sustainable manufacturing under industry 4 · mcmd Ö command position vcmd Ö command speed tcmd...
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MAG Stiftungslehrstuhl für Fertigungstechnologien
Chinesisch-Deutsches Hochschulkolleg der Tongji-Universität
Prof. Dr. –Ing ZHANG Weimin
Sustainable Manufacturing under
Industry 4.0
Energy efficiency research of the machine tool for Industry 4.0
Outline
Background 1
Resource and energy comprehensive evaluation of
machining process 2
Energy consumption online monitoring based on the
machine tool servo parameters & working status 3
Manufacturing System environmental properties
evaluation 4
1. Background
Sustainable development
The core is Develop & Sustainable
To eliminate poverty and hunger;
To feed, nurture, house, educate
and employ the global population
To ensure peace, security
and freedom;
To preserve the Earth’s basic life
support systems.
Global sustainable development problem
213.46
258.68 291.45
324.94
361.73
152.51 184.95
209.30 232.02
252.46
123.00 151.27
172.11 189.41 205.67
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
2004 2006 2008 2010 2012
Total Energy Consumption (Bntce) Total industrial energy consumption (Bntce) Total manufacturing energy consumption Bntce)
• Sustainability of China
0.218
0.519
0.118 0.124
0.255
0.306
15094
7298
5869
3577
0
2000
4000
6000
8000
10000
12000
14000
16000
0.000
0.100
0.200
0.300
0.400
0.500
0.600
USA China Japan Germany World Asia
Energy consumption per unit GDP(TCE/k$) GDP(bn$)
Source:IMF2012
GDP USA(1) China(2) Japan(3) Germany(4)
Energy consumption per unit GDP rank USA(9) China(55) Japan(7) Germany(27)
World rank(2012)
The past 30 years, China's GDP grew by 15 times, while energy consumption increased by nearly four times, the main resource consumption and pollutant emissions per unit GDP is much higher than in developed countries
Source:IMF2012
The total energy consumption increase every year, but the growth rate decreased. Manufacturing share of energy consumption is about 56%
Energy consumption Source:NSBC
Green Energy
Smart Urban Factory
Smart Products
Green Manufacture
High efficiency Machine tools
Green Material
Advanced manufacturing process
Industry 4.0 smart Product is consist of Smart Urban Factory and Green Manufacture. And high efficiency workshop, high efficiency machine tools and advanced production process are important supports.
High efficiency workshop
Industry 4.0 and Green Manufacturing
Manufacture process Research objectives:
•Clearly analyzing resource consumption in manufacturing process.
•Systematic evaluation of the low-carbon properties of manufacturing resources,
carbon footprint modeling
•The allocation of resources for the assessment of low-carbon manufacturing
process planning and optimization
Resource consumption
analysis
High efficiency and low carbon manufacturing resources evaluation and optimization
low-carbon evaluation
Manufacturing Resource Optimization for low-carbon and
efficient
Evaluation, control and optimization under constraint of resources and ecological environment during the production
process
Resource consumption analysis
Ecological constraints
Optimize the allocation of resources
Research content:
Analysis of resource consumption in
manufacturing process
Systematic evaluating low-carbon properties
of resources
Resources optimization
High efficiency low-carbon manufacturing
low-carbon Grading
2 Resource and energy comprehensive evaluation of machining process
Carbon footprint (CFP) assessment system of machining process
More energy efficient and environmentally responsible: • Environmental Initiatives • Education & Awareness • Carbon Reduction • Energy & Resource Efficiency • Local Initiatives
Climate protection
Products for Avoiding GHG
emissions Adapting to climate
change
Reduction for GHG emissions in
Production
Value chain
Advanced Technologies
Maximize economic benefits Energy & Resource Sustainable Development
CFP has historically been defined as "the total set of greenhouse gas (GHG) emissions caused by an organization, event, product or person.
• evaluate indicator for greenhouse gas emissions due to energy consumption
• Measured by produced equivalent CO2
• More CO2 emission, larger carbon footprint, and vice versa.
By Nicks J
CFP Assessment
source: Daimler AG, PPA/GSU
In production process, the GHG emissions exist in each process, which includes direct discharge(DD), optional discharge(OD) and indirect discharge(ID).
Qualified products
Raw materials
Auxiliary materials
(lubricants, cutting
fluids...)
