMMBMS)	$SEC = P s tan dby + k spindle n + b spindle + λ v c α F f β F a p ψ F MRR$	×	The effect of feed rate on specific energy consumption is less than that of cutting velocity and cutting depth. A large feed rate can be used in energy efficient machining process
	Zhang et al. [58]	Turning (EMMBMS)	$E electrical = (P air + P material + P loss-spindle) t cutting + P standby t standby-preparation + P air t air + P standby t tool-changing + P auxiliary (t standby-preparation + t air + t cutting + t tool-changing)$	Noise emission, machining cost	A large feed rate and cutting depth minimize the energy consumption of the machining process. The influence of cutting speed on energy is insignificant. A conflict is found between minimizing energy consumption and noise emission
	Bagaber and Yusoff [59]	Turning (EMMBMS)	$E electrical-dry = P startup t startup + P standby t standby-preparation + P air t air + (P air + k m MRR) t cutting + P standby t tool-changing,$ $E electrical-wet = P startup t startup + P standby t standby-preparation + P air t air + (P air + k m MRR) t cutting + P standby t tool-changing + E fluid-embodied$	Machining cost, surface roughness	Minimum energy consumption can be achieved with the highest value of feed rate and lowest value of cutting depth. Feed rate is the most significant factor affecting energy consumption
	Hu et al. [60]	Turning (EMMBMS)	$E electrical = (P material + P unload-feed + P unload-spindle + P spraying-cooling + P standby) t cutting + E approaching + E rotation-changing$	×	Simulated annealing (SA) outperforms expectation–maximization (EM) because it requires less computation time with minimal sacrifice in solution quality compared with EM
	Li et al. [61]	Milling (EMMBMS)	$E electrical = E standby + E air + E cutting + E tool-changing$	Machining time	Cutting depth and width are the most influential factors for specific energy consumption. A trade-off is found between specific energy consumption and machining time
	Wang et al. [62]	Milling (EMMBMS)	$E electrical = E s tan dby + E approaching + E leaving + E cutting$	×	The range of cutting parameters increases with cutting tool diameter. Accordingly, a large MRR can be used to reduce energy consumption
	Chen et al. [32]	Milling (EMMBMS)	$E footprint = E standby + E air + E cutting + E tool-changing + E tool-embodied$	Machining time	Multiobjective optimization strikes a balance between minimum energy consumption and minimum machining time

Tab.2 Studies on energy efficient cutting parameter optimization by using energy models

Fig.5 Flowchart of an exemplary nondominated sorting genetic algorithm II.

Fig.6 Schematic representation of metareinforcement learning of cutting parameter optimization [93].

Fig.7 On-board cutting parameter optimization.

a_c	Clearance angle of the tool tip
a_e	Cutting width
a_l	Lead angle of the tool tip
a_p	Cutting depth
a_p,max	Maximum cutting depth
a_p,min	Minimum cutting depth
b_spindle	Unload power coefficient of spindle system
B(n_s)	Viscous damping coefficient of main transmission system equivalently transformed to motor shaft
B_SA	Coefficient of the spindle acceleration energy
B_SRD	Coefficient of the spindle deceleration energy
C_F	Coefficient of cutting force
C_SA	Coefficient of the spindle acceleration energy
C_SRD	Coefficient of the spindle deceleration energy
C_T	Coefficient of tool life
D_avg	Average diameter of workpiece
D_milling	Diameter of milling tool
E_ac	Spindle acceleration energy
E_air	Air cutting energy
$E a i r i$	Air cutting energy of the ith pass
$E a p p r o a c h i n g i$	Energy consumption for tool approaching of the ith pass
E_cutting	Cutting energy
$E c u t t i n g i$	Cutting energy of the ith pass
E_dc	Spindle deceleration energy
E_electrical	Electrical energy of the machining process
E_{electrical-dry}	Electrical energy of the machining process under dry condition
E_{electrical-wet}	Electrical energy of the machining process under wet condition
E_embodied	Electrical energy of machine tool and the embodied energy of consumable material
E_{fluid-embodied}	Embodied energy consumption of cutting fluid
