Robust Design Optimization using optiSLang

The one-day seminar gives a short introduction to probabilistic theory and provides the background of the terms robustness and reliability. The application of robustness and reliability assessments will be demonstrated by using an application example of an optimized design.

Virtual product development is conducted using deterministic parameters, hence under idealized conditions. Particularly in the context of product optimization, the product performance under random influences (tolerances, external influences) has to be warranted to fulfill the requirements.

For a robustness analysis, input parameters are modelled as random variables. The result of a robustness assessment is a prognosis of the variation of the product performance in connection with the prognosis quality of the simulated system response. Moreover, the most relevant inputs influencing the scatter of the system response can be identified.

For the proof of quality or safety requirements, optiSLang offers methods of reliability analysis, which compute the rare event of exceeding a –possibly non-linear– limit state.

Agenda

  • Robustness evaluation: introduction, terms, Design for Six Sigma
  • Iterative Robust Design Optimization (RDO)
  • Coupled RDO
  • Basics of reliability analysis
  • Methods of reliability analysis: FORM, Monte Carlo und varianz-reducing methods
  • Training project: robustness and reliability assessment of an optimized design

 

 

As a prerequisite, basic knowledge about statistics and optiSLang is required which can be obtained by attending the training "optiSLang basics". We recommend the training "Statistics on Structures" for further study of the subject.

Details

Date:
Nov 8, 2017 in Weimar

Map

Please register not later than 14 days prior to the event date.

Time:
9am - 5pm

Fee: 
600 Euro (excluding VAT) per participant in Weimar or Vienna
2000 Euro (excluding VAT) per on-site training including maximum 12 participants

Discount:
50% for students
30% for university members / Ph.D. students