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O. Nejadseyfi

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Robustimizer

A graphical user interface application for efficient uncertainty quantification, robust optimization, and reliability-based optimization of processes and designs

The primary goal of this work is to provide easy-to-use and cutting-edge optimization software designed to handle uncertainties, intended for use in research and education. Robustimizer offers efficient uncertainty quantification through exact analytic formulas using specific sur ...
In this work, metamodel-based robust optimization is performed using measured scatter of noise variables. Principal component analysis is used to describe the input noise using linearly uncorrelated principal components. Some of these principal components follow a normal probabil ...
Production efficiency in metal forming processes can be improved by implementing robust optimization. In a robust optimization method, the material and process scatter are taken into account to predict and to minimize the product variability around the target mean. For this purpo ...
A robustness criterion that employs skewness of output is presented for a metamodel-based robust optimization. The propagation of a normally distributed noise variable via nonlinear functions leads to a non-normal output distribution. To consider the non-normality of the output, ...
Optimization under uncertainty requires proper handling of those input parameters that contain scatter. Scatter in input parameters propagates through the process and causes scatter in the output. Stochastic methods (e.g. Monte Carlo) are very popular for assessing uncertainty pr ...
Robust optimization is a powerful method to find the parameters for a process at which its output is least sensitive to the variation of the input parameters. In this method, measured or estimated noise parameters are used to estimate the scatter of the output. At the optimum des ...