2022
DOI: 10.2478/scjme-2022-0002
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Comparison of FEM Simulations of Rubber-Metal Mount Element

Abstract: Increasing of electric vehicles volume brings new challenges in NVH (noise, vibration and harshness) area. Presented paper deals with comparison of simulations of rubber-metal motor mount using finite element method. For the future work it is important to define static characteristics of rubber-metal mounts. It leads to study possibilities of simulations of rubber-metal mount in different software environment. Preparing of computational models are realized in MSC Marc and MSC Nastran. Results from simulations … Show more

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Cited by 5 publications
(3 citation statements)
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“…11) neural network with two layers. This can give us an insight into how accuracy between mathematical method and neural network compares [8]. In order to evaluate the methods, another 5 shape variants of rubber mount had to be created, these have been input into FEM analysis and reduced order models.…”
Section: Creating Reduced Order Modelsmentioning
confidence: 99%
“…11) neural network with two layers. This can give us an insight into how accuracy between mathematical method and neural network compares [8]. In order to evaluate the methods, another 5 shape variants of rubber mount had to be created, these have been input into FEM analysis and reduced order models.…”
Section: Creating Reduced Order Modelsmentioning
confidence: 99%
“…The finite element method (FEM) is applied widely in the evaluation of structures with reliable results [10][11][12][13]. Numerical tests were carried out using commercial finite element software Abaqus (CAE 6.13-1) with ISO3165 standard specimens.…”
Section: Simulation Proceduresmentioning
confidence: 99%
“…CAE analysis has become indispensable for the development and design of new products [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Abaqus and Marc are well-known analytical software programs, but recently Ansys and Nastran have also become popular.…”
Section: Introductionmentioning
confidence: 99%