The present paper proposes a tire model based in finite element analyses that predicts displacement on its outer shell as a result of inflation pressure variation. The model is composed of wire axisymmetric elements, under pressure, applying displacement on the edges to represent the wheel deformation. The next step is to divide the geometry in sections, addressing the problem of defining a virtual material for each section that represents with good fidelity the material combinations found on the actual tire. The classical Yeoh hyperelastic material model was adopted. One parameter per section has to be obtained based on physical tests. An initial set of parameters was chosen at random by a Design of Experiment methodology. The first step towards this objective was to measure displacement data for four different values of inflation pressures of a tire using optical 3D metrology technique. The resulting images were numerically treated to obtain the displacements and tire thickness for each section. A set of points on the tire was used as a reference to assess the tire deformation. The model was run and its results were compared to 3D experimental data in order to establish a discrepancy metric. This set of parameters was then fed into a Gaussian Process Regression Algorithm as inputs and the corresponding discrepancy values as outputs. The discrepancies were minimized in terms of the material parameters. The initial material model predictions were used to create a second, more refined, DoE for the Yeoh material parameters. This process was repeated iteratively in order to calibrate with increasing accuracy of the adopted material model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tire Material Characterization Using Gaussian Process Regression and Optimization


    Additional title:

    Sae Technical Papers



    Conference:

    2020 SAE Brasil Congress & Exhibition ; 2020



    Publication date :

    2021-03-26




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Tire Material Characterization Using Gaussian Process Regression and Optimization

    Namba, Victor Lyra / Souza, André Pelisser Cavalcante / de Almeida, Sérgio Frascino Müller et al. | British Library Conference Proceedings | 2020


    Acquisition of Interaction Characteristics between Tire and Ground by Gaussian Process Regression

    Yamakawa, Junya / Aota, Katsuyoshi / Eto, Ryosuke | TIBKAT | 2022



    Thermal matching using Gaussian process regression

    Pearce, Robert / Ireland, Peter / Romero, Eduardo | SAGE Publications | 2020

    Free access

    Optimization of Gaussian Process Regression Model for Characterization of In-Vehicle Wet Clutch Behavior

    Shui, Huanyi / Zhang, Yijing / Yi, Elbert et al. | British Library Conference Proceedings | 2022