Correction published on 20 December 2018, see
Metals 2019, 9(1), 6.
Strength Prediction for Pearlitic Lamellar Graphite Iron: Model Validation
1
Swerea SWECAST, 550 02 Jönköping, Sweden
2
Materials and Manufacturing-Foundry Technology, Jönköping University, Gjuterigatan 5, 553 18 Jönköping, Sweden
*
Author to whom correspondence should be addressed.
Metals 2018, 8(9), 684; https://doi.org/10.3390/met8090684
Received: 11 July 2018
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Revised: 19 August 2018
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Accepted: 30 August 2018
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Published: 31 August 2018
(This article belongs to the Special Issue Cast Irons: Properties and Applications)
The present work provides validation of the ultimate tensile strength computational models, based on full-scale lamellar graphite iron casting process simulation, against previously obtained experimental data. Microstructure models have been combined with modified Griffith and Hall–Petch equations, and incorporated into casting simulation software, to enable the strength prediction for four pearlitic lamellar cast iron alloys with various carbon contents. The results show that the developed models can be successfully applied within the strength prediction methodology along with the simulation tools, for a wide range of carbon contents and for different solidification rates typical for both thin- and thick-walled complex-shaped iron castings.
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Keywords:
lamellar graphite iron; ultimate tensile strength; primary austenite; gravity casting process simulation
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MDPI and ACS Style
Fourlakidis, V.; Belov, I.; Diószegi, A. Strength Prediction for Pearlitic Lamellar Graphite Iron: Model Validation. Metals 2018, 8, 684. https://doi.org/10.3390/met8090684
AMA Style
Fourlakidis V, Belov I, Diószegi A. Strength Prediction for Pearlitic Lamellar Graphite Iron: Model Validation. Metals. 2018; 8(9):684. https://doi.org/10.3390/met8090684
Chicago/Turabian StyleFourlakidis, Vasilios, Ilia Belov, and Attila Diószegi. 2018. "Strength Prediction for Pearlitic Lamellar Graphite Iron: Model Validation" Metals 8, no. 9: 684. https://doi.org/10.3390/met8090684
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