E_{fluid-material}	Energy used to fabricate the material of cutting fluid
E_footprint	Energy footprint of the machining process
E_{functional-modules}	Energy consumption by main machine tool functional modules
E_{idle-auxiliary}	Idle energy of auxiliary system
E_insert	Energy to fabricate the cutting insert material
E_leaving	Energy consumption for tool leaving
E_loss-motor	Additional load loss energy of main motor
E_loss-moving	Inertia energy loss of moving components
E_m	Changed energy of electromagnetic field
E_material	Material removal energy
E_{rotation-changing}	Energy consumption for spindle rotation changing (non-cutting)
E_standby	Standby energy
$E s tan d b y i$	Standby energy of the ith pass
E_{standby-preparation}	Standby energy used to bring the workpiece and cutting tool to the about-to cut position and to set up the numerical control program before machining
E_startup	Startup energy
E_{tool-changing}	Standby energy used for changing the worn cutting tool
E_{tool-embodied}	Embodied energy consumption of cutting tool
f	Feed rate
f_max	Maximum feed rate
f_min	Minimum feed rate
f_z	Feed rate per tooth
F_cutting	Cutting force
h	Deformed chip thickness
J_m(n_s)	Rotational inertia of main transmission system equivalently transformed to motor shaft
k_m	Constant for material removal power
k_spindle	Unload power coefficient of spindle system
K	Cutting pressure
l	Cutting length of workpiece
m	Number of machining passes
M_om(n_s)	Load torque of electric motor in the main transmission system
n	Spindle speed
n_teeth	Average number of engaged tool teeth
$n E j pq$	Final spindle speed for the jth speed change in spindle rotation
$n S j pq$	Initial spindle speed for the jth speed change in spindle rotation
n(t)	Spindle speed varying with time
N	Number of cutting edges of each insert
P_air	Air cutting power
P_auxiliary	Power of auxiliary system
$P c j pq$	Power consumption of spindle system during the jth speed change of the spindle rotation in noncutting operations from feature F_p to feature F_q
P_cutting	Cutting power
P_feed-fast	Power for fast feeding
P_{idle-auxiliary}	Idle power of auxiliary system
P_loss	Additional load loss power of spindle system and feed systems
P_loss-spindle	Additional load loss power of spindle system
P_m	Nominal motor power of spindle
P_material	Material removal power
P_{rated-compressed}	Rated power of compressed air motor
P_removal	Material removal power
P_{spraying-cooling}	Power for spraying cooling fluid
P_standby	Standby power
P_startup	Startup power
P_unload	Unload power of spindle and feed systems
P_unload-feed	Unload power of feed system
P_{unload-spindle}	Unload power of spindle system
Ra	Surface roughness
Ra_max	Permitted maximum surface roughness
t_ac	Time duration of spindle acceleration
t_air	Air cutting time
t_cutting	Cutting time
$t c j pq$	Time duration during the jth speed change of the spindle rotation in noncutting operations from feature F_p to feature F_q
t_end	Spindle acceleration ending at this time point
t_feed-fast	Time for fast feeding
t_{insert-changing}	Time for changing an insert
t_{spraying-cooling}	Time for spraying cooling fluid
t_st	Spindle acceleration starting at this time point
t_{standby-preparation}	Standby time used to bring the workpiece and cutting tool to the about-to cut position and to set up the numerical control program before machining
t_startup	Startup time
t_{tool-changing}	Tool changing time
T_fluid	Replacement cycle of cutting fluid
T_e	Economic tool life
T_SA	Coefficient of the spindle acceleration energy
T_tool	Tool life
U_fluid	Unit embodied energy of cutting fluid
U_tool	Unit embodied energy of cutting tool
V_additional	Additional volume of cutting fluid
v_c	Cutting velocity
v_c,max	Maximum cutting velocity
v_c,min	Minimum cutting velocity
V_initial	Initial volume of cutting fluid
V_insert	Volume of one insert
x_F	Coefficient of cutting force
y_F	Coefficient of cutting force
z	Number of cutting inserts
z_F	Coefficient of cutting force
α_A	Coefficient of the spindle system
α_F	Coefficient of cutting force
