JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Sensing, Controls and Automation Group June 9-10, 2004 John M. Griffith Technical Fellow Structural Technologies, Prototyping and Quality Phantom Works, St. Louis The Boeing Company (314)

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Expertise/Background Advanced Composite Materials, Processing and Manufacturing/Quality…….. –New Technology Development –Technology Maturation –Application Transition Associated with Advanced Composites for 38 Years

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Definitions Technology Developer and Technology Customers Have Significantly Different Perspectives These Metrics Do Not Take Into Account Different Perspectives From Multiple Disciplines Typical Time Frames Going From Initial Development to Production Range From 1 Year to Years. Content related to Accelerated Insertion of Materials – Composites (AIM-C) which is jointly accomplished by a Boeing Led Team and the U.S. Government under the guidance of NAVAIR. Work funded by DARPA/DSO (Dr. Leo Christodoulou) and administered by NAVAIR through TIA N Technology Maturity

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Definitions Sensing –Directed at Variable Measurement (Directly or Indirectly) –Used In-Process and/or Embedded in Parts Control –Directed at Variability Areas/Items and Limits –Includes Materials, Processing, Indirect/Support Materials, Equipment and Tooling –Covers In-Process Control and Final Part Control –Used at Three Different Times Initial-Setup Production Changes/Change Control

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Definitions Automation –Relative to Processing/Manufacturing Operations or Steps Preforms (If Applicable) Injection (Material(s) Into Preform or Tool) Secondary Operations –Relative to Equipment Established and Available Special Purpose

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop State of The Art (SOTA) Assessment Sensing –Tend to Minimize Sensors in Production –Tend to Maximize Sensors During Maturation and Startups –Temperature and Pressure Primary Variable Sensors –Few Qualified For Batch Type of Aerospace Production –Several Key Process Variables Not Presently Measurable by Sensors Control –Tend to Over Control Everything Through Specifications (Knowns and Known Unknowns) –Unknown Unknowns Create Problems –6 Sigma is Helping –Control of Change is an Issue Automation –Has Been Difficult for Aerospace Composites Liquid Molding Because of Low Rates, Low Quantities and Batch Type of Mfg –Previous Focuses Were on Labor Intensive Areas Such As Preform Layup Automation –Driven By Economics, Not Technology (Need Good Understanding of Variability Areas and Items for Automation) (General Observations/Comments)

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Future Vision Science Based Understanding of Material, Processing, Indirect/Support Material, Equipment and Tooling Variability Along With Variability Limits and Interactions for Intelligent Control and Repeatable Components. This Includes Sensor Based Control of Key Variables.

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Perceived Gaps Understanding Part Impact From Primary Variable Limits Understanding Material Chemistry Variability Impact On Processing Understanding Processing Variability Impact On Material Chemistry Understanding of Part Impact From Material Chemistry Variability and Limits Verification and Validation of Variable Models Pedigree of Information on Variability Research Activities Capability to Integrate Variability/Control Models and Tie To Part Performance Capability to Use Models/Integrated Models By Industry Understanding of “As-Built” Relative to “As-Designed” Variability For Intelligent Controls …….….Transition/Implementation/Usage is Based on Perceived Risk of Customers

JMG NSF/DOE/APC Future of Modeling in Composites Molding Processes Workshop Research Thrusts Chemistry Based Understanding of Variability and Variability Control Increased Emphasis on Model Verifications and Validations “As-Built” Variability Understanding Relative to “As-Designed” Architecture/Infrastructure for Integrated Variability Modeling or Simulation Information Data Bases with Known Pedigree of the Information Increased Standards and/or Common Metrics for Variability Measurements to Enable Multi-Scale Multi-Model Simulation of Variability Factors and Interactions Directed Towards Part Impacts