Optimization Parameters for Energy Efficiency in End milling

Available online at www.sciencedirect.com

ScienceDirect Procedia CIRP 69 (2018) 312 – 317

25th CIRP Life Cycle Engineering (LCE) Conference, 30 April ± 2 May 2018, Copenhagen, Denmark

Optimization Parameters for Energy Efficiency in End milling Lirong Zhoua, Jianfeng Lia*, Fangyi Lia, Gamini Mendisb, John W. Sutherlandb a

National Demonstration Center for Experimental Mechanical Engineering Education, Shandong Universiy, 17923 Jinshi Road, Jinan 250000,China b School of Environmental and Ecological Engineering, Purdue University, West Lafayette, Indiana 47906, United States

* Corresponding author. Tel.: +86-88399876; fax: +86-0531-88574135. E-mail address: [email protected]

Abstract

One way to manage the modern challenges of global resource depletion and climate change is to reduce energy consumption. The use stage of machines in manufacturing operations consumes the majority of energy and brings serious emissions over a PDFKLQH¶V life cycle. With this in mind, a multi-objective cutting parameter optimization model is proposed, focusing on minimizing the process time and energy consumption per unit of removed material. Constraint conditions, such as the processing capacity of the machine tool, the tool life, the surface roughness of the part, and wasted ploughing energy are considered. A genetic algorithm is used to solve the optimization model and the effects of the parameters on the energy consumption of the machine are discussed. To verify the proposed method, experiments were designed for an end milling operation, using Taguchi design principles. ©201 2017The The Authors. Published by Elsevier B.V. © Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference Keywords: Energy Efficiency; Optimization Parameters; Milling Process

1. Introduction Sustainability requires coordinated development and protection of the economic, societal, and environmental resources. Energy resources are essential to production activities and can be a significant part of production cost. However, the use of energy also produces a range of environmental emissions. The sustainable development of the manufacturing industry can be improved by rational use of energy and energy efficiency technologies. End milling is an important machining process and is widely used to create auto parts, aircraft parts, etc. In milling processes, machine tools are the mainly electricity consumption devices and are responsible for carbon dioxide emissions. Furthermore, end milling processes can cause particulate or chemical emissions which can adversely affect worker health. Since the end milling process is both the use stage of an end milOLQJ PDFKLQH¶V life cycle and the manufacturing stage of other products¶ life cycle, the end

milling process has important life cycle implications which need to be quantified. The European Union¶s Eco-design Directive 2009/125/E has designated machine tools as category of regulatory priority, which calls for the reduction of the energy consumption in a machine tools¶ life cycle, especially for the use stage. Energy efficient operations of machine tools, such as selection of the optimal cutting parameters, may promote machining sustainability and reduce environmental impact in a machine tools¶use stage. Research on this topic has been widely studied by academics. Many machining optimization problems are extremely complex and require multi-objective optimization models to analyse the trade-offs between parameters. Yan and Li[1] solved a multi-objective optimization model for milling parameters using a weighted grey relational analysis and a response surface methodology. The optimization objectives in their study were surface roughness, material removal rate (MRR), and cutting energy, respectively representing cutting quality, production rate, and sustainability. Their research

2212-8271 © 201 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference doi:10.1016/j.procir.2017.12.005

Lirong Zhou et al. / Procedia CIRP 69 (2018) 312 – 317

focused on energy in cutting status, and for the whole machining process, it can be better if energy in air-cutting and tool change status is also considered. Rajemi et al.[2-3] established an energy consumption model for a single pass turning process, which included energy during machine setup, machining, tool change, and tool production. The optimal tool economic life and optimal cutting speed under a minimum energy criterion could be derived through the energy model. Their model can be improved if machine standby power was not used to replace power consumption during tool changing operations in their study. Camposeco-Negrete[4] developed a optimization model for cutting parameters in order to minimize the energy needed by a machine tool to remove unit volume of material (UEC) and surface roughness, and maximize the MRR during the turning process. A desirability method was applied to determine the optimal parameters, and the feed rate of the tool was found to be the most significant factor to optimize, but the scope of the study did not include several constraint conditions, which could be relevant to certain manufacturers. He et al.[5] built an optimization model for machining parameters and used it to analyse face milling and turning operations. Their study used a simplified model of process time, and assumed that time of the feed motion, spindle motion, standby state, and spray coolant are all effectively the same. As many scholars have shown, the selection of cutting parameters is related to the environmental impact of the machining process and significant work has been invested to optimize cutting parameters[6]. Cutting parameter may also effect chemical or particulate emissions. Limited research has been performed on milling optimization and due to complexities in the milling process, existing optimization models still need to be improved. In an attempt to develop a better optimization model, this work proposes a model which can improve processing time and reduce energy consumption of the milling process, and can establish constraint equations such as surface roughness, tool life, ploughing energy waste, and machining capability of tools and machine. Minimum process time and Unit Energy Consumption (UEC) were set as optimization objectives. End milling experiments were performed to generate data and a genetic algorithm was used to calculate the optimal cutting parameters. 2. Optimization model In this paper, the minimum process time, Tprocess , and the UEC are set as optimization objectives, representing the production rate and the energy efficiency in the milling process, respectively. Additionally, the variables to be optimized are spindle rotation speed, feed rate, axial cut depth, and radial cut depth, which affect both machining time and machine power. Using a dimensionless processing method, the final optimization objective can be expressed as: F (n, vf , ap, ae) min[ F 1(UECprocess)  F 1(Tprocess)] (1) where F 1() is dimensionless function can be written as: F1

