Effect of boron on the microstructure and tensile properties of dual-phase steel

Shen, Xingquan; Priestner, R.

doi:10.1007/bf02647000

Cited by 34 publications

(27 citation statements)

References 5 publications

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“…titanium and boron synergistically retard the austenite (g)→ferrite (a) transformation. [7][8][9] Unless such expert knowledge is also fed to the system, it cannot learn the real relationship between the inputs and outputs. In spite of this, the network is seen to have learnt satisfactorily in most of the cases except for a few instances where a fair prediction could not be achieved.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, copper (Cu) being an element known to strengthen through solid solution strengthening in ferrite as well as through precipitation hardening; a Ti, B, microalloyed steel has been alloyed with Cu to utilize the individual effect of Ti, B and Cu as well as synergistic effect of the combination of Ti, B and Cu, B for improving the hardenability of austenite [7][8][9][10] which in turn is expected to give rise a DP microstructure depending on the finish rolling temperature and the subsequent cooling rate.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

et al. 2005

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The effects of copper along with some microalloying elements and the processing parameters are modeled with artificial neural network and adaptive neuro-fuzzy inference system. Both the tools are found to be useful for modeling the effect of copper and other alloying additions along with the processing parameters on the hardness of microalloyed DP steels. In case of the neural network, the proposed committee of models is found to be effective in handling the problem of mapping the input-output relation in these steels. The increase in the number of rules is found to improve the predictability of the neuro-fuzzy inference system. The predictions made by both the models substantiate the knowledge of physical metallurgy principles.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

et al. 2005

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“…The influence of B is exhibited in terms of widening the temperature range for bainitic transformation and Cu extends the bainitic transformation domain towards much slower cooling rates. Figure 3(c) clearly reveals the synergistic effect 13) for the combined addition of Ti and B in terms of lowering the Ac 3 and Ac 1 temperatures. Lowering of the austenite transformation temperatures is more prominently captured in the results predicted by ANN modeling.…”

Section: Validation Of the Model Of Cct Diagrammentioning

confidence: 94%

Design of the Directly Air-cooled Pearlite-free Multiphase Steel from CCT Diagrams Developed Using ANN and Dilatometric Methods

et al. 2008

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Present study aims to predict the effect of Ti, B, Cu and Ni on continuous cooling transformation diagrams of low carbon (0.04-0.05 wt%) steels by artificial neural network model. The predicted results are validated with dilatometric studies. Comparison of the phase fields in different continuous cooling transformation diagrams demonstrated that in Ni containing 1.5 wt% Cu-added Ti-B microalloyed steel it is possible to achieve dual phase (ferrite-martensite) microstructure in directly air-cooled condition (i.e., at cooling rate close to 1°C/s) by suppressing pearlite formation. Addition of Cu has remarkably improved the hardness of the dilatometric samples.

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“…[2] Microalloying with elements such as Ti and B also improve the hardenability of austenite. [3,4] It is also known that addition of Cu retards the austenite (c) fi ferrite (a) transformation. [5][6][7] In the present study, the necessary hardenability of austenite is achieved by microalloying (Ti, B) and Cu addition (1.5 wt pct) without using the partitioning effect during intercritical annealing with an aim to develop directly air-cooled low-carbon (0.035 to 0.055 wt pct) steels.…”

Section: Introductionmentioning

confidence: 99%

Development of High-Strength Cu-Ni-Ti-B Multiphase Steel by Direct Air Cooling

Ghosh

Haldar

Ganguly

et al. 2008

Metall Mater Trans A

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The present study concerns development of high-strength low-carbon Cu-Ni-Ti-B multiphase steels in the as-hot-rolled condition. An attempt has been made to predict the microstructural evolution of the chosen steels during continuous cooling transformation by artificial neural network modeling as well as dilatometric study. Experimental results have demonstrated that direct air cooling of the hot-rolled Cu-Ni-Ti-B steel resulted in a microstructure containing intercritical ferrite and finer low-temperature transformation products of austenite. An attractive strength-ductility combination was achieved in the case of as-hot-rolled Cu-Ni-Ti-B steel with a dual-phase-like microstructure. Age hardening of the steels has further enhanced the mechanical properties. Analysis of the work-hardening behavior by differential Jaoult-Crussard (J-C) analysis has revealed the effect of aging on the work-hardening behavior.

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Effect of boron on the microstructure and tensile properties of dual-phase steel

Cited by 34 publications

References 5 publications

Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

Design of the Directly Air-cooled Pearlite-free Multiphase Steel from CCT Diagrams Developed Using ANN and Dilatometric Methods

Development of High-Strength Cu-Ni-Ti-B Multiphase Steel by Direct Air Cooling

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