ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
ISSN-L : 0915-1559
Multiscale Analysis of MnS Inclusion Distributions in High Strength Steel
Ryota SakaguchiTakayuki ShiraiwaPornthep ChivavibulTadashi KasuyaManabu EnokiNorio YamashitaHideo YokotaYutaka MatsuiAkira KazamaKeita OzakiHiroyuki Takamatsu
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JOURNALS OPEN ACCESS Advance online publication

Article ID: ISIJINT-2019-739


In the present study, manganese sulfide (MnS) inclusions in the high-strength steel were observed by mainly three observation methods (optical microscope, ultrasonic test and serial sectioning) to characterize the size, location and shape distributions across multiple length scales. For the inclusion size, ultrasonic C-scan imaging and three-dimensional internal structure observation with serial sectioning were used to measure the distributions of the square root of the projected area of the inclusion. The obtained size distributions were combined by setting the threshold of ultrasonic amplitude. The validity of the amplitude threshold was verified by observing several inclusions with X-ray CT. The spatial distributions of inclusions were also obtained by the three observation methods, and analyzed on the basis of the coefficient of variation of the mean near-neighbor distance of inclusions (COVd). The results of analyzing COVd in both 2D and 3D spaces revealed that the inclusions in this material were arranged in clusters. For the inclusion shape, the three-dimensional geometries of inclusions were reconstructed from the images obtained by the serial sectioning method, and simplified to ellipsoid by principal component analysis. From the above results, the distributions of inclusion size, aspect ratio and direction (angle between rolling direction and major axis) were successfully obtained. The inclusion distributions were applied to fatigue prediction model, and the fatigue crack initiation life and total fatigue life of the high-strength steel were calculated. The calculation results showed that the multiscale analysis of inclusions would be useful for fatigue life prediction.

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