Energy (electricity,
heat...)
Others
Optimization technology
Input Disposals output
Output
Liquid wastes
Others
Dust
Carbon footprint
Other discharges
CH4
CO2
Process 1
Process 2
Process i
I1
I2
Ii
Process nIn
O1
O2
Oi
On
Transportation/
inventory Status 1
…
Status i
Production process for products
Production control
C1
Disposal Weights
C2
…
P1
P2
...
Pm
ωC1
ωC2
ωP1
ωP2
...
ωPm
…
Solid wastes
Gas C ω
…
Transportation/
inventory
Transportation/
inventory
ci
n
iiCC
1
Pi
m
iiPP
1
Ci (i=1,2,…,n) -- optional CO2e emissions induced from the use of materials/chemicals in an assessed process; ωCi --emission factor of the nth type of material. Pi --the discharge unrelated to carbon footprint(such as noise, dusts, etc.) ωPi --the emission factor of the mth type of material.
CFP Assessment Model for manufacturing system
Metal Cutting process (based on CFP)
1
m
nToolTotal Machine Coolant Lubricant Chip Others
i i i i i i i
m
C C C C C C C
Ci
machine = EFe´ (E
servomotor+ E
spindlemotor+E
coolingsystem+ E
compressor
+Ecoolantpump
+Echipconveyor
+ EATC
+ Etoolmagazine
+ Estand-by
)
*coolantcprod cdisp coolant coolant water water water
update
{( ) ( ) ( )}coolant
i
tC EF EF V V EF V V
t
lubricant
spindlelub slidewaylubiC C C
{( ) }( 1)
prodtool
toolprod tooldisp tool re re
toolife re
tC EF EF m N C
t N
workpiece product density chip( )chip
iC V V EF
The total CO2e emissions of each product can be calculated out
• Put forward a concept that taking CFP of unit product as the main efficiency evaluation index of electrical energy , resource and and environmental emissions.
Carbon footprint per kilogram(CFK)
Total
ii
i
CCFK
m im removal material Weight of product
CFK can used for resources consumption Evaluation
Energy consumption modeling based on servo parameters
and machine status
Current loop, velocity loop and servo parameters relation model
Motor power and cutting power Relationship Modeling
PLC Status and Components Enable
Component Energy analysis
Servo parameters and power Relationship Modeling
PLC Status and Components Relationship Modeling
Research objectives:
•Establish a method to monitor the servo system energy by the servo parameters
•Establish a method to monitor componets energy consumption by the machine tool
status
•Establish a method to monitor the machine tool energy consumption bye servo
parameters and PLC status
3. Energy consumption online monitoring based on the machine tool
servo parameters & working status
Research content:
Kp PK1V/S+PK2VMCMD + VCMD +
PK2V×α 1/(JL·S) 1/S
TCMD
+ 1/(Jm·S)-
-
-
Speed feedback
Position feedback
Motor
Spring couplling
Machine speed
MachinePK1V: Velocity loop integral gainPK2V: Velocity loop proportional gainα : Machine speed feedback gain
SPEED
SPSD
MCMD:Command position VCMD:Command Speed TCMD:Command Torque
SPEED:Motor speed SPSD:Output speed
The three-loop control of servo system: (Current loop, Speed loop, Position loop)
Power calculating based servo parameters
For digital servo system: CNC can read and write servo parameters to servo system
• Current main servo motor with constant torque - constant power control
• Taking Fanuc as example, CNC control the motor by using parameters Command torque(TCMD) and motor speed(SPEED)
Define the apparent power PST FANUC αiI22/7000HV motor
maxST
ST max
15002 T
TCMD SPEED 60
1500
P Me
P
SPE
P TCM
ED
SP DD EE
,
, >
Define the cutting power PSP
SP60
Y CF VP
Servo motors & servo parameters
Pi Machine tool input power PiPSM PSM input power PiSPM SPM input power ……
Power
grid
PSM
SVM
SPM Spindle
Motor
CNC
PLC
…
Hioki
3390
Servo
Guide
Kistler
9129A
Power transmitting
sensor
Pi PiPSM
PoSPM
PiSVM
PiSPM
PSP
PST
(a) (b)
(c) (d)
Kistler dynamometerCincinnati HTC 200M
Kistler Amplifier
HIOKI 3390 Measuring
clamp
Hall current sensor & voltage clamp
HIOKI 3390power analyzer
PCMCIA LAN Card
Experiment 1: The cylindrical longitudinal cutting , establish relationship mode between PST and Pi, PiPSM, PiSPM, PSP .etc.