f ( x)  f ( x) min f ( x) max  f ( x) min

(2)

313

2.1. Modelling of the process time A PRGHO RI WKH SURFHVV¶ WLPH LV GHYHORSHG E\ WDNLQJ WKH sum of the standby time, the air-cutting time, the cutting time, and the auxiliary processing time. These are defined as follows: (a) standby time, tstan (min), refers to the time used for preheating and adjusting the machine tool, waiting for the ZRUNSLHFH¶V DUULYDO the installation of the workpiece, and workpiece¶V disassembly. tstan can be related to the experience level of the machine operator, and it is relatively stable in the workshop. Thus, standby time tstan can be approximated as a constant Cstan according to investigation. (3) tstan Cstan (b) air-cutting time, tidle (min), refers to the time when spindle axis and feed axis are moving but the cutting tool is not touching the workpiece. tidle consists of the movement time of the unloaded spindle ts_air , the movement time of the unloaded feed tf_air , and the movement time of the unloaded spindle and feed tsf_air . For energy efficiency consideration, it is assumed that there is no ts_air in the numerical control code, so then tidle can be calculated as: tsf_air  tf_air

tidle

Lsf_x  Lf_x Lsf_y  Lf_y Lsf_z  Lf_z   vf_x vf_y vf_z

(4)

where Lsf_x (mm), Lsf_y (mm), and Lsf_z (mm) are the aircutting stroke length in the x, y, and z directions respectively, when the spindle axis and feed axis moving together. Lf_x (mm), Lf_y (mm), and Lf_z (mm) are the air-cutting stroke length in the x, y, and z directions of the feed axis respectively. vf_x (mm/min), vf_y (mm/min) and vf_z (mm/min) are the feed rates in x, y, and z directions respectively. (c) the cutting time, tcut (min), refers to the time used for cutting the workpiece. Cutting time is an inverse function of the MRR. Vm MRR

tcut

Vm vf ˜ ap ˜ ae

(5)

where Vm (mm3) is the total material volume that needs to be removed, MRR (mm3/s) is the material removal rate in the milling process, ap (mm) is the axial cut depth, ae (mm) is the radial cut depth, and vf (mm/min) is the feed rate. (d) the auxiliary processing time, taux (min), refers to the time when the auxiliary devices, such as the lighting device, the spray coolant device, and the tool change device, begin operating until cease to operate. taux tco ˜ i1  tlt ˜ i 2  tch ˜ i 3 (6) where tco , tlt , and tch are the operation times for the auxiliary light, spray coolant, and tool change devices, respectively. Here, i1 ~ i 3 0 or 1, where 0 represents the on state, and 1 represents the off state of the device. Here the following assumptions are made: x The light will remain on throughout the milling process, tlt

tpro

x The coolant spray time is the same as the cutting time, tco

tcut

x Tools need to be changed when its wear width VB=0.3mm. The total time required to change tools is related to tool life Tlife (min) and single tool change time tsc (min).

