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Cognitive Production Metrology for Flexible Small Series Production Meeting Prof. Dr. Jomi Fred Hübner Cognitive Production Metrology for Flexible Small Series Production Meeting Prof. Dr. Jomi Fred Hübner – Project Overview March, 10 th 2011 Florianópolis, DAS/UFSC M. Eng. Alberto Pavim Laboratory for Machine Tools WZL at the RWTH Aachen M. Eng. Mário Lucio Roloff S 2 i/DAS/UFSC Seite 1

Project team Brazil (UFSC, CERTI) Germany (RWTH Aachen) n Coordinator – Prof. Dr. -Ing. Project team Brazil (UFSC, CERTI) Germany (RWTH Aachen) n Coordinator – Prof. Dr. -Ing. Marcelo Stemmer (S 2 i) – Prof. Dr. -Ing. Carlos Schneider (CERTI) n Coordinator – Prof. Dr. -Ing. Robert Schmitt (WZL) – Prof. Dr. -Ing. Tilo Pfeifer (WZL) n Researchers – Dr. Marcos Doro (LABElectron) n Engineers – Dipl. Ing. Philipp Jatzkowski (WZL) n Engineers – M. Eng. Günther Pfeiffer (CERTI) – M. Eng. Thiago Mantovani (LABElectron) n Doctoral students – M. Eng. Alberto Pavim (WZL) n Doctoral students – M. Eng. Mario Roloff (S 2 i) n Master students – Eng. Jaqueline Vargas (S 2 i) – Tecnol. Daniel Melo (S 2 i) n Bachelor students – Mauricio Dal Pont (S 2 i) – Felipe de Carvalho (S 2 i) – Priscila Antunes (S 2 i) n Master students – Maximilian Wegener (WZL) n Bachelor students – Marcelo Faria (WZL) – Elena Martín (WZL) – Christian Silvano (WZL) – Leonardo Adams (WZL) – Priscila Antunes (WZL) – Maximilian Engelbracht (WZL) – Thiago Bacic (WZL) Seite 2

Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Cognitive Production Metrology 4 Status 5 Conclusion and perspectives Seite 3

Small Series Production Characterisation Challenges for the Inspection of Small Series Production Small series Small Series Production Characterisation Challenges for the Inspection of Small Series Production 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. Production volume Boundary conditions and inspection requirements in SSP Mass production n Lack of predictability about the process and product behaviour n Constant creation of quality documentation n Increased setup cycles and no or just few products for rigging processes n Short time to observe and provide feedback to processes during production n Difficulties for reusing information and performing corrective Scientific challenge! actions Is it possible to maintain an economical production in small series and at the same time monitor the big Product variety n Lack of data for decision taking 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. Seite 4

Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Cognitive Production Metrology 4 Status 5 Conclusion and perspectives Seite 5

Flexible metrology supports flexible production systems Self-optimisation: reduce planning/costs of complex systems scale valueorientation Flexible metrology supports flexible production systems Self-optimisation: reduce planning/costs of complex systems scale valueorientation planningorientation scope flexibility self-optimisation 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 n Benefits of optical sensors Higher flexibility increases – Touchless, non-invasive and the planning of the system 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 6

Conceptual definition Cognitive production metrology and self-optimising systems Flexibility and mutability 1. Analysis of Conceptual definition Cognitive production metrology and self-optimising systems Flexibility and mutability 1. Analysis of the current situation n. Focus: 2. Determination of (new) system objectives Selfoptimised systems Autonomy Cognition Quality planning: 1) automatic and dynamic inspection plan generation 2) prediction of process and product quality Measurement systems: 1) flexible integration of different measurement systems 2) conception of adaptive measurement systems, combining multidimensional information acquired from different sources 3. Adaption of system behaviour to surrounding conditions Cognitive Production Metrology Automatic definition and application of inspection tasks for several product variants, using different flexible and adaptive measurement and inspection systems Seite 7

Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Cognitive Production Metrology 4 Status 5 Conclusion and perspectives Seite 8

Vision of cognitive production metrology Updated vision overview Roadmap to reach the vision 2020 Vision of cognitive production metrology Updated vision overview Roadmap to reach the vision 2020 2009 today + 2 years Cognitive Metrology Quality prediction of products; anticipation of optimized process parameters new generation of Dynamic inspection optical measurement planning based on systems product and process Ensuring total utilisation of new data exchangeability physical of CAD, CAM, CAQ, . . . phenomena in production and others. . . metrology (e. g. terahertz technology, soft X-rays, etc. ) data Quality planning Providing more flexible and robust geometric measuring systems Focus: CERTI Measurement systems and others. . . Others Not part of BRAGECRIM project Focus: WZL, S 2 i Seite 9

