Flexible Optical Metrology Strategies for the Control and Quality Assurance of Small Series Production SPIE Europe – Optical Metrology 2009 Optical Measurement Systems for Industrial Inspection June 14th, 2009, International Congress Centre Munich – ICM Munich, Germany
Prof. Dr.-Ing. R. Schmitt, A. Pavim Chair of Metrology and Quality Management Laboratory for Machine Tools and Production Engineering WZL RWTH Aachen University © RWTH Aachen University
The research goal of production technology for the 21st century: Resolution of the polylemma of production Vision of Integrative Production Technology resolution of the polylemma of production reduced dilemma
in order to optimise the value-adding processes Modelling, simulation Knowledge, information and data generation planningorientation
Critical masses,
standards Specific, mastered individual processes Minimised, fixed cycle times High synchronisation
Extensive planning
2020
scale
Focus on value adding
processes 2008 Less planning, preparation, rework, handling, transport value Standardised orientation (work) methods © RWTH Aachen University
dilemma scope timeline
One-piece-flow Alterable, dynamic
processes Limited output Limited synchronisation Seite 1
Small Series Production Characterisation Challenges for the Inspection of Small Series Production Small series production (SSP): In many cases, SSP focuses on the manufacturing of a big product variety in a short period of time, while having a low production volume (possibly unitary). Time for processing the complete batch is unknown and products usually have different complexity levels. Boundary conditions and requirements for the inspection in SSP – – – – – –
Lack of predictability about the process and product behaviour Constant creation of quality documentation Increased setup cycles and no or just few products for rigging processes Short time to observe and provide feedback to processes during production Difficulties for reusing information and performing corrective actions Lack of data for decision taking
The (rigid) metrology strategy used within mass production is unable to cope with such conditions. Demand for new flexible metrology strategies. Source: Pyzdek [1], Del Castillo [2]; Lin [3]; Doro [4], Juran [5]; Black [6] © RWTH Aachen University
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Content Motivation: inspection requirements of small series production
Flexible metrology strategies
Application scenario – Self-optimised and automated assembly of a solid-state laser
Conclusions
© RWTH Aachen University
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Flexible metrology supports flexible production systems Self-optimisation: reduce planning/costs of complex systems
scale
valueorientation
planningorientation
scope
flexibility self-optimisation
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Reduce the dilemma between scale and scope: flexible production systems Flexible metrology supports the secured performance of flexible production systems: optical sensors provide adequate benefits Benefits of optical sensors Higher flexibility increases Touchless, non-invasive the –planning of the system and non-destructive – High measurement speed Increased planning efforts can (inline, in-process) be reduced with – Small encapsulation, self-optimised systems integration to production – Wide inspection range by combination and data fusion Seite 4
Self-optimisation for reducing the planning efforts The knowledge about the system is used to find new objectives
1
Analysis of the current situation
(flexible) metrology
Demand on supporting technologies
Flexibility and mutability
2
Determination of (new) system objectives
cognition, autonomy
Selfoptimised systems
Autonomy
3
Adaption of the system behavior according to surrounding conditions
Cognition
flexibility, mutability
Source: Gausemeier [31] © RWTH Aachen University
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Conception of self-optimised (SO) systems Combination of supporting technologies to build up a SO system
Flexibility and mutability
Sensor data fusion: combination of data from multiple sensors or even data acquired from a single sensor in different time intervals for the improvement of the measurement uncertainty, robustness, time etc.
[sensor data fusion] Agent-based systems: agents are autonomous entities that perceive their environment and act back to it in a goal-oriented way. Agents cooperate to split the system control complexity: optimal use of resources over a distributed system.
Selfoptimised systems
Autonomy [agent-based system]
Cognition [knowledge-based system]
Knowledge-based systems: provide technical systems the basis for knowledge representation and inference skills, in order to accomplish cognitive tasks such as reasoning, planning and learning.
