Robust product design means controlling the variability inherent in the production of a product. These variabilities come from various sources of uncertainty that must be modeled and considered in the design and dimensioning process. The ultimate objective is to be able to size the product while ensuring a certain level of reliability or performance. In order to achieve this objective, Phimeca’s expertise is based on a set of complementary probabilistic analyses.
Probabilistic modelingThe variabilities are represented by a probabilistic model. Several inference techniques exist: statistical or Bayesian. The modeling of the dependencies between the variabilities is an important point and can be carried out thanks to the use of copulas.
Sensitivity analysisAnalysing the dispersion of the variables of interest allows us to determine which random variables are the most influential. Several types of sensitivity analysis exist: size reduction (screening analysis), prioritisation of influences (analysis of variance).
Reliability analysisThe reliability level is determined by calculating a probability that one or more quantities of interest exceed a critical threshold. Numerous methods exist: Monte Carlo, FORM, Subset or advanced techniques combining simulation and response surfaces.
Performance calculationUnlike the reliability calculation, it involves calculating the performance of the product in order to ensure a given target reliability level (i.e. probability). This calculation is equivalent to the estimation of quantiles of rare events.
Robust optimisationThe purpose is to optimise design variables (constant or random) while ensuring an acceptable level of reliability. The methods combine optimisation algorithms and advanced reliability methods to provide accurate results within an acceptable calculation time.
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