Integration and cooperation between the project partners Two different case studies to validate the Integration and cooperation between the project partners Two different case studies to validate the concept focus on product specifications focus on process specifications Predictive Quality Planning Inspection Planning Sensor Integration and Data Fusion Quality Evaluation Planned information: Inspection plan Real information: Information about product and process 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 based inspection of freeform parts (e. g. automotive headlights) Seite 10

Chronogram 2 + 2 years (2009 – 2013) Seite 11 Chronogram 2 + 2 years (2009 – 2013) Seite 11

Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Cognitive Production Metrology 4 Status 5 Conclusion and perspectives Seite 12

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

Use Case DE: Autonomous Inspection of Freeform Parts Integration of CPM concepts to the Use Case DE: Autonomous Inspection of Freeform Parts Integration of CPM concepts to the automated test stand at WZL n Four main tasks: – Task 1: Development of an agent-based machine control structure for the automated test stand § Work in progress (90%) by doctor student Alberto Pavim and supported by Brazilian exchange students Christian Silvano, Leonardo Adams and Priscila Antunes – Task 2: Integration and control of new visual inspection systems into the automated test stand § Work in progress (70%) by doctor student Alberto Pavim and bachelor student Maximilian Engelbracht and supported by Brazilian exchange student Priscila Antunes – Task 3: Development of sensor data fusion strategies for the quality analysis of free form parts § Work in progress (20%) by doctor student Alberto Pavim and exchange student Thiago Bacic – In experimental phase – Task 4: Development of a dynamic inspection planning, quality prediction and evaluation system § Cooperation work to be executed (10%) by doctor student Alberto Pavim and supported by Brazilian exchange doctor student Mario Roloff and master student Jacqueline Vargas – In preliminary study phase Seite 14

Achieved results: DE: Autonomous Inspection of Freeform Parts Agent-based machine control approach Quality agent Achieved results: DE: Autonomous Inspection of Freeform Parts Agent-based machine control approach Quality agent Security agent Routing agent Inspection Config. Inspection (IP) agent Planning agent GUI agent Inspection (IA) agent Statistic/Log agent Calibration agent DF agent Process agent Product agent product Transporting system I/O agent Station agent AMS agent Seite 15

Achieved results: DE: Autonomous Inspection of Freeform Parts Agent-based machine control approach Security Leave Achieved results: DE: Autonomous Inspection of Freeform Parts Agent-based machine control approach Security Leave Fusion of Final agent quality Inspection Z! I am inspection result A with evaluation! monitoring the system! result B! me to Take system Inspection inspection Z Y! consistency! station 4! GUI agent Quality agent Providing I am product X. I Presenting I am a am OK!Take me to Product I must inspection Inspection plan! inspection Y calibration Next step! recognition! inspectioncollect summary!station 3!data product! summary! about myself! Calibration agent Process agent Statistic/Log I am Take. I meis the Where there need to monitoring stations MV know who system inputs 1 and 2! inspection? Product outputs! I am! and agent product Transporting system Routing Feedback tolerance. Product Y: Two moves Ok, Z: Inside of to C! Fusion result =previous. Inspection X Inspection (IP) agent Inspection Config. clockwise and further counter are OK! identified! deal! B! You agent result = A! agent processes! route through stations Ok, 1 - Inspection Y Get 1 and with routing 2! deal! 2 - Inspection Z 3 - Data fusion agent! Ok, Planning 4 - Quality I am registering evaluation deal! agent system events Inspection (IA) and statistics! I/O agent Go to Handle agent first with transport MV Ok, station! station agents! DF deal! agent Create product agents! Station agent! Product Ok, agent arrived! deal! Station agent AMS agent Seite 16

BR Main Tasks Task 1: Development of a dynamic inspection planning and quality prediction BR Main Tasks Task 1: Development of a dynamic inspection planning and quality prediction and evaluation system for PCB Work begun by researcher Marcos Doro and continued by master student Jaqueline Vargas Task 2: Development of a visual inspection system for PCB Work begun by German exchange student Maximilian Wegener (DA) and continued by master student Daniel Fritzke Ferreira de Melo Task 3: Development of an agent-based machine control structure for the entire PCB production line Work executed by doctor student Mario Lucio Roloff with cooperation with doctor student Alberto X. Pavim at WZL Seite 17

