Construction of fatigue criteria through Positive Unlabeled Learning
Résumé
Vehicles reliability is a major issue for automotive manufacturers. In particular, mechanical fatigue is an important preoccupation of the design office. In order to accelerate the development of new mechanical parts, car manufacturers want to rely more on numerical simulation and drastically reduce the number of validation tests on prototypes. To do this, they need efficient fatigue criteria, able to correctly identify critical zones on a numerical model. However, the current fatigue criteria used to post process numerical results fail to correlate well on fatigue test rig. In this paper, we first propose a probabilistic Dang Van criterion that accounts for the dispersion of fatigue results in a multiaxial setting. We then introduce a fatigue database built upon numerical results and fatigue test reports on automotive chassis components. A novel approach, based on Positive-Unlabeled learning (PU learning), is developed to leverage this source of data and improve the predictivity of the fatigue criterion. The methodology is applied to the fatigue database to illustrate the interest of the approach.
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