α_feed	Unload power coefficient of feed system
α_spindle	Unload power coefficient of spindle system
α_T	Coefficient of tool life
β_F	Coefficient of cutting force
β_feed	Unload power coefficient of feed system
β_spindle	Unload power coefficient of spindle system
β_T	Coefficient of tool life
γ_compressed	Load factor of compressed air motor
γ_feed	Unload power coefficient of feed system
γ_spindle	Unload power coefficient of spindle system
γ_T	Coefficient of tool life
δ	Concentration of cutting fluid
ξ_loss	Additional load loss coefficient
η_m	Overall efficiency of spindle motor
λ	Coefficient of cutting force
λ_loss	Additional load loss coefficient
μ_F	Coefficient of cutting force
μ_feed	Unload power coefficient of feed system
ρ	Density of the cutting fluid
ψ_F	Coefficient of cutting force

1	IEA. Key energy statistics. Available at IEA website. 2020-08-20
2	W Cai, C Liu, K Lai, et al.. Energy performance certification in mechanical manufacturing industry: A review and analysis. Energy Conversion and Management, 2019, 186: 415–432 https://doi.org/10.1016/j.enconman.2019.02.041
3	W Cai, F Liu, X Zhou, et al.. Fine energy consumption allowance of workpieces in the mechanical manufacturing industry. Energy, 2016, 114: 623–633 https://doi.org/10.1016/j.energy.2016.08.028
4	X Chen, C Li, Y Tang, et al.. An Internet of Things based energy efficiency monitoring and management system for machining workshop. Journal of Cleaner Production, 2018, 199: 957–968 https://doi.org/10.1016/j.jclepro.2018.07.211
5	P Liu, F Liu, H Qiu. A novel approach for acquiring the real-time energy efficiency of machine tools. Energy, 2017, 121: 524–532 https://doi.org/10.1016/j.energy.2017.01.047
6	ISO. Machine tools—Environmental evaluation of machine tools—Part 1: Design methodology for energy-efficient machine tools. 2017. Available at ISO website. 2020-08-20
7	S T Newman, A Nassehi, R Imani-Asrai, et al.. Energy efficient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 2012, 5(2): 127–136 https://doi.org/10.1016/j.cirpj.2012.03.007
8	H S Yoon, E S Kim, M S Kim, et al.. Towards greener machine tools—A review on energy saving strategies and technologies. Renewable & Sustainable Energy Reviews, 2015, 48: 870–891 https://doi.org/10.1016/j.rser.2015.03.100
9	C Camposeco-Negrete. Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 2013, 53: 195–203 https://doi.org/10.1016/j.jclepro.2013.03.049
10	M Aslani, M S Mesgari, M Wiering. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events. Transportation Research Part C, Emerging Technologies, 2017, 85: 732–752 https://doi.org/10.1016/j.trc.2017.09.020
11	R Kumar, P S Bilga, S Singh. Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of Cleaner Production, 2017, 164: 45–57 https://doi.org/10.1016/j.jclepro.2017.06.077
12	J B Dahmus, T G Gutowski. An environmental analysis of machining. In: Proceedings of 2004 ASME International Mechani-cal Engineering Congress and RD&D Expo. Anaheim: ASME, 2004, 643–652
13	M F Rajemi, P T Mativenga, A Aramcharoen. Sustainable machining: Selection of optimum turning conditions based on minimum energy considerations. Journal of Cleaner Production, 2010, 18(10–11): 1059–1065 https://doi.org/10.1016/j.jclepro.2010.01.025
14	L C Moreira, W Li, X Lu, et al.. Energy-efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies. Robotics and Computer-Integrated Manufacturing, 2019, 58: 1–12 https://doi.org/10.1016/j.rcim.2019.01.011
15	T T Nguyen. Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling. Measurement, 2019, 136: 525–544 https://doi.org/10.1016/j.measurement.2019.01.009
16	M Arif, I A Stroud, O Akten. A model to determine the optimal parameters for sustainable-energy machining in a multi-pass turning operation. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2014, 228(6): 866–877 https://doi.org/10.1177/0954405413508945
17	C Lu, L Gao, X Li, et al.. Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm. Journal of Cleaner Production, 2016, 137: 1516–1531 https://doi.org/10.1016/j.jclepro.2016.07.029
