Artificial Neural Networks for Modelling of the Impact Toughness of Steel

Dunne, D. P.; Tsuei, H.; Sterjovski, Zoran

doi:10.2355/isijinternational.44.1599

Cited by 24 publications

(15 citation statements)

References 9 publications

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“…In addition, this method been reviewed thoroughly [11], as have been its applications [16]. Indeed, there have been diverse applications which lead to useful and verifiable predictions in the context of low-cycle fatigue [17], the estimation of bainite plate thickness [18], the calculation of ferrite number in stainless steels [19], the estimation of tensile strength [20,27], impact strength [21,26], the effect of processing parameters on marageing steels [22], the modelling of strain induced martensitic transformation [23], and the reduction in steel varieties [28], to name but a few. There has even been an assessment of procedures needed to design networks which are well-assessed in their performance [24].…”

Section: Methodsmentioning

confidence: 99%

Fatigue crack growth rate model for metallic alloys

Dimitriu

Bhadeshia

2010

Materials & Design

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A model has been created to allow the quantitative estimation of the fatigue crack growth rate in steels as a function of mechanical properties, test-specimen characteristics, stress-intensity range and test-frequency. With this design, the remarkable result is that the method which is based on steels, can be used without modification, and without any prior fatigue test, to estimate the crack growth rates in nickel, titanium and aluminium alloys. It appears therefore that a large proportion of the differences in the fatigue crack growth rate of metallic alloys can be explained in terms of the macroscopic tensile properties of the material rather than the details of the microstructure and chemical composition.

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Section: Methodsmentioning

confidence: 99%

Fatigue crack growth rate model for metallic alloys

Dimitriu

Bhadeshia

2010

Materials & Design

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“…Neural networks have also successfully been applied in prediction of product properties [8][9][10][11] as well as process simulation and control [12 and 13] .…”

Section: Introductionmentioning

confidence: 99%

Neural network based prediction of roughing and finishing times in a hot strip mill

Colla¹,

Vannucci²,

Valentini³

2010

REVMETAL

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The paper presents a model based on neural networks which is able to predict the time required to pass the different gauges of a roughing and finishing mill as function of some slab features and process parameters. The final aim of the work is to increase the rolling efficiency while avoiding collisions and queues that cause time and energy losses. Neural networks are suitable to this prediction task as they are particularly able to cope with unknown non linear relationships between input and output variables. Moreover they can learn from real industrial data and therefore do not require prior assumptions or mathematical modelling of the process and transferability is ensured by the possibility to use different databases coming from different rolling mills. In the paper, two different kinds of neural network-based models have been proposed, their performances have been discussed and compared. KeywordsHot rolling steel; Slab; Coil; Mill pacing; Neural networks. Predicción basada en redes neuronales del tiempo de procesamiento de los trenes desbastador y laminador en una instalación de laminación en caliente ResumenEn este artículo se presenta un modelo basado en redes neuronales capaz de predecir el tiempo necesario para pasar las diferentes galgas de un tren desbastador y acabador en función de ciertas características del desbaste y paráme-tros de proceso. El objetivo final es aumentar la eficacia de la laminación evitando colisiones y colas que provoquen pérdidas de tiempo y energía. Se propone utilizar para esta tarea redes neuronales por su capacidad de predicción en aquellos casos en los que existen relaciones no lineales desconocidas entre las variables de entrada y las de salida. Además, son capaces de aprender de datos industriales reales y, por tanto, no requieren suposiciones previas o modelos matemáticos del proceso, estando la transferibilidad asegurada ya que es posible utilizar distintas bases de datos procedentes de diferentes trenes de laminación. Palabras claveLaminación en caliente; Desbaste; Bobina; Ritmo de laminado; Red neuronal.

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“…A few studies in this area have shown that ANN analyses enable a model to be found which went the prediction of e.g. impact energy with a relatively high degree of accuracy [18,19]. Very often processing parameters have been correlated to final properties using artificial neural networks [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of fracture toughness temperature dependance from tensile test parameters

Dlouhý¹,

Smida²,

Babjak³

2010

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The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation of these properties. Three-point bend specimens were applied to determine fracture toughness. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. Different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A good correlation of predicted and experimentally determined values of reference temperature was found.K e y w o r d s : steels, brittle to ductile transition, fracture, toughness, artificial neural network (ANN)

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Artificial Neural Networks for Modelling of the Impact Toughness of Steel

Cited by 24 publications

References 9 publications

Fatigue crack growth rate model for metallic alloys

Fatigue crack growth rate model for metallic alloys

Neural network based prediction of roughing and finishing times in a hot strip mill

Prediction of fracture toughness temperature dependance from tensile test parameters

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