Microstructural evolution during continuous annealing of a 980 MPa cold rolled steel grade

Arruda, Marcus Vinícius Pereira; Melo, Túlio Magno Füzessy de; Costa, F. P.; Santos, Dagoberto Brandão

doi:10.1088/1742-6596/1270/1/012020

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The material used in this study was a DP 980 steel obtained from US Steel, Pittsburgh, PA, USA, with a nominal composition of 0.1% C, 0.15% Si, and 2.35% Mn. While the detailed processing conditions are typically confidential for commercial steels, thermomechanical processing of DP 980 steel starts with austenitising of the material, then reducing the temperature to an appropriate intercritical level, in order to achieve the desired portions of ferrite and austenite 36 37 …”

Section: Methodsmentioning

confidence: 99%

Phase determination in dual phase steels via HREBSD‐based tetragonality mapping

Adams

Miles

Homer

et al. 2020

Journal of Microscopy

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Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (body centred cubic and body centred tetragonal, with small tetragonality) phases in steels using standard EBSD software. One method to tackle the problem of phase distinction is to measure the tetragonality of the phases, which can be done using simulated patterns and cross‐correlation techniques to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both kinetically simulated and dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate ∼0.2% error in carbon content estimation. Such an error makes the technique unsuitable for estimation of total carbon content of most commercial steels, which often have carbon levels below 0.1%. However, even in the DP steel for this study (0.1 wt.% carbon) it can be used to map carbon in regions with higher accumulation (such as in martensite with nonhomogeneous carbon content). Lay Description Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (BCC and BCT) phases in steels using standard EBSD software due to the small difference in crystal structure. One method to tackle the problem of phase distinction is to measure the tetragonality, or apparent ‘strain’ in the crystal lattice, of the phases. This can be done by comparing experimental EBSD patterns with simulated patterns via cross‐correlation techniques, to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon conte...

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Section: Methodsmentioning

confidence: 99%

Phase determination in dual phase steels via HREBSD‐based tetragonality mapping

Adams

Miles

Homer

et al. 2020

Journal of Microscopy

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“…In the case of cold rolled sheets, the microstructure is strongly dependent on the chemical composition and on the steel sheet processing, especially at the continuous annealing stage. Since the applicability of these industrial products relies on the guarantee of their mechanical properties, here expressed by the yield strength (YS), ultimate tensile strength (UTS), and elongation (EL), a better understanding of the effects and interactions of the fundamental variables becomes of great importance for the development and improvement of the DP steels grades [1,2].…”

Section: Introductionmentioning

confidence: 99%

Mechanical properties prediction of dual phase steels using machine learning

Tavares¹,

Finamor²,

Zorzi³

2022

Tecnol. Metal. Mater. Min.

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The use of artificial intelligence techniques, with the increase of data generation capacity and the advancement of computational resources, has enabled the industries to develop and improve products without compromising laboratory and industrial resources. In this paper, a supervised machine learning (ML) based technique was used to predict the yield strength (YS), ultimate tensile strength (UTS), and elongation (EL) of dual phase steels with minimum tensile strengths of 590 and 780 MPa. The computational analysis was done from industrial data information containing the chemical composition and the thermomechanical processing parameters of the referred materials. The proposed ML model reached values of coefficient of determination above 0.94, with an accuracy of ±30 MPa for YS and UTS, and ±3% for EL. These results demonstrated the rationality and reliability of the tested method, allowing its application in future research works and in decision making that aim to optimize the steels industrial processing parameters.

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