A Lightweight Dash Insulator Development and Engineering Application for the Vehicle NVH Improvement

Zhang, Jun; Pang, Jian; Zhang, Siwen; Zhang, Xiaoxuan; Liu, Congguang

doi:10.4271/2015-01-2342

Cited by 2 publications

(1 citation statement)

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“…In addition to forward propagation, convolutional neural networks also have the characteristics of back propagation. The back propagation process updates the model parameters based on the error calculated by the loss function [39,40]. The loss function is an important index to determine the direction of the network training process.…”

Section: Research Methods 21 1d-cnn Network Introductionmentioning

confidence: 99%

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

Peng,

Cheng,

Sun

et al. 2024

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The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multiobjective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path. This approach involves introducing a 1D-Convolutional Neural Network (1D-CNN) model for predicting the sound insulation performance of the floor acoustic package, thereby avoiding the limitations of conventional CAE approaches that rely solely on "data-driven" methods. The proposed method was applied and tested using specific vehicle models, and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression (SVR) models.

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Section: Research Methods 21 1d-cnn Network Introductionmentioning

confidence: 99%