Title: Applicability and comparison of surrogate techniques for modeling of selected heating problems
Authors: Pavlíček, Karel
Kotlan, Václav
Doležel, Ivo
Citation: PAVLÍČEK, K., KOTLAN, V., DOLEŽEL, I. Applicability and comparison of surrogate techniques for modeling of selected heating problems. Computers & Mathematics with applications, 2019, roč. 78, č. 9, s. 2897-2910. ISSN 0898-1221.
Issue Date: 2019
Publisher: Elsevier
Document type: článek
URI: 2-s2.0-85062150170
ISSN: 0898-1221
Keywords in different language: induction-assisted laser welding;surrogate modeling;metamodeling;computational cost reduction;weld depth prediction;regression analysis
Abstract in different language: Possibilities of using surrogate techniques for modeling selected strongly nonlinear coupled problems of heating are evaluated. The main purpose is to significantly reduce the computing time in the case of computations of many variants of a given task by the finite element method on the condition of obtaining results of a still acceptable accuracy. Frequently used surrogate techniques (based on Kriging, neural network etc.) are tested on a particular problem of induction-assisted laser welding that represents a very complicated 3D problem. Here, the most important output quantities are the internal structure of weld (decisive for its mechanical parameters) and its depth that depend on a number of input parameters (power of laser beam, velocity of shift of the welded parts, overall geometry and material properties etc.) and must be known before the process of welding itself. The paper presents both full model of this process and considered surrogate algorithms, and compares the results obtained. It is shown that a careful selection of the surrogate technique together with suitable choice of its input data is very beneficial and may result in high savings in design of the process. Implementation performance and suitability of particular techniques of this kind are also evaluated.
Rights: Plný text je přístupný v rámci univerzity přihlášeným uživatelům.
© Elsevier
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Články / Articles (KTE)

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