BR Main Tasks Task 1: Development of a dynamic inspection planning and quality prediction BR Main Tasks Task 1: Development of a dynamic inspection planning and quality prediction and evaluation system for PCB Work begun by researcher Marcos Doro and continued by master student Jaqueline Vargas Task 2: Development of a visual inspection system for PCB Work begun by German exchange student Maximilian Wegener (DA) and continued by master student Daniel Fritzke Ferreira de Melo Task 3: Development of an agent-based machine control structure for the entire PCB production line Work executed by doctor student Mario Lucio Roloff with cooperation with doctor student Alberto X. Pavim at WZL Seite 18

LABelectron Use Case BR Figure: New assembly line at LABelectron (2011) Figure: Design of LABelectron Use Case BR Figure: New assembly line at LABelectron (2011) Figure: Design of Assembly Line at LABelectron Seite 19

LABelectron SMA – 1 st Statistic/Log agent Option Planning agent PCBA agent SCADA agent LABelectron SMA – 1 st Statistic/Log agent Option Planning agent PCBA agent SCADA agent Quality agent Inspection (SPI) agent Product agent Security agent Process agent Inspection (Stencil) agent DF agent Inspection (AOI) agent AMS agent product Loader agent Printer agent Conveyor agent Insertion agent Oven agent Unloader agent Seite 20

LABelectron SMA – 2 nd Option (Agents + Artifacts) Quality agent Process agent Inspection LABelectron SMA – 2 nd Option (Agents + Artifacts) Quality agent Process agent Inspection (SPI) agent Inspection (Stencil) agent Product agent DF agent Inspection (AOI) agent Conveyor artifact product Loader artifact Printer artifact Security agent Planning agent PCBA agent SCADA agent Statistic/Log agent Inserter artifact Oven artifact AMS agent Unloader artifact Seite 21

LABelectron SMA (Agents + Artifacts + Organization) PCBA agent SCADA agent Quality agent Statistic/Log LABelectron SMA (Agents + Artifacts + Organization) PCBA agent SCADA agent Quality agent Statistic/Log agent Organization Specifications Inspection (Stencil) agent Inspection (SPI) agent Product agent Security agent Process agent Inspection (AOI) agent Conveyor artifact product Loader artifact Printer artifact Inserter artifact Planning agent Oven artifact DF agent AMS agent Unloader artifact Seite 22

LABelectron SMA (Quality Prediction - Expert System) HMI data SCADA agent Quality agent Decision LABelectron SMA (Quality Prediction - Expert System) HMI data SCADA agent Quality agent Decision support data Knownledge Base Expert System Process Data Process Action product Seite 23

Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Contents 1 Introduction 2 Self-optimisation and the Cognitive Production Metrology concept 3 Vision of Cognitive Production Metrology 4 Status 5 Conclusion and perspectives Seite 24

Conclusion and Perspectives SSP complicates production and quality assurance tasks planningorientation scale n CPM Conclusion and Perspectives SSP complicates production and quality assurance tasks planningorientation scale n CPM defines a new paradigm in terms of metrological and quality assurance systems within SSP n Goal: to make the SSP economically viable while flexibly guaranteeing the quality of processes and products valueorientation scope an efficient quality management and metrology application for flexible SSP (based on SO systems) n First phase: – implementation of flexible multisensory-based inspection platforms – development of modules for the generation of automatic inspection plans for the two validation scenarios Flexibility and mutability Selfoptimised systems Autonomy n Development of methods, technologies and services for Cognition n Second phase: cognitive software modules – predict and evaluate process and product quality – allow intelligent and autonomous decision making and adaption processes Seite 25

Publications Conference proceedings, magazines n Papers in conference proceedings: – – n T. Pfeifer, Publications Conference proceedings, magazines n Papers in conference proceedings: – – n T. Pfeifer, R. Schmitt, A. Pavim, M. Stemmer, M. Roloff, C. Schneider, M. Doro: Cognitive Production Metrology: A new concept for flexibly attending the inspection requirements of small series production. Proceedings of the 36 th International MATADOR Conference, Springer, p. 359 -362, ISBN 978 -1 -84996431 -9, July 2010, Manchester, England M. Stemmer, M. Roloff, C. Schneider, M. Doro, T. Pfeifer, R. Schmitt, A. Pavim: Handling small series production inspection requirements through the use of cognitive and flexible metrology strategies. Proceedings of the CIRP ICME 2010 – 7 th CIRP International Conference on Intelligent Computation in Manufacturing Engineering – Innovative and Cognitive Production Technology and Systems, June 2010, Gulf of Naples, Italy Magazine article: – R. Schmitt, T. Pfeifer, A. Pavim: Cognitive Production Metrology – Ein neues Konzept zur qualitativen Absicherung der Kleinserienproduktion. Industrie Management, February 2011, Special Issue about Brazil. Printing procedure Seite 26

Thank You n Project partners n The depicted research has been funded by the Thank You n Project partners n 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. Seite 27

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