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Lirong Zhou et al. / Procedia CIRP 69 (2018) 312 – 317

Additionally, the cutting parameters will affect Tlife ; these effects can be expressed using the Taylor tool life equation : Tlife

CT vcD ˜ vf E ˜ apT ˜ aeJ

(7)

where CT is a constant associated with the workpiece and the tool material. D , E , T , J are coefficients for tool life. Although the auxiliary operation times, such as tco and tlt , will overlap with the other time mentioned above, the energy calculation which use these times are evaluated in parallel. The total time of milling process, Tprocess (min) , can be written as: Tprocess tsb  tidle  tcut  tch (8) 2.2. Milling power model The UEC of the milling process is calculated using the machine tool power model. Machine tool power model takes into account the power consumption in WKHPDFKLQH¶Vstandby mode, the power consumption in its air-cutting mode, the power in its cutting mode, and the power in its auxiliary mode[7]. The functional forms of these models are defined as follows: (a) The power consumption of the machine in standby mode, Psb (kW), refers to the power consumed by machine tool fan, computer, displayer, and lubrication system, Psb Pfan  Pcpt  Pdpy  Plub (9) where Pfan , Pcpt , Pdpy , and Plub are fan power, NC computer power, display power, and lubrication system power respectively. The first three items in equation (9) are constant power. However, for the lubrication system, its power, Plub , is variable. The centralized lubrication system in modern machine tools has a periodically changing operational status, according to the measured oil pressure and temperature, and is controlled by PLC program code to start or stop the lubrication system. After the machine has been turned on, the lubrication system will continuously operate to prepare for machining. When the machine is in cutting status, the lubrication system will operate intermittently, in order to reduce lubrication waste and oil pollution. As an approximation, the standby power can be expressed as piecewise function: ­ Psb 0 Csb 0 0  t d W 0 ° (10) ® Psb1 Csb1 t  (W oil ˜ ncyci ), ncyci 1, 2,3 ° ¯ Psb 2 Csb 2 t  (W uoil ˜ ncyci ) where t (min) is the amount of time the machine is

Psb

operating, W 0 (min) is the pre-heat time of machine, W oil (min) is the amount of time oil is needed for one work cycle of the lubrication system, W uoil (min) is the non-oil supply time during one work cycle of lubrication system, ncyci is the ith lubrication work cycle, Psb0 is the standby power consumption in pre-heat time, Psb1 is the standby power consumption in oil supply time, Psb 2 is the standby power consumption in nonoil supply time, Csb0 ~ Csb 2 are constant, namely average measured value of the standby power consumption. Over the duration of the process, the number of times which the oil has been cycled on and off can be calculated as (Tprocesso  W 0) (W oil  W uoil ) .

(b) The power consumption of the machine in air-cutting mode, Pidle , refers to the input power when the spindle and feed axis have already moved but the cutter has not yet reached the workpiece. Pidle includes the standby power consumption, Psb , spindle power consumption, Psp , and feed power consumption, Pfd , at any instantaneous time in aircutting mode. Spindle power is a function of the rotation speed and the feed power is function of feed rate[8], Pidle Psb  Psp  Pfd (11) Psp A1  A2 u n (12) (13) ( Pfd ) _ x / y / zu / zd ( B1  B 2 u vf 2  B3 u vf ) _ x / y / zu / zd where A1 , A2 , B1 , B 2 , B3 are fitting parameters, Pfd_x and Pfd_y are feed power in x and y directions, Pfd_zu and Pfd_zd are feed power consumption for upward movement and downward movement in the z direction. (c) the power consumption in the cutting mode Pin_cut refers to the input power when tool cuts the workpiece. Pin_cut includes the standby power consumption Psb , the spindle power consumption Psp , the feed power consumption Pfd , and power consumed during material removal, Pma , at any instantaneous time in cutting status. Pin_cut Psb  Psp  Pfd  Pma (14) The feed power is very small at commonly used feed rates, and can be thought as constant. According to our previous work[9], an improved power model for material removal with milling machine tools can be expressed as: Pma SCE ˜ MRR c ( tc )c (Vc)c ˜ MRR (15) 1

tc

2

3

fz ˜ ae

(16)

R ˜ cos 1 (1  ae ) R

where SCE (J/mm3) is the specific cutting energy. The SCE in milling process is found can be a function of the average chip thickness tc (mm) and cutting speed Vc (m/s). fz (mm/tooth) is the feed rate per tooth. R (mm) is the tool radius. c1 , c 2 , and c 3 are fitting coefficients. (d) The power consumption in the auxiliary mode, Paux , refers to the power consumption of the auxiliary components, such as power consumption of the lighting device, Pli , the power consumption of the spray coolant device, Pco , the power consumption of the tool change device, Pch . (17) Paux Pco ˜ i1  Pli ˜ i 2  Pch ˜ i3 where i1 ~ i 3 0 or 1, 0 represents the power consumed during machine operation and 1 represents the power in the off/idle state. In summary, according to the model and assumptions mentioned above, the energy needed by a milling machine tool to remove a unit volume of material, unit energy consumption UECprocess (J/mm3), can be calculated as: (³

tstan

0

UECpro

Psbdt  ³

tidle

0

( Psb  Psp  Pfd )dt  ³

tcut

0

tch

tco

Tprocess

0

0

0

 ³ ( Psb  Pch)dt  ³ Pcodt  ³

Vm

( Psb  Psp  Pfd  Pma)dt

Pltdt )

