Virtual Test and Optimisation
Virtual Test is about:
- Allocation of resources to do more early simulation
- Development of trust in simulation results
- Applying true Systems Engineering including upfront analysis
- Applying set-based engineering and develop knowledge
To investigate the total Design space (the possible variations of all involved parameters), the Test and the Calculation need to be in an organised way to ensure finding the best combinations of parameters
Virtual Optimisation is to find the optimal robust concept and includes the following steps:
- Design Of Experiment (DOE)
- Meta-Modelling
- Optimisation
- Robust Optimisation
- Uncertainty & Sensitivity Analysis
Virtual Test and Optimisation
Problems to address
- Long design cycle time
- Too many costly physical prototypes and testing
- High design cost considering narrow requirement tolerances
- Costly design loopbacks due to late design changes
- Poor product quality due to limited product knowledge leads to
sub-optimisation of specific characteristics
Benefits
- Reduced design cycle time and costly physical prototypes and
testing
- Improved tolerance settings based on the output variability
- Reduced late design changes and improved product quality due
to systematic knowledge building
- Good balancing between all product characteristics
Presentation of Virtual Test and Optimisation The corresponding PPP is for sale (also describing the Taguchi method used when creating a complete strength curve).
[1] Taguchi, G. , "Taguchi on Robust Technology Development, Bringing Quality Engineering
Upstream", ASME Press, New York, 1993.
[2] T. W. Simpson, J. D. Peplinski, P. N. Koch, and J. K. Allen, "Metamodels for Computer-
based Engineering Desing: Survey and Recommendations", Engineering with Computers
17, 129–150, 2001.
[3] K. Deb, "Multi-Objective Optimization Using Evolutionary Algorithms", John Wiley & Sons,
Ltd., Chichester, 2001.
[4] D. E. Goldberg, "Genetic Algorithm in Search, Optimization and Machine Learning",
Addison-Wesley Publishing Company, Inc., Reading, Massachusetts, 1989.
[5] A. Saltelli, "Global Sensitivity Analysis – An Introduction", Tutorial Lecture for The
International Conference on Sensitivity Analysis, Santa Fe, New Mexico, March 8 – 11,
2004.
[6] A. Sudjianto, X. Du, and W. Chen, "Probabilistic Sensitivity Analysis in Engineering Design
Using Uniform Sampling and Saddlepoint Approximation", Detroit Michigan, USA, 2005.