Modelling phase transformations and material properties critical to the prediction of distortion during the heat treatment of steels

Guo, Ziqi; Saunders, N.; Miodownik, Peter; Schillé, Jean Philippe

doi:10.1504/ijmmp.2009.028632

Cited by 30 publications

(23 citation statements)

References 4 publications

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“…The thermodynamic modeling for the equilibrium and phase relationships, austenite composition at quenching temperature for HCCI alloy were simulated using JmatPro software [19,20], (Java-based Materials Property simulation software, version 7.0.0) (Sente Software Ltd., Surrey Technology Center, 40 Occam Road, Guildford, Surrey GU2 7YG, UK). Figure 2 shows thermodynamic simulation for Fe-12Cr-2.08C alloy.…”

Section: Methodsmentioning

confidence: 99%

Precipitation of M23C6 Secondary Carbide Particles in Fe-Cr-Mn-C Alloy during Heat Treatment Process

Oanh

Việt

2020

Metals

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The initial as-cast microstructure of high-chromium white cast iron (2.08% C, 12.1% Cr), including austenitic dendrites in a matrix of a eutectic mixture of austenite and M7C3 carbides. Heat treatment of as-cast iron leads to a transformation of the matrix from austenite to martensite phase. The secondary M23C6 carbide precipitated from matrix improving the hardness of sample from 48 HRC to 62 HRC. JMatPro simulations of volume fraction phase for Fe-C-Cr-Mn alloy shows M7C3 and austenite phases are stable at austenitization temperature (1000 °C). Eutectic carbide of M7C3 is a stable phase in the alloy which is undissolved in the matrix during the heat treatment process. The HR-TEM images of heat-treated sample revealed that both eutectic M7C3 carbide and ultrafine secondary M23C6 carbide particles distributed on the martensite matrix.

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Section: Methodsmentioning

confidence: 99%

Precipitation of M23C6 Secondary Carbide Particles in Fe-Cr-Mn-C Alloy during Heat Treatment Process

Oanh

Việt

2020

Metals

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“…Different rates of cooling may lead to different transformation products, and in turn different properties, as demonstrated in Ref. 40 for steels and Ref. 25 for titanium alloys.…”

Section: Drawbacks Of Equation-based Materials Flow Lawsmentioning

confidence: 99%

Introduction of Materials Modelling into Processing Simulation

Guo

Turner

Silva

et al. 2013

MSF

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The introduction of materials modelling into computer-aided engineering (CAE) processing simulation has become popular in recent years, whereas the fundamental challenge lies in the development of material models that can calculate the properties essential for processing design and simulation. This paper reviews the recent development of such models and the material data that can be calculated include physical, thermophysical, and mechanical properties, as well as phase transformation kinetics. The calculated material data has been used as input to numerous CAE packages for the simulation of casting, welding, forming and heat treatments. Two case studies are presented here, one on the simulation of residual stress in linear friction welding of titanium alloys, and the other on the prediction of distortion and residual stress in heat-treated large steel rings.

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“…In order to obtain adequate simulation results the material data should be accurate. The following information on materials properties have to be known for distortion prediction caused by quenching through numerical simulations [17]:…”

Section: Numerical Simulationmentioning

confidence: 99%