(18)

2.3. Constraint models To enable the calculation of meaningful data, several constraints are used in the model, as discussed below. These constraints were chosen based on physical limits on the system, as imposed by machine limits and quality constraints.

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Lirong Zhou et al. / Procedia CIRP 69 (2018) 312 – 317

(a) machine and cutter processing ability constraint: (19) (20) The maximum and minimum value of spindle rotation speed, feed rate, radial cut depth, and axial cut depth are set according to the rated speeds for the machine, and the recommended cutting parameters for certain material categories: vf_x_quik c1 , vf_y_quik c 2 , vf_z_quik c 3 (21) where c1 , c 2 , c 3 are fast feed rates in the x, y, z axial directions, respectively. The use of fast feed rates during air stroke steps may improve production efficiency. (22) (b) spindle load constraint: ( Psp  Pma ) K d Pj max where K is the transmission efficiency of the spindle, Pj max is the spindle rated power under the jth gear of spindle system. (c) surface roughness constraint: Ra Kr ˜ ap b1 ˜ nb 2 ˜ vf b 3 ˜ aeb 4 d [ Ra] (23) where [ Ra] is the allowable maximum roughness value. Kr and b1 ~ b4 are fitting coefficients determined based on experimental results. (24) (c) tool life constraint: Tlife t [T ] where [T ] is the minimum allowable tool durability. (25) (d) ploughing energy waste constraint: tc re t 1 where t (mm) is the average chip thickness, and re (mm) is the cut edge radius. For tc re t 1 , more energy will be transferred as shear cutting energy. For tc re  1 , higher friction between the tool and the work piece will lead to a high ploughing energy and significant energy waste[10]. ap min d ap d ap max , ae min d ae d ae max n min d n d n max , vfmin d vf d vfmax

2.4. Genetic Algorithm procedure Genetic algorithms are widely used to solve nonlinear and multi-objective optimization problems. This paper converts multiple objective functions into a single objective function and searches the optimal milling process parameters using a genetic algorithm. Several steps are made in procedure. The first step is to initialize the chromosome population. The second step is to choose a fitness function, and genetic operators. The third step is to select the conditions for the termination of the calculation. More information about this procedure can be found in literature[11]. In this study, the population type is set as double vector. Population size is set at 40. A roulette selection function and a rank fitness scaling function were chosen. The crossover fraction probability is 0.8 and the crossover function uses two points. The stopping criteria were set to be 200 generations and a time limit was set at 1 minute. 3. Case study End milling cutters GM-4E-D12.0 (tool diameter is 12mm, number of teeth is 4) were used to cut AISI 1045 steel workpieces on a XKA714B/B vertical milling machine. The removal volume for one workpiece is 115 u 70 u10 (mm3), and the tool path can be seen in Fig.1. The optimization objective is to minimize the process time and UEC in this end milling process. Five workpieces are used for each experimental condition and the data are reported below for the processing of all five workpieces. The test data are shown in Table 1 and

Table 2. These set of tests were performed by varying the cutting parameters by using a Taguchi orthogonal method to test the power, surface roughness, and tool life. A Yokogawa CW240 clamp type power meter was used to record power. A TIMETR200 hand-held roughness meter was used to record roughness. Tool wear is monitored by a JTVMS2010 camera. The cutting tool was replaced when the tool wear width reached VB=0.3mm. Each test was performed using a down milling operation without coolant.

