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Multiagent-based approach for the automation and quality assurance of the small series production

Multiagent-based approach for the automation and quality assurance of the small series production. 16 th IEEE International Conference on Emerging Technologies and Factory Automation – ETFA 2011 September, 7 th 2011 University Toulouse 1 Capitole, Toulouse, France

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Multiagent-based approach for the automation and quality assurance of the small series production

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  1. Multiagent-based approach for the automation and quality assuranceof the small series production 16th IEEE International Conference on Emerging Technologies and Factory Automation – ETFA 2011 September, 7th 2011University Toulouse 1 Capitole, Toulouse, France Robert Schmitt, Tilo Pfeifer, Alberto PavimLaboratory for Machine Tools WZL, RWTH Aachen Marcelo Stemmer, Jomi Hübner, Mario RoloffDepartment of Automation and Systems DAS, UFSC Florianópolis

  2. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  3. Small series production (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. Small Series Production Characterisation Mass production Challenges for the Inspection of Small Series Production Production volume Product variety • Boundary conditions and inspectionrequirements 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 Scientific challenge! Is it possible to maintain an economical production in small series and at the same time monitor the big diversity of product variants and process parameters, in order to guarantee the production quality? The (rigid) metrology strategy used within mass production is unable to cope with such conditions.Demand for new flexible and adaptive metrology strategies.

  4. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  5. Flexible production requires also flexible metrology strategies Self-optimization for reducing production control complexity flexibility self-optimization Reduce the dilemma between scale and scope: flexible production systems planning- orientation scale Flexible metrology supports the secured performance of flexibleproduction systems:optical sensors provideadequate benefits value- orientation scope • Benefits of optical sensors • Touchless, non-invasive and non-destructive • High measurement speed (inline, in-process) • Small encapsulation, integration to production • Wide inspection range by combination and data fusion Higher flexibility levels increasethe planning of the system Increased planning efforts canbe reduced withself-optimizing systems

  6. Conceptual definition Cognitive production metrology and self-optimizing systems Cognitive Production MetrologyAutomatic definition and application of inspection tasks for several product variants, using different flexible and adaptive measurement and inspection systems Flexibilityand mutability • Focus:Quality planning:1) automatic and dynamic inspection plan generation2) prediction of process and product quality Measurement systems:1) flexible integration of different measurement systems2) conception of adaptive measurement systems, combining multidimensional information acquired from different sources 1. Analysis of the currentsituation 2. Determination of (new) systemobjectives Self-optimizingsystems Autonomy Cognition 3. Adaption of system behaviour to surrounding conditions

  7. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  8. Integration and cooperation between project partners Two different case studies to validate the concept focus on product specifications focus on process specifications Expertise: inspection planning, artificial intelligence modules Expertise: optical metrology, sensor fusion Use Case 1 Use Case 2 Small batch assembly of printed circuit boards (PCB) Multi-sensor basedinspection of freeform parts (e.g. automotive headlights) PredictiveQuality Planning InspectionPlanning Sensor Integration and Data Fusion QualityEvaluation Planned information:Inspection plan Real information:Information about product and process

  9. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  10. Splitting the system complexity into distinct responsibility levels Multiagent hybrid control structure: Hierarchical view Coordination level • Product planner Planning level • Process planning • Route planning • Inspection planning Task level • Transport • Calibration • Quality Inspection Work level • Robots/axes • Product • Tools/sensors Security level Productplanning • Watch-dog Productionplanning Productiontasks Actors Infor-mationcarrier Sensors

  11. Towards self-optimization through combination of capabilities Multiagent hybrid control structure: Heterarchicalview Inspectionplanner Productionplanner Process planner Routeplanner Inspection configuration Stationagent Process agent Qualityagent DirectoryFacilitator Watchdog AgentManagementSystem Calibrationagent Imageacquisition IOagent GUIagent Imageprocessing Statisticagent Logagent Productagent Flexible Production Environment Agents with individual orcollective self-optimizing behavior Message Transporting System

  12. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  13. Use Case 2: Autonomous Inspection of Freeform Parts Automated test stand at WZL in Germany • Not a typical SSP (similar characteristics): diversity of product variants with different design elements worked in parallel • 100% inspection of the headlight glasses is required • Different failure types (geometry, material, scratches, cracks, dirt) • Automated inspection approach consists of optical stations • Imperfections of mechanical system and inflexible software lead to false rejection (improve flexibility and sensing capabilities!) Example: automotive headlights

