Energy and Process Monitoring of Production Lines

Transcription

1 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

2 AGENDA Challenges from environmental aspects and standards Energy efficiency Resource effectiveness Process monitoring Machine monitoring Energy Management Robust production lines Conclusions, outlook 2

3 1. Challenges from environmental aspects and standards 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 Concept Description for SRI of the machine tool industry C ISO 14955: Environmental evaluation of machine tools Users target: Cost efficient products and production Target of eniprod: - 30% energy and resource saving Status report ISO TC39 WG 12 Update 3

4 1. Challenges from environmental aspects and standards Energy Management System Standards ISO 50001, EN Scheme: Plan-Do-Check-Act Energy policy, Planning and Implementation Monitoring, Measurement Corrective & Preventative actions Audit, management review 4

5 2. Energy efficiency evaluation method related to technical, energetic and economical aspects of machine tools = E = W P( t) dt T Potentials for energy savings: Peak power: P reduction base load: t reduction Reactive power: Q compensation Process requirements : flushing optimization Machining centre: energy flow in cutting operation 5

6 2. Energy efficiency electric (active) power consumption of main components during machining Drive system block model of a machining centre in Matlab SimPowerSystem CNC-control + control circuit circuit 5,4% lighting hydraulic 5.4% 2,2% 0,6% 2.2% 0.6% rotating wiper 2,2% 2.2% chip conveyer 3.8% 3,8% cooling cooling (drives) 14.4% 14,4% servo drive servo drive unit unit 42,6% 42.6% air air cleaning 9.0% 9,0% coolant 19.8% 19,8% equipment for securing network ESNQ quality (ESNQ) 2,9% 2.9% line-side converter LSC-module (LSC) module 1,1% 1.1% axes support drives, drives idle running 9,1% 9.1% support axes drives, processing 1,5% 1.4% converter for for spindle spindle 1,1% 13.1% spindle, spindle idle running 13,4% 13.4% spindle, processing 13,4% 13.5% E in = E el Main switch D (ESNQ) C (LSC-module) servo drive unit B Energy storage system (ESS) A Potentials for energy savings: % servo drive unit % cutting process (13.5% + 1.4%) % coolant auxiliary drive systems 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 6

7 3. Resource effectiveness Energy & resource flow Machine level Main switch E electric E loss E in Compressed air supply E sealing air E lub,air E process Etherm Chips, Burrs Properties Blank workpiece WP in WP out Machined workpiece New tool Coolant supply Tool in Coolant in Process level Tool out Coolant out Used tool Cooling lubricant 7

8 4. Process monitoring in cutting processes Influence of cooling strategy and cutting speed in grooving Targets: High energy and resource efficiency, lower cost, higher productivity Higher removal rates, high speeds and feeds Increase tool life, Chip control (length) High pressure coolant jet Chip lenght Tool v c f Workpiece TiAl6V4 8

9 4. Process monitoring in cutting processes Influence of cooling strategy and cutting speed on energy balance in grooving Process level Energy on the cutting edge Machine level E in = E machine + E high pressure pump Specific energy [Ws/mm³] 2,6 2,5 2,4 2,3 120 bar 150 bar Specific energy [Ws/mm³] E in E machine 120 bar 150 bar E high pressure pump 2, Cutting speed v c [m/min] Restriction Chip length Cutting speed v c [m/min] Restriction Tool wear 9

10 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 10

11 5. Machine monitoring Condition Monitoring System Equipment Measurement category Data aquisition and preprocessing Data storage, Diagnostic, Maintenance instruction Condition of press frame Condition of bearings, gearboxes, guidances Server & Router Condition of oil (temperature, humidity ratio, number of particles) Analysis software air consumption, condition of pneumatic cylinders Energy consumption Operating data logging Network Data base condition trend time diagnostic analysis visualization trend history Maintanance message - by / SMS 11

12 Predictive Maintenance: Machine and process monitoring Optical inspection systems Object Identification by recognition of patterns part geometry inspection 2D / 3D Photometry Error Detection on surfaces Process Control Multi Criteria Process Monitoring and Control - Grinding Process Multi Sensor Data Analysis laser welding system Preventive Maintenance breakdown prediction of components 12

13 Predictive Maintenance: Machine and process monitoring multi sensor Laser welding system of sheet metals in process detection of welding error Virtual Reference Grinding on cylinders used for paper production Detection of ripples on a transparent surface a) standard image acquisition b) optical contrast image 13

14 6. Energy Management Control & Measurement Structure Peak Power, time of operation Coordinated control actions Ethernet network Actual Power [kw/min]; Mode of operation [1/0]; control data (fan control), Ready (1/0) Assistent system 14

15 7. Robust production lines Reliability, Availability Redundant Components Sensors Failure Detection & Isolation / Supervisor System Sensor-/ / Time Sequence Control 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 15

16 8. Conclusions, outlook Fraunhofer IWU R&D for automotive component suppliers 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 16

17 Reference of our customers ThyssenKrupp Drauz Nothelfer 17