Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5666
Title: Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network
Authors: Kafkas, F.
Karataş, C.
Sözen, A.
Arcaklıoğlu, E.
Sarıtaş, S.
Keywords: Artificial neural network
Electrochemical machining
Layer removal technique
Post-sintering
Powder metallurgy steels
Residual stresses
Issue Date: 2007
Publisher: Elsevier Ltd
Abstract: This paper presents a new approach based on artificial neural networks (ANNs) to determine the residual stresses in PM steel based nickel (FLN2-4405). This study consists of two cases: (i) The experimental analysis: The measurements of residual stresses were carried out by electrochemical layer removal technique. The values and distributions of residual stresses occurring in PM steel processed under various densities (6.8, 7.05, 7.2 and 7.4 g/cm3) and heat treatment conditions (sintered at 2050 °F, sintered at 2300 °F, quenching-tempered, and sinter-hardened) were determined. In most of the experiments, tensile residual stresses were recorded in surface of samples. The residual stress distribution on the surface of the PM steels is affected by the heat treatment conditions and density. Maximum values of residual stresses on the surface were observed sinter hardened condition and 7.4 g/cm3 density. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the residual stresses based on heat treatment conditions and densities in PM steel using results of experimental analysis. The back propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four and five neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R2 values are 0.999244, 0.999025, 0.999664 and 0.999322 for sintered at 2050 °F, sintered at 2300 °F, quenching-tempered, and sinter-hardened, respectively. Similarly, these values for testing data are 0.998354, 0.99706, 0.999607 and 0.999205, respectively. As seen from the results of mathematical modeling, the calculated residual stresses are obviously within acceptable uncertainties. © 2006 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.matdes.2006.09.003
https://hdl.handle.net/20.500.11851/5666
ISSN: 0261-3069
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

24
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

21
checked on Sep 24, 2022

Page view(s)

8
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.