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