Evaluation and assessment of non-normal output during robust optimization

Omid Nejadseyfi (Corresponding Author), Hubertus J.M. Geijselaers, Antonius H. van den Boogaard

    Research output: Contribution to journalArticleAcademicpeer-review

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    Abstract

    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, a skew-normal distribution is used. Mean, standard deviation, and skewness of the output are calculated by applying an analytical approach. To show the applicability of the proposed method, a metal forming process is optimized. The optimization is defined by an objective and a constraint, which are both nonlinear. A Kriging metamodel is used as nonlinear model of that forming process. It is shown that the new robustness criterion is effective at reducing the output variability. Additionally, the results demonstrate that taking into account the skewness of the output helps to satisfy the constraints at the desired level accurately.
    Original languageEnglish
    JournalStructural and multidisciplinary optimization
    Issue number1
    DOIs
    Publication statusPublished - 2018

    Fingerprint

    Robust Optimization
    Output
    Metal forming
    Evaluation
    Normal distribution
    Skewness
    Metamodel
    Robustness
    Skew-normal Distribution
    Metal Forming
    Non-normality
    Kriging
    Nonlinear Function
    Standard deviation
    Nonlinear Model
    Propagation
    Optimization
    Demonstrate

    Keywords

    • UT-Hybrid-D
    • skewness
    • non-normal output
    • robust optimization

    Cite this

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    title = "Evaluation and assessment of non-normal output during robust optimization",
    abstract = "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, a skew-normal distribution is used. Mean, standard deviation, and skewness of the output are calculated by applying an analytical approach. To show the applicability of the proposed method, a metal forming process is optimized. The optimization is defined by an objective and a constraint, which are both nonlinear. A Kriging metamodel is used as nonlinear model of that forming process. It is shown that the new robustness criterion is effective at reducing the output variability. Additionally, the results demonstrate that taking into account the skewness of the output helps to satisfy the constraints at the desired level accurately.",
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    Evaluation and assessment of non-normal output during robust optimization. / Nejadseyfi, Omid (Corresponding Author); Geijselaers, Hubertus J.M.; van den Boogaard, Antonius H.

    In: Structural and multidisciplinary optimization, No. 1, 2018.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Evaluation and assessment of non-normal output during robust optimization

    AU - Nejadseyfi, Omid

    AU - Geijselaers, Hubertus J.M.

    AU - van den Boogaard, Antonius H.

    N1 - Springer deal

    PY - 2018

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    AB - 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, a skew-normal distribution is used. Mean, standard deviation, and skewness of the output are calculated by applying an analytical approach. To show the applicability of the proposed method, a metal forming process is optimized. The optimization is defined by an objective and a constraint, which are both nonlinear. A Kriging metamodel is used as nonlinear model of that forming process. It is shown that the new robustness criterion is effective at reducing the output variability. Additionally, the results demonstrate that taking into account the skewness of the output helps to satisfy the constraints at the desired level accurately.

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    KW - robust optimization

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