How Boeing Commercial is Leveraging Evolutionary Computation in Design

Transcription

1 How Boeing Commercial is Leveraging Evolutionary Computation in Design Tom Dickens Associate Technical Fellow The Boeing Company Seattle WA, USA BOEING is a trademark of Boeing Management Company.

2 Outline Goal of EC in Industry EC/Optimization Papers From Boeing Current State of EC in Boeing Potential Future Uses of EC Conclusions References

3 Goal of EC in Industry Automate (pieces of) the Design Process Explore Large Possible Design Space Consider More Alternatives Make Good Designs Better Optimize Manufacturing Processes Optimize Scheduling Other

4 Overview of Boeing Papers Johnston Autoclave Process Control George Autoclave Underbrink Scheduling Blom Fairing Design Dickens Reflector Design Dickens Image Transformation von Doenhoff Hydraulic Brake Model Herling 3DOPT Neves Design Explorer

5 Paper Johnston 2001 WHO: Boeing Canada with National Research Council Canada. WHAT: Composite Materials Process Control. EC/GA USE: To optimize the autoclave process in terms of temperature and time. FOCUS: Use by non-experts, use with existing equipment, handling sensor failure. GOAL: Reduce manufacturing costs and time, improve quality of composite parts.

6 Results Johnston 2001 Optimized Original 25% Time Reduction

7 Paper George 2002 WHO: Boeing, Convergent Manufacturing Technology, Northrop Grumman, NAST, DARPA. WHAT: Robust Design Computational System (RDCS) SW Tool. EC/GA USE: Parametric design-space search engine. FOCUS: Used a Composite-Curing autoclave problem as a test case. GOAL: Provide mature RDCS SW tool.

8 Results George 2002

9 Paper Underbrink 1994 WHO: Boeing. WHAT: Assembly Line Scheduling. EC/GA USE: Search around both temporal constraints and resource constraints. FOCUS: Scheduling for manufacturing. GOAL: Proof-of-concept for scheduling algorithm. NOTES: Used multiple chromosomes with chromosome repair.

10 Presentation Blom 2003 WHO: Boeing. WHAT: Wing/Body fillet design. EC/GA USE: Parametric design-space search engine coupled with geometry-generation and CFD solver. FOCUS: Production use for aerodynamic design. GOAL: Find optimal fillet. RESULTS: First month-long run found errors in gridding code.

11 Paper Dickens 1997 WHO: Boeing. WHAT: Light Reflector Design for test equipment. EC/GA USE: Parametric design-space search engine coupled with geometry-generation and simulation. FOCUS: Production use for a one-time reflector design for PSP test equipment. GOAL: Optimal reflector design, was manufactured.

12 Results Dickens Control Points Light Source Brackets Outlet 20% more-efficient design

13 Paper Dickens 1999 WHO: Boeing. WHAT: Image transformation for target matching. EC/GA USE: Search engine driving values in a 4x4 transformation matrix. FOCUS: Transform a set of images into the same image space for feature analysis. GOAL: Use in registration of images in a Pressure Sensitive Paint (PSP) data-acquisition system.

14 Results Dickens Image 1 Image 2 Registration Targets T21 Calculate T21 Transformed Image 2 Image 2 T21 All Pixels Transform Image 2

15 Paper von Doenhoff 1996 WHO: Boeing, university of Washington. WHAT: Develop accurate simulation of airplane brake system hydraulics. EC/GA USE: Parametric design-space search engine to find a good set of model parameters. FOCUS: Proof-of-concept of generating an accurate model. GOAL: Replace expensive hydraulic test-stand use for tuning brake controllers with an accurate and cost-effective simulation model.

16 Results von Doenhoff Measured Versus Simulated Match Well 2 Plots of Measured Versus Simulated The 2 worst cases, still had very good match!

17 Paper Herling 1998 WHO: Boeing and Air Force Research Laboratory. WHAT: 3DOPT, and 3D Geometry Design Optimization System. EC/GA USE: GA to locate global optimum region, local hill-climbing. FOCUS: Airplane Wing or Wing/Body Configurations. GOAL: Geometry optimization system.

18 Paper Herling 1998

19 Paper Herling 1998

20 Paper Neves 1999 WHO: Boeing. WHAT: Design Explorer (DE), a Collaboration between Boeing and Rice University. EC/GA USE: None. FOCUS: 3D-Geometry Optimization. GOAL: Provide a robust easy-to-use system for design optimization.

