Banca de DEFESA: JOELTON FONSECA BARBOSA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE : JOELTON FONSECA BARBOSA
DATA : 22/07/2019
HORA: 08:30
LOCAL: Sala 414 do CTEC - UFRN
TÍTULO:

PERFORMANCE OF FATIGUE LIFE MODELS AND A NEW ARTIFICIAL CONSTANT LIFE DIAGRAM APPLIED TO MATERIALS AND STRUCTURAL DETAILS

PALAVRAS-CHAVES:

Fatigue; mean stress; high cycle; neural networ.

PÁGINAS: 85
GRANDE ÁREA: Engenharias
ÁREA: Engenharia Mecânica
RESUMO:

Mechanical failures of machine equipment and components cause loss of required function performance and unexpected stops, resulting in an increase in the need for corrective maintenance, which increases maintenance costs and reduces the reliability of mechanical systems. The effect of medium stress plays an important role in fatigue life prediction, its influence significantly changes the high-cycle fatigue behavior (HCF), decreasing the value of the fatigue limit with the increase of the mean stress. Geometric discontinuities - such as a change of cross-section, holes, notches, among others - cause a considerable increase in the value of the nominal stress acting in the adjacent vicinity of the stress concentrator. This potentiates the effects of the positive mean stress on the damage over the life cycle of the material, causing direct influence on the calculation of the fatigue resistance factor (Kf). Numerous empirical models, such as Gerber, Goodman, Soderberg and Morrow, have been developed to describe the mean stress effects, but despite the advances, a unified model that considers the stochastic behavior of fatigue failure that predicts the stresses maximum amplitude values supported in the high cycle region for the notched material. In this way, the purpose of this work is the developing of a new artificial constant life diagram (stress amplitude vs. mean stress) based on an artificial neural network applied to metallic materials and structural details, capable of estimating the fatigue resistance reduction factor for different mean stresses. The results showed that the trained neural network was able to determine regions of reliability of operation of the material under the aspects of the mean stress, stress amplitude, stress ratio and stochastic behavior of the number of cycles to failure. In addition, it was possible to estimate the values of the fatigue strength reduction factor corresponding to the resistance limit using a small amount of experimental data.

MEMBROS DA BANCA:
Externo à Instituição - ABÍLIO MANUEL PINHO DE JESUS - FEUP
Interno - 434906 - AVELINO MANUEL DA SILVA DIAS
Externo à Instituição - JOSÉ ANTÓNIO FONSECA DE OLIVEIRA CORREIA - FEUP
Externo ao Programa - 1378360 - MARCO ANTONIO LEANDRO CABRAL
Presidente - 1338331 - RAIMUNDO CARLOS SILVERIO FREIRE JUNIOR
Interno - 1445637 - WALLACE MOREIRA BESSA