On the Choice of Basis in Proper Orthogonal Decomposition-Based Surrogate Models

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    To reduce scrap in metal forming processes, one should aim for robustness by means of optimization, control or a combination of both. Due to the high computational costs, a Finite Element (FE) model of a metal forming process cannot be used in optimization routines or control algorithms directly. Alternatively, a surrogate model of the process response to certain variables
    can be created that enables efficient control or optimization algorithms. When the process response is more than a scalar function only, reduction methods such as Proper Orthogonal Decomposition (POD) can be applied to obtain a surrogate model. In this work, the results of a set of FE analyses are decomposed using a single and separated snapshot matrices using different preprocessing methods. Additionally, a new method for projecting in different parts of the snapshot matrix is proposed. The bases obtained using different preprocessing methods are compared. Thereafter, the surrogate models of the process are built by interpolating the amplitudes obtained in different bases. The accuracy of all surrogate models is assessed by comparing the reduced results with the
    results from the FE analyses.
    Original languageEnglish
    Title of host publicationProceedings of the 22nd International ESAFORM Conference on Material Forming: ESAFORM 2019
    EditorsLander Galdos, Pedro Arrazola, Eneko Saenz de Argandona, Nagore Otegi, Joseba Mendiguren, Aitor Madariaga, Mikel Saez de Buruaga
    Number of pages6
    ISBN (Electronic)978-0-7354-1847-9
    Publication statusPublished - 2 Jul 2019
    Event22nd International Conference on Material Forming 2019 - Votoria-Gasteiz, Spain
    Duration: 8 May 201910 May 2019
    Conference number: 22

    Publication series

    NameAIP Conference Proceedings


    Conference22nd International Conference on Material Forming 2019
    Abbreviated titleESAFORM 2019
    Internet address


    • Proper orthogonal decomposition
    • snapshot preprocessing
    • Kriging
    • Surrogate model
    • Surrogate Modeling
    • proper orthogonal decomposition (POD)

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