18	C Li, Y Tang, L Cui, et al.. A quantitative approach to analyze carbon emissions of CNC-based machining systems. Journal of Intelligent Manufacturing, 2015, 26(5): 911–922 https://doi.org/10.1007/s10845-013-0812-4
19	Q Yi, C Li, Y Tang, et al.. Multi-objective parameter optimization of CNC machining for low carbon manufacturing. Journal of Cleaner Production, 2015, 95: 256–264 https://doi.org/10.1016/j.jclepro.2015.02.076
20	P C Priarone, M Robiglio, L Settineri, et al.. Modelling of specific energy requirements in machining as a function of tool and lubricoolant usage. CIRP Annals-Manufacturing Technology, 2016, 65(1): 25–28 https://doi.org/10.1016/j.cirp.2016.04.108
21	W Li, A Zein, S Kara, et al.. An investigation into fixed energy consumption of machine tools. In: Hesselbach J, Herrmann C, eds. Glocalized Solutions for Sustainability in Manufacturing. Berlin: Springer, 2011, 268–273 https://doi.org/10.1007/978-3-642-19692-8_47
22	L Zhou, J Li, F Li, et al.. Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. Journal of Cleaner Production, 2016, 112: 3721–3734 https://doi.org/10.1016/j.jclepro.2015.05.093
23	G Y Zhao, Z Y Liu, Y He, et al.. Energy consumption in machining: Classification, prediction, and reduction strategy. Energy, 2017, 133: 142–157 https://doi.org/10.1016/j.energy.2017.05.110
24	P Renna. Energy saving by switch-off policy in a pull-controlled production line. Sustainable Production and Consumption, 2018, 16: 25–32 https://doi.org/10.1016/j.spc.2018.05.006
25	C Li, X Chen, Y Tang, et al.. Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. Journal of Cleaner Production, 2017, 140: 1805–1818 https://doi.org/10.1016/j.jclepro.2016.07.086
26	L Hu, Y Liu, N Lohse, et al.. Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed. Energy, 2017, 139: 935–946 https://doi.org/10.1016/j.energy.2017.08.032
27	M Emami, M H Sadeghi, A D Sarhan, et al.. Investigating the minimum quantity lubrication in grinding of Al2O3 engineering ceramic. Journal of Cleaner Production, 2014, 66: 632–643 https://doi.org/10.1016/j.jclepro.2013.11.018
28	I Hanafi, A Khamlichi, F M Cabrera, et al.. Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. Journal of Cleaner Production, 2012, 33: 1–9 https://doi.org/10.1016/j.jclepro.2012.05.005
29	X Chen, C Li, Y Jin, et al.. Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials. International Journal of Advanced Manufacturing Technology, 2018, 96(1–4): 775–788 https://doi.org/10.1007/s00170-018-1647-0
30	A M M S Ullah, K Kitajima, T Akamatsu, et al.. On some eco-indicators of cutting tools. In: Proceedings of ASME 2011 International Manufacturing Science and Engineering Conference. Corvallis: ASME, 2011, 105–110 https://doi.org/10.1115/MSEC2011-50071
31	M Arif, I A Stroud, O Akten. A model to determine the optimal parameters in a machining process for the most profitable utilization of machining energy. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2015, 229(2): 266–274 https://doi.org/10.1177/0954405414527960
32	X Chen, C Li, Y Tang, et al.. Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time. Energy, 2019, 175: 1021–1037 https://doi.org/10.1016/j.energy.2019.02.157
33	Q Wang, F Liu, X Wang. Multi-objective optimization of machining parameters considering energy consumption. International Journal of Advanced Manufacturing Technology, 2014, 71(5–8): 1133–1142 https://doi.org/10.1007/s00170-013-5547-z
34	D Fratila, C Caizar. Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3. Journal of Cleaner Production, 2011, 19(6–7): 640–645 https://doi.org/10.1016/j.jclepro.2010.12.007
35	C Li, Q Xiao, Y Tang, et al.. A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. Journal of Cleaner Production, 2016, 135: 263–275 https://doi.org/10.1016/j.jclepro.2016.06.097
36	G Campatelli, L Lorenzini, A Scippa. Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 2014, 66: 309–316 https://doi.org/10.1016/j.jclepro.2013.10.025
37	A Bhattacharya, S Das, P Majumder, et al.. Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Production Engineering, 2009, 3(1): 31–40 https://doi.org/10.1007/s11740-008-0132-2