Fig. 1. End milling tool path Table 1. Cutting power and surface roughness test results No.

n

vf

ap

ae

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

(r/min) 800 800 800 800 800 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 2000 2000 2000 2000 2000 2400 2400 2400 2400 800

(mm/min) 240 290 340 380 420 240 290 340 380 420 240 290 340 380 420 240 290 340 380 420 240 290 340 380 240

(mm) 0.5 1 1.5 2 2.5 1 1.5 2 2.5 0.5 1.5 2 2.5 0.5 1 2 2.5 0.5 1 1.5 2.5 0.5 1 1.5 0.5

(mm) 2 3 4 5 6 4 5 6 2 3 6 2 3 4 5 3 4 5 6 2 5 6 2 3 2

Ra ( Pm ) 2.141 2.793 2.837 3.512 4.013 2.171 2.331 2.424 1.924 2.187 2.024 1.897 1.799 2.173 2.373 1.533 1.565 1.684 1.76 1.741 1.497 1.728 1.415 1.321 2.141

Power (kW) 0.020 0.060 0.100 0.200 0.300 0.069 0.134 0.244 0.119 0.047 0.140 0.086 0.155 0.060 0.150 0.143 0.253 0.068 0.178 0.113 0.290 0.115 0.085 0.185 0.020

Table 2. Tool life test results No

n

vf

ap

ae

Vc

Tlife

1 2 3 4 5 6 7 8 9

(r/min) 1300 1300 1300 1700 1700 1700 2100 2100 2100

(mm/min) 120 140 180 120 140 180 120 140 180

(mm) 2.5 3 3.5 3 3.5 2.5 3.5 2.5 3

(mm) 3 6 9 9 3 6 6 9 3

(m/s) 0.816 0.816 0.816 1.068 1.068 1.068 1.319 1.319 1.319

(min) 80 62 53 49 55 50 20 30 25

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Lirong Zhou et al. / Procedia CIRP 69 (2018) 312 – 317

3.1. Optimization model and results Table 3 lists the empirical models for the power, surface roughness, tool life, and so on. The fitting parameters were determined using experimental results. Time models and constraint conditions are also can be seen in Table 3. Machining requirements include Ra d 2.5ȝm , Tlife t 30min , tc re t 1 , and the spindle power load. A comprehensive consideration for the cutting tool manufacturer, the ability of the machine to process workpieces, and the workpiece material is needed for cutting parameters range constraint. Table 3. Equations of the case Model

Equations

Time model

tsb

8,

tsf_air

tcut

tch 0.6 u tsc

vf

ae

ap

145 § 70 15 · 70 15 30 u 2  15 § 70 15 · u  ( ) u 400 ¨© ae ap ¸¹ vf ¨© ae ap ¸¹ vf ap V 115 u 70 u 15 MRR ap ˜ ae ˜ vf

tf_air

Power model

15  15 u ( 70 u 15 )

115 u 70 u 15 ap u ap ˜ ae ˜ vf

0.6 ,

0.450

˜ vc1.576 ˜ ae0.149 ˜ v f 0.212 e5.674

tlt Tprocess

Psb

­ Psb 0 0.71 ° ® Psb1 0.66 ° ¯ Psb 2 0.63

Psp

0.0674  1.8483 u 10 4 u n

0  t d 10 t  (10 ˜ ncyci ), ncyci

Table 4. Comparison of optimal and empirical schemes for objective

1, 2,3

t  (15 ˜ ncyci )

Pfd_x 0.01471  1.0526 u 106 u vf  1.8318 u 108 u vf 2 Pfd_y 0.0193  1.0017 u 10 5 u vf  1.5835 u 10 8 u vf 2 4

9

Pfd_zu 0.1662  1.3352 u 10 u vf  3.0637 u 10 u vf

Pfd_zd

Constraint model

2

0.1683 ,

Pma 0.00283( tc )0.1261 (Vc )0.4557 ˜ MRR Pco 0.26 , Pch 0.66 , Pli 0.05 500r/min  n d 3500r/min

6mm/min  vf d 730mm/min 0.05mm  ap d 3.6mm , 0.05mm  ae d 12mm vf_z_quik 400mm/min , vf_x_quik vf_y_quik 800mm/min Psp  Pma d 7.5kW 1000 u 0.92 Tlife

e5.674 t 30min ap 0.450 ˜ Vc1.576 ˜ ae 0.149 ˜ vf 0.212

Ra

25.234 ˜ ap 0.027 ˜ n 0.649 ˜ vf 0.322 ˜ ae0.259 d 2.5ȝm

tc re

Higher feed rate reduces the process time, and higher spindle speed will lead to high power consumption. However, the increment in power is less than the decrement in process time. As a result, E1 minimizes the time of the machining operation and the UEC, but also increases tool wear. When tools are frequently changed, processing continuity and part quality may be effected, and more cutting tools will need to be purchased, and more energy will be consumed during tool changing operations consumption. E2 has a large energy consumption and a long process time and does not meet energy saving requirements. This process is unlikely to be a cost-effective process for long term use. G3 and G4 trade off the time and energy consumption, and make these two objects values decrease. In addition, the tool life, surface roughness, ploughing energy waste constraints and so on can all be matched. The genetic algorithm solutions show that the constraints on roughness and tool life are preventing the algorithm from reaching a more optimal solution. However, these constraints were imposed by the manufacturer, and are necessary to meet production/cost goals. The constraints could be altered in other simulations to identify the impacts of tolerance and cost requirements on the energy consumption of the process.

