Improving nondestructive characterization of dual phase steels using data fusion

•Data fusion was applied on NDE parameters to characterize dual phase steel features.•Microstructure, mechanical properties and thickness were assessed, nondestructively.•The errors of measurements were reduced, significantly, by fusion of NDE parameters.•Combining the results of the two NDE techniq...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of magnetism and magnetic materials 2018-07, Vol.458, p.317-326
Hauptverfasser: Kahrobaee, Saeed, Haghighi, Mehdi Salkhordeh, Akhlaghi, Iman Ahadi
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•Data fusion was applied on NDE parameters to characterize dual phase steel features.•Microstructure, mechanical properties and thickness were assessed, nondestructively.•The errors of measurements were reduced, significantly, by fusion of NDE parameters.•Combining the results of the two NDE techniques led to better measuring reliability. The aim of this paper is to introduce a novel methodology for nondestructive determination of microstructural and mechanical properties (due to the various heat treatments), as well as thickness variations (as a result of corrosion effect) of dual phase steels. The characterizations are based on the variations in the electromagnetic properties extracted from magnetic hysteresis loop and eddy current methods which are coupled with a data fusion system. This study was conducted on six groups of samples (with different thicknesses, from 1 mm to 4 mm) subjected to the various intercritical annealing processes to produce different fractions of martensite/ferrite phases and consequently, changes in hardness, yield strength and ultra tensile strength (UTS). This study proposes a novel soft computing technique to increase accuracy of nondestructive measurements and resolving overlapped NDE outputs related to the various samples. The empirical results indicate that applying the proposed data fusion technique on the two electromagnetic NDE data sets nondestructively, causes an increase in the accuracy and reliability of determining material features including ferrite fraction, hardness, yield strength, UTS, as well as thickness variations.
ISSN:0304-8853
1873-4766
DOI:10.1016/j.jmmm.2018.03.049