38	G Kant, K S Sangwan. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production, 2014, 83: 151–164 https://doi.org/10.1016/j.jclepro.2014.07.073
39	Y Zhang, P Zou, B Li, et al.. Study on optimized principles of process parameters for environmentally friendly machining austenitic stainless steel with high efficiency and little energy consumption. International Journal of Advanced Manufacturing Technology, 2015, 79(1–4): 89–99 https://doi.org/10.1007/s00170-014-6763-x
40	C Camposeco-Negrete, J de Dios Calderón Nájera, J C Miranda-Valenzuela. Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. International Journal of Advanced Manufacturing Technology, 2016, 83(5–8): 1341–1347 https://doi.org/10.1007/s00170-015-7679-9
41	P S Bilga, S Singh, R Kumar. Optimization of energy consumption response parameters for turning operation using Taguchi method. Journal of Cleaner Production, 2016, 137: 1406–1417 https://doi.org/10.1016/j.jclepro.2016.07.220
42	R S Altıntaş, M Kahya, H Ö Ünver. Modelling and optimization of energy consumption for feature based milling. International Journal of Advanced Manufacturing Technology, 2016, 86(9–12): 3345–3363 https://doi.org/10.1007/s00170-016-8441-7
43	R K Bhushan. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 2013, 39(1): 242–254 https://doi.org/10.1016/j.jclepro.2012.08.008
44	J Yan, L Li. Multi-objective optimization of milling parameters: The trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 2013, 52: 462–471 https://doi.org/10.1016/j.jclepro.2013.02.030
45	O V Arriaza, D Kim, D Lee, et al.. Trade-off analysis between machining time and energy consumption in impeller NC machining. Robotics and Computer-Integrated Manufacturing, 2017, 43: 164–170 https://doi.org/10.1016/j.rcim.2015.09.014
46	S A Bagaber, A R Yusoff. Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. Journal of Cleaner Production, 2017, 157: 30–46 https://doi.org/10.1016/j.jclepro.2017.03.231
47	E Suneesh, M Sivapragash. Parameter optimisation to combine low energy consumption with high surface integrity in turning Mg/Al2O3 hybrid composites under dry and MQL conditions. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019, 41(2): 89 https://doi.org/10.1007/s40430-019-1587-0
48	D Y Jang, J Jung, J Seok. Modeling and parameter optimization for cutting energy reduction in MQL milling process. International Journal of Precision Engineering and Manufacturing—Green Technology, 2016, 3(1): 5–12 https://doi.org/10.1007/s40684-016-0001-y
49	J Li, Y Lu, H Zhao, et al.. Optimization of cutting parameters for energy saving. International Journal of Advanced Manufacturing Technology, 2014, 70(1–4): 117–124 https://doi.org/10.1007/s00170-013-5227-z
50	S Velchev, I Kolev, K Ivanov, et al.. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning. Journal of Cleaner Production, 2014, 80: 139–149 https://doi.org/10.1016/j.jclepro.2014.05.099
51	P Albertelli, A Keshari, A Matta. Energy oriented multi cutting parameter optimization in face milling. Journal of Cleaner Production, 2016, 137: 1602–1618 https://doi.org/10.1016/j.jclepro.2016.04.012
52	F Ma, H Zhang, H Cao, et al.. An energy consumption optimization strategy for CNC milling. International Journal of Advanced Manufacturing Technology, 2017, 90(5–8): 1715–1726 https://doi.org/10.1007/s00170-016-9497-0
53	Y Xiong, J Wu, C Deng, et al.. Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing. International Journal of Advanced Manufacturing Technology, 2016, 87(4): 1237–1246 https://doi.org/10.1007/s00170-013-4881-5
54	Z Deng, H Zhang, Y Fu, et al.. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. Journal of Cleaner Production, 2017, 166: 1407–1414 https://doi.org/10.1016/j.jclepro.2017.08.022
55	K He, R Tang, M Jin. Pareto fronts of machining parameters for trade-off among energy consumption, cutting force and processing time. International Journal of Production Economics, 2017, 185: 113–127 https://doi.org/10.1016/j.ijpe.2016.12.012
56	H Zhang, Z Deng, Y Fu, et al.. A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. Journal of Cleaner Production, 2017, 148: 174–184 https://doi.org/10.1016/j.jclepro.2017.01.077
