energy and process monitoring of production lines
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© Fraunhofer IWUProf. Neugebauer
Energy and Process Monitoring of Production Lines
ACMA – Fraunhofer Technology Day on Resource Efficiency in Car Manufacturing
September 8, 2011, New Delhi
Dr.-Ing. Hans-Joachim Koriath
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
AGENDA
� Challenges from environmental aspects and standards
� Energy efficiency
� Resource effectiveness
� Process monitoring
� Machine monitoring
� Energy Management
� Robust production lines
� Conclusions, outlook
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
A EU: ECODESIGN Directive 2009/125/EG
Ecodesign of energy-related products, ENTR Lot 5: machine tools
B CECIMO Taskforce
Self-Regulation initiative (SRI) of machine tool manufacturers
C ISO 14955: Environmental evaluation of machine tools
1. Challenges from environmental aspects and standards
Users’ target: Cost efficient products and production
Target of eniPROD: - 30% energy and resource savingStatus report ISO TC39 WG 12 Update
www.ecomachinetools.eu
�
�
Concept Description for SRI of the machine tool industry
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
1. Challenges from environmental aspects and standards
� Energy Management System Standards ISO 50001, EN 16001
� Scheme: Plan-Do-Check-Act
� Energy policy, Planning and Implementation
� Monitoring, Measurement
� Corrective & Preventative actions
� Audit, management review
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
2. Energy efficiencyevaluation method related to technical, energetic and economical aspects of machine tools
Potentials for energy savings:
Peak power: P reductionbase load: t reduction
Reactive power: Q compensationProcess requirements : flushing optimization
∫==
T
dttPWE )(
Machining centre: energy flow in cutting operation�
�
�
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
2. Energy efficiency
Potentials for energy savings:- 42.6% servo drive unit- 14.9% cutting process (13.5% + 1.4%)- 19.8% coolant
electric (active) power consumption of main components during machining
Drive system block model of a machiningcentre in Matlab SimPowerSystem
rotating wiper
2,2%
air cleaning
9,0%
cooling
14,4%
chip conveyer
3,8%
hydraulic
0,6%
CNC control + control circuit
5,4%lighting
2,2%
coolant
19,8%spindle, processing
13,4%
spindle
13,4%
converter for spindle
1,1%
support drives, processing
1,5%
LSC-module
1,1%
ESNQ
2,9%
support drives
9,1%
servo drive unit
42,6%
axes drives, processing
1.4%
13.5%
Level A: individual drives of the drive unit
Level B: energy storage system (���� development)
Level C: servo drive unit (ESNQ)
Level D: all electrical energy consumers
B
AD
C
Mainswitch
Ein = EelEnergy storage
system (ESS)
auxiliary drive
systems
(ESNQ)
(LSC-module)equipment for securing
network quality (ESNQ)
2.9%
line-side converter
(LSC) module
1.1%
spindle, idle running
13.4%
converter for spindle
13.1%
spindle, processing
13.5%
rotating wiper
2.2%
lighting
2.2%
CNC-control + control circuit5.4%
axes drives, idle running
9.1%
hydraulic
0.6%
chip conveyer
3.8%
cooling (drives)
14.4%
air cleaning
9.0% coolant
19.8%
servo drive
unit
42.6%
servo drive unit�
�
�
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
3. Resource effectiveness
Machine level
Process level
Eprocess Etherm
Eelectric Eloss
Coolantin
Chips, Burrs
WPin WPout
Esealing air
Coolantout
Toolin Toolout
Main switch
Compressed
air supply
Blank workpiece
New tool Used tool
Properties
Cooling lubricant
Machined
workpiece
Elub,air
Coolant
supply
Ein
Energy & resource flow
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
Targets:•High energy and resource efficiency, lower cost, higher productivity•Higher removal rates, high speeds and feeds•Increase tool life, Chip control (length)
4. Process monitoring in cutting processes
Influence of cooling strategy and cutting speed in grooving
Too
l
Workpiece TiAl6V4
vcf
Chip lenght
High pressurecoolant jet
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
4. Process monitoring in cutting processes
2,2
2,3
2,4
2,5
2,6
50 75 100 125 150 175
Sp
ecific
ene
rgy
[Ws/m
m³]
Cutting speed vc [m/min]
Process level Machine level
120 bar
150 bar
120 bar
150 bar
Ein = Emachine + Ehigh pressure pump
Ein
Ehigh pressure pumpEmachine
Restriction
Chip length
Restriction
Tool wear
Energy on the cutting edge
Influence of cooling strategy and cutting speed on energy balance in grooving
0
6
12
18
24
30
50 75 100 125 150 175
Sp
ecific
energ
y [W
s/m
