ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
PUBLISHED
SINCE 1952
 
Proceeding cover
proceedings
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Determination of residual stresses and material properties by an energy-based method using artificial neural networks; pp. 296–305

Full article in PDF format | doi: 10.3176/proc.2012.4.04

Authors
Hongping Jin, Wenyu Yang, Lin Yan

Abstract

With the help of an energy-based method and dimensional analysis, an artificial neural network model is constructed to extract the residual stress and material properties using spherical indentation. The relationships between the work of residual stress, the residual stress, and material properties are numerically calibrated through training and validation of the artificial neural network (ANN) model. They enable the direct mapping of the characteristics of the indentation parameters to the equi-biaxial uniform residual stress and the elastic–plastic material properties. The proposed ANN can quickly and effectively predict the residual stress and material properties based on the load–depth curve of spherical indentation.


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