Effects of Cooling Conditions on Microstructure, Tensile Properties, and Charpy Impact Toughness of Low-Carbon High-Strength Bainitic Steels

Abstract Three steels were designed based on HSLA-100 with additional levels of Mn, Ni, Cr and Cu. The steels were prepared by controlled rolling and tempered at temperatures in range of 550–700°C. The continuous cooling time curves were shifted to longer times and lower temperatures with the increased tendency for the formation of martensite at lower cooling rates. The microstructures revealed that controlled rolling results in austenite with uniform fine grain structure. The steel with the highest amount of Mn showed the greatest strength after tempering at 750 °C. The top strength was attributed to the formation of Cu-rich particles. The steel with 1.03 wt.% Mn, tempered at 650 °C exhibited the best Charpy impact toughness at –85°C. On the other hand, the steel that contained 2.11 wt.% Mn and tempered at 700 °C showed the highest yield strength of 1 097.5 MPa (∼159 ksi) and an impact toughness of 41.6 J at –85°C.

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INCO-WELD C-276 WELDING ELECTRODE

Alloy Digest ◽

10.31399/asm.ad.ni0754 ◽

2019 ◽

Vol 68 (8) ◽

Keyword(s):

Impact Toughness ◽

Tensile Properties ◽

Charpy Impact ◽

Minimum Requirement ◽

Charpy Impact Toughness ◽

Welding Electrode

Abstract Inco-Weld C-276 Welding Electrode (ENiCrMo-4) is a NiCrMo welding electrode with increased Mo, W, and reduced Nb. The alloy offers strength levels consistently exceeding the minimum requirement of the 9% nickel steels and excellent Charpy impact toughness at –196 deg C (–321 deg F). This datasheet provides information on composition and tensile properties. It also includes information on joining. Filing Code: Ni-754. Producer or source: Special Metals Welding Products Company.

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Effect of Rolling Process on Impact Toughness in High Strength Low Carbon Bainitic Steel

Materials Science Forum ◽

10.4028/www.scientific.net/msf.816.743 ◽

2015 ◽

Vol 816 ◽

pp. 743-749 ◽

Cited By ~ 1

Author(s):

Xiao Long Yang ◽

Xiao Dong Tan ◽

Yun Bo Xu ◽

Zhi Ping Hu ◽

Yong Mei Yu ◽

...

Keyword(s):

Impact Toughness ◽

Ductile Fracture ◽

Cleavage Fracture ◽

Charpy Impact ◽

High Strength ◽

Rolling Process ◽

Bainitic Steel ◽

Low Carbon ◽

Austenite Recrystallization ◽

The Impact

Based on TMCP and UFC technology, the microstructures and impact toughness of low carbon bainitic steel were studied in this paper. The bainite morphology and fracture surfaces of Charpy impact specimens were observed by SEM, and mechanical properties of bainitic steel were measured by tensile and impact test. The results showed that the yield and tensile strengths of steel were 804MPa and 1015MPa, and elongation was 15.7% when the rolling was finished in the austenite recrystallization region. The steel rolled below Tnr temperature obtained tht yield strength of 930 MPa, tensile strength of 1090 MPa and elongation of 16.2%. However, the impact toughness was deteriorated in the steel rolled above Tnr temperature while the excellent impact toughness existed in the steel rolled below Tnr temperature. The impact toughness of steel rolled below Tnr temperature was 140J at-60°C, while the impact toughness of 15J at the same temperature was obtained for the steel rolled above Tnr temperature. The large cleavage fracture region on the fracture surface occured with the decrease of tested temperature in the steel rolled above Tnr temperature and inevitably reduced the impact toughness, while the main ductile fracture existed in the steel rolled below Tnr temperature at the same temperature. The rolling process of steel can strongly affect impact toughness of low carbon bainitic steel. Hence, the different rolling processes can adjust the occurrence of cleavage fracture and ductile fracture in order to improve the impact toughness.

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Application of Artificial Neural Network to the Prediction of Tensile Properties in High-Strength Low-Carbon Bainitic Steels

Metals ◽

10.3390/met11081314 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1314

Author(s):

Sang-In Lee ◽

Seung-Hyeok Shin ◽

Byoung-Chul Hwang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Yield Strength ◽

Tensile Properties ◽

Uniform Elongation ◽

High Strength ◽

Low Carbon ◽

Ann Model ◽

Bainitic Steels ◽

Artificial Neural

An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), and BF (bainitic ferrite). The input parameters of the model were the fraction of constituents, while the output parameters of the model were composed of the yield strength, yield-to-tensile ratio, and uniform elongation. The ANN model to predict the tensile properties exhibited a higher accuracy than the multi linear regression (MLR) model. According to the average index of the relative importance for the input parameters, the yield strength, yield-to-tensile ratio, and uniform elongation could be effectively improved by increasing the fraction of AF, bainitic microstructures (AF, GB, and BF), and PF, respectively, in terms of the work hardening and dislocation slip behavior depending on their microstructural characteristics such as grain size and dislocation density. The ANN model is expected to provide a clearer understanding of the complex relationships between constituent fraction and tensile properties in high-strength, low-carbon bainitic steels.

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