57	Q Zhong, R Tang, T Peng. Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 2017, 140: 1819–1827 https://doi.org/10.1016/j.jclepro.2016.07.084
58	L Zhang, B Zhang, H Bao, et al.. Optimization of cutting parameters for minimizing environmental impact: Considering energy efficiency, noise emission and economic dimension. International Journal of Precision Engineering and Manufacturing, 2018, 19(4): 613–624 https://doi.org/10.1007/s12541-018-0074-3
59	S A Bagaber, A R Yusoff. Energy and cost integration for multi-objective optimisation in a sustainable turning process. Measurement, 2019, 136: 795–810 https://doi.org/10.1016/j.measurement.2018.12.096
60	L Hu, R Tang, W Cai, et al.. Optimisation of cutting parameters for improving energy efficiency in machining process. Robotics and Computer-Integrated Manufacturing, 2019, 59: 406–416 https://doi.org/10.1016/j.rcim.2019.04.015
61	C Li, L Li, Y Tang, et al.. A comprehensive approach to parameters optimization of energy-aware CNC milling. Journal of Intelligent Manufacturing, 2019, 30(1): 123–138 https://doi.org/10.1007/s10845-016-1233-y
62	H Wang, R Y Zhong, G Liu, et al.. An optimization model for energy-efficient machining for sustainable production. Journal of Cleaner Production, 2019, 232: 1121–1133 https://doi.org/10.1016/j.jclepro.2019.05.271
63	W Li, S Kara. An empirical model for predicting energy consumption of manufacturing processes: A case of turning process. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2011, 225(9): 1636–1646 https://doi.org/10.1177/2041297511398541
64	P Chauhan, M Pant, K Deep. Parameter optimization of multi-pass turning using chaotic PSO. International Journal of Machine Learning and Cybernetics, 2015, 6(2): 319–337 https://doi.org/10.1007/s13042-013-0221-1
65	J Huang, F Liu, J Xie. A method for determining the energy consumption of machine tools in the spindle start-up process before machining. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2015, 230(9): 1639–1649 https://doi.org/10.1177/0954405415600679
66	P T Mativenga, M F Rajemi. Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Annals-Manufactuing Technology, 2011, 60(1): 149–152 https://doi.org/10.1016/j.cirp.2011.03.088
67	L Li, J Yan, Z Xing. Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. Journal of Cleaner Production, 2013, 52: 113–121 https://doi.org/10.1016/j.jclepro.2013.02.039
68	J Lv, R Tang, S Jia. Therblig-based energy supply modeling of computer numerical control machine tools. Journal of Cleaner Production, 2014, 65: 168–177 https://doi.org/10.1016/j.jclepro.2013.09.055
69	T Gutowski, M Branham, J Dahmus, et al.. Thermodynamic analysis of resources used in manufacturing processes. Environmental Science & Technology, 2009, 43(5): 1584–1590 https://doi.org/10.1021/es8016655
70	T Gutowski, J Dahmus, A Thiriez. Electrical energy requirements for manufacturing processes. In: Proceedings of the 13th CIRP International Conference on Life Cycle Engineering. Leuven, 2006, 623–628
71	Y Altintas. Manufacturing Automation. Cambridge: Cambridge University Press, 2012
72	N Diaz, S Choi, M Helu, et al.. Machine tool design and operation strategies for green manufacturing. In: Proceedings of the 4th CIRP International Conference on High Performance Cutting (HPC2010). Gifu, 2010, 271–276
73	M P Sealy, Z Liu, D Zhang, et al.. Energy consumption and modeling in precision hard milling. Journal of Cleaner Production, 2016, 135: 1591–1601 https://doi.org/10.1016/j.jclepro.2015.10.094
74	J Lv, R Tang, S Jia, et al.. Experimental study on energy consumption of computer numerical control machine tools. Journal of Cleaner Production, 2016, 112: 3864–3874 https://doi.org/10.1016/j.jclepro.2015.07.040
75	L Hu, C Peng, S Evans, et al.. Minimising the machining energy consumption of a machine tool by sequencing the features of a part. Energy, 2017, 121: 292–305 https://doi.org/10.1016/j.energy.2017.01.039
76	F Han, L Li, W Cai, et al.. Parameters optimization considering the trade-off between cutting power and MRR based on linear decreasing particle swarm algorithm in milling. Journal of Cleaner Production, 2020, 262: 121388 https://doi.org/10.1016/j.jclepro.2020.121388
