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1
Content available remote Artificial intelligence in the AHSS steel mechanical properties and microstructure analysis
Krajewski S., Nowacki J.
Przegląd Elektrotechniczny
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2016
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R. 92, nr 6
102--105
EN
The assessment of results method of calculation tensile strength and yield strength of this cold rolled steels using the artificial neural networks in modelling relationship of elements composition (chromium, manganese, silicon, carbon), thermal treatment and properties of HCT600X, HCT780X, HCT980X steels was proposed. Was made further research using the new element chromium.
PL
W artykule użyto sztucznych sieci neuronowych do obliczenia zależności między wytrzymałością na rozciąganie i umowną granicą plastyczności, a pierwiastkami (chrom, mangan, krzem i węgiel), obróbką cieplną i właściwościami stali HCT600X, HCT780X, HCT980X walcowanych na zimno należących do stali karoseryjnych dwufazowych. Dokonano kontynuację badań z uwzględnieniem nowego pierwiastkachromu.
2
Content available Modeling of two-stage solidification: Part II computational verification of the model
Wodo O., Gawrońska E.
Archives of Foundry Engineering
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2013
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Vol. 13, iss. 1
125--130
EN
In Part I of this article, two-stage solidification model was presented. In this part we use our model to simulate solidification of the Al 7% Si alloy for two cooling rates - 2 deg/s and - 20 deg/s. Simulations have been performed for two eutectic transformation modes, typical for modified and unmodified alloys. Obtained cooling curves are qualitatively consistent with the typical cooling curves for modified and unmodified alloys. Moreover, evolution of cooling-curve characteristics is compared with the analytical model and found to be in close agreement.
3
Content available Modeling of two-stage solidification: Part I model development
Wodo O., Gawrońska E.
Archives of Foundry Engineering
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2012
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Vol. 12, iss. 4
151-156
EN
The paper presents a new numerical model of solidification processes in hypoeutectic alloys. The model combines stochastic elements, such as e.g. random nucleation sites and orientation of dendritic grains, as well as deterministic methods e.g. to compute velocity of dendritic tips and eutectic grains. The model can be used to determine the temperature and the size of structure constituents (of both, the primary solid phase and eutectics) and the arrangement of individual dendritic and eutectic grains in the consecutive stages of solidification. Two eutectic transformation modes, typical to modified and unmodified hypoeutectic alloys, have been included in the model. To achieve this, cellular automata and Voronoi diagrams have been utilized.
4
Role of computer modelling in thermomechanical processing.
Sellars C.M.
Inżynieria Materiałowa
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1998
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R. XIX, nr 3
100-107
EN
Off-line models involving microstructural evolution are widely used in the steel industry to optimize process parameters. The models are generally based on empirical relationships, which are less succesful when applied to aluminium alloys. The reasons for this difference in usefulness are examined, and the development of physically based internal state variable models is discussed. The value of taking a multidisciplinary approach to the development of the next generation of models is argued on the basis of the complexity of modelling industrial processing.
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