21 Paper Neves 1999

22 Current State of EC Implementations Very Local and Specialized Implementations Many Proof-of-concept Efforts to Sell The Idea Some Limited Production Use GA-Expert-Centric, Requires Specialized Knowledge and Dedication of GA-Expert Efforts and Investigates Since the Early 1990s Leveraging Faster Computers Leveraging Networks of Computers

23 Other Potential Uses of EC Training Artificial Neural Systems Decision-making with Sparse or Rough Data Heat Distribution in Autoclaves Real-time Adaptation of Machinery to Forecast Wear Resource Allocation, Work Assignment, and Work Positioning in the Factory More 3D Geometry Optimization Scheduling Optimization

24 Evolutionary Computing Papers by The Presenter Dickens, Thomas P, Smooth Non-Linear Floating-Point Bit-Mapping Technique, The Fifth International Workshop on Frontiers in Evolutionary Algorithms (FEA 2003), Under the umbrella of 7th Joint Conference on Information Sciences, September 26-30, 2003, Cary, North Carolina. Thomas P. Dickens, Image-Calibration Transformation Matrix Solution using a Genetic Algorithm, in Industrial Applications of Genetic Algorithms, edited by Charles L. Karr and L. Michael Freeman, CRC Press, New York, Thomas P. Dickens, Method to Achieve Better Performance in Genetic Algorithms Applied to Time- Constrained, Multi-Solution Problems, Master s thesis, University of Alabama through the National Technical University, Dickens, Thomas P. and Charles L. Karr, Method to Achieve Better Performance in Genetic Algorithms Applied to Time-Constrained, Multi-Solution Problems, IEEE International Joint Symposia on Intelligence and Systems, May 21-23, 1998, Rockville, Maryland. Thomas P. Dickens, Genetic Algorithms and Genetic Programming, Boeing TM&T Technical Exchange, February 6, 1997, Renton Washington.

25 Evolutionary Computing Papers by Boeing Employees Blom, Gordon A. and Huy V. Cao, Navier-Stokes/genetic optimization of multi-element airfoils, AIAA, Applied Aerodynamics Conference, 14th, New Orleans, LA, June 18-20, Pete George, John Griffith, George Orient, Alison West, Robert Courdji, and Calvin Teng, Exploration of Composites Processing and Producibility by Analysis, 34th International Society For the Advancement of Material and Process Engineering (SAMPE) Technical Conference, November 2002, Baltimore. Maryland. Theron R. Fennel, A. J. Underbrink, Jr., and George P. W. Williams, Jr., Scheduling With Genetic Algorithms, Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space, i-sairas 94, October 18-20, 1994, Pasadena, California. William W. Herling, Stephen T. LeDoux, Robert R. Ratcliff, David A. Treiber, and Matthew J. Warfield, 3DOPT - An Integrated System for Aerodynamic Design Optimization, AIAA Applied Aerodynamics Conference, AIAA , June William W. Herling, Stephen T. LeDoux, Robert R. Ratcliff, Application Studies using the 3DOPT Integrated Design System, AIAA Applied Multi-disciplinary Optimization Conference, AIAA , September George P. W. Williams, Jr., Evolutionary Computation: What is it and why Boeing should care, April 29, A. J. Underbrink, Jr., and George P. W. Williams, Jr., Genetic Algorithms Applied to Assembly Operations, SPIE Conference on Applications of Artificial Intelligence XII:Knowledge-Based Artificial Intelligence Systems in Aerospace and Industry: April 5-6, 1994 Orlando, Florida. Thomas A. Grandine, A Quick Summary of Evolutionary Computing, Streifel, R J; Von Doenhoff, R C; Choi, J J; Healy, M J; Valaas, A M, Application Of Genetic Algorithms To Hydraulic Brake System Parameter Identification, Boeing/University of Washington White Paper, June 3, Dr. Kenneth W. Neves, Industrial "Power Grid" Computing: The Next High Performance Challenge, November 10, 1999, Many more that are Boeing proprietary.

26 Evolutionary Computing Papers Sponsored by Boeing Boeing has funded GA/EC research for the past decade. Some reference I found include: Grant A. E. Soremekun, Genetic Algorithms for Composite Laminate Design and Optimization, MS in Engineering Mechanics Thesis to Virginia Polytechnic Institute and State University, February 5, 1997, Blacksburg, Virginia. Robert E. Smith, Ph.D., A Framework for Evolutionary Component Capabilities in Agent-Based Systems, Associate Professor Department of Aerospace Engineering and Mechanics, The University of Alabama, Tuscaloosa, Alabama. Dr. Bryce Roth, Mr. Brian German, Prof. Dimitri Mavris, Adaptive Selection Of Engine Technology Solution Sets From A Large Combinatorial Space, Georgia Institute of Technology, Atlanta, Georgia. (The technique is applied to a commercial turbofan engine technology selection problem of practical interest. A pool of forty technology concepts is proposed and evaluated, the objective being to determine which subset of technologies is the best candidate to go forward into development given conflicting objectives on performance, engine manufacturing cost, and design risk )

27 Conclusions There is a Wide and Growing Interest for EC in Industry Many Good Examples of Successes Application of EC is Too Specialized: Need to Embed EC (and/or DE) Into the Design Process For the General User Off-the-shelf or Common EC Engines That Can Be Used In Multiple Code Are Needed

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