77	S Hu, F Liu, Y He, et al.. Characteristics of additional load losses of spindle system of machine tools. Journal of Advanced Mechanical Design, Systems and Manufacturing, 2010, 4(7): 1221–1233 https://doi.org/10.1299/jamdsm.4.1221
78	H Xu, Q Jiang, T Cao. Milling Processing Manual. Beijing: China Machine Press, 2012
79	W A Yang, Y Guo, W H Liao. Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm. International Journal of Advanced Manufacturing Technology, 2011, 54(1–4): 45–57 https://doi.org/10.1007/s00170-010-2927-5
80	A R Yildiz. Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Science, 2013, 220: 399–407 https://doi.org/10.1016/j.ins.2012.07.012
81	L Gao, J Huang, X Li. An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Applied Soft Computing, 2012, 12(11): 3490–3499 https://doi.org/10.1016/j.asoc.2012.06.007
82	M S Shunmugam, S V B Reddy, T T Narendran. Optimal selection of parameters in multi-tool drilling. Journal of Materials Processing Technology, 2000, 103(2): 318–323 https://doi.org/10.1016/S0924-0136(00)00500-8
83	L Li, C Li, Y Tang, et al.. An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining. Journal of Cleaner Production, 2017, 162: 458–473 https://doi.org/10.1016/j.jclepro.2017.06.034
84	N Yusup, A M Zain, S Z M Hashim. Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). Expert Systems with Applications, 2012, 39(10): 9909–9927 https://doi.org/10.1016/j.eswa.2012.02.109
85	H G Resat, B Unsal. A novel multi-objective optimization approach for sustainable supply chain: A case study in packaging industry. Sustainable Production and Consumption, 2019, 20: 29–39 https://doi.org/10.1016/j.spc.2019.04.008
86	H Peng, H Wang, D Chen. Optimization of remanufacturing process routes oriented toward eco-efficiency. Frontiers of Mechanical Engineering, 2019, 14(4): 422–433 https://doi.org/10.1007/s11465-019-0552-z
87	X Duan, B Wu, Y Hu, et al.. An improved artificial bee colony algorithm with MaxTF heuristic rule for two-sided assembly line balancing problem. Frontiers of Mechanical Engineering, 2019, 14(2): 241–253 https://doi.org/10.1007/s11465-018-0518-6
88	M H Gholami, M R Azizi. Constrained grinding optimization for time, cost, and surface roughness using NSGA-II. International Journal of Advanced Manufacturing Technology, 2014, 73(5–8): 981–988 https://doi.org/10.1007/s00170-014-5884-6
89	S Zhu, H Zhang, Z Jiang, et al.. A carbon efficiency upgrading method for mechanical machining based on scheduling optimization strategy. Frontiers of Mechanical Engineering, 2020, 15(2): 338–350 https://doi.org/10.1007/s11465-019-0572-8
90	C Tian, G Zhou, F Lu, et al.. An integrated multi-objective optimization approach to determine the optimal feature processing sequence and cutting parameters for carbon emissions savings of CNC machining. International Journal of Computer Integrated Manufacturing, 2020, 33(6): 609–625 https://doi.org/10.1080/0951192X.2020.1775303
91	B Kirsch, C Effgen, M Büchel, et al.. Comparison of the embodied energy of a grinding wheel and an end mill. Procedia CIRP, 2014, 15: 74–79 https://doi.org/10.1016/j.procir.2014.06.037
92	S J Shin, J Woo, S Rachuri. Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters. Journal of Cleaner Production, 2017, 161: 12–29 https://doi.org/10.1016/j.jclepro.2017.05.013
93	Q Xiao, C Li, Y Tang, et al.. Meta-reinforcement learning of machining parameters for energy-efficient process control of flexible turning operation. IEEE Transactions on Automation Science and Engineering, 2021, 18(1): 5–18 https://doi.org/10.1109/TASE.2019.2924444
94	N Tapoglou, J Mehnen, J Butans, et al.. Online on-board optimization of cutting parameter for energy efficient CNC milling. Procedia CIRP, 2016, 40: 384–389 https://doi.org/10.1016/j.procir.2016.01.072

[1]	Pai LIU, Yi YAN, Xiaopeng ZHANG, Yangjun LUO. A MATLAB code for the material-field series-expansion topology optimization method[J]. Front. Mech. Eng., 2021, 16(3): 607-622.
