Prediction of hardness of the tempered martensitic rim of TMT rebars

Mukherjee, Madhumita; Dutta, Chaitali; Haldar, Arunansu

doi:10.1016/j.msea.2012.02.041

Cited by 29 publications

(10 citation statements)

References 24 publications

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“…It seems that the increase of hardness due to the addition of alloying elements can be expressed by using the compositiondependent C regardless of tempering stages. Recently, Mukherjee et al 21) suggested a regressed equation to predict the tempered martensite hardness for alloy steels considering various alloying elements. They selected the best combination of tempering temperature, tempering time, and composition term with no consideration of the tempering parameter.…”

Section: Verification With Discussionmentioning

confidence: 99%

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Prediction of Tempered Martensite Hardness Incorporating the Composition-Dependent Tempering Parameter in Low Alloy Steels

Kang

Lee

2014

Mater. Trans.

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We investigated a composition-dependent tempering parameter for more accurate prediction of tempered martensite hardness in low alloy steels. The proposed composition-dependent tempering parameter decreased with the addition of alloying elements related to the activation energy for tempering. The tempered martensite hardness calculated using the composition-dependent tempering parameter showed a reliable accuracy compared with experimental data.

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Section: Verification With Discussionmentioning

confidence: 99%

“…They selected the best combination of tempering temperature, tempering time, and composition term with no consideration of the tempering parameter. Figure 3 compares the prediction accuracy by using three different equations: regressed equation, 21) eq. (5) with a constant C value of 20, and eq.…”

Section: Verification With Discussionmentioning

confidence: 99%

Prediction of Tempered Martensite Hardness Incorporating the Composition-Dependent Tempering Parameter in Low Alloy Steels

Kang

Lee

2014

Mater. Trans.

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“…where C s is solute carbon content, d £ is prior austenite grain size (µm) and I is tempering parameter. Mukherjee et al 9) derived the empirical model for predicting the Vickers hardness of the tempered martensite. The Mukherjee's model contains 14 alloying elements and 2 tempering parameters.…”

Section: Linear Regression Modelmentioning

confidence: 99%

“…This traditional approach has resulted in successful predictions in various fields. In the field of mechanical properties, Mukherjee et al 9) proposed a multivariate regression model to predict the hardness of thermo-mechanically treated rebars as a function of chemical composition and tempering parameters. Ozerdem and Kolukisa 10) predicted multiple mechanical properties of copper alloy by using neural network.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fatigue Strength in Steels by Linear Regression and Neural Network

2018

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This paper examines machine learning methods to predict fatigue strength with high accuracy using existing database. The fatigue database was automatically classified by hierarchical clustering method, and a group of carbon steels was selected as a target of machine learning. In linear regression analyses, a model selection was conducted from all possible combinations of explanatory variables based on cross validation technique. The derived linear regression model provided more accurate prediction than existing empirical rules. In neural network models, local and global sensitivity analyses were performed and the results of virtual experiments were consistent with existing knowledge in materials engineering. It demonstrated that the machine learning method provides prediction of fatigue performance with high accuracy and is one of promising method to accelerate material development.

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“…The martensitic case formed in the outer surface of the bars increases their hardness [8], while the remaining ferritic-perlitic core maintains the typical ductility of hot rolled bars. The final strength of the TMT rebars depends on the thickness of the outer tempered martensitic case as well as on the distribution of other phases inside the core of bar [9]. Nowadays, the use of TMT carbon steel bars has become common for reinforcing of concrete structures in Europe.…”

Section: Introductionmentioning

confidence: 99%

Influence of the microstructure of TMT reinforcing bars on their corrosion behavior in concrete with chlorides

Bautista

Pomares

González

et al. 2019

Construction and Building Materials

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Thermomechanically treated (TMT) carbon steel bars, often known as "TempCore" bars, are commonly used as reinforcements in concrete structures. TMT causes the formation of a martensite case in the outer surface of the bars, increasing their hardness, while the remaining ferritic-perlitic core maintains the typical ductility of hot rolled bars. In this work, the effect of this TMT induced microstructure on the development of pitting attacks in chloride media has been analyzed in depth. Electrochemical impedance spectroscopy (EIS) measurements and polarization curves have been carried out in simulated pore solutions to understand the effect of the presence of different phases in the microstructure and to quantify the strength of the galvanic couple that the outer martensite can form with the inner core. Moreover, accelerated corrosion tests in chloride contaminated concrete slabs have also been performed. Bars from six different slabs where corrosive attack has been forced for different times have been studied. The shape of the main pits in the bars corroded in concrete has been analyzed through optoelectronic microscopy and the results obtained prove that the depth of the attack is related to the microstructure of the TMT bars.

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Prediction of hardness of the tempered martensitic rim of TMT rebars

Cited by 29 publications

References 24 publications

Prediction of Tempered Martensite Hardness Incorporating the Composition-Dependent Tempering Parameter in Low Alloy Steels

Prediction of Tempered Martensite Hardness Incorporating the Composition-Dependent Tempering Parameter in Low Alloy Steels

Prediction of Fatigue Strength in Steels by Linear Regression and Neural Network

Influence of the microstructure of TMT reinforcing bars on their corrosion behavior in concrete with chlorides

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