Prediction of mechanical properties of DP steels using neural network model

Bahrami, Abbas; Anijdan, S.H. Mousavi; Ekrami, A.

doi:10.1016/j.jallcom.2004.09.014

Cited by 85 publications

(45 citation statements)

References 18 publications

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“…The increase in yield and ultimate tensile strengths may be due to the DSA effects. The effects of high temperature deformation on tensile properties of DP steels are discussed in our previous articles (Ref 20,23). The previous results showed that at 300°C the DSA effects are dominant and result in the increase of strength.…”

Section: Tensile Propertiesmentioning

confidence: 95%

“…Li and Leslie (Ref 22) reported that the fatigue strengths of 1008-and 1020-renitrogenized steels increases as a result of DSA effects. Previous studies on high temperature deformation of DP steels showed that DSA occurs in the temperature range of 150-450°C, and deformation at 300°C has the highest effects on room temperature tensile properties ( Ref 20,23).…”

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

The Effect of Dynamic Strain Aging on Subsequent Mechanical Properties of Dual-Phase Steels

Molaei

Ekrami

2009

J. of Materi Eng and Perform

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Dual-phase (DP) steels with different martensite contents were produced by subjecting a low carbon steel to various heat treatment cycles. In order to investigate the effect of dynamic strain aging (DSA) on mechanical properties, tensile specimens were deformed 3% at 300°C. Room temperature tensile tests of specimens which deformed at 300°C showed that both yield and ultimate tensile strengths increased, while total elongation decreased. The fatigue limit increased after pre-strain in the DSA temperature range. The effects of martensite volume fraction on mechanical properties were discussed.

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Section: Tensile Propertiesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 94%

The Effect of Dynamic Strain Aging on Subsequent Mechanical Properties of Dual-Phase Steels

Molaei

Ekrami

2009

J. of Materi Eng and Perform

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“…The choice of a specific class of networks for the simulation of a nonlinear and complex problem depends on a variety of factors such as the accuracy desired and the prior information concerning the input-output pairs . The most popular ANN in materials science and engineering investigations is the feedforward multi-layer perceptron, where the neurons are arranged into an input layer, one or more hidden layers, and an output layer (Muc & Gurba, 2001;Bahrami et al, 2005;Mousavi Anijdan et al, 2005;Song et al, 1995). A schematic description of a three-layer feedforward network is given in Fig.…”

Section: Artificial Neural Network; An Overviewmentioning

confidence: 99%

Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials

Sadati¹,

Kaklar²,

Ghajar³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…Sterjovski et al [10] made three different models of artificial neural network, the first model was for predicting the impact toughness of quenched and tempered pressure vessel steel, the second model for predicting hardness of the simulated heat affected zone in pipeline, the third model for predicting the hot ductility and hot strength of various microalloyed steels for casting process. Bahrami et al [11] researched the use of artificial neural networks for prediction of mechanical properties based on different morphology and volume fractions of martensite.…”

Section: Introductionmentioning

confidence: 99%

Intelligent system for prediction of mechanical properties of material based on metallographic images

et al. 2015

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Original scientific paper This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample. Inteligentni sustav za predviđanje mehaničkih svojstava materijala na osnovu metalografskih slika Izvorni znanstveni članak U radu se predstavlja razvijeni inteligentni sustav za predviđanje mehaničkih svojstava materijala na temelju metalografskih slika. Sustav se sastoji od dva modula. Prvi je modul algoritam za dobivanje karakteristika iz metalografskih slika. Prvi algoritam očitava metalografsku sliku dobivenu mikroskopom, zatim se dobivaju karakterisike razvijenim algoritmom, i na kraju algoritam izračunava omjere mikrostrukture materijala. U ovom istraživanju potrebno je što točnije odrediti omjere grafita, ferita i ausferita iz metalografskih slika. Drugi modul razvijenog sustava je sustav za predviđanje mehaničkih svojstava materijala. Predviđanje mehaničkih svojstava materijala izvršeno je pomoću feed-forward umjetne neuronske mreže. Kao ulazi u umjetnu neuronsku mrežu rabljeni su izračunati omjeri grafita, ferita i ausferita, dok su mehanička svojstva materijala upotrebljena kao ciljevi za uvježbavanje. Uvježbavanje umjetnih neuronskih mreža obavljeno je na prilično maloj bazi podataka, no mijenjajući parametre nama je to uspjelo. Umjetna neuronska mreža je naučila do te mjere da je greška bila prihvatljiva. S orijentiranom neuronskom mrežom uspješno smo predvidjeli mehanička svojstva izuzetog uzorka. KeywordsKljučne riječi: faktor faznog prijanjanja između površina; maksimalna vlačna čvrstoča; mehanička svojstva; metalografska slika; naprezanje tečenja; obrada slike; žilavost loma; umjetna neuronska mreža

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Prediction of mechanical properties of DP steels using neural network model

Cited by 85 publications

References 18 publications

The Effect of Dynamic Strain Aging on Subsequent Mechanical Properties of Dual-Phase Steels

The Effect of Dynamic Strain Aging on Subsequent Mechanical Properties of Dual-Phase Steels

Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials

Intelligent system for prediction of mechanical properties of material based on metallographic images

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