1 / 7

Design and Optimization: Status & Needs

Design and Optimization: Status & Needs. Dr. Wei Chen Associate Professor I ntegrated DE sign A utomation L aboratory ( IDEAL ) Department of Mechanical Engineering Northwestern University weichen@northwestern.edu , (847)491-7019 Http://ideal.mech.northwestern.edu//.

Télécharger la présentation

Design and Optimization: Status & Needs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Design and Optimization: Status & Needs Dr. Wei Chen Associate Professor Integrated DEsign Automation Laboratory (IDEAL) Department of Mechanical Engineering Northwestern University weichen@northwestern.edu, (847)491-7019 Http://ideal.mech.northwestern.edu//

  2. Research Areas of IDEAL Pratt & Whitney US Army Ford Motorola GM Industrial Applications • Major Topics • Robust Design & Optimization under Uncertainty (NSF) • Metamodeling for Simulation-Based Design (Ford) • Enterprise-Driven Multidisciplinary Decision Based Design (NSF) • Model Validation (NSF) Alcoa, JD Power, etc.

  3. State-of-the-Art: Efficient Probabilistic Optimization f(u1, u2) pdf RA Constr 1 RA Constr 1 RA Constr n RA Constr n pdf of g Red Area = Prob(ggR)=R Cycle 2 Cycle 1  u1 MPP u2 MPP g=0 Opt 1 Deter 0 gR g Opt 2 Deter Most Probable Point (MPP) Method for Efficient Reliability Assessment Inverse MPP Strategy Sequential Optimization and Reliability Assessment (SORA) Method

  4. State-of-the-Art: Metamodeling Techniques for Simulation Based Design D. Analytical Uncertainty Propagation E. Probabilistic Optimization B. Sequential Metamodeling Confirmation & Metamodel Updating A. Optimal Design of Experiments (DOE) • C. Analytical Probabilistic Global Sensitivity Analysis • Reduce the size of problem • Identify source for variance reduction Classification of Variables Noise Factors Responses Control Factors Product/ Process CAE Model

  5. State-of-the-Art:Multidisciplinary Decision-Based Design Framework ACCOUNTING ENGINEERING GROUPS 2 3 Total Lifecycle Cost Optimized Design and Price Engineering Attributes Design Options 5 Utility (Profit) Key Customer Attributes Demand 4 1 CORPORATE CUSTOMER MANAGEMENT PREFERENCES MARKETINGTEAM

  6. Vision Rapid concurrent design of material, product, and the associated manufacturing processes, optimizing quality, costs, and performance based on high fidelity modeling spanning the whole product realization and life cycle. Material Design Design Driven Performance Process Design Product Design Properties Structure Mapping Relation Processing Concurrent/Collaborative Optimal Material , Product, and Process Design Decisions

  7. Challenges/Research Thrusts • Seamless communication with languages/representations across material scientists, product designers, and manufacturing process engineers. • Problem decomposition/recomposition methods and modeling approach to reduce the interdependency (complexity) but to maintain the concurrency of subproblems. • Rapid design optimizationmethods to employ high fidelity simulation programs that capture the life cycle requirements, with the consideration of uncertainty. • Design synthesis methods to accumulate knowledge and experience that adapt to changes of design requirements. • Adaptive design framework with decision support, knowledge accumulation, and support for incorporating business and costs modeling, for multi-level users with distributed, concurrent, and collaborative access. • Model validation approach that requires the minimum amount of physical experiments and improves the confidence of using the result from optimization.

More Related