m³]
Cutting speed vc [m/min]
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
4. Process monitoring in forming processes
LearnForm (FP7 NMP collaborative project)
�Title: Self-learning sheet metal forming system
�The concept of the project LearnForm bases on the following four main ideas:- A self-learning sheet metal forming system based on work piece energy and thermal quality control- Intelligent drawing dies including multi-sensors- Multi die cushion axes with adaptronic force oscillation actuators- An open architecture motion control system extended by self-learning control strategies
�Three tasks of self-learning control- sliding friction- forming - clamping tasks� supervised by: energy level with thermo quality check
�industrial leadership
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
Condition Monitoring System
Data aquisition and
preprocessingEquipment Measurement
category
Data storage, Diagnostic,
Maintenance instruction
Network
Condition of oil(temperature, humidityratio, number of particles)
air consumption, condition of pneumaticcylinders
Operating data logging
Data base
Server&
Router
trend
time
con
ditio
n
time
con
ditio
n
Analysis software
• diagnostic• analysis
• visualization• trend• history
• Maintanance message-by Email / SMS
Energy consumption
Condition of bearings,
gearboxes, guidances
Condition of press frame
5. Machine monitoring
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
Predictive Maintenance: Machine and process monitoring
Process Control
� Multi Criteria Process Monitoringand Control - Grinding Process
� Multi Sensor Data Analysis
laser welding system
� Preventive Maintenance
breakdown prediction of components
Optical inspection systems
� Object Identification
by recognition of patterns
� part geometry inspection
2D / 3D Photometry
� Error Detection
on surfaces
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
multi sensor Laser welding Laser welding Laser welding Laser welding system system system system of sheet metals
in process detection of welding error
Virtual Reference GrindingVirtual Reference GrindingVirtual Reference GrindingVirtual Reference Grindingon cylinders used for paper production
Detection of ripplesDetection of ripplesDetection of ripplesDetection of ripples on a transparent surface
a) standard image acquisitionb) optical contrast image
Predictive Maintenance: Machine and process monitoring
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
6. Energy Management
Actual Power [kW/min]; Mode of operation [1/0]; control data (fan control), Ready (1/0)
Eth
ern
et
netw
ork
Control & Measurement Structure
Ass
iste
nt
syst
em
Peak Power, time of operationCoordinated control actions
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
7. Robust production lines
Redundant Redundant Redundant Redundant ComponentsComponentsComponentsComponentsSensors Sensors Sensors Sensors
FailureFailureFailureFailure DetectionDetectionDetectionDetection & Isolation / & Isolation / & Isolation / & Isolation / SupervisorSupervisorSupervisorSupervisor System System System System
SensorSensorSensorSensor----/ Time / Time / Time / Time SequenceSequenceSequenceSequence ControlControlControlControl
� active, passive, „cold"
redundance
� necessary information:
same / alternative
principle of operation
� Parallel network
(safety is not increased)
� Model based failure detection, -
isolation, -identification
� System models in detail requested
� Decision derived from the condition
check
� Supervisor system actions on system
components
� "Watchdog"-Function
� Alternativ sequence / next step
� Continue Sensor/Time condition
� Acknowledgement / Reset
� Message, Warning, Failure
Reliability, Availability
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
8. Conclusions, outlook
Energy Management
� Measurement, Monitoring KPI
� Power, time of operation control
� Energy recovery, use of regenerative energies
Resource effectiveness
� Material for tool, work piece
� Process emissions: chips, coolant, oil
Process Monitoring
� Fingerprint: successful process conditions
� Correct failure detection and prediction, Predictive maintenance
� Robust manufacturing systems
Fraunhofer IWU R&D for automotive component suppliers
© Fraunhofer IWUProf. Neugebauer
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Archivierungsangaben
Reference of our customers
ThyssenKrupp ThyssenKrupp ThyssenKrupp ThyssenKrupp DrauzDrauzDrauzDrauz NothelferNothelferNothelferNothelfer
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