vf ae 1 ˜ ˜ t1 4 ˜ n 6 ˜ cos 1 (1  ae ) 0.030 6

3.2. Optimization results and discussion Table 4 shows four different sets of processing parameters, the first two are based upon operator experience (E1, E2) and the second two are based upon optimized parameters from the genetic algorithm (G3, G4). The two experience-based approaches have extra associated costs. E1 has the highest spindle speed and feed rate, and affects tool life significantly.

No .

n

vf

ap ae

Ra

Tlife

tc re

Tprocess

UECprocess

E1 E2 G3 G4

3427

706

12

3.6

2.1

8.4

1.1

119.3

84.4

1600

360

7

3.5

2.4

35.3

1.3

259.9

132.1

1704

347

10

3.6

2.5

30.1

1.2

194.4

104.4

1722

355

10

3.5

2.5

30.0

1.2

197.0

105.8

From the Table 5, the energy consumption is largely dependent on the standby energy consumption, cutting energy and spindle energy, while the feed energy and tool change energy are relatively small. In order to reduce standby energy, higher feed rates are needed to shorten the idle time and reduce non-productive energy. However, large feed rates may cause high large surface roughness, therefore better understanding of the trades off between these indicators is need. Furthermore, spindle energy, cutting energy, and tool life are affected significantly by cutting speed, therefore, a suitable spindle rotation speed needs to be selected to make these indicators meet constraint requirements. Table 5. Comparison of optimal and empirical schemes for energy consumption (kJ) No.

Spindle energy

Standby energy

Feed energy

Cutting energy

E1 E2 G3 G4

1049.5 1880.5 1398.0 1417.8

4912.1 10789.9 8130.7 8192.8

309.1 575.8 395.3 405.2

3728.2 2588.4 2671.6 2680.3

Tool change energy 140.0 115.1 95.1 96.5

Total energy 10138.9 15949.7 12690.8 12792.6

As shown in Fig.2, with the vf increases, the UEC and processing time decrease. The larger radial cut depth and axial cut depth decrease the UEC, because MRR increases while processing time decreases.

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Lirong Zhou et al. / Procedia CIRP 69 (2018) 312 – 317

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Fig.3 shows that with increasing spindle rotation speed, the UEC increases, because the spindle rotation speed directly affects spindle and material removal power. However, the cutting speed effect the process time lightly. 



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Fig. 2. Effect of feed rate on the UEC and the process time

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4. Conclusion The optimization result shows suitable milling parameters can be obtained to minimize the processing time and UEC through GA. In addition, an improved machine tool power and some assumption to calculate the process time are proposed to make optimization results more reasonable. The effects that the parameters have on the UEC, time and ploughing energy waste were analyzed. Suitable parameter selections are discussed in order to meet energy saving requirements.







 









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Fig. 3. Effect of spindle rotation speed on the UEC and the process time

Fig.4.a shows when the radial cut depth and the feed rate decrease, the average chip thickness decreases, then more energy is wasted as ploughing. With the tc re increases, more energy can be used to shear the material and the ploughing energy waste can be reduced. Fig.4.b shows when the spindle rotation speed increases, more points came in the plough energy waste area. That is because higher spindle rotation speed makes the lower feed rate per tooth, and then leads smaller average chip thickness. In this work, chip thickness is the main factor which effects the ploughing energy, and this situation may need to be considered when selecting cutting parameters. There are a few limitations in this work, such as the lack of consideration of a cost model in this process and the use of only one environmental impact (energy consumption) in the analysis. There are several other impacts (particulate emissions, CO2 emissions, worker health implications, chip waste, etc.) which would be valuable to quantify and include in the model. Further investigations into other the effects of cutting parameters on environmental impacts (particulate emissions, waste chip generation, etc.) is still needed to better incorporate trade-offs into the model. To further calculate the economic sustainability of the process and compare the tradeoffs between economic and environmental sustainability, a cost model for the end milling process is needed in the future.

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