An Investigation of Creep Resistance in Grade 91 Steel through Computational Thermodynamics

This study was conducted to understand the relationship between various critical temperatures and the stability of the secondary phases inside the heat-affected-zone(HAZ)of welded Grade 91(Gr.91)steel parts.Type IV cracking has been observed in the HAZ,and it is widely accepted that the stabilities of the secondary phases in Gr.91 steel are critical to the creep resistance,which is related to the crack failure of this steel.In this work,the stabilities of the secondary phases,including those of the M23C6,MX,and Z phases,were simulated by computational thermodynamics.Equilibrium cooling and Scheil simulations were carried out in order to understand the phase stability in welded Gr.91 steel.The effect of four critical temperatures—that is,Acl(the threshold temperature at which austenite begins to form),Ac3(the threshold temperature at which ferrite is fully transformed into austenite),and the M23C6 and Z phase threshold temperatures—on the thickness of the HAZ and phase stability in the HAZ is discussed.Overall,the simulations presented in this paper explain the mechanisms that can affect the creep resistance of Gr.91 steel,and can offer a possible solution to the problem of how to increase creep resistance at elevated temperatures by optimizing the steel composition,welding,and heat treatment process parameters.The simulation results from this work provide guidance for future alloy development to improve creep resistance in order to prevent type IV cracking.

Thermodynamic aspects of inclusion engineering in steels

Tailoring non-metallic inclusions in accordance to the desired effect on steel properties has gained widespread acceptance in the last decades and has become known as “inclusion engineering”. Effective inclusion engineering involves three steps： （a） a good knowledge of how inclusions influence properties, （b） understanding what is the effect of each type of inclusions on these properties and thus which is the most desirable inclusion in a given product and （c） adjusting the processing parameters to obtain these inclusions. A significant portion of the process adjustment is done during steel refining, where the steel can be tailored so that the desired chemical composition of the non-metallic inclusions that will precipi- tate can be altered. Understanding the relations between steel chemistry, processing variables and inclusion chemical composition requires significant understanding of the thermodynamics of the systems involved. These complex equilibrium calculations are best done using computational thermodynamics. In this work some of the basic techniques used to control inclusion composition are reviewed and the thermodynamic information required to perform this task is presented. Several examples of the application of computational thermodynamics to inclusion engineering of different steels grades are presented and compared with experimental results, whenever possible. The potential and limitations of the method are highlighted, in special those related to thermodynamic data and databases.

Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning

A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the optimal additional amount of Sr in A356 alloys,but their results are inevitably inconsistent.In this paper,a combination of computational thermodynamic and machine learning approaches was employed to determine the optimal Sr content in A356 alloys with the best mechanical properties.First,a self-consistent thermodynamic database of quaternary Al-Si-Mg-Sr system was established by means of the Calculation of PHAse Diagram technique supported by key experiments.Second,the fractions for solidified phase/structures of A356-xSr alloys predicted by Scheil simulation,together with the measured mechanical properties were set as the input dataset in the machine learning model to train the relation of“composition-microstructure-properties”.The optimal addition of Sr in A356 alloy was designed as 0.005 wt.%and validated by key experiments.Furthermore,such a combinatorial approach can help to understand the strengthening/toughening mechanisms of Sr-modified A356 alloys.It is also anticipated that the present approach may provide a feasible means for efficient and accurate design of various casting alloys and understanding the alloy strengthening/toughening mechanisms.

Thermodynamic Analysis of Inclusions of Low Melting Point Area in Spring Steel

The activity data of each component of a CaO–SiO2–Al2O3 system were calculated by thermodynamic software Factsage. The composition of low melting point inclusions in a CaO–SiO2–Al2O3–MgO system was analyzed by thermodynamic calculation. The results show that the area of low melting point inclusions first increases then decreases with accumulating the alumina and magnesium oxide contents, respectively; the low melting area of CaO–SiO2–Al2O3–MgO inclusion is the biggest when the content of MgO and Al2O3 is 15%. To obtain low melting point inclusions, the alumina and magnesium contents should be approximately controlled to be 15%, and the CaO should be 40%.