Selection of the optimization method for identification of phase transformation models for steels

Material models for steels, used widely in numerical simulations of manufacturing chains, require identification of their coefficients on the basis of measurements obtained from laboratory test. Precision of the identification highly influences modelling reliability. This is visible especially in th...

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Bibliographic Details
Published in:Materials and manufacturing processes 2017-08, Vol.32 (11), p.1248-1259
Main Authors: Bachniak, Daniel, Rauch, Lukasz, Pietrzyk, Maciej, Kusiak, Jan
Format: Article
Language:English
Subjects:
Analysis
identification
inverse
optimization
phase
transformation
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Summary:Material models for steels, used widely in numerical simulations of manufacturing chains, require identification of their coefficients on the basis of measurements obtained from laboratory test. Precision of the identification highly influences modelling reliability. This is visible especially in the case of phase transformation models, which are crucial in predicting of the modern Advanced High Strength Steels (AHSS) properties after applied heat treatment. However, identification of phase transformation models for steels based on dilatometric tests presents serious difficulties. Two problems are investigated in the paper i.e. (i) efficiency of the inverse algorithms used for identification of phase transformation models, (ii) final reliability of the identified models in numerical simulations of manufacturing processes. In the work two phase transformation models were selected as an example. The first was a modified JMAK (Johnson-Mehl-Avrami-Kolmogorov) equation. The second was an upgrade of the Leblond equation, in which second order derivative with respect to time was introduced. The identification was performed by coupling the selected model with nature inspired optimization techniques and performing inverse analysis for the experimental data. Dilatometric tests performed for various cooling rates were used as an experiment, which supplies data for the inverse analysis. Finally, validation of identified models is presented by using industrial data.
ISSN:1042-6914
1532-2475
DOI:10.1080/10426914.2017.1292035