[2]	Kai LONG, Xiaoyu YANG, Nouman SAEED, Ruohan TIAN, Pin WEN, Xuan WANG. Topology optimization of transient problem with maximum dynamic response constraint using SOAR scheme[J]. Front. Mech. Eng., 2021, 16(3): 593-606.
[3]	Zunpu HAN, Yong WANG, De TIAN. Ant colony optimization for assembly sequence planning based on parameters optimization[J]. Front. Mech. Eng., 2021, 16(2): 393-409.
[4]	J. ZHANG, H. DU, D. XUE, P. GU. Robust design approach to the minimization of functional performance variations of products and systems[J]. Front. Mech. Eng., 2021, 16(2): 379-392.
[5]	Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG. Efficient, high-resolution topology optimization method based on convolutional neural networks[J]. Front. Mech. Eng., 2021, 16(1): 80-96.
[6]	Jinghua XU, Hongsheng SHENG, Shuyou ZHANG, Jianrong TAN, Jinlian DENG. Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number[J]. Front. Mech. Eng., 2021, 16(1): 133-150.
[7]	Genshen LIU, Huaiju LIU, Caichao ZHU, Tianyu MAO, Gang HU. Design optimization of a wind turbine gear transmission based on fatigue reliability sensitivity[J]. Front. Mech. Eng., 2021, 16(1): 61-79.
[8]	Peng WEI, Wenwen WANG, Yang YANG, Michael Yu WANG. Level set band method: A combination of density-based and level set methods for the topology optimization of continuums[J]. Front. Mech. Eng., 2020, 15(3): 390-405.
[9]	Yongliang YUAN, Liye LV, Shuo WANG, Xueguan SONG. Multidisciplinary co-design optimization of structural and control parameters for bucket wheel reclaimer[J]. Front. Mech. Eng., 2020, 15(3): 406-416.
[10]	Zhen-Pei WANG, Zhifeng XIE, Leong Hien POH. An isogeometric numerical study of partially and fully implicit schemes for transient adjoint shape sensitivity analysis[J]. Front. Mech. Eng., 2020, 15(2): 279-293.
[11]	Song ZHANG, Lelun WANG, Anze YI, Honggang GU, Xiuguo CHEN, Hao JIANG, Shiyuan LIU. Dynamic modulation performance of ferroelectric liquid crystal polarization rotators and Mueller matrix polarimeter optimization[J]. Front. Mech. Eng., 2020, 15(2): 256-264.
[12]	Shuo ZHU, Hua ZHANG, Zhigang JIANG, Bernard HON. A carbon efficiency upgrading method for mechanical machining based on scheduling optimization strategy[J]. Front. Mech. Eng., 2020, 15(2): 338-350.
[13]	Xianda XIE, Shuting WANG, Ming YE, Zhaohui XIA, Wei ZHAO, Ning JIANG, Manman XU. Isogeometric topology optimization based on energy penalization for symmetric structure[J]. Front. Mech. Eng., 2020, 15(1): 100-122.
[14]	Emmanuel TROMME, Atsushi KAWAMOTO, James K. GUEST. Topology optimization based on reduction methods with applications to multiscale design and additive manufacturing[J]. Front. Mech. Eng., 2020, 15(1): 151-165.
[15]	Jiali ZHAO, Shitong PENG, Tao LI, Shengping LV, Mengyun LI, Hongchao ZHANG. Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level[J]. Front. Mech. Eng., 2019, 14(4): 474-488.

Viewed

Full text

Abstract

Cited

Shared

Discussed