Source: Husser [15]; Russel [20]; Beierle [30] © RWTH Aachen University
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Model for the conception of self-optimised systems Synergetic combination of the supporting technologies Flexible Manufacturing Environment Agent Management System
watchdog
route planner
tolerance matching
product planner
pickandplace
actor (hardware)
Directory Facilitator
sensor (hardware)
process planner
inspection planner
adjusting
Agent
joining
sensor data fusion
Environment model
Sensing
goals
quality assurance
capabilities
Behaviour
information
Message Transporting System © RWTH Aachen University
with self-optimised individual and/or collective agents’ behaviour
knowledge
Acting
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Content Motivation: inspection requirements of small series production
Flexible metrology strategies
Application scenario – Self-optimised and automated assembly of a solid-state laser
Conclusions
© RWTH Aachen University
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Self-optimised assembly of a diode-pumped solid-state laser (DPSS) The planar laser design enables an automated assembly Assembly is currently
Assembly tool Laser baseplate Lens array Crystal
Manual assembly is
responsible for up to 80% of the production costs Diode-pumped solid-state
laser with planar configuration
Laser electronic
All optic components are
Laser diode Input coupling mirror
manually performed in a non-systematic and empiric way (expertise)
Output coupling mirror
soldered or bonded from above on a coated ceramic carrier plate New assembly approach:
cooperative robots assisted by different sensors! Source: Fraunhofer ILT © RWTH Aachen University
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Metrology is essential for the control of the assembly process Each new assembly iteration results in a unique product Each robot handles a
different task: handling, joining and inspecting The alignment precision of
the components is required in the range of a few microns: high precision alignment gripper Flexibility of the cell: allows
assembly of different laser types Different sensors / inspection
configurations are applied according to laser type Constant monitoring of the
assembly steps: enables to learn with past experiences!
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Multi agent hierarchical control structure Splitting the system complexity into autonomous entities Security level Watch-dog Collision avoidance
Coordination level Product planner
Product planning
Planning level Assembly planning
Process planning Path/route planning Inspection planning
Task level Pick-and-place
Assembly tasks
Adjusting/joining Quality assurance
Work level Actors
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Information carrier
Sensors
Robots/axes Product Tools/sensors Seite 11
Quality assurance through sensor data fusion Data fusion happens in 3 different levels
Fusion levels
Examples
3rd level (decision level) Combined decisions
2nd level (information level) Extracted object features
1st level (data level) Raw data from sensors
X
e.g. identification and resolution of Failure States (expert system) e.g. image correlation for a complete laser baseplate image acquisition (reference marks)
e.g. filtering noise, different illumination strategies
Challenge: scientific aspect of the decision level (cognition) Source: Husser [15]; Esteban [16] © RWTH Aachen University
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Methods for identifying/solving Failure States Expert systems are needed for providing expertise Model
Failure State
Interpretation
Action
ideal state
measurement deviation
reason for the failure
resolution of the failure
Measurements
Reference value: laser beam at the CCD Sensor
Measurements + Expert system
Effective value: unexpected behaviour of the laser beam
Decisions
Laser beam is strongly reflected by the crystal housing
Choose adequate optic elements
robot-based vision system
camera-based laser beam analysis © RWTH Aachen University
Combination of different sensors for the assessment of the complete system current state Seite 13
Content Motivation: inspection requirements of small series production
Flexible metrology strategies
Application scenario – Self-optimised and automated assembly of a solid-state laser
Conclusions
© RWTH Aachen University
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Conclusions Optical metrology contributes significantly to self-optimisation scale
planningorientation
Small series production complicates considerably
production and quality assurance tasks Self-optimised behaviour enables reducing planning
efforts and increasing production flexibility simultaneously valueorientation
scope
Self-optimised systems require: – Flexibility and mutability: sensor data fusion – Autonomy: agent-based systems – Cognition: knowledge-based systems Optical metrology presents many benefits for developing
multi-sensing systems to support small series production requirements The combination of the supporting technologies allows
the automated and self-optimised assembly of the diodepumped solid-state laser system
© RWTH Aachen University
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Thank You Project partners
The depicted research has
been funded by the German Research Foundation DFG as part of the BRAGECRIM and Excellence Cluster research initiatives.
Further information
http://www.production-research.de/ © RWTH Aachen University
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