Building models to support synthesis in early stage product design

  • Authors:
  • R. Bharat Rao;Stephen C-Y. Lu

  • Affiliations:
  • Learning Systems Department, Siemens Corporate Research, Inc.;Knowledge-based Engg., Systems Res. Lab, University of Illinois at Urbana-Champaign

  • Venue:
  • AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
  • Year:
  • 1993

Quantified Score

Hi-index 0.00

Visualization

Abstract

Current computer-aided engineering paradigms for supporting synthesis activities in engineering design require the designer to use analysis simulators iteratively in an optimization loop. While optimization is necessary to achieve a good final design, it has a number of disadvantages during the early stages of design. In the inverse engIneering methodology, machine learning techniques are used to learn a multidirectional model that provides vastly improved synthesis (and analysis) support to the designer. This methodology is demonstrated on the early design of a diesel engine combustion chamber for a truck.