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A Hierarchical Model Validation of Predictive Models for Engineering Product Development

[+] Author Affiliations
Byeng D. Youn, Byung C. Jung, Zhimin Xi

University of Maryland - College Park, College Park, MD

Sang Bum Kim

LG Electronics, Pyeongtaek, Korea

Paper No. DETC2009-87571, pp. 1229-1238; 10 pages
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 35th Design Automation Conference, Parts A and B
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4902-6 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME


As the role of predictive models has increased, the fidelity of computational results has been of great concern to engineering decision makers. Often our limited understanding of complex systems leads to building inappropriate predictive models. To address a growing concern about the fidelity of the predictive models, this paper proposes a hierarchical model validation procedure with two validation activities: (1) validation planning (top-down) and (2) validation execution (bottom-up). In the validation planning, engineers define either the physics-of-failure (PoF) mechanisms or the system performances of interest. Then, the engineering system is decomposed into subsystems or components of which computer models are partially valid in terms of PoF mechanisms or system performances of interest. Validation planning will identify vital tests and predictive models along with both known and unknown model parameter(s). The validation execution takes a bottom-up approach, improving the fidelity of the computer model at any hierarchical level using a statistical calibration technique. This technique compares the observed test results with the predicted results from the computer model. A likelihood function is used for the comparison metric. In the statistical calibration, an optimization technique is employed to maximize the likelihood function while determining the unknown model parameters. As the predictive model at a lower hierarchy level becomes valid, the valid model is fused into a model at a higher hierarchy level. The validation execution is then continued for the model at the higher hierarchy level. A cellular phone is used to demonstrate the hierarchical validation of predictive models presented in this paper.

Copyright © 2009 by ASME



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