(a) Taper turning
(b) Variable cut-depth turning
Experiment 2: Taper machining test to verify the correctness of the relational model
Experiment 1 shows that there has a good correlation between PST and Pi, PiPSM, PiSPM, PSP .etc.
Relational model is built as form of f(PST) = p1 * PST + p2.
p1 p2 R2 AdjR2 SSE RMSE
Pi 1.499 1266 0.9963 0.9962 1.76e+06 115.9
PiPSM 1.451 353.4 0.9962 0.9962 1.664e+06 169.4
PiSPM 1.455 274.8 0.9958 0.9957 1.842e+06 178.2
PoSPM 1.366 223.5 0.9966 0.9965 1.328e+06 151.3
PSP 1.14 -184.4 0.9869 0.9865 3.6e+06 249.1
Pi PiPSM PiSPM PoSPM PSP PST
Pi 1.000 1.000 1.000 1.000 0.996 0.998
PiPSM 1.000 1.000 1.000 1.000 0.996 0.998
PiSPM 1.000 1.000 1.000 1.000 0.996 0.998
PoSPM 1.000 1.000 1.000 1.000 0.996 0.998
PSP 0.996 0.996 0.996 0.996 1.000 0.993
PST 0.998 0.998 0.998 0.998 0.993 1.000
Power correlation analysis
(a) No.31 ap=2.2~0.2 Vc=370m/min f=0.1mm/r
(b) No.38 ap 2.2~0.2 Vc=370m/min f=0.1mm/r
Experiment 2 shows that: prediction model and the actual value of the good goodness of fit
No. Pi PiPSM PiSPM PoSPM PSP
31 2.04% 0.75% 2.61% 2.89% -5.45%
32 2.44% 0.85% 2.81% 2.90% -4.65%
33 2.87% 1.31% 3.07% 3.28% -2.84%
34 2.92% 2.19% 3.58% 3.80% -0.20%
35 4.49% 3.63% 5.38% 4.79% 1.54%
36 4.42% 4.52% 5.15% 4.71% 1.51%
37 5.76% 5.25% 6.36% 5.84% 3.05%
38 6.81% 6.78% 7.72% 6.81% 3.88%
Prediction error analysis
Taking ETC3650 machine as an example: Load-independent can be calculated out by querying PLC status
Component power monitoring based on PLC status
In CNC machine tools, CNC system issues Instructions, enable PLC action and control the relays, then drive corresponding motor working.So each action corresponds to a PLC status.
CNC machine tool energy consumption
Load dependent Load independent
Spindle
Feed-axis
Lubricating and cooling
Hydraulic system
Peripheral Systems
Auxiliary systems
…… ……
System Corresponding major components
Spindle Spindle Servo, Spindle motor
Feed-axis Feed-axis servo, Feed-axis motor
Hydraulic System Hydraulic motor, electromagnetic valve
Lubrication& Cooling Lubrication pump motor, Cooling pump motor
Auxiliary system CRT, IO devices, ATC, Chip conveyor motor, etc
Peripheral Systems Light, Fan
Construction of the test platform ① Monitor the power at different position under different states. ② Monitor the PLC status, match it with the power curve.
machine energy diagram with PLC cycle Start-up energy consumption
Measuring Position
Current MeasuringVoltage Measuring
③ Establish the correspondence between power and PLC status. ④ Calculated out the component power.