  14. Use Case 2: Autonomous Inspection of Freeform PartsIntegration of visual inspection systems into the test stand Calibration Pallet Stereo System Visi Scratch System Visi Wave System Flexible MV- System

  15. Use Case 2: Autonomous Inspection of Freeform Parts Inspection Config.agent Agent-based machine control approach Qualityagent Securityagent Routingagent Inspection (IP)agent Planningagent GUIagent Inspection (IA)agent Statistic/Log agent Calibrationagent DFagent Processagent Productagent Stationagent product Stationagent AMSagent I/Oagent Transporting system I am monitoring the system consistency! Fusion result = C! Inside of tolerance. You are OK! Feedback to previous and further processes! Two moves counter clockwise through stations 1 and 2! Inspection Z: result = B! Inspection Y: result = A! Product X identified! Ok, deal! Leave inspection system! Final quality evaluation! Fusion of result A with result B! Inspection Z! Ok, deal! 1- Inspection Y 2- Inspection Z 3- Data fusion 4- Quality evaluation Get route with routing agent! Take me to inspection Z station 4! Inspection Y! Ok, deal! I am registering system events and statistics! I am a calibration product! Providing inspection summary! I am OK! Next step! I must collect data about myself! I am product X. Inspection plan! Take me to inspection Y station 3! Product recognition! Presenting inspection summary! Go to first MV station! Handle transport with station agents! I am monitoring system inputsand outputs! Ok, deal! Take me there stations 1 and 2! Where is the MV inspection? I need to know who I am! Create product agent! Create agents! Product arrived! Ok, deal!

  16. Contents 1 Introduction 2 Self-optimizing systems and the concept of Cognitive Metrology 3 Vision of the project Cognitive Metrology 4 Agent-based approach as basis for the Cognitive Metrology concept 5 Application example: Agentification of an automated inspection machine 6 Conclusions and outlook

  17. ConclusionsSSP complicates production and quality assurance tasks Flexibilityand mutability Self-optimizingsystems planning- orientation scale Autonomy Cognition value- orientation scope • CPM defines a new paradigm in terms of metrological and quality assurance systems within SSP • Goal: to make the SSP economically viable while flexibly guaranteeing the quality of processes and products • Development of methods, technologies and services for an efficient quality management and metrology application for flexible SSP (based on SO systems) • Implementation basis on top of a hybrid multiagent control structure • Flexible integration and autonomous control of different hardware and software modules with reactive/intelligent behaviors • Dynamic handling of the inspection of different product variants through a set of metrology agents • Introduction of parallelism factors (transport and self-organization) within a serial industrial inspection machine

  18. Outlook: Cognitive Assembly of Printed Circuit Boards PCB assembly chain at LABelectron in Brazil Surface Mount Technology - SMT Solder Paste Printing SMT Components Insertion Optical Inspection Solder Paste Reflow Oven Optical Inspection

  19. Outlook: Cognitive Assembly of Printed Circuit Boards PCB assembly chain at LABelectron in Brazil Qualityagent Securityagent PCBA agent Inspection (AOI) agent Inspection (SPI) agent Planningagent SCADAagent Productagent Inspection(Stencil) agent Statistic/Log agent DFagent Processagent AMSagent OrganizationSpecifications product Conveyorartifact Unloaderartifact Ovenartifact Loaderartifact Printerartifact Inserterartifact

  20. Thank You • Project partners • The depicted research has been funded by the German and Brazilian Research Foundations DFG, CAPES, FINEP and CNPq as part of the BRAGECRIM collaborative research initiative.

  21. Backup

  22. Tendence in production: Innovative Products and Processes Customisation of products Increasing number of product variants Shorter product life cycles and smaller production batches Flexible small series production One productive line for different products (flexibility) Short setup time with reduced production costs Autonomy and robustness Self-learning production system Metrology within the small series aims at the improvement of: Quality, Robustness, Flexibility, Autonomy Challenges for the inspection of small series Batchsize Mass production Individualised production Today Future

  23. Generic model of an agent Agent Sensing environmentmodel goals Behaviour Environment capabilities information knowledge Acting • Platform based on services • Decomposition of a problem • Distribution of responsibilities • Decentralisation of the control • Flexibility for the integration of hardware and software modules • Independent of programming language and operational system • Characteristics of an agent: • Encapsulation • Autonomy • Goal oriented • Reactivity • Proactivity • Interaction • Intelligence

  24. Comparison between programming paradigms monolithic system object oriented system distributed system dynamic integration agent-based system

  25. Measurement on DemandFlexible inspection of PCB assembly Source: FLIR

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