Optimization

Simulations are great way to understand structural or fluidics performance. Simulations are mostly performed with geometry from CAD models at nominal dimensions and mean values of material properties. As these results are great for design work, effects of tolerances is not captured with results set with nominal values. To understand the effects of tolerance and robustness of design, we propose performing a DOE (Design Of Experiment). DOE techniques allow for the exploration of design space by considering all variables simultaneously and predicting system’s response over a wide range of values. The DOE results provide greater understanding of performance of the system.

In many engineering problems design parameters may be uncertain: tolerances, fluctuating operating conditions, etc. These uncertainties impact the design process both in terms of reliability (i.e. the probability that a certain design will not fail to meet a predefined criterion or performance function) and in terms of robustness of the optimal solution ( i.e. the stability of an optimization outcome against the variations in the input parameters). In these circumstances, traditional optimization techniques tend to “over-optimize”, producing solutions that perform well at the design point, but have poor off-design characteristics.

For a long time these types of efforts were limited to few industries and in academics, now can be cost effective. We have industry leading algorithms and hardware resources. We can help you with selecting a good DOE technique for given set of variables and recommend the need for optimization (if any).