Progress in mesoscopic modeling of microstructure evolution in steels

Xiao, Namin; Chen, Yun; Li, Dianzhong; Li, Yiyi

doi:10.1007/s11431-011-4699-z

Cited by 16 publications

(8 citation statements)

References 120 publications

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“…Mathematical model and numerical simulation are often used to explore the distributions of solute elements during solidification . One dimensional mathematical model basing on the scale of the secondary dendrite arm spacing can describe the micro segregation behaviors of solute elements, and the phase field method is a good way to demonstrate the microstructural evolutions . Combined with the concentration field, the phase field model can be used to analyze the effect of dendritic morphology on the distributions of solute elements during solidification .…”

Section: Introductionmentioning

confidence: 99%

Study on Micro Segregation of High Alloy Fe–Mn–C–Al Steel

Shen

Yang

Liu

et al. 2019

steel research int.

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The segregation patterns of solute elements in the Fe–17.1Mn–0.24C–1.38Al steel are systemically investigated by experiments and simulations. The electron probe microanalyzer (EPMA) is conducted to assess possible micro segregation. The result indicates that with the increase of solid fraction in dendrite arms, Mn shows a positive segregation tendency, C uniformly distributes, and Al content slightly fluctuates. Compared with micro segregation models, experimental results of C match well with the Brody‐Fleming model. The segregation ratios of Mn are in good agreement with the lever‐rule. There is no certain regularity displayed between the experimental and calculation results about the segregation ratios of Al. The matrix of the steel is inhomogeneous with severe Mn and C micro segregation in the inter dendritic zone, whereas Al shows the opposite trend. A multicomponent phase field method coupled to thermodynamic calculations is used to simulate the concentration profiles of solute elements in the dendrite arms, and the numerical results are in favorable agreement with the experimental ones. Finally, the roles of undercooling and cooling rate on dendrite morphology and micro segregation of Mn are investigated. It reveals that large undercoolings could improve Mn segregation, while cooling rates have little effect on the maximum Mn segregation concentration but contribute to reduce the total inter dendritic Mn amount. They both have refinement effects on the dendritic structure. Those are helpful to understand and improve the segregations in Fe–Mn–C–Al steels.

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Section: Introductionmentioning

confidence: 99%

Study on Micro Segregation of High Alloy Fe–Mn–C–Al Steel

Shen

Yang

Liu

et al. 2019

steel research int.

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“…These methods can not only successfully provide the general average microstructure properties, but can also model the evolution of the grains [60,61]. The common characteristic of all these models is that they try to use just one basic physical concept to summarize the different microstructural evolution phenomena [62]. A representative example is the cellular automate (CA) models, which are algorithms that describe the discrete spatial and temporal evolution of complex systems by applying local deterministic or probabilistic transformation rules to the cells of a regular (or non-regular) lattice [63].…”

Section: Introductionmentioning

confidence: 99%

Prediction of microstructural evolution during hot forging

2014

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-Microstructural evolution, which is governed by temperature, strain and strain rate during hot forging, is a key factor influencing mechanical properties. Understanding the microstructural evolution of metals and alloys in hot forging has a great importance for the designers of metal forming processes. The principal objective of this paper is to provide an overview of the models for the prediction of microstructural evolution for metals and alloys during the hot forging process. In this review paper, the models are divided into four categories, including the phenomenological, physically-based, mesoscale and artificial-neural-network models, to introduce their developments, prediction capabilities and application scopes. Additionally, some limitations and objective suggestions for the further development of the modelling of microstructural evolution during hot forging are proposed.

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“…Modelling of thermal conditions during manufacturing processes and resultant metallurgical features is a wellestablished field of research. [201][202][203][204][205][206][207][208] In the case of SLM, however, the application of these techniques of microstructure predictions is still limited. Nonetheless, the plethora of experimental studies in the literature focusing on characterizing and empirically predicting the microstructure during SLM of materials [209][210][211][212] clearly indicates the scope and need of thermo-metallurgical modelling studies.…”

Section: Thermo-metallurgical Modelling Of Slmmentioning

confidence: 99%

Multiphysics modelling of manufacturing processes: A review

Jabbari

Baran

Mohanty³

et al. 2018

Advances in Mechanical Engineering

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Numerical modelling is increasingly supporting the analysis and optimization of manufacturing processes in the production industry. Even if being mostly applied to multistep processes, single process steps may be so complex by nature that the needed models to describe them must include multiphysics. On the other hand, processes which inherently may seem multiphysical by nature might sometimes be modelled by considerably simpler models if the problem at hand can be somehow adequately simplified. In the present article, examples of this will be presented. The cases are chosen with the aim of showing the diversity in the field of modelling of manufacturing processes as regards process, materials, generic disciplines as well as length scales: (1) modelling of tape casting for thin ceramic layers, (2) modelling the flow of polymers in extrusion, (3) modelling the deformation process of flexible stamps for nanoimprint lithography, (4) modelling manufacturing of composite parts and (5) modelling the selective laser melting process. For all five examples, the emphasis is on modelling results as well as describing the models in brief mathematical details. Alongside with relevant references to the original work, proper comparison with experiments is given in some examples for model validation.

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Progress in mesoscopic modeling of microstructure evolution in steels

Cited by 16 publications

References 120 publications

Study on Micro Segregation of High Alloy Fe–Mn–C–Al Steel

Study on Micro Segregation of High Alloy Fe–Mn–C–Al Steel

Prediction of microstructural evolution during hot forging

Multiphysics modelling of manufacturing processes: A review

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