Damage variables for the life-time prediction of rubber components

2011 ◽  
pp. 191-196
Author(s):  
V Mehling ◽  
H Baaser ◽  
T Hans
Keyword(s):  
Life Time ◽  
Time Prediction

Lifetime Distribution Estimation of Boot Seals in Automotive Applications by Bayesian Method

2006 ◽  
Vol 129 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Fabrice Guerin ◽  
Ridha Hambli
Keyword(s):  
Finite Element ◽  
Bayesian Method ◽  
Life Time ◽  
Automotive Applications ◽  
Fatigue Lifetime ◽  
Time Prediction ◽  
Steering Mechanism ◽  
Market Requirements ◽  
Distribution Parameters ◽  
Lifetime Testing

The constantly increasing market requirements of high quality vehicles ask for the automotive manufacturers to perform lifetime testing to verify the reliability levels of new products. A common problem is that only a small number of examples of a component of system can be tested. In the automotive applications, mechanical components subjected to cyclic loading have to be designed against fatigue. Boot seals are used to protect velocity joint and steering mechanisms in automobiles. These flexible components must accommodate the motions associated with angulation of the steering mechanism. Some regions of the boot seal are always in contact with an internal metal shaft, while other areas come into contact with the metal shaft during angulation. In addition, the boot seal may also come into contact with itself, both internally and externally. The contacting regions affect the performance and longevity of the boot seal. In this paper, the Bayesian estimation of lognormal distribution parameters (usually used to define the fatigue lifetime of rubber components) is studied to improve the accuracy of estimation in incorporating the available knowledge on the product. In particular, the finite element results and expert belief are considered as prior knowledge. For life time prediction by finite element method, a model based on Brown–Miller law was developed for the boot seal rubber-like material.

scholarly journals A Use Case to Implement Machine Learning for Life Time Prediction of Manufacturing Tools

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 1484-1489 ◽  
Author(s):  
Robin Oberlé ◽  
Sebastian Schorr ◽  
Li Yi ◽  
Moritz Glatt ◽  
Dirk Bähre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Life Time ◽  
Use Case ◽  
Time Prediction

Life Time Prediction of Ceramic Turbocharger Rotor

1986 ◽  
Author(s):  
M. Matsui ◽  
T. Soma ◽  
Y. Ishida ◽  
I. Oda
Keyword(s):  
Life Time ◽  
Time Prediction
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