Energy consumption components
Power(W)Energy consumption components
Power(W) Description Remarks
Cooling motor 392b/305c Lubrication pump 42.6a a. Only when directly open, not constant when running b. Only when directly open and during the open process , not constant when running. c. When directly open and 1 second later, not constant when running. d. Only when the cooling motor running. f Non-running state after power on. Spindle and feed axis power demand alone fit
Spindle fan 63 Light 18 Hydraulic motor 565 Trafo2.1 15/110/111.6 T 3×220 30/55 Trafo2.2 18.22/25
T 220C 59 ATC 130a
T 110A 6.5/10d X-axis/Z-axis 2f/Px /Pz
Fan1 20 Spindle 20f/Pms
Fan2 20
When load experiment,fit the cutting experimental data
Execution state model in Matlab / Simulink
Contrast with Measurement and Simulation (no-load state)
⑤Validate the model
Define the state-flow model
The Simulation error under no-load state is less than 5%;
Simulation status results
The error of cutting state is less than 7%
CNC system can directly read the servo parameters and the PLC status, so energy modules can developed integrated in CNC machine tools, which can achieve network connection (Taking Shenyang i5 CNC as example)
Simulation status results
Contrast with Measurement and Simulation (cuttingstate)
Production process evaluation system
Resources
consumption
Ecological
influences
MaterialCutting
toolCoolant
Lubricating
oil Electricity
Pungent
odorOil mist Dust Noisy Security Others
Goal
Criterial
Machining Strategy
AlternativesMachine tool 1 Machine tool 2 Machine tool n. . .
CFKComprehensive index
system
CFK as resources consumption evaluation factor ; Mist, noise, etc., as environmental factors.
4. Manufacturing System environmental properties evaluation
AHP method
CPS module : Mist, noise sensors, etc. connect to the CNC system
①CFK Grade Evaluation Index
D = CFKi-CFK CFK
Evaluation Index
Level 1 2 3 4 5
Influence level Very slight Slight Moderate Serious Extremely serious
Weights 0-1 1-2 2-3 3-4 4-5
Obtain the process mean reference value by statistics, collect the CFK value of the process to be evaluated in itscycle, calculate difference with the reference average according formula , grade it.
Level 1 2 3 4 5
Difference between the
average valueD (%) 0-10 10-20 20-40 40-50 ≥50
②Noise Level in processing cycle
Level 1 2 3 4 5
Noise(dB/cycle) ≤75 75-85 85-90 90-100 ≥100
③ dust concentration value in processing cycle
Level 1 2 3 4 5
Dust concentration
( mg/m3/cycle) ≤1 1-3 3-8 8-10 ≥10
④ Oil mist concentration in processing cycle
Level 1 2 3 4 5
Oil mist concentration
( mg/m3/cycle) ≤1 1-3 3-8 8-10 ≥10
⑤ Pungent odor
Level 1 2 3 4 5
Irritation level
Non-irritating
Irritating weak,
almost not
perceive out
In general, no cause significant discomfort
More serious irritation
Serious irritation,
obvious discomfort
without protective
measures
• A milling evaluation case
Workpiece & tool path
100±0.14
10
0±
0.1
4
37
±0
.1
42
28
48
50±0.1
6
5
17
.5
R6
Tool diameter 10mm with 30 °helix angle, XK714 CNC mill
Name Unit Value
electricity kg-CO2/kWh 0.5482
Coolant producing kg-CO2/L 2.85
Coolant processing kg-CO2/L 0.189
Dilution (water) kg-CO2/L 0.189
Spindle and guideway
lubricant producing kg-CO2/L 2.85
Spindle and guideway
lubricant processing kg-CO2/L 0.189
Tool producing kg-CO2/kg 24
Tool processing kg-CO2/kg 24
Regrinding kg-CO2/time 2.47
Chips processing kg-CO2/kg 2.47
CFP factor
Name Data Grade
Spindle & servo motor(g-CO2) 3.721
Auxiliary electricity(g-CO2) 75.895
Lubricant (g-CO2) 0.065
Chip (g-CO2) 10.99 ―
Tool (g-CO2) 12.361
Coolant (g-CO2) 1.747
Total CFP (g-CO2) 104.779
CFK(kg-CO2/kg) 0.606 3
CFK Noise Dust Mist Irritant gas Safety
CFK 1 5 3 3 3 2
Noise 1/5 1 1/3 1/3 1/3 1/4
Dust 1/3 3 1 1 1 1/3
Mist 1/3 3 1 1 1 1/3
Irritant gas 1/3 3 1 1 1 1/3
Safety 1/2 4 3 3 3 1
Carbon emissions
Manufacturing resources configuration judge matrix
133342/1
3/111133/1
3/111133/1
3/111133/1
4/12/12/12/115/1
233351
A
TW )262.0,112.0,112.0,112.0,058.0,343.0(
CFK weight 2.079
Grade 1 2 3 4 5
Degree Very
slight
sligh
t
Med
ium
Seve
re
Very
Severe
Weight 0-1 1-2 2-3 3-4 4-5
Weight calculating
Resource environment property Grading
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