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NSF Engineering Research Center for Reconfigurable Machining Systems

NSF Engineering Research Center for Reconfigurable Machining Systems. Reconfigurable Manufacturing Systems A. Galip Ulsoy , Center Deputy Director William Clay Ford Professor of Manufacturing University of Michigan, College of Engineering. April 11, 2002.

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NSF Engineering Research Center for Reconfigurable Machining Systems

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  1. NSF Engineering Research Center forReconfigurable Machining Systems Reconfigurable Manufacturing Systems A. Galip Ulsoy, Center Deputy Director William Clay Ford Professor of Manufacturing University of Michigan, College of Engineering April 11, 2002

  2. Reconfigurable Manufacturing Systems (RMS) Basic concepts ERC/RMS research plan Capacity Management Policies Combined Design and Control Concluding Remarks Outline

  3. Product design time reduced by CAD Present Develop Product C GOAL Develop Product B Product A in-market Develop Product A Design & Build Manufacturing System Ramp Up Produce A Produce A & B Produce B & C RU RU Time Exactly the functionality needed… … Exactly when needed Reconfiguration Economic Goal: Shorten SystemDesign & Reconfiguration Lead-Time Concept Product A in-market Develop Product A Produce A for 10 - 20 years Design & Build Manufacturing System Ramp-Up System Lead Time

  4. R M S B + C R M S A + B R M S A Comparison of Three Types of Systems:Capacity & Functionality Dedicated Mfg. Line Capacity F M S Product Product Product Multiple A A + B B + C Products Functionality Exactly the functionality and capacity needed . . . . . . Exactly when needed.

  5. A Line 1 Years 3&6 A & B Line 2 Product A or B; 200,000 parts/year B Product A; 200,000 parts/year A & B Product A or B; 250,000 parts/year A Line 1 Year 8 Line 2 Laser Stn. B Reconfiguration Scenario Year 0 Product A; 300,000 parts/year A A Product A; 200,000 parts/year

  6. Optical Measurement for Quick Ramp-Up

  7. 6 [mm] 10.0 without SoV 8.0 6.0 with SoV 4.0 (Data from a real plant) 2.0 Time [weeks] 24 36 48 12 Installation Ramp-Up Methodology Rapid ramp-up is critical for successful reconfiguration, but it’s also useful for traditional systems • New methodology for systematic ramp-up of large systems based on stream-of-variation theory (SoV) • • Utilizes state-space modeling combined with statistical analysis methods • • Needs high-speed high-accuracy measurement

  8. Reconfigurable? Flexible Dedicated ? • Reconfigurablity concept -wrenches • RMS: reconfigure capacity and functionality • Reconfigurable vs dedicated vs flexible is an economic decision Reconfigurable

  9. RMS Science Base A set of theories and laws that are applicable to the synthesis and analysis of RMSs and share key characteristics RMS Science Base RMT machine diagnosis & calibration Life-cycle economic Modeling Reconfigurable machine design theory Stream-of-variation theory System configuration rules f(machine reliability, quality,…) Controller configuration methodology Networked Control Modularity Customization Scalability Convertibility Integrability Diagnosability

  10. Library of Machine Modules & Machines Part Family Market Demand: Volume & Mix Reconfigurable Machines and Controls Reconfigurable System Design CoP 1 System-Level Design CoP 2 Machine / Control-Level Design Ramp-Up Methodology CoP 3 Calibration and Ramp-Up Major Research Issues in RMS

  11. CoP 3 CoP 2 CoP 1 Center Projects Market Changes Volume & Mix Part Family M1 Reconfigurable Machine Design Methodology S1 System-Level Configurator & Process Plan S2 Scalability & Material Handling M4 Network-Based Control S3 System Configuration Impact R2 Reconfigurable Process Monitoring R4 Machine Vision M2&M5 Spindles For RMTs M3 Modular Logic Control R1 Stream-of-Variations For System Diagnostics S4 Life-Cycle Economic Modeling R3 Rapid Optical Measurement of Parts Reconfigurable Machines Exploratory Projects Testbed Projects for proof-of-concept and integration RMS Ramp-Up RMS Design

  12. Center Testbed Facility A brief (1.5 minute) video presentation. Also an ERC Testbed web cam is available live at http://erc.engin.umich.edu/webcam.htm

  13. Delphi study involving experts from around the world: Reconfigurable Manufacturing Systems identified as one of six grand challenges Adaptable and reconfigurable systems - priority technology number 1 http://www.nap.edu/readingroom/books/visionary/ National Research Council:Visionary Manufacturing Challenges for 2020 RMS featured in CIRP 99 keynote paper, JUSFA 00 and JIMTOF 00 keynote papers, ASME conferences/publications, 1st CIRP RMS conference 01 and Feb. 02 issue of ASME ME magazine. The impact of the ERC/RMS, in just 5 years, has been worldwide

  14. Reconfigurable Manufacturing Systems (RMS) Capacity Management Policies Capacity management via feedback Combined Design and Control Concluding Remarks Outline

  15. A Reconfiguration Decision Consists of Two Parts: When? How Much? • How much? How much? When? Capacity Management:Motivation Market Demand Capacity Time

  16. Optimize Cost Function Wi(Ci, Di,yi) w.r.t: Capacity Management Problem Time

  17. What has been done? Capacity Management Policy Uniform capacity expansion [Manne, 1961 & 1967] ExpansionSize (x) Optimal Solutions: Capacity Management Problem Capacity & Market Demand Demand (Slope=d) Time

  18. Cost Function: Optimum Value Cost (W) Expansion Size (x) Capacity Management - Background Expansion Size Discount Rate Variable Cost Fixed Cost Demand Slope

  19. Manne’s Example: Optimal Results

  20. What are the issues? Slope might change Discount factor might change In addition to minimum cost, we need to consider sensitivity and robustness Manne’s Example: Results

  21. Capacity Management via Feedback

  22. Manne’s Example: Feedback Approach Controller Design i i C(t)

  23. Manne’s Example: Results w/ Feedback

  24. Manne’s Example: Results w/ Feedback

  25. Stochastic Demand Example Stochastic Market Demand [Freidenfelds, 1980 & 1981]

  26. Stochastic Demand Example: Results Stochastic Market Demand [Freidenfelds, 1980 & 1981] Mean and variance of cost are both reduced using the feedback approach

  27. Numerical Example and Results (A=R=0) Capacity Management: Optimal Policies

  28. Introduce Dynamics In The Problem Capacity & Market Demand Time Capacity Management:Effect of Delay • New Design • Change • Respond • Quick • Time Delay

  29. Capacity Management:Problems Due to Delay

  30. Capacity Management:Optimal Policy Which Accounts for Delay

  31. Reconfigurable Manufacturing Systems (RMS) Capacity Management Policies Combined Design and Control Combined optimality conditions and coupling Concluding Remarks Outline

  32. Plant/Controller Optimization Strategies Optimize the plant Optimize the system by varying both plant & control Simultaneous Optimize the controller Sequential Optimize the system by varying the plant Optimize the plant without compromising control performance Optimize the controller Optimize the controller Iterative Nested

  33. Definitions Theorem 1: The plant and controller optimization problems are coupled in the sense that their sequential solution does not necessarily give a combined optimum. Vector of plant design variables Vector of controller design variables Combined objective function Set of feasible plant/controller designs Sequential vs. Simultaneous Strategies

  34. Combined Plant/LQR Control Optimization

  35. Combined Optimality Conditions

  36. The coupling term: Sufficient decoupling conditions: Pareto limit condition (special case of boundary decoupling). Zero coupling term condition (interior decoupling). Necessary decoupling condition (interior & boundary decoupling): When the contribution of the optimal attainable control performance to the system objective cannot be enhanced without violating active plant design constraints, the two problems decouple. The Coupling Term/Decoupling Cond.s

  37. Reconfigurable Machining Systems (RMS) Can be (re)configured in response to market Reduce lead time, including ramp up Center research organization Summary and Conclusions • Capacity Management Policies • Capacity management problem • Optimal robust capacity reconfiguration policy based on feedback • Results (deterministic & stochastic) and effect of delay • Combined Design of Plant and Controller • Sequential, iterative, nested and simultaneous strategies • Coupled problem formulated and existence of coupling proven • Optimality conditions and coupling

  38. Acknowledgements Collaborators: Farshid Asl, Hosam Fathy, Yoram Koren, Panos Papalambros Sponsor:NSF Grant EEC 9529125 Acknowledgements and References • References • RMS: • Koren et al, CIRP Keynote Paper 1999 • Dynamic Modeling of RMS: • Asl, Ulsoy and Koren, JUSFA 2000 • Asl, Ulsoy, ACC 2000, JUSFA 2002 & IMECE 2002 • Combined Design and Control: • Fathy, Papalambros and Ulsoy, ACC 2001 & DET 2001

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