Integration of design of experiments and artificial neural networks for achieving affordable concurrent design

Wei Chen*, Sriram Varadarajan

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

For designs involving computer intensive systems analyses, approximation techniques are commonly used to create a simplified approach to evaluating the system behavior. These techniques help in reducing the product development time and in finding the optimal solutions. Two important types of approximation techniques are the Design of Experiments (DOE) and the Artificial Neural Networks (ANN). While these techniques have their own unique features, they have certain important advantages as well as disadvantages over each other. In this paper, an integration strategy is presented in which both methods complement one another in achieving affordable current systems design. The proposed strategy is verified by comparing the DOE and ANN approaches to the approximations of typical nonlinear behaviors in design. The high speed civil transport (HSCT) aircraft design is used as an example in this study.

Original languageEnglish (US)
Pages (from-to)1316-1324
Number of pages9
JournalCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Volume2
StatePublished - Jan 1 1997
EventProceedings of the 1997 38th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Part 4 (of 4) - Kissimmee, FL, USA
Duration: Apr 7 1997Apr 10 1997

ASJC Scopus subject areas

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Integration of design of experiments and artificial neural networks for achieving affordable concurrent design'. Together they form a unique fingerprint.

Cite this