Effects of metallic microstructures on fatigue fracture of Q345 steel

Liu, H.Q.; Wang, Chun Ming; Zhang, Hong; Zhi, Huang; 王, 清遠; Chen, Qiang

doi:10.1007/s42243-019-00347-2

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…The Q345 is extensively used to produce pressure vessels with low and medium pressure in China because of its good mechanical properties and low cost. 5 Many studies have investigated the microstructure, thermal deformation behavior, mechanical properties, fatigue performance and corrosion resistance of the Q345, [5][6][7][8][9][10] but few studies have investigated its SSC behavior. The SSC failure of steels has been identified as often occurring at hydrogen congregation sites.…”

Section: Introductionmentioning

confidence: 99%

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Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment

Tian,

Zhang,

Wang

et al. 2024

Materials Science and Technology

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The steel Q345 is extensively used in pressure vessels with low and medium pressure because of good mechanical properties and low cost. Good resistance to sulfide stress cracking (SSC) is required for the pressure vessel steel, but the SSC behavior of the Q345 has received little attention. This work investigated the SSC behavior of the Q345 in a wet H2S environment. The microstructure contained the irregular various-sized polygonal-like ferrite grains plus a carbon-rich degenerate pearlite banded structure. The Q345 had high ductility but failed to pass the SSC tests. The SSC cracking involved hydrogen embrittlement. The SSC preferentially occurred at the carbon-rich degenerate pearlite banded structure. The elimination of the carbon-rich degenerate pearlite banded structure is necessary to improve the SSC resistance of the Q345 pressure vessel steel.

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Section: Introductionmentioning

confidence: 99%

“…Many studies have investigated the microstructure, thermal deformation behavior, mechanical properties, fatigue performance and corrosion resistance of the Q345, 5–10 but few studies have investigated its SSC behavior. The SSC failure of steels has been identified as often occurring at hydrogen congregation sites.…”

Section: Introductionmentioning

confidence: 99%

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment

Tian,

Zhang,

Wang

et al. 2024

Materials Science and Technology

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show abstract

“…Since the concept of artificial intelligence was first introduced at the Dartmouth Conference in 1956, it has developed through highs and lows and is now in its third wave of development [9]. In the background of big data and strong computing computer developments, machine learning algorithms have made breakthrough advances in a variety of sectors such as computer visualisation, speech recognition and natural language processing, and their applications based on artificial intelligence (AI) technology have matured for industrial fields.…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

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In the wake of the era of big data, the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry. The sintering process is an extremely complex industrial scene. As the main process of the blast furnace ironmaking industry, it has great economic value and environmental protection significance for iron and steel enterprises. It is also one of the fields where deep learning is still in the exploration stage. In order to explore the application prospects of deep learning techniques in iron ore sintering, a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures. Firstly, the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail, and then, the development of iron ore sintering simulation techniques was introduced. Secondly, deep learning techniques were introduced, including commonly used models of deep learning and their applications. Thirdly, the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction, controlling, and optimisation of key parameters. Generally speaking, deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

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“…This is based on the evidence that the cooling rate created microzones in microstructure, capable of the SSC resistance. Many studies investigated the microstructure, thermal deformation behavior, mechanical properties, fatigue performance, and corrosion resistance of the Q345 steel, [3,[13][14][15] but few studies specifically investigated its microstructure evolution affected by cooling rate. In the actual industrial manufacturing procedure of the Q345 steel, the cooling rate has been largely based on the established production experience.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cooling Rate on Sulfide Stress Cracking Resistance of a Q345 Pressure Vessel Steel Subject to Hydrogen Sulﬁde

Tian,

Zhang,

Wang

et al. 2023

steel research int.

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As an important equipment for the separation, store, and transport of the oil and natural gas in energy industry, the pressure vessels are subjected to harsh service environments of sulfide stress cracking (SSC). In this work, the effect of the cooling rate on the SSC susceptibility of a Q345 pressure vessel steel is studied. A pronounced distinction in microstructure is presented as the cooling rate is increased from ≈1 to ≈15 °C s−1. At ≈1 °C s−1, the microstructure is consisted of polygonal‐like ferrite and distinct banded pearlite. At ≈5 °C s−1, the microstructure is consisted of polygonal‐like ferrite and faintly banded pseudo‐pearlite. At ≈15 °C s−1, the microstructure is acicular ferrite with a complete elimination of banded structure. The cooling rate decides the level of the banded structure. As the cooling rate increases, the banded structure becomes relatively slight and even disappears. Strength in sort ascending is ≈1, ≈ 5, and ≈15 °C s−1. The SSC failure happens at ≈1 and ≈5 °C s−1, while does not happen at ≈15 °C s−1. The strength is not the only dominant factor responsible for the SSC failure. The SSC is preferentially generated at the banded structure. The elimination of the banded structure is necessary to improve the SSC resistance of the Q345 pressure vessel steel.

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Effects of metallic microstructures on fatigue fracture of Q345 steel

Cited by 12 publications

References 21 publications

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment

Application of deep learning in iron ore sintering process: a review

Effect of Cooling Rate on Sulfide Stress Cracking Resistance of a Q345 Pressure Vessel Steel Subject to Hydrogen Sulﬁde

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Effects of metallic microstructures on fatigue fracture of Q345 steel

Cited by 12 publications

References 21 publications

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H2S environment

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H2S environment

Application of deep learning in iron ore sintering process: a review

Effect of Cooling Rate on Sulfide Stress Cracking Resistance of a Q345 Pressure Vessel Steel Subject to Hydrogen Sulﬁde

Contact Info

Product

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Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment

Sulfide stress cracking (SSC) of a commercial Q345 pressure vessel steel in a wet H₂S environment