Energy and Process Monitoring of Production Lines

© 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


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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


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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


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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


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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�

�

�


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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�

�

�


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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


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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


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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]


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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


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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


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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


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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


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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


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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


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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


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Reference of our customers

ThyssenKrupp ThyssenKrupp ThyssenKrupp ThyssenKrupp DrauzDrauzDrauzDrauz NothelferNothelferNothelferNothelfer

Documents

Energy and Process Monitoring of Production Lines