The Influence of Mo, Cr and B Alloying on Phase Transformation and Mechanical Properties in Nb Added High Strength Dual Phase Steels

Dual-phase steels are the most important AHSS grades for automotive applications. Microalloying elements such as Ti, Nb or B are widely used to improve the strength of dual-phase steels. Thus, understanding the influence of these elements on the microstructure and mechanical properties of dual-phase steels along the processing route is critical for the development of new steel grades. In this work, different microalloying elements were investigated, separately or in combination. The influences of the different elements on the microstructure and mechanical properties of dual-phase steels in the hot-rolled condition and after annealing of cold-rolled material. Dilatometer measurements were performed to investigate phase transformation during a typical continuous annealing treatment of dual-phase steel after cold rolling. It was shown that, for example, Ti has a strong influence on the mechanical properties of hot-rolled material while its influence on annealed materials after cold rolling was relatively small. Conversely, B had a strong influence on cold-rolled materials but an insignificant influence on hot-rolled materials.

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Relationships Between Stretch-Flangeability and Microstructure-Mechanical Properties in Ultra-High-Strength Dual-Phase Steels

Metals and Materials International ◽

10.1007/s12540-019-00270-x ◽

2019 ◽

Vol 25 (5) ◽

pp. 1161-1169 ◽

Cited By ~ 9

Author(s):

Jae Ik Yoon ◽

Jaimyun Jung ◽

Hak Hyeon Lee ◽

Jin You Kim ◽

Hyoung Seop Kim

Keyword(s):

Mechanical Properties ◽

High Strength ◽

Dual Phase ◽

Dual Phase Steels

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Effects of cooling rate on the mechanical properties of dual phase treated vanadium steels

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100074896 ◽

1983 ◽

Vol 41 ◽

pp. 220-221

Author(s):

L.J. Chen ◽

H.C. Cheng ◽

J.R. Gong ◽

J.G. Yang

Keyword(s):

Mechanical Properties ◽

Cooling Rate ◽

Automobile Industry ◽

High Strength ◽

Weight Percent ◽

Low Alloy Steels ◽

Dual Phase ◽

Resource Requirement ◽

Alloy Steels ◽

Potential Weight

For fuel savings as well as energy and resource requirement, high strength low alloy steels (HSLA) are of particular interest to automobile industry because of the potential weight reduction which can be achieved by using thinner section of these steels to carry the same load and thus to improve the fuel mileage. Dual phase treatment has been utilized to obtain superior strength and ductility combinations compared to the HSLA of identical composition. Recently, cooling rate following heat treatment was found to be important to the tensile properties of the dual phase steels. In this paper, we report the results of the investigation of cooling rate on the microstructures and mechanical properties of several vanadium HSLA steels.The steels with composition (in weight percent) listed below were supplied by China Steel Corporation: 1. low V steel (0.11C, 0.65Si, 1.63Mn, 0.015P, 0.008S, 0.084Aℓ, 0.004V), 2. 0.059V steel (0.13C, 0.62S1, 1.59Mn, 0.012P, 0.008S, 0.065Aℓ, 0.059V), 3. 0.10V steel (0.11C, 0.58Si, 1.58Mn, 0.017P, 0.008S, 0.068Aℓ, 0.10V).

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Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

Materials Science Forum ◽

10.4028/www.scientific.net/msf.773-774.268 ◽

2013 ◽

Vol 773-774 ◽

pp. 268-274

Author(s):

Amir Ghiami ◽

Ramin Khamedi

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Total Elongation ◽

Ultimate Tensile Stress ◽

Dual Phase ◽

Dual Phase Steels ◽

Good Ability ◽

Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

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