US 2026/0194884 A1Published application

Material selection apparatus and method for vehicle parts using a brs (buzz, rattle, squeak) noise prediction algorithm

AssigneesHyundai Motor Company, Kia Corporation, Korea University Research and Business Foundation

InventorsKyung Woo Lee, Dae Un Sung, Seong Ho Yoon, Jun Young Yoon, Byoung Ho Choi, Sang Min Lee

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Abstract

A material selection method using an optimized machine learning prediction model that learns the correlation between materials and environmental factors includes acquiring material properties, test conditions, and BRS noise data required for model training, generating a Gaussian process regression model using Bayesian optimization based on the training data, predicting an affective quality index for each dissimilar material using the model, and selecting an optimal material by synthesizing the indices. The optimal material may be selected based on predicting a BRS noise signal and an affective quality index value that may occur for each condition through a machine learning model, identifying the principle of BRS noise occurrence, and identifying the range of material properties and environmental factors in a training database according to the design criteria using a predicted value of the machine learning model.

Claims13

Dependent claims

A material selection apparatus for vehicle parts using a BRS (Buzz, Rattle, Squeak) noise prediction algorithm, the material selection apparatus comprising:

an input unit configured to receive an external input signal for setting one or more conditions;
a physical property data acquisition unit configured to acquire physical property information of a rubber material through one or more tests;
a BRS noise data acquisition unit configured to acquire BRS noise data descriptive of BRS noise due to friction between the rubber material and a dissimilar material while changing a surface shape, friction velocity, and friction load of the rubber material for vehicle parts;
a controller configured to
control the physical property data acquisition unit and the BRS noise data acquisition unit according to test conditions input via the input unit, and
select an optimal rubber material using a Gaussian process regression model, wherein the optimal rubber material is optimal according to the Gaussian process regression model;
a display unit configured to display information according to a result of operation of the controller;
and
a storage unit configured to store information acquired through the input unit, the physical property data acquisition unit, and the BRS noise data acquisition unit and information associated with the Gaussian process regression model.

6 more elements

Dependencies are inferred from the claim text.