Energy efficient cutting parameter optimization

doi:10.1007/s11465-020-0627-x

Frontiers of Mechanical Engineering

2021, Vol. 16

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

本期目录

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

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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.

Key words： energy efficiency cutting parameter optimization machining process

收稿日期: 2020-09-02 出版日期: 2021-06-15

Corresponding Author(s): Congbo LI

引用本文:

. [J]. Frontiers of Mechanical Engineering, 2021, 16(2): 221-248.
Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI. Energy efficient cutting parameter optimization. Front. Mech. Eng., 2021, 16(2): 221-248.

链接本文:

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

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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

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

Fig.5

Fig.6

Fig.7

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

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