NondestructiveApproachforDetermination of Steel MechanicalProperties

EDGAR LOPEZ MARTINEZ; JazmÃ­n Y. JuÃ¡rez-ChÃ¡vez; S. Serna; B. Campillo

doi:10.24297/ijct.v14i9.7078

Authors

EDGAR LOPEZ MARTINEZ Facultad de QuÃmica, Universidad Nacional AutÃ³noma de MÃ©xico, Mexico
JazmÃn Y. JuÃ¡rez-ChÃ¡vez CIICAp, Universidad AutÃ³noma del Estado de Morelos, MÃ©xico
S. Serna CIICAp, Universidad AutÃ³noma del Estado de Morelos, MÃ©xico
B. Campillo Facultad de QuÃmica-Instituto de Ciencias FÃsicas, Universidad Nacional AutÃ³noma de MÃ©xico, MÃ©xico

DOI:

https://doi.org/10.24297/ijct.v14i9.7078

Keywords:

Artificial neural network, HSLA steel, welding, heat affected zone, hardness

Abstract

It was proposed the design of an artificial neural network (ANN) to estimate the yield strength in the welding zone of HSLA experimental steels. The input parameters of the ANN were the chemical composition and hardness. The information needed to training and testing the ANN was obtained by searching the literature of the yield strength as a function of the input parameters. The design was of the type perceptron multilayer with a rule learning of backpropagation type and sigmoidal transfer function, varying the number of nodes in the hidden layer. It was determined that the design of the ANN with 11 nodes is able to estimate the yield strength of high strength low alloy steels and ultra-high strength steels according to their chemical composition and hardness.