A Model for The Prediction of Fracture Toughness Using Neural Network

The purpose of this research programme is to develop quantitative models for the prediction of mechanical properties (fracture toughness) using experimental data collected from the literature, together with a powerful computational technique known as neural network. Creating a truly general model re...

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Main Authors: Aseel abdulbaky, Dhafer Al-Fattal, Harry Bhadeshia, Talal Abdul Jabbar
Format: Article
Language:English
Published: Unviversity of Technology- Iraq 2012-03-01
Series:Engineering and Technology Journal
Subjects:
Online Access:https://etj.uotechnology.edu.iq/article_35476_3aa753d1887c8d2c677f128480ba47fa.pdf
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author Aseel abdulbaky
Dhafer Al-Fattal
Harry Bhadeshia
Talal Abdul Jabbar
author_facet Aseel abdulbaky
Dhafer Al-Fattal
Harry Bhadeshia
Talal Abdul Jabbar
author_sort Aseel abdulbaky
collection DOAJ
description The purpose of this research programme is to develop quantitative models for the prediction of mechanical properties (fracture toughness) using experimental data collected from the literature, together with a powerful computational technique known as neural network. Creating a truly general model requires a combination of available data and metallurgical knowledge. This model is proposed for martensitic and ordinary bainitic steels in addition to the more recent class of non-structural super-bainitic steels. Super-bainitic steels are attractive for many applications such as armour. The model of fracture toughness, based on chemical composition, heat treatment an d mechanical properties is proposed. The predictions of fracture toughness are generally acceptable but the uncertainties are high and more input data need to be collected for super-bainitic steels when available in the future to improve the predictions of this model.
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spelling doaj.art-6268900142814172a1440ca9a3a174212024-02-04T17:38:49ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582012-03-0130586888510.30684/etj.30.5.1335476A Model for The Prediction of Fracture Toughness Using Neural NetworkAseel abdulbakyDhafer Al-FattalHarry BhadeshiaTalal Abdul JabbarThe purpose of this research programme is to develop quantitative models for the prediction of mechanical properties (fracture toughness) using experimental data collected from the literature, together with a powerful computational technique known as neural network. Creating a truly general model requires a combination of available data and metallurgical knowledge. This model is proposed for martensitic and ordinary bainitic steels in addition to the more recent class of non-structural super-bainitic steels. Super-bainitic steels are attractive for many applications such as armour. The model of fracture toughness, based on chemical composition, heat treatment an d mechanical properties is proposed. The predictions of fracture toughness are generally acceptable but the uncertainties are high and more input data need to be collected for super-bainitic steels when available in the future to improve the predictions of this model.https://etj.uotechnology.edu.iq/article_35476_3aa753d1887c8d2c677f128480ba47fa.pdffracture toughnesspredictionsneural network
spellingShingle Aseel abdulbaky
Dhafer Al-Fattal
Harry Bhadeshia
Talal Abdul Jabbar
A Model for The Prediction of Fracture Toughness Using Neural Network
Engineering and Technology Journal
fracture toughness
predictions
neural network
title A Model for The Prediction of Fracture Toughness Using Neural Network
title_full A Model for The Prediction of Fracture Toughness Using Neural Network
title_fullStr A Model for The Prediction of Fracture Toughness Using Neural Network
title_full_unstemmed A Model for The Prediction of Fracture Toughness Using Neural Network
title_short A Model for The Prediction of Fracture Toughness Using Neural Network
title_sort model for the prediction of fracture toughness using neural network
topic fracture toughness
predictions
neural network
url https://etj.uotechnology.edu.iq/article_35476_3aa753d1887c8d2c677f128480ba47fa.pdf
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