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    Complementing Drawability Assessment of Deep-Drawn Components With Surrogate-Based Global Sensitivity Analysis

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 010 ):;issue: 003::page 31204-1
    Author:
    Lehrer, Tobias
    ,
    Kaps, Arne
    ,
    Lepenies, Ingolf
    ,
    Raponi, Elena
    ,
    Wagner, Marcus
    ,
    Duddeck, Fabian
    DOI: 10.1115/1.4065143
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions. Complementing this process with a global sensitivity analysis (GSA) can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component. Here, we show in an exemplary application problem, that both a standard Gaussian process regression (GPR) model and an ensemble model can provide commendable results at a fraction of the computational cost. We compare our surrogate models to existing approaches in the field. Furthermore, by comparing drawability measures we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase.
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      Complementing Drawability Assessment of Deep-Drawn Components With Surrogate-Based Global Sensitivity Analysis

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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    contributor authorLehrer, Tobias
    contributor authorKaps, Arne
    contributor authorLepenies, Ingolf
    contributor authorRaponi, Elena
    contributor authorWagner, Marcus
    contributor authorDuddeck, Fabian
    date accessioned2024-12-24T19:18:22Z
    date available2024-12-24T19:18:22Z
    date copyright4/22/2024 12:00:00 AM
    date issued2024
    identifier issn2332-9017
    identifier otherrisk_010_03_031204.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303701
    description abstractIn the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions. Complementing this process with a global sensitivity analysis (GSA) can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component. Here, we show in an exemplary application problem, that both a standard Gaussian process regression (GPR) model and an ensemble model can provide commendable results at a fraction of the computational cost. We compare our surrogate models to existing approaches in the field. Furthermore, by comparing drawability measures we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleComplementing Drawability Assessment of Deep-Drawn Components With Surrogate-Based Global Sensitivity Analysis
    typeJournal Paper
    journal volume10
    journal issue3
    journal titleASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
    identifier doi10.1115/1.4065143
    journal fristpage31204-1
    journal lastpage31204-10
    page10
    treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 010 ):;